FerroTag: A Paper-based mmWave-Scannable Tagging ...wenyaoxu/papers/conference/xu-sensys2019.pdfFerroTag: A Paper-based mmWave-Scannable Tagging Infrastructure SenSys ’19, November

FerroTag A Paper-based mmWave-Scannable TaggingInfrastructure

Zhengxiong Li1 Baicheng Chen1 Zhuolin Yang1 Huining Li1Chenhan Xu1 Xingyu Chen1 Kun Wang2 Wenyao Xu1

1 University at Buffalo the State University of New York Buffalo New York USA2 University of California Los Angeles Los Angeles California USA

ABSTRACTInventory management is pivotal in the supply chain to supervisethe non-capitalized products and stock items Item counting index-ing and identification are the major jobs of inventory managementCurrently the most adopted inventory technologies in productcountingidentification are using either the laser-scannable barcodeor the radio-frequency identification (RFID) However the laser-scannable barcode is entangled by an alignment issue (ie the laserreader must align with one barcode in line-of-sight) and the RFIDis economically and environmentally unfriendly (ie high-cost andnot naturally disposable) To this end we propose FerroTag whichis a paper-based mmWave-scannable tagging infrastructure for thenext generation inventory management system featuring ultra-lowcost environment-friendly battery-free and in-situ (ie multipletags can be simultaneously processed outside the line-of-sight)FerroTag is developed on top of the FerroRF effects Specifically themagnetic nanoparticles within the ferrofluidic ink reply to prob-ing mmWave with classifiable features (ie the FerroRF response)By designating the ink pattern and hence the location of particlesthe related FerroRF response can be modified Thus a specificallydesignated ferrofluidic ink printed pattern which is associatedwith a unique FerroRF response is a remotely retrievable (akammWave-scannable) identity Furthermore we augment FerroTagby designing a high capacity pattern system and a fine-grainedidentification protocol such that the capacity and robustness ofFerroTag can be systematically improved in mass product manage-ment in inventory Last but not least we evaluate the performanceof FerroTag with 201 different tag design patterns Results showthat FerroTag can identify tags with an accuracy of more than 99in a controlled lab setup Moreover we examine the reliability ro-bustness and performance of FerroTag under various real-worldcircumstances where FerroTag maintains the accuracy over 97Therefore FerroTag is a promising tagging infrastructure for theapplications in inventory management systems

CCS CONCEPTSbull Computer systems organizationCorresponding E-email wenyaoxubuffaloedu (WX) wangkuclaedu (KW)

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page Copyrights for components of this work owned by others than ACMmust be honored Abstracting with credit is permitted To copy otherwise or republishto post on servers or to redistribute to lists requires prior specific permission andor afee Request permissions from permissionsacmorgSenSys rsquo19 November 10ndash13 2019 New York NY USAcopy 2019 Association for Computing MachineryACM ISBN 978-1-4503-6950-31911 $1500httpsdoiorg10114533562503360019

ACM Reference FormatZhengxiong Li1 Baicheng Chen1 Zhuolin Yang1 Huining Li1 and Chen-han Xu1 Xingyu Chen1 Kun Wang2 Wenyao Xu1 2019 FerroTag APaper-based mmWave-Scannable Tagging Infrastructure In The 17th ACMConference on Embedded Networked Sensor Systems (SenSys rsquo19) Novem-ber 10ndash13 2019 New York NY USA ACM New York NY USA 14 pageshttpsdoiorg10114533562503360019

1 INTRODUCTIONAn impressive amount of capital in the US about 11 trillion dol-lars in cash which are equivalent to 7 of the entire US GDP istightly associated with inventories [13] As a result inventory man-agement (ie the supervisory mechanism for tracking the flow ofgoods from manufacturers to warehouses and from storage to thepoint of sale [2]) has become a critical component in the wholecommercialized business According to the newest 2019 report fromStatista [15] business respondents who are all manufacturers andretailers rated the warehouse management as the most importantbusiness investment in 2017 as 90 of the inventory are stationary(eg stored in warehouses) [14] The reports also indicate that mostof companies are willing to upgrade the current existing inventorymanagement systems to further promote efficiency in daily routinesand manage business growth [4]

Currently there are two types of tagging technologies employedin most of existing inventory management systems ie barcodeand radio-frequency identification (RFID) [1] Barcode is a printedpatternized identity which is read by a laser scanner [7] Howeversince barcode technology relies on invisiblevisible light (ie amedium can hardly travel through obstacles) for information ac-quisition the scanner must align with the barcode in line-of-sightto recognize the identity [65] In addition ordinal scanners limit toprocessing one barcode at a time [21] Different from the printablebarcode an RFID is an electronics consisting of a circuit and anantenna which encodes and transmits the dataidentity in RF wave(ie a medium can pass through barriers) [26] Although RFID hasthe advantage over the barcode in term of scanning efficiency thereis a significant hindrance causing the financial and the environ-mental costs One RFID tag costs from 018 minus 30 US dollars [3 16]Moreover RFID tags cannot be naturally degraded after the use andbecome a kind of e-waste in most of cases [55] Giving the fact thatthe inventory system is essential for business growth there is anurgent need to develop a better paradigm for tagging technologies

To this end we introduce FerroTag as an advanced tagging infras-tructure to promote inventory management system ie a criticalpart in the modern commercialized business In particular Ferro-Tag is featured as (1) Ultra-low cost the tag is highly affordablefor large scale deployment (2) Environment-friendly the tag isbased on ordinal papers and with non-toxic inks which are safely

SenSys rsquo19 November 10ndash13 2019 New York NY USA Z Li et al

Figure 1 FerroTag a paper-based mmWave-scannable tag-ging infrastructure can replace the traditional tagging tech-nologies for mass product counting and identification in in-ventory management

disposable and naturally degradable (3) Battery-free the tag iscompletely passive requiring no power supply (4) In-situ multi-ple tags are accurately read by a scanner outside the line-of-sightAs shown in Figure 1 the application scenes of FerroTag includemanufacturing factories warehouses and retail offices

Specifically FerroTag is a paper-based mmWave-scannable tag-ging infrastructure A pattern printed by naturally degradable inkis served as an identity [61] Thus it is highly economical environ-mentally harmless and fully passive in its origin In order to realizeFerroTag we need to address two technical challenges in this work(a) design and implement a paper-based mmWave-scannable tagginginfrastructure for the inventory management system The foundationof FerroTag rests on the FerroRF effects When RF signals meetsurfaces and objects a responsive RF signal will be generated [78]In our application when there is an RF signal passing through a fer-rofluidic ink print will generate a recognizable response (hereafterthe FerroRF response) which is associated with the ferrofluidic printpattern (ie the tag identity) To retrieve and recognize the identitya fine-grained response analysis protocol is developed First of allto protect the FerroRF response from distortions a range resolutionanalysis and an envelope correlation function are applied Secondlywe extract a set of critical scalar features (n = 25) including tenmost impactful ones based on Mel-Frequency Cepstral CoefficientsAt the end the selected features are fed into a classifier containinga cluster of decision trees (m = 150) for identification In additionby analyzing the angle of arrival multiple tags can be simultane-ously detected and identified (b) Model and optimization of theFerroRF effects for high capacity We first investigate and establish amathematical model of the FerroRF effects The FerroRF responseis generated by the magnetic nanoparticles within the ferrofluidicink Since the quantity and the arrangement (ie three-dimensionallocations) of particles are varying along with any variation in aferrofluidic ink printed pattern a unique pattern can be served as anon-contact retrievable identity as it can generate a unique FerroRFresponse Secondly with the in-depth modeling and understanding

of the FerroRF effects and response we study a systematic approachto optimize the variation of the FerroRF effects by designate tag pat-terns such that it contains only succinct geometric features but canbe used to room high-capacity identities in the tagging infrastruc-ture More specifically we investigate and evaluate an innovativenested pattern for tagging design in FerroTag which can providea large number of identities with regulations to a vast amount ofproducts in inventories

Our contribution can be summarized in three-fold

bull We investigate the FerroRF effects and discover the mag-netic nanoparticles within the ferrofluidic ink modulatesprobing mmWave with classifiable characteristics Moreoverwe model and advance the FerroRF infrastructure with aninnovative nested tag patternbull We design and implement FerroTag with one working proto-type machine Specifically we develop a new hardware andsoftware stack support and retrofit a commodity printer intothe application of FerroTag fabrication It is a paper-basedmmWave-scannable tagging infrastructure Each tag is madewith low-cost naturally degradable paper and ferrofluidicink Outside the line-of-sight multiple tags can be remotelyscanned by a cost-efficient mmWave probe to extract theidentitiesbull We evaluate FerroTag from three aspects First we study thespec of our infrastructure including cost time budget sens-ing accuracy and more Second we test the reliability withvariances in sensing distances orientations environmentsbarriers and more Finally we investigate the performanceof FerroTag and measure the stability in a case study oncomplex scenes

2 BACKGROUND AND PRELIMINARIES21 FerroRF EffectsFerrofluidic ink is colloidal liquids whose core components aremagnetic nanoparticles (eg ferrite compound-Magnetite powder)carrier fluid that suspends the nanoparticles (eg organic solvent)and the surfactant that coats each magnetic nano-particles [77]The quantity and the arrangement of these magnetic nanoparticlesreflect into the unique characteristic frequency responses whentags are probed by broadband radio frequency (RF) signals as shownin Figure 2 When a fundamental tone is passed through the fer-rofluidic ink magnetic nanoparticles modulate the response signaland generate additional frequency tones besides the fundamentalone as formulated in Section 22

Figure 2 The ferrofluidic pattern generates a FerroRF re-sponse under the RF beam The FerroRF response is associ-ated with intrinsic physical characteristics of tag pattern

FerroTag A Paper-based mmWave-Scannable Tagging Infrastructure SenSys rsquo19 November 10ndash13 2019 New York NY USA

mmWave is an emerging technology (eg 5G network) and it isworth to mention that object (eg tags) presence detection trackingand localization through mmWave signals are intensively studiedin the past years [19 75 81 83 85] In contrast to other RF sens-ing modalities such as WiFi [79] and acoustics [48] mmWave hasan excellent performance in term of the directionality and ownsthe superiority in high tolerance to ambient noises (eg soundlight and temperature) and less surface scattering FurthermoremmWave facilitates a micron-level shape change resolution makingit feasible to monitor the pattern shape change which in case ofshape size occurs at the range of 2-3mm [69] Considering thatmmWave is becoming the key carrier in the next-generation wire-less communication we will investigate the FerroRF effects underthe applications of mmWave signalsHypothesis It is indeed the FerroRF response (ie harmonics orinter-modulation) that contains the unique characteristics of thetag which can be treated as an intrinsic and persistent identity ofthe tag Different geometric shapes and sizes of ink patterns on tagscan be meticulously designed these discrepancies are sufficientto alter the frequency responses and induce unique measurablefingerprints which associate with the ink patterns Therefore itis possible to utilize a mmWave probe to force tags to radiate theresponse signature that reflects their unique properties and can beused for object counting and identification

22 Modeling on FerroRF EffectsIn this part we further explore the FerroRF effects Before com-pletely understanding the tag modulation on the response signal itis crucial to model the FerroRF effects When a fundamental toneis passed through the tags the ferrofluidic patterns modulate theresponse signal and generate additional frequency tones besidesthe fundamental one as formulated in Equation (1)

r (f τ t ) = Rf t (τ )otimes

hf (t ) (1)

where r (f τ t) is the signal modulation function of ferrofluidicdue to the stimulation of millimeter wave [74] Rf t (τ ) is the sig-nal reflection function based on τ -volume makeup of ferrofluidicf-range of frequency and t-time instant

otimesstands for convolution

computing and hf (t) is the ideal band-pass filter function for thecarrier bandwidth [41] In the following equations we model therelation between τ -volume makeup of ferrofluidic and signal mod-ulation function in which the volume makeup consisting of threedimensions is the geometric pattern that can be manipulated withtag pattern design

Rf t (τ ) =Rf t (τ )1Rf t (τ )2

Rf t (τ )1 = exp(2γd (τ )L)(γd (τ ) minus γ0(τ ))(γ (τ )+γd (τ )) + (γ0(τ ) + γd (τ ))(γ (τ ) minus γd (τ ))

Rf t (τ )2 = minusexp(2γd (τ )L)(γd (τ ) + γ (τ ))(γ0(τ )+γd (τ )) + (γ0(τ ) minus γd (τ ))(γ (τ ) minus γd (τ ))

γ 2(τ ) =π 2

a2minus ω2ϵ0micro0ϵ microlowast(τ )

Rf t (τ ) has two parts in the mathematical presentation γ0 andγ are the propagation constants in the empty and ferrofluidic partsof the wave-guide respectively γd is the propagation constant ofthe wave in the dielectric L is the thickness of dielectric insertion

d represents the diameter of the ferromagnetic particles a is thesize of the wide wall of the wave-guide π is the ratio of a circlersquoscircumference to its diameter ϵ0 and micro0 are the electric and mag-netic constants respectively ϵ and microlowast are the permittivity and thepermeability of the medium that fills the wave-guide cross sectionin which ferrofluidic presents respectively This shows the relation-ship between ferrofluidicrsquos permeability of millimeter wave andand the volume makeup which we further model with Equation 3

microlowast = 1 + χmprime(τ ) minus jχm rdquo(τ )

χmprime(τ ) =

γ τML(σ )ωHn

(1 + η2)2Hn4 + (η2 minus 1)Hn

(1 + η2)2Hn4 + 2(η2 minus 1)Hn

χm rdquo(τ ) =γ τML(σ )ωHn

ηHn2(1 + Hn

2(1 + η2))(1 + η2)2Hn

4 + 2(η2 minus 1)Hn2 + 1

where τ is the volume makeup of ferrofluid which is manipulatedwith our tag design as shown in Figure 3 χm

prime and χmrdquo are the

real and imaginary parts of the magnetic susceptibility respectively[74] Hn is the reduced magnetic field η = ξ ( 1

L(σ ) minus1σ ) Hn = γ H

σ =micro0MdVkT H σ is a combined parameter of the magnetic fluid

Md is a saturation magnetization of the solid magnetic V = πd36 is

the volume of a ferromagnetic particle ξ is the damping constantof the electromagnetic wave in the magnetic fluid For sphericalferromagnetic particles it is assumed that the dielectric propertiesof the magnetic fluid are independent of the magnetic field [73 74]To summarize the foregoing exploration the FerroRF response canbe affected by adjusting the ferrofluidic ink pattern shape and size

Figure 3 The FerroRF responses (in the red box) of six differ-ent patterns (in the upper right corner) are distinct in bothfrequency spectrum and amplitude after themodulation in-dicating the feasibility of FerroTag counting and identifica-tion

23 The FerroRF Effects on TagsProof-of-concept To gain evidence on the validation of the Hy-pothesis we conducted a preliminary experiment using 6 differenttags with different patterns Each tag pattern is attached to theright in Figure 3 These tag patterns are made by ferrofluidic inkwith a regular copy paper following the Arabic numerals shapes of1 to 6 which are different from aspects of the size and the shapeSix different tags are stimulated with the mmWave probe with adistance from the devices The reflected signal profile is explored inthe spectrum domain As shown in the range frequency spectrumgraph (see Figure 3) the x-axis is the frequency and the y-axis isthe amplitude of the received signal The various sub-carrier fre-quencies can be clearly observed that separated from the clutter ofthe artifacts their FerroRF responses are distinct at the frequencyand amplitude To conclude the results are significantly promisingimplying that given the massive amount of tags variations havesufficient space to be served as powerful identity resourcesA Study on Package BoxAfter we confirm the FerroRF responsesfrom different tag patterns are diverse there is still one remainingchallenge In real-world applications FerroTag can be placed on thepackage box As a result we need to investigate whether the sur-roundings such as package box will disturb the FerroRF responseand interfere the tag identification We investigate an example ofthe interference from the package box when a mmWave signalcomes through the tag in Figure 4 We first put a package boxwithout the tag before the mmWave probe As shown in the rangefrequency spectrum graph (see Figure 4(a)) where the x-axis is thefrequency of the received signal the y-axis is the power spectraldensity (PSD) [82] of the received signal we can observe that thereflective signal from the package box is stable and visible Further-more we attach a tag on the package box and then reacquire andanalyze the mmWave signal as shown in Figure 4(b) A comparisonbetween these two trial results confirms that these two PSDs aresignificantly distinguishable which proves the feasibility of thetagging infrastructure in real practice

(a) The object package PSD analysiswithout the tag

(b) The object package PSD analysiswith the tag

Figure 4 An example of the object package PSD estimationanalysis without and with interference from FerroTag

3 FERROTAG SYSTEM OVERVIEWWe introduce FerroTag a paper-based mmWave-scannable tagginginfrastructure to facilitate mass object countingidentification ofthe inventory management The end-to-end system overview isshown in Figure 5Tag Fabrication The primary physical components of FerroTagare a series of ferrofluid patterns printed on a normal substrate eg

copy paper The tag patterns can be highly customized The tag canbe manufactured via a variety of mass-produced easy-to-use andwidely accessible manufacturing methodsTag Scanning and Identification A mmWave probe is proposedto remotely and robustly acquire the tagrsquos FerroRF response foridentification Specifically the probe transmits a mmWave signaland processesdemodulates the reflected response signal Oncereceiving the data the tag identification module first performsthe preprocessing to correct the distortion and extracts the spatial-temporal features After that an effective classification algorithm isdeveloped to count the tag number and recognize the tag identity

4 TAG DESIGN AND IMPLEMENTATION41 Basic Tag Pattern StudyTo diffuse ferrofluidic materials in to the substrate we print a fer-rofluidic ink pattern directly on the substrate surface In this processthe depth variation compared to its length and width is negligible(typically less than 01 on a tag that is 20x20mm) and the patterncan be considered pseudo-2D As a result we utilize 2D geometricshapes to characterize these ferrofluid printed pseudo-2D patternsand test the FerroRF response

For designing an appropriate tag pattern we analyze the areato perimeter ratios of some typical geometric shapes The area toperimeter ratios of triangle rectanglesquare pentagon and circleare around 014l 025l 02l and 025l respectively It is worth tomention that other shapes (eg hexagon octagon and decagon)can be decomposed by two or more regular shapes Among ourdesign in Figure 6 both the square and the circle have the highestarea to perimeter ratio but the circle saves more than 21 spacecompared to the square Also we notice while the tag pattern ismore complex the pattern has more forms of presentation (ie morepotential component combinations and identity capacity under cer-tain layouts) We further introduce a formal approach to generatinga nested geometric for robust and high-capacity tag design

42 Prototyping of FerroTag421 FerroTag Pattern Design Systemization In order to bettercharacterize the tag capacity we propose the pattern complexityscore ϕ(z) of a tag pattern to evaluate the pattern complexity Specif-ically ϕ(z) is estimated in Equation (4)

ψ =Lminus1sum0zip(zi )

ϕ(z) =Lminus1sum0(zi minusψ )2p(zi )

where z is the grayscale of the image p(middot) is the histogram of theimage L is the grayscale levelψ is the average of z and ϕ(z) repre-sents the complexity of the tag which is also the variance of thehistogram [62] As shown in Figure 7 all scores of the basic patterns((a)-(h)) are below 200 Besides the complexity of a nested pattern(see Section 5) is at least twice than that of the basic pattern There-fore we define the threshold as 400 empirically When the score islarger than 400 the tag is treated as a nested one Based on ϕ(z) thetag can then be categorized according to the corresponding rulesin Section 41 and 5

Figure 5 The system overview for FerroTag to in-situ identify the tag patterns It comprises of the ultra-low cost tag with onemmWave sensing module in the front-end and one tag identification module in the back-end

Figure 6 The design of four basic tag patterns

Figure 7 Representative tag designs with different complex-ity scores

422 FerroTag PrintingFerroTag Substrate A paper based ferrofluidic ink printed tag isselected to achieve ultra-low cost wireless and secure countingof target objects Papers are extremely low cost nowadays and itsprice can become minimal in mass production [6]FerroTag Printing Machine To rapidly prototype the tags wedesign a new FerroTag printing machine that is capable of produc-ing printed tags The prototype is based on a commodity printerincluding two system support components

Hardware Development For the ease of implementation we em-ploy the modal of inkjet printers as the base of our printing machineThemain task is to revitalize an ink cartridge for ferrofludic printingAs shown in Figure 8(a) in order to replace printer ink or refill withferrofluidic ink a syringe is used to transfer ferrofluidic ink fromcontainer to cartridge safely Syringe with needle diameter greaterthan 3mm is preferred due to the sealing nature of ferrofluidic inkwhich causes plunge from pushing it into a cartridge The needleneeds to be kept at least 1cm below the surface of container andcartridge as the ferrofluidic ink tends to form bubbles and splashover working area In case that a cartridge is not revitalizable wefabracate a 3D printable Cartridge that is physically compatiblewith manufacturersquos cartridge After 3D printing the Cartridge weinject the ferrofluidic ink without the need to remove ink residue

which is time-efficient and prevents the residue from contaminat-ing ferrofluid After injecting the ferrofluidic ink to the Cartridgeand replacing manufacture cartridge with 3D printed Cartridgeprocedures including print-head alignment cleaning and testingare carried out to remove ink residue in the printer To preventthe printer from printing with an empty Cartridge we design theSupply Manager module that estimates the ferrofluidic ink levelin the printer based on Cartridgersquos usage

Software Development To reduce the tag edit time we develop aFerroTag Generator in the software stack that can efficiently gener-ate tags in a mass scale as shown in Figure 8(b) Users first inputthe number of tags to be printed then the FerroTag Generatoractivates the Randomizer to generate parameters for the Execu-tor Afterwards the Executor utilizes the parameters and followsAlgorithm 1 (mentioned in Section 5) to generate tag image files

After obtaining the generated patterns we need a layout man-agement to efficiently arrange tags on the substrate Therefore weadd a custom-built Layout manager that transforms a set of tagimages into a print-friendly printing layout which is compatiblewith native printer drivers The Layout editor first trims the tagimages by removing white or transparent pixels then the size ofeach image is adjusted to user defined value from the Size controllervia image processing technique such as bi-linear interpolation [47]After that the Layout editor concatenates images in a layout thatbest fits the paper size selected by users

Figure 8 (a) shows the experimental setup for the retrofit-ted off-the-shelf printer employed for the mass manufac-ture (b) illustrates the improved system architecture withhardware and software developments

Alternative Fabrication SolutionWith consumer-grade 3D print-ers becoming more pervasive accessible and reliable users employ3D printing technology to print FerroTagmolds As shown in Figure9 we propose a new method of producing FerroTag utilizing theinexpensive reusable and durable 3D printed molds with basicgeometric shape as an alternative of modified inkjet printing Afterthe 3D printing stage a firm tag can be drawn on the paper with abasic brush in seconds

Figure 9 Several molds by 3D printing for the accessiblemanufacture

Cost Analysis The total printing costs originate from the printerand the consumable items The commercial printer costs around$60 [12] In our system the 3D printing mold costs less than $1the ferrofluidic ink costs around $9 for 60ml (around 015$mL)[8] and the Staples copy paper we use costs $57 per 5000 sheets[9] We find that per 10ml of ferrofluidic ink could produce morethan 500 sheets and one sheet can carry more than 24 tag patternsTherefore the cost of one tag (ie ferrofluidic ink and paper) isless than one cent Moreover the cost can be further reduced undermassive production

5 FERROTAG ADVANCEMENTIn this section we further investigate the advanced pattern designto enlarge the tag capacity and strengthen the robustness The tradi-tional approach is to utilize the stripe pattern design [28] which hasa fixed stripe layout and completes the tag pattern change throughthe line width However such design is not applicable for this ap-plication since the ink can expand and involve the adjacent lineswhich can severely devastate the corresponding FerroRF responseTherefore we develop an advanced nested geometric design In-spired by the concentric zone model [5] the nested pattern is amultiple-layer pattern produced through a series of iterations ofcomponents With this multiple-layer iteration mode the nestedpattern allows tag capacity to increase exponentially with respectto the increment in number of layers which shows a huge capacityfor this application Besides we introduce a component Hollowbetween layers to aid resisting the ink expansion interference Fur-thermore learned from our preliminary design exploration fromthe volume of ferrofluidic ink the design complexity as shown inFigure 3 and Figure 7 respectively we propose the design protocolcontaining three key components spiral line hollow connectionas shown in Figure 10

Figure 10 The illustration of the advanced tag pattern de-sign including three components hollow connection andspiral lineSpiral Line FerroTag is based on a set of nested circles that effec-tively utilizes two dimensional substrate surface space To reduceferrofluid usage on paper substrate we apply a set of nested arcs(ie spiral lines) to substitute nested circles The parameters ofspiral lines can be adjusted to manipulate the tag capacity We firstdefine the number of spiral line layers and the cardinality as NandC respectivelyCstar tpos is the cardinality of starting position

of the spiral line in term of angle in degrees for each layer giventhe ability to rotate 360 we set each possible starting positionexactly 1 apart resulting Cstar tpos = 360 Cendpos is the cardi-nality of ending position relative to starting position in degreesfor any layer this determines the length of the spiral line length= endposWe set Cendpos = 361 to hold values 0 to 360 where 0represents absence of spiral line and 360 represents a completering In summary the capacity contributed by spiral lines can becalculated as (Cstar tpos times(Cendpos minus2)+2)N where (Cendpos minus2)acknowledges empty spiral line and full ring being same in anystarting positionHollow To allow a range of error tolerance we develop hollowstructure As the ink takes time to sink into paper substrate it canoverlay with another line from the printer head a hollow structurecan reduce the probability that ink spreads and overlays on topof other undesired lines In addition enlarging hollow space canreduce ink usage and lower costConnection Adding connections increases tag capacity with thevariations of connection between every two layers We first de-fine K the number of connections between two layer and CK the number of connections between adjacent layers CKV is thebinary cardinality for each connection to be present or not a con-nection is absent represented by KV = 0 and present presentedwith KV = 1 The capacity of connections alone can be calculatedby CKV (CK )

(Nminus1)

Figure 11 Four different advanced ferrofluidic nested tagpatterns

Algorithm 1 Nested Pattern Design Generation in FerroTagInput N The number of spiral line layer

R Array of radius for spiral linesL Array of length of spiral linesS Array of starting point of spiral linesK Array of connection positions

Output I The generated tag pattern1 Initialize N R L S2 Iappend(SpiralLine(index 0 L S))3 index larr 14 while index lt N do ▷ Iterate for each component5 Iappend(SpiralLine(index R L S))6 Iappend(Connection(index R Rminus1 K))7 index + +8 end while9 return I

Tag Design The design of ferrofluid nested pattern is illustratedin Algorithm 1 For a nested pattern (see Figure 11) the numberof spiral line layers radius of each spiral line length of each spiralline and starting position of each spiral line are preset by users

or randomly distributed as input We establish SpiralLine() andConnection() functions based on the preset parameters By callingthese functions array of tuples that stores spiral line and connec-tion points are generated The algorithm iterates layers by layersto generate tag patterns In detail the algorithm first transformsbetween polar coordinate and Cartesian coordinate and then per-forms trigonometric calculations for each position such that pixelcoordinates in the pattern are stored directly after calculationThe Tag Capacity Estimation We estimate the capacity of thetag with the model of Equation (5) where N is set as five for highcapacity and proper size and K is set as six to avoid overlappingconnections as well as enable enough tag capacity CN is the totalcardinality based on N Based on these settings the tag capacitycan be mathematically modeled as follows

N gt 1 CK = 2 CKV = 2Cstar tpos = 6Cendpos = 7

CN = (Cstar tpos times (Cendpos minus 2) + 2)N timesCKV (CK )(Nminus1)

= (6 times (7 minus 2))5 times (224) gt 85 times 1012

6 MMWAVE-SCANNABLE IDENTIFICATIONSCHEME

61 FerroRF Response Signal AcquisitionWe introduce the RF hardware in FerroTag to stimulate and acquirethe FerroRF response from tags Pulse-Doppler radar that emitsa set of periodic powerful pulse signals has been largely used inairborne applications [46] such as the target range and shape de-tection However when a short-time pulse stimulus which hasan infinite frequency band is applied to illuminate the electron-ics the corresponding FerroRF response will be overlapped withthe stimulus signal and difficult to recognize Therefore FerroTagselects a frequency-modulated continuous-wave (FMCW) radarwith a narrow passband filter [60] The FMCW radar continuouslyemits periodic narrow-band chirp signals whose frequency variesover time Non-linear interrelation to these narrow-band stimuliwill generate distinct frequency response and the received signalswill carry the distinguishable FerroRF responses when the stim-uli signals hit the target tag Specifically the transmitted FMCWstimulation signal is formulated as Equation (6)

T (t) = e j(2π f0t+int t0 2π ρt dt ) 0 lt t lt Tr (6)

where T (t) is the transmitted signal Tr is one chirp cycle f0 is thecarrier frequency and ρ is the chirp rate In our application whenT (t) passes through the tag the ferrofluidic patterns on the tag actas a passive convolutional processor (see Figure 2) and generatesnon-linear distortions toT (t) After the manipulated signal radiatesfrom the tag the non-linear spectrum response will be captured bythe RF probe receiver antenna (Rx) Once the FerroRF responsesignals are received FerroTag will first preprocess the signal andextract the effective feature vector from the FerroRF response Afterthat an identification model is developed to identify the tag

62 Signal PreprocessingIn practice the received FerroRF response always contain the signaldistortion A natural countermeasure is to model a digital filter and

Table 1 List of features extracted from the FerroRF response

Category Features

Temporal Feature

Mean Value 50th percentile 75th percentileStandard Deviation Skewness [49] Kurtosis[22] RMS Amplitude Lowest Value Highest

Spectral Feature Mean Value Standard Deviation SkewnessKurtosis Crest Factor [11] Flatness [39]

Others MFCC-10 [64]

compensate for the distortion accordingly However such a solu-tion hardly handles cumulative errors over time that are unlikelyto be accessible to end-users [53] To address this issue we employthe range resolution analysis solution [51] The range resolutionis ∆RES = c

2B where c is the light speed and B is the bandwidthof one periodic chirp This solution can automatically align thesignal distortion based on different conditions When the distor-tion displacement lt ∆RES the signal has a minute frequency shiftthat is invisible on the FerroRF response When the distortion dis-placement gt ∆RES it will result in the misalignment issue of theFerroRF response To align spectrum response S(w) we utilize theenvelope correlation function between the shifted spectrum and itscorresponding reference spectrum Q(w) formulated as

E(τ ) =sumw|Q (w ) | middot |S (w minus τ ) | (7)

where τ is the frequency shift The optimal frequency shift can becalculated as

τ opt = argmax E(τ ) (8)

63 Spatial-temporal FerroTag IntrinsicFingerprint Extraction

As the above mentioned the FerroRF response contains the uniqueidentity of the tag As a result we exploit the internal traits inthe FerroRF response signal by extracting scalar features in spatial-temporal domains The feature are listed in Table 1 These featuresrepresent the FerroRF response signal from different aspects No-tably we employ 10 features inMel-Frequency Cepstral Coefficients(MFCC) [64] MFCC are coefficients which are based on a linearcosine transform of a log power spectrum on a nonlinear Mel scaleof frequency as illustrated in Equation (9) (10) and (11) [67]

Bm [k ] =

0 k lt fmminus1 and k gt fm+1

k minus fmminus1fm minus fmminus1

fmminus1 le k le fm

fm+1 minus kfm+1 minus fm

fm le k le fm+1

Y [m] = log(fm+1sum

k=fmminus1

|X [k ] |2Bm [k ]) (10)

c[n] =1M

Msumm=1

Y [m] cos(πn(m minus 05)M

) (11)

where X [k] is the input signal applied by the Fast Fourier Trans-form (FFT) Bm [k] is the Mel filter bank Y [m] is the Mel spectralcoefficients m is the number of filter bank n is the number ofcepstral coefficients Intuitively the fingerprint with more MFCCcoefficients will contain more unique physical characteristics of thetag MFCC combines the advantages of the cepstrum analysis with

a perceptual frequency scale based on critical bands and can im-prove the tag recognition performance by boosting high-frequencyenergy and discovering more intrinsic information

However it also increases the computation overhead To balancethis trade-off we empirically choose n = 10 Thus MFCC-10 collec-tively make up the robust representation of the short-term powerspectrum in the received signal which is related to the tag patternBesides apart from the MFCC-10 for example the skewness isa scale of symmetry to judge if a distribution looks the same tothe left and right of the center point and flatness describes thedegree to which they approximate the Euclidean space of the samedimensionality Thus after analyzing with the forward selectionmethod for the feature selection [18] a fingerprint containing 25features from the temporal and spectral parts is formed

64 FerroTag Identification AlgorithmAfter we obtain the input data a 25-dimensional feature vector ex-tracted from the FerroRF response we use FerroTag for the tag iden-tification We utilize our FerroTag identification algorithm whichcan be formulated as a multi-class classification problem as shownin Algorithm 2 A traditional approach to address this problem isthe Convolutional Neural Network (CNN) classification Althoughsome scholars adopted the CNN approach in their particular ap-plications [84] the CNN is intractable requiring intricate networktopology tuning long training time and complex parameters se-lection Moreover CNN based solutions which learn features au-tomatically are not always effective due to the local convergenceproblem Thus we employ a customized random forest methodwhich is an ensemble learning method for classification task thatoperates by constructing a multitude of decision trees at trainingtime and outputting the class that is the mode of the classes of theindividual trees [42] The advantage of adopting the random forestis that it utilizes the unbiased estimate to achieve the generalizationerror and subsequently has an outstanding performance in gen-eralization Moreover the random forest can resist the overfittingbe implemented without massive deployment and has an excel-lent ability to be interpreted [25] Therefore we use an ensemblelearning method for classification of the FerroRF response signalParticularly we deploy a random forest with 150 decision treesas a suitable classifier for our system The set of features selectedthrough Section 63 are used to construct a multitude of decisiontrees at training stage to identify the corresponding tags for every20ms segment of the RF signal in the classification stage

65 Multiple Tags Counting and IdentificationTo further facilitate inventory management FerroTag can count andidentify multiple tags at the same time This ability helps FerroTagwith scanning multiple items in the same box or shelf which canmagnificently improve the scanning efficiency To count the mul-tiple tags the spectral signature based solution is widely adopted[33] The solutions detect if there are several wavelengths muchgreater than their footprints when the designed tags resonate atfrequencies However the resonation only occurs when the inkon the tag is conductive while the ferrofluidic ink is the oppositeThus owing to multiple Rx on the probe we employ the Angleof Arrival (AoA) to count the tags The distances from the tags to

Algorithm 2 FerroTag Identification AlgorithmInput γ The FerroRF response traces from a tagOutput R The identification result1 ϑ larr 150 ▷ Set the tree number2 δ larr 0 ▷ Initial the count3 ϱ(i) = Preprocessinд(γ (i)) ▷ Preprocesse signal4 for i isin 1 n do ▷ Extract features5 ϵ1(i) = FeaturesTemp(ϱ(i))6 ϵ2(i) = FeaturesFreq(ϱ(i))7 ϵ3(i) = FeaturesMFCC(ϱ(i))8 ϵ(i) = [ϵ1(i) ϵ2(i) ϵ3(i)]9 end for10 for i isin 1 n do11 R(i) = RandomForest(ϵ(i)ϑ ) ▷ Classify traces12 δ + +13 end for14 return R

different Rx (the distance of propagation) vary which causes phaseshifts among the received signal on multiple Rx The AoA solutionis to calculate these phase shifts to induce tag signal sources andcount tags [66] The phase shifts ∆ω can be formulated by

∆ω =2π∆d sinθ

λ (12)

where ∆d is the distances between two Rx and θ is the AoA Ac-cording to the above equation we can get the AoA resolution ∆θas

∆ωRES =2π∆dmax

λ(sin(θ + ∆θ ) minus sinθ ) gt 2π

νsin(θ+∆θ )minussin θasymp∆θ cos θminusminusminusminusminusminusminusminusminusminusminusminusminusminusminusminusminusminusminusminusminusminusrarr ∆θ gt

ν∆dmax cosθ

where ν is the number of sample points

7 SYSTEM PROTOTYPE AND EVALUATIONSETUP

Experimental PreparationThe deployment of FerroTag is shownin Figure 12 The distance between the tag and the probe is denotedas D Note that D is adjustable based on the requirement of realapplications In this setting D is set as 60cm empirically The corre-sponding tag sizes are made in 28mm (111in) (diagonal) Each tagis attached to three standard boxes for scanning two USPS packageboxes and an Amazon package box

Figure 12 System implementation (designated FerroTagswith a mmWave sensing probe)FerroTag Fabrication Without loss of the generality we employtwo off-the-shelf economic printers (ie Epson Expression Home

XP-400 and Canon Pixma MG2922) and retrofit printers to producemassive tags with 201 different nested patterns in the experimentEach tag pattern is printed with different size (see Section 84 indetail) We collect a total of 300 traces with a 441K sampling ratefor each tag including 210 traces randomly selected for trainingand 90 traces for testing As a result there are entirely 42210 tracesfor training and 18090 for testingApplication Protocol FerroTag has the enrollment and identifica-tion mode At the enrollment mode FerroTag extracts features fromtags introduced and stores them in a database At the identificationmode FerroTag extracts features introduced and compares themto the template ones FerroTag returns the tag ID and counts theobject numberScannerDesignThe FerroTag prototype employs an ordinal FMCWmmWave system [59] The mmWave signal is processed using stan-dard FMCW and antenna array equations to generate the FerroRFresponse which is then utilized for the tag identification and count-ing The transmission power is less than 12W The cost is lowerthan $100 Besides the probe can be easily mounted on the wall orintegrated with other devices like laptops smartphones and mobileinspection instrumentsPerformance Metric To better evaluate the system performancewe adopted a confusion matrix in addition to straightforward tech-niques such as accuracy precision and recall In the confusionmatrix plot the darker the shade the higher the classification per-formance [24]

8 FERROTAG SYSTEM EVALUATION81 Identification PerformanceWe evaluate the ability of FerroTag to recognize the different tags inthe controlled lab environment The resulting average classificationperformance for each tag is shown on a heat map (aka a confusionmatrix) in Figure 14 The rows represent the selected tag ID clas-sified by the random forest classifier and the columns representthe actual tag ID Evidently the diagonal cells are the darkest blueimplying that the traces from tag i were indeed classified as class i While little light blue cells are appearing outside the diagonal cellsinstances of misclassification are rare The identification accuracy is9954 with 9952 precision and 9949 recall which implies thatthe feature vector effectively reflects the unique FerroRF responsecharacteristics in each tag To better illustrate the classification per-formance we select ten types and enlarge their confusion matricesUpon careful analysis we notice that No77 tag is misclassified asNo80 tag even though their shapes are significantly different Thereason is due to the nature of our algorithm which describes theFerroRF response primarily based on the FerroRF effects ratherthan their visual characteristics In conclusion our results demon-strate that paper-based printed tags can be precisely recognized byFerroTag

82 FerroTag Sensing TestsIt is essential to detect and recognize tag identifies in a tagginginfrastructure To evaluate this performance we employ the radarcross-section (RCS) that is an important characteristic that indi-cates the power of the backward tag signal [56] The RCS can be

Figure 13 The identification performance for FerroTagwith201 different type tags Confusion matrices of ten types areenlarged in the green box These ten types are the same asothers following the same pattern design These ten typesverify that most are classified correctly

represented as

ξ =Pr

PtGtGr

(4π )3d4

λ2 (14)

where ξ is the RCS Pr is the power of the received modulated tagsignal Pt is the power transmitted by the probe Gt is the gainof the probe transmit antenna Gr is the gain of the probe receiveantenna λ is the wavelength and d is the distance to the tag [20]To evaluate the RCS of tags we utilize 30 different tags The RCS ofthe tags are between minus37dBsm to minus29dBsm under mmWave whichimplies that the tag owns a similar exceptional performance topassive RFID tags [76]

Figure 14 The tag uniqueness analysis

83 Tag UniquenessFor a tagging infrastructure it is essential to recognize the overalldistinguishability of tags In this evaluation we employ 201 dif-ferent fingerprints in Section 81 and differentiate the fingerprintsinto two groups ie intra-group (within the same tag) and inter-group (among different tags) We leverage Euclidean distance awidely used technique for measuring the fingerprint distance Thefingerprint distance between intra- and inter-groups is illustrated inFigure 14 The average of intra group is 251 which is significantlysmaller than the average of inter group 3204 Also the results showthe tags can be completely separated with little overlap Therebywe prove the independence between two tags even with the similarpattern

84 Performance Characterization of DifferentFerroTag Configurations

In this section to further reflect the actual performance in realpractice (eg the tag size the tag ink intensity the reading ratethe tag pattern complexity and substrate material) we conduct thefollowing experiments

Figure 15 Tag detection andrecognition with differentsizes

Figure 16 Performance un-der different ink densities

Tag Size The tag size is a crucial consideration in a real applicationwhich is related to the tag cost and the occupancy specifically wedesign 50 different advanced tags with four different size settingsbetween 10mmtimes 10mm (039intimes 039in) to 30mmtimes 30mm (118intimes118in) For each size setting we follow the same methodologydescribed in Section 7 and re-prepare the training and testing setFigure 15 shows the performance results For the smallest size of10mm times 10mm FerroTag only obtains the accuracy of 8722 Theresults are because the ferrofluidic patterns are too small to bedifferentiated by the mmWave signal After increasing the size theperformance gradually increases Generally we find that a turningpoint at 20mm times 20mm where the performance saturates afterward(reaching the accuracy of 9954) Considering the printed-basedtag eg barcode whose width is at least 30mm [10] the resultsimply that FerroTag is applicable for various applicationsTag Ink Density Intuitively it is well known that the ink densityof tags profoundly affects the tag cost Thus we propose to measurethe tag ink intensity (mLtag) that shows the ink amount usage formaking a tag We design 50 different advanced tags with six differ-ent density settings varying from 0004mLtaд to 0030mLtaд andevaluate how it affects the accuracy of the tag identification Figure16 shows the accuracy is only 927 For the lowest ink density of0004mLtaд FerroTag only obtains 9274 precision9262 recalland 9276 accuracy which shows that lower ink density can dimin-ish the accuracy The performance improves along the ink densitygrows until 0020mLtaд Then the performance keeps constantaround 9957 precision9952 recall and 9959 accuracy Thisobservation can guide us to select the proper density to ensure theidentification accuracy with a minimal cost in specific applicationsScanning Time We know that objects screening tasks are chal-lenging due to concise budgeted time for efficiency As a result weare interested in analyzing the performance of FerroTag concerningdifferent time budgets ie a different time needed for scanning onetag Specifically we select four different time settings between 5msto 200ms For each time setting we follow the same methodologydescribed in Section 7 and re-prepare the training and testing setFigure 17 shows the performance results For the lowest durationof 5ms FerroTag only obtains the accuracy of 9017 The results

Figure 17 Tag identificationwith different scanning time

Figure 18 Performance un-der different tag complexi-ties

are because the contained information in traces with 5ms cannotcomprehensively represent the characteristics of FerroTag Afterincreasing the scanning time the performance gradually increasesGenerally we find that a turning point at 20ms where the per-formance saturates afterward (reaching 9954 accuracy) whichmeans we can scan 50 tags per second which is a magnificentimprovement in scanning speed compared to the optical-basedsolutionsTag Pattern Complexity In the practical implementation the tagpattern can be highly customized to satisfy different manufacturemethods and appearance requirements To understand its impacton the identification accuracy we set basic group and advancedgroup according to the tag complexity and 50 different tags areemployed for each The results are shown in Figure 18 The identifi-cation accuracy for both groups remains high (above 991) whichimplies that the system performance is insensitive to the tag patterncomplexitySubstrate MaterialWe consider the scenario where the user mayuse different substrates to build the tags according to various appli-cations Particularly we collect four different daily-achievable mate-rials as shown in Figure 19 The cumulative distribution function isplotted in the figure where we can see that the overall identificationerror rate for each is less than 1 Certain materials slightly affectthe performance to some extent This is because FerroTag utilizeshigh-frequency signal and therefore has small wavelength andlimited penetration ability As a result it is prone to the scatteringreflection upon some specific materials But in general FerroTagstill provides reliable performance in tag recognition

Figure 19 The objects detection under different substratematerial

9 ROBUSTNESS ANALYSISPerformance in Different Distances To validate the usabilityand effectiveness of FerroTag in the non-contact identification sce-nario we set up the device to stimulate the operation distance from

30cm to 100cm considering the mmWave coverage of the tags inrelation to their response magnitude In this experiment 50 dif-ferent tags (see Section 7) are recruited Figure 20 manifests thattheir performances can achieve up to 9959 accuracy Besides theidentification performance remains above 99 when the sensingdistance varies within 1m Thereby FerroTag can facilitate in-situand convenient tag scanning in real practice

Figure 20 Performance under different sensing distances

Impact of Environmental Dynamics The ambient environmentcan introduce random noises or even interfere with the tag proper-ties or the probe hardware operation We consider common noisesin the daily workplace in terms of human factors and ambient fac-tors Typically we select four conditions where (1) the environmenttemperature is 10C (50F ) (2) the humidity of the testing locationis controlled at 70 (3) the magnetic field strength is set at 400microT (4) the light intensity is 1000Lux Moreover we use the result ofthe optimal lab environment as the comparison target (the tem-perature the humidity the magnetic field strength the ultrasoundwave and the light intensity are 20C (68F ) 30 50microT and 300Lux respectively) Again we evaluate the above four conditions using50 tags Figure 21 demonstrates that their performances all achieveabove 9914 In conclusion FerroTag presents a strong toleranceto different ambient environments

Figure 21 The tags detection under different environments

Under Blockage FerroTag usage scenarios may involve non-lineof sight (NLOS) environment eg in inventory management wherethe object (and tag) may be placed inside a package We mimicsuch NLOS cases by blocking the light of sight between the tagand probe using different materials paper wood glass and plasticSpecifically we evaluate the above four conditions using 50 tagsFigure 22 reveals that blockage has trivial impact on FerroTagrsquoscounting and identification The experiment verifies that FerroTagworks in NLOS scenarios where camera-based approaches will fail

Impact of ScanningOrientation In practical scenarios tags mayplace towards different orientations Therefore it is important to in-vestigate whether the sensing angle will affect system performance

Figure 22 The tags detection under different occlusions

Specifically we measure the different tag orientations (from 0 to180 ) The results are shown in Figure 23 As for the orientationthe effective sensing range is among (60-120) since the mmWavehas a narrow beam forming Although the reflected signal slightlychanges due to the different probe angles for each tag the inter-device distinguishability among 30 tags is significant such that eachtag can be correctly recognized Thereby FerroTag shows a strongcapacity in real practiceDurability Study Proving the permanence of identification is cru-cial Our generated dataset has included multiple sessions as partof a longitudinal approach to establishing a baseline comparison oflong-term persistence 30 different tags participated in the longi-tudinal study lasting three weeks as shown in Figure 24 Notablythis study has two phases the enrollment phase and authenticationphase In the enrollment phase training data were collected for eachsubject on the first day of this longitudinal study Each tag finishesfive trials in data collection events with the duration of each testset as 10 seconds After that the long-term authentication phase iscarried out in the following three weeks Each tag performed fiveidentification trials every 2 days and each identification durationis 5 seconds in this study The accuracy measurement is depictedin Fig 24 In the three weeks duration mean values of accuracymeasurement are between 99 and 100 and standard deviationsare between 022 and 051 We concluded that the accuracy has nosignificant performance decreasing or ascending tendency whichdemonstrates FerroTag is robust against aging in time

Figure 23 Impact of vary-ing sensing angles

Figure 24 A three-week in-vestigation on the accuracystability of FerroTag

10 A CASE STUDY ON COMPLEX SCENESAs the application environments of tags are mainly in retail ware-house or places where a large amount of coexisting tags hiddenin the package under NLOS environment we aim to count theirnumber and know their types immediately and conveniently with-out opening the package and protecting the privacy To achievethis goal there are two existing primary concerns in practice 1)the unavoidable variance in the signalrsquos scaling and magnitudeand 2) the interference from the containing items and the ambient

noises To explore whether FerroTag can identify multiple hiddentags simultaneously we continue with the setup in Section 7 Specif-ically we employ 6 tags and select four conditions where (i) and(iii) randomly select 2 tags out of 6 (ii) and (iv) randomly select 3tags out of 6 In (i) and (ii) tags are attached to cardboard boxeswithin the package box while in (iii) and (iv) tags are attached toT-shirts This study is a 6-by-6 scanning with different combinedWe enumerate all possible combinations in these conditions Figure25 shows the AoA range profile where the red color areas implytag locations and then we count the tags based on this range pro-file The identification results are illustrated in Figure 26 showingthat their performances keep between 97 to 99 This is owingto the fact that the FerroRF effects change if we combine two tagsalong with the FerroRF response To sum up FerroTag suffices asan accurate and reliable way of ultra-low cost and in-situ tagginginfrastructure in the real-world scene

Figure 25 The graph showsthe three tags counting

Figure 26 Identification ofcombined tags

11 DISCUSSIONPotential Health Hazard There are two aspects for the poten-tial health consideration wireless sensing and ferrofluidic printsCompared to other wireless sensing techniques (eg WiFi spots)FerroTag has a much smaller radiation factor eg a 12W powerconsumption and an 8dBm radio transmission power Besides theferrofluidic ink is not inherently dangerous or toxic and has beenapplied to the medical domain [17] Moreover the ferrofluidic printcan be naturally degradable [35 61] Therefore FerroTag is a consid-erably safe identification tool with limited health hazard concernsSecurity and Privacy Privacy-preserving is another fundamentalfactor for the object counting and identification as many ownershave extremely high requirements on their privacy protection Oursystem only requires the FerroRF response with a small informationdisclosure (ie mmWave signal) of the corresponding tagCustom Designed Patterns It is significant to customize the di-mensions of patterns as well as the paper substrate in FerroTag tosatisfy different application requirements The system robustnessto the tag size and pattern is discussed in Section 84Future Applications Beyond the use case of the mass objectscounting and identification owing to the concept of the FerroRFeffects and the advantages of ultra-low cost and in-situ FerroTagcan serve as a generic solution to extend the interaction area of IoTdevices extend the network to new physical dimensions or serveas the lsquofingertipsrsquo of the next-generation Internet It can also beapplied to a variety of applications eg agricultural monitoringcell tracking monitoring patients and anti-counterfeiting [29 5063 68]

12 RELATEDWORKPaper-based (Chipless) Electronics Paper-based electronics havebeen developed over the last few years There are three categoriesin terms of the way to interact The first one is the contact-basedsolution that (eg microfluidic paper [70] wet sensor [38] touchsensor [27 36] ubiquitous device [30 31 37 54 71] etc Howeverthey all need to measure the resistor inductor and capacitor (RLC)change in a contact manner which are not convenient The secondcategory is based on a non-contact optical method There are someresearch works like linear barcode [7] and QR code [72] Howeverthese solutions are limited by the line of sight requirement Thesolutions in the third category can work in a remote and invisibleway such as using the passive RFID [26 32] ink-tattoo ID [29 32]and mental-shape ID [23] However these solutions all employ theconductive ink or the complex toxic specific-designed chemical inkwhich is neither low-cost nor environment-friendly Up to date noexisting work utilizes the FerroRF effects to perform an ultra-lowcost and naturally degradable tagging infrastructuremmWave Sensing In recent years mmWave has been utilizedfor the material characterization and object detection There aretwo main mmWave sensing schemes In the first category thesetechnologies are based on the detection of objectsrsquo geometry andmicro-motion [34 43ndash45 52 80 86] These mmWave sensing worksmainly rely on analyzing the object boundaries and the Dopplereffect of moving objects thus their techniques cannot be appliedto sense the intrinsic tag pattern Second there are some applica-tions focusing on the material response for material componentcharacterization [57 58] and the non-linear response introduced byconductive components [40 41] when stimulated by the mmWavesignal FerroTag is the first mmWave sensing work to combine thematerial response and the pattern design for identification

13 CONCLUSIONIn this paper we presented a paper-based and mmWave-scannabletagging infrastructure FerroTag to promote the inventory manage-ment technologies FerroTag is easy to use and low-cost which is onthe basis of ferrofluidic ink on the paper print and its interference tothe mmWave signal In this study we modeled the FerroRF effects tooptimize the tag pattern design prototyped an end-to-end FerroTagsolution with a retrofited paper printer and a low-cost mmWaveprobe Also we developed a software framework of detecting andrecognizing a newly designed nested tag pattern in practice Exten-sive experiments including one case study with complex scenesimply that FerroTag can achieve the accuracy of 9954 in a 20ms re-sponse time Different levels of evaluation proved the effectivenessreliability and robustness of FerroTag FerroTag has a potential totransform the sensing to new physical dimensions and serve asthe object identity of the next-generation Internet

ACKNOWLEDGMENTSWe thank all anonymous reviewers for their insightful commentson this paper This work was in part supported by the NationalScience Foundation under grant No 1718375

REFERENCES[1] [n d] 4 Types of Inventory Control Systems ([n d]) httpswww2camcodec

omasset-tagsinventory-control-systems-types[2] [n d] Inventory Management ([n d]) httpssearcherptechtargetcomdefin

itioninventory-management[3] [n d] RFID vs Barcode Comparison Advantages amp Disadvantages ([n

d]) httpswwwpeak-ryzexcomarticlesrfid-vs-barcode-comparison-advantages-disadvantages

[4] 2013 Policy Sustainable development goals for people and planet Nature 4957441 (2013) 305 ndash 307 httpsearchebscohostcomgatelibbuffaloeduloginaspxdirect=trueampdb=mnhampAN=EPTOC86214517ampsite=ehost-liveampscope=site

[5] 2017 The Burgess Urban Land Use Model (Dec 2017) httpstransportgeographyorgpage_id=4908

[6] 2018 Printing Cost Calculator - Calculate Your Cost of Printing (Jul 2018)httpswwwuniprintnetenprinting-cost-calculator-calculate-cost

[7] Accessed 2019-1-13 GS1 BARCODE CHART httpwwwgs1usorgresourcesstandardsean-upc-visuals

[8] Accessed 2019-1-3 MakingCosmetics - Triethanolamine - 20floz 60ml -Cosmetic Ingredient httpswwwamazoncomMakingCosmetics-Triethanolamine-2-0floz-60mldpB01GDZYRK4ref=sr_1_fkmrnull_1

[9] Accessed 2019-1-9 Staples Copy Paper Multi-Purpose Copier and FaxMachine Carton Letter Size Acid Free 92 Bright 20 lb White 5000SheetsCase httpswwwamazoncomStaples-Multi-Purpose-Copier-Machine-CartondpB00FGIFL0Aref=sr_1_3

[10] Accessed 2019-2-15 Barcode Dimensions httpsworldbarcodescombarcode-standards

[11] Accessed 2019-2-16 Crest factor httpsenwikipediaorgwikiCrest_factor[12] Accessed 2019-2-3 Epson Expression Home XP-440 Wireless Color

Photo Printer with Scanner and Copier Amazon Dash ReplenishmentEnabled httpswwwamazoncomgpproductB06W9K5FD2ref=oh_aui_search_asin_titleie=UTF8amppsc=1

[13] Accessed 2019-4-5 10 Shocking Inventory Management Statistics httpsblogcapterracominventory-management-statistics

[14] Accessed 2019-4-5 Everything You Need to Know About InventoryManagement httpswwwwarehouseanywherecomresourcesinventory-management

[15] Accessed 2019-4-5 Which area are you investing in most heavily to evolve yourinventory management practices httpswwwstatistacomstatistics780763inventory-management-investments-retailers-manufacturers

[16] Accessed 2019-8-10 YARONGTECH UHF tag httpswwwamazoncomYARONGTECH-860-960MHZ-Alien-73-5x21-2mm-AdhesivedpB01L97ULR4[2]

[17] Christoph Alexiou Roland Jurgons Roswitha J Schmid Christian BergemannJulia Henke Wolf Erhard Ernst Huenges and Fritz Parak 2003 Magnetic drugtargeting - biodistribution of themagnetic carrier and the chemotherapeutic agentmitoxantrone after locoregional cancer treatment Journal of drug targeting 113 (2003) 139ndash149

[18] F Guillaume Blanchet Pierre Legendre and Daniel Borcard 2008 Forwardselection of explanatory variables Ecology 89 9 (2008) 2623ndash2632

[19] Christian Carlowitz Axel Strobel Tobias Schaumlfer Frank Ellinger and MartinVossiek 2012 A mm-wave RFID system with locatable active backscatter tagIn 2012 IEEE International Conference onWireless Information Technology andSystems (ICWITS) IEEE 1ndash4

[20] Luca Catarinucci Danilo De Donno Matteo Guadalupi Fabio Ricciato and Lu-ciano Tarricone 2011 Performance analysis of passive UHF RFID tags with GNU-radio In 2011 IEEE International Symposium on Antennas and Propagation(APSURSI) IEEE 541ndash544

[21] Shilin Chen Hui Yao Jianping Han Chang Liu Jingyuan Song Linchun ShiYingjie Zhu Xinye Ma Ting Gao Xiaohui Pang et al 2010 Validation of theITS2 region as a novel DNA barcode for identifying medicinal plant species PloSone 5 1 (2010) e8613

[22] Robert I Davis Wei Qiu Nicholas J Heyer Yiming Zhao MS Qiuling Yang NanLi Liyuan Tao Liangliang Zhu Lin Zeng Daohua Yao et al 2012 The use ofthe kurtosis metric in the evaluation of occupational hearing loss in workers inChina Implications for hearing risk assessment Noise and Health 14 61 (2012)330

[23] Gerald DeJean Vasileios Lakafosis Anya Traille Hoseon Lee Edward GebaraManos Tentzeris and Darko Kirovski 2011 RFDNA A wireless authenticationsystem on flexible substrates In 2011 IEEE 61st Electronic Components andTechnology Conference (ECTC) IEEE 1332ndash1337

[24] Sanorita Dey Nirupam Roy Wenyuan Xu Romit Roy Choudhury and SrihariNelakuditi 2014 AccelPrint Imperfections of Accelerometers Make SmartphonesTrackable In NDSS

[25] Jerome Friedman Trevor Hastie and Robert Tibshirani 2001 The elements ofstatistical learning Vol 1 Springer series in statistics New York

[26] Chuhan Gao Yilong Li and Xinyu Zhang 2018 LiveTag Sensing human-objectinteraction through passive chipless WiFi tags In 15th USENIX Symposiumon Networked Systems Design and Implementation (NSDI 18) 533ndash546

[27] Chuhan Gao Xinyu Zhang and Suman Banerjee 2018 Conductive Inkjet PrintedPassive 2D TrackPad for VR Interaction In Proceedings of the 24th AnnualInternational Conference on Mobile Computing and Networking ACM 83ndash98

[28] Jerry Zeyu Gao Lekshmi Prakash and Rajini Jagatesan 2007 Understanding 2d-barcode technology and applications in m-commerce-design and implementationof a 2d barcode processing solution In 31st Annual International ComputerSoftware and Applications Conference (COMPSAC 2007) Vol 2 IEEE 49ndash56

[29] Moshe Glickstein 2004 Firewall protection for paper documents RFID Journalinternet article Feb (2004)

[30] Nan-Wei Gong Steve Hodges and Joseph A Paradiso 2011 Leveraging con-ductive inkjet technology to build a scalable and versatile surface for ubiqui-tous sensing In Proceedings of the 13th international conference on Ubiquitouscomputing ACM 45ndash54

[31] Nan-Wei Gong Steve Hodges and Joseph A Paradiso 2011 LeveragingConductive Inkjet Technology to Build a Scalable and Versatile Surface forUbiquitous Sensing In Proceedings of the 13th International Conference onUbiquitous Computing (UbiComp rsquo11) ACM New York NY USA 45ndash54 httpsdoiorg10114520301122030120

[32] Takahiro Hashizume Takuya Sasatani Koya Narumi Yoshiaki Narusue YoshihiroKawahara and Tohru Asami 2016 Passive and contactless epidermal pressuresensor printed with silver nano-particle ink In Proceedings of the 2016 ACMInternational Joint Conference on Pervasive and Ubiquitous Computing ACM190ndash195

[33] Cristian Herrojo Jordi Naqui Ferran Paredes and Ferran Martiacuten 2016 Spectralsignature barcodes implemented by multi-state multi-resonator circuits for chip-less RFID tags In 2016 IEEE MTT-S International Microwave Symposium (IMS)IEEE 1ndash4

[34] M C Huang J J Liu W Xu C Gu C Li and M Sarrafzadeh 2016 A Self-Calibrating Radar Sensor System for Measuring Vital Signs IEEE Transactionson Biomedical Circuits and Systems 10 2 (April 2016) 352ndash363

[35] Robert Kaiser 1972 Ferrofluid composition (Oct 24 1972) US Patent 3700595[36] Ccedilağdaş Karataş and Marco Gruteser 2015 Printing multi-key touch interfaces In

Proceedings of the 2015 ACM International Joint Conference on Pervasive andUbiquitous Computing ACM 169ndash179

[37] Yoshihiro Kawahara Steve Hodges Benjamin S Cook Cheng Zhang and Gre-gory D Abowd 2013 Instant inkjet circuits lab-based inkjet printing to sup-port rapid prototyping of UbiComp devices In Proceedings of the 2013 ACMinternational joint conference on Pervasive and ubiquitous computing ACM363ndash372

[38] Yoshihiro Kawahara Hoseon Lee and Manos M Tentzeris 2012 SensproutInkjet-printed soil moisture and leaf wetness sensor In Proceedings of the 2012ACM Conference on Ubiquitous Computing ACM 545ndash545

[39] Benjamin LrsquoHuillier and Arman Shafieloo 2017 Model-independent test of theFLRW metric the flatness of the Universe and non-local estimation of H0 rdJournal of Cosmology and Astroparticle Physics 2017 01 (2017) 015

[40] Zhengxiong Li Fenglong Ma Aditya Singh Rathore Zhuolin Yang BaichengChen Lu Su and Wenyao Xu 2020 WaveSpy Remote and Through-wall ScreenAttack via mmWave Sensing In To appear in IEEE Symposium on Security andPrivacy 2020 (SampPrsquo20)

[41] Zhengxiong Li Zhuolin Yang Chen Song Changzhi Li Zhengyu Peng andWenyao Xu 2018 E-Eye Hidden Electronics Recognition through mmWaveNonlinear Effects In Proceedings of the 16th ACM Conference on EmbeddedNetworked Sensor Systems ACM 68ndash81

[42] Andy Liaw Matthew Wiener et al 2002 Classification and regression by ran-domForest R news 2 3 (2002) 18ndash22

[43] Jaime Lien Nicholas Gillian M Emre Karagozler Patrick Amihood CarstenSchwesig Erik Olson Hakim Raja and Ivan Poupyrev 2016 Soli UbiquitousGesture Sensing with Millimeter Wave Radar ACM Trans Graph 35 4 Article142 (July 2016) 19 pages httpsdoiorg10114528978242925953

[44] Feng Lin Chen Song Yan Zhuang Wenyao Xu Changzhi Li and Kui Ren 2017Cardiac Scan A Non-contact and Continuous Heart-based User AuthenticationSystem In Proceedings of the 23rd Annual International Conference on MobileComputing andNetworking (MobiCom rsquo17) ACM NewYork NY USA 315ndash328

[45] F Lin Y Zhuang C Song AWang Y Li C Gu C Li andW Xu 2017 SleepSenseA Noncontact and Cost-Effective Sleep Monitoring System IEEE Transactionson Biomedical Circuits and Systems 11 1 (Feb 2017) 189ndash202

[46] BassemRMahafza 2017 Introduction to radar analysis Chapman andHallCRC[47] Heikki Mannila and Pekka Orponen 2010 Algorithms and Applications

Springer[48] Wenguang Mao Mei Wang and Lili Qiu 2018 AIM Acoustic Imaging on a

Mobile In Proceedings of the 16th Annual International Conference on MobileSystems Applications and Services (MobiSys rsquo18) ACM New York NY USA468ndash481 httpsdoiorg10114532102403210325

[49] Kanti V Mardia 1970 Measures of multivariate skewness and kurtosis withapplications Biometrika 57 3 (1970) 519ndash530

[50] Moataz M Mekawy Atsushi Saito Akira Sumiyoshi Jorge J Riera HiroakiShimizu Ryuta Kawashima and Teiji Tominaga 2019 Hybrid magneto-fluorescent nano-probe for live apoptotic cells monitoring at brain cerebral

ischemia Materials Science and Engineering C (2019)[51] Adriano Meta Peter Hoogeboom and Leo P Ligthart 2007 Signal processing

for FMCW SAR IEEE Transactions on Geoscience and Remote Sensing 45 11(2007) 3519ndash3532

[52] I V Mikhelson S Bakhtiari T W Elmer II and A V Sahakian 2011 RemoteSensing of Heart Rate and Patterns of Respiration on a Stationary Subject Us-ing 94-GHz Millimeter-Wave Interferometry IEEE Transactions on BiomedicalEngineering 58 6 (June 2011) 1671ndash1677 httpsdoiorg101109TBME20112111371

[53] Joseacute M Muntildeoz-Ferreras and Feacutelix Peacuterez-Martiacutenez 2010 Subinteger range-binalignment method for ISAR imaging of noncooperative targets EURASIP Journalon Advances in Signal Processing 2010 (2010) 14

[54] Koya Narumi Steve Hodges and Yoshihiro Kawahara 2015 ConductAR anaugmented reality based tool for iterative design of conductive ink circuits InProceedings of the 2015 ACM International Joint Conference on Pervasive andUbiquitous Computing ACM 791ndash800

[55] Pavel V Nikitin Sander Lam and KVS Rao 2005 Low cost silver ink RFIDtag antennas In 2005 IEEE Antennas and Propagation Society InternationalSymposium Vol 2 IEEE 353ndash356

[56] Pavel V Nikitin KVS Rao and Roberto D Martinez 2007 Differential RCS ofRFID tag Electronics Letters 43 8 (2007) 431ndash432

[57] Turgut Ozturk 2019 Characterization of Liquids Using Electrical Properties inMicrowave and Millimeter Wave Frequency Bands Journal of NondestructiveEvaluation 38 1 (2019) 11

[58] Turgut Ozturk 2019 Classification of measured unsafe liquids using microwavespectroscopy system by multivariate data analysis techniques Journal ofhazardous materials 363 (2019) 309ndash315

[59] Zhengyu Peng Joseacute-Mariacutea Muntildeoz-Ferreras Roberto Goacutemez-Garciacutea Lixin Ranand Changzhi Li 2016 24-GHz biomedical radar on flexible substrate for ISARimaging In Wireless Symposium (IWS) 2016 IEEE MTT-S International IEEE1ndash4

[60] Zhengyu Peng Lixin Ran and Changzhi Li 2015 A 24-GHz low-cost contin-uous beam steering phased array for indoor smart radar In 2015 IEEE 58thInternational Midwest Symposium on Circuits and Systems (MWSCAS) IEEE1ndash4

[61] R Perez-Castillejos J Esteve MC Acero and JA Plaza 2001 Ferrofluids fordisposable microfluidic systems In Micro Total Analysis Systems 2001 Springer492ndash494

[62] Jukka Perkiouml and Aapo Hyvaumlrinen 2009 Modelling image complexity by inde-pendent component analysis with application to content-based image retrievalIn International Conference on Artificial Neural Networks Springer 704ndash714

[63] Stevan Preradovic Isaac Balbin and Nemai Karmakar 2008 The developmentand design of a novel chipless RFID system for low-cost item tracking In 2008Asia-Pacific Microwave Conference IEEE 1ndash4

[64] Zakariya Qawaqneh Arafat Abu Mallouh and Buket D Barkana 2017 Deepneural network framework and transformed MFCCs for speakerrsquos age and genderclassification Knowledge-Based Systems 115 (2017) 5ndash14

[65] Jie Ren Lin Wang Yuefeng Mo Chen Feng Yunxin Ouyang Hui Li and JunYin 2019 Illuminator for a barcode scanner (Feb 21 2019) US Patent App16055776

[66] Peng Rong and Mihail L Sichitiu 2006 Angle of arrival localization for wirelesssensor networks In 2006 3rd annual IEEE communications society on sensorand ad hoc communications and networks Vol 1 Ieee 374ndash382

[67] Md Sahidullah and Goutam Saha 2012 Design analysis and experimentalevaluation of block based transformation in MFCC computation for speakerrecognition Speech Communication 54 4 (2012) 543ndash565

[68] Adeel Saleem Atif Iqbal Adnan Sabir and Adil Hussain 2019 Real-Time PhysicalChanging 3d Display using Magnetic Liquid Technology for Visually ImpairedPatients In 2019 2nd International Conference on Computing Mathematics andEngineering Technologies (iCoMET) IEEE 1ndash5

[69] S Scherr S Ayhan B Fischbach A Bhutani M Pauli and T Zwick 2015 AnEfficient Frequency and Phase Estimation Algorithm With CRB Performancefor FMCW Radar Applications IEEE Transactions on Instrumentation andMeasurement 64 7 (July 2015) 1868ndash1875 httpsdoiorg101109TIM20142381354

[70] Hiroyuki Shibata Yuki Hiruta and Daniel Citterio 2019 Fully inkjet-printeddistance-based paper microfluidic devices for colorimetric calcium determinationusing ion-selective optodes Analyst (2019)

[71] Tung Ta Masaaki Fukumoto Koya Narumi Shigeki Shino Yoshihiro Kawaharaand Tohru Asami 2015 Interconnection and double layer for flexible electroniccircuit with instant inkjet circuits In Proceedings of the 2015 ACM InternationalJoint Conference on Pervasive and Ubiquitous Computing ACM 181ndash190

[72] Iuliia Tkachenko William Puech Christophe Destruel Olivier Strauss Jean-Marc Gaudin and Christian Guichard 2016 Two-level QR code for private mes-sage sharing and document authentication IEEE Transactions on InformationForensics and Security 11 3 (2016) 571ndash583

[73] DA Usanov AV Skripal and SA Ermolaev 1996 Microwave and UltrasoundMethods for Determining the Size of Ferromagnetic Particles and Magnetic

Fluid Agglomerates MAGNETOHYDRODYNAMICS CC OF MAGNITNAIAGIDRODINAMIKA 32 (1996) 481ndash486

[74] DA Usanov Al V Skripal An V Skripal and AV Kurganov 2001 Determina-tion of the magnetic fluid parameters from the microwave radiation reflectioncoefficients Technical Physics 46 12 (2001) 1514ndash1517

[75] Marco Vari and Dajana Cassioli 2014 mmWaves RSSI indoor network local-ization In 2014 IEEE International Conference on Communications Workshops(ICC) IEEE 127ndash132

[76] Arnaud Vena Etienne Perret Smail Tedjini Guy Eymin Petot Tourtollet Anasta-sia Delattre Freacutedeacuteric Garet and Yann Boutant 2013 Design of chipless RFIDtags printed on paper by flexography IEEE Transactions on Antennas andPropagation 61 12 (2013) 5868ndash5877

[77] W Voit DK Kim W Zapka M Muhammed and KV Rao 2001 Magnetic behaviorof coated superparamagnetic iron oxide nanoparticles in ferrofluids MRS OnlineProceedings Library Archive 676 (2001)

[78] Anran Wang Vikram Iyer Vamsi Talla Joshua R Smith and Shyamnath Gol-lakota 2017 FM Backscatter Enabling Connected Cities and Smart Fabrics In14th USENIX Symposium onNetworked SystemsDesign and Implementation(NSDI 17) 243ndash258

[79] Chen Wang Jian Liu Yingying Chen Hongbo Liu and Yan Wang 2018 TowardsIn-baggage Suspicious Object Detection Using Commodity WiFi 2018 IEEEConference on Communications and Network Security (CNS) (2018) 1ndash9

[80] Teng Wei and Xinyu Zhang 2015 mtrack High-precision passive trackingusing millimeter wave radios In Proceedings of the 21st Annual InternationalConference on Mobile Computing and Networking ACM 117ndash129

[81] Teng Wei and Xinyu Zhang 2016 Gyro in the air tracking 3D orientation ofbatteryless internet-of-things In Proceedings of the 22nd Annual InternationalConference on Mobile Computing and Networking ACM 55ndash68

[82] Peter Welch 1967 The use of fast Fourier transform for the estimation of powerspectra a method based on time averaging over short modified periodogramsIEEE Transactions on audio and electroacoustics 15 2 (1967) 70ndash73

[83] Klaus Witrisal Paul Meissner Erik Leitinger Yuan Shen Carl Gustafson FredrikTufvesson Katsuyuki Haneda Davide Dardari Andreas F Molisch Andrea Contiet al 2016 High-accuracy localization for assisted living 5G systems will turnmultipath channels from foe to friend IEEE Signal Processing Magazine 33 2(2016) 59ndash70

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[86] Yibo Zhu Yanzi Zhu Zengbin Zhang Ben Y Zhao and Haitao Zheng 201560GHz mobile imaging radar In Proceedings of the 16th International Workshopon Mobile Computing Systems and Applications ACM 75ndash80

Abstract

1 Introduction

2 Background and Preliminaries

21 FerroRF Effects

22 Modeling on FerroRF Effects

23 The FerroRF Effects on Tags

3 FerroTag System Overview

4 Tag Design and Implementation

41 Basic Tag Pattern Study

42 Prototyping of FerroTag

5 FerroTag Advancement

6 mmWave-Scannable Identification Scheme

61 FerroRF Response Signal Acquisition

62 Signal Preprocessing

63 Spatial-temporal FerroTag Intrinsic Fingerprint Extraction

64 FerroTag Identification Algorithm

65 Multiple Tags Counting and Identification

7 System Prototype and Evaluation Setup

8 FerroTag System Evaluation

81 Identification Performance

82 FerroTag Sensing Tests

83 Tag Uniqueness

84 Performance Characterization of Different FerroTag Configurations

9 Robustness Analysis

10 A Case Study on Complex Scenes

11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

Figure 1 FerroTag a paper-based mmWave-scannable tag-ging infrastructure can replace the traditional tagging tech-nologies for mass product counting and identification in in-ventory management

disposable and naturally degradable (3) Battery-free the tag iscompletely passive requiring no power supply (4) In-situ multi-ple tags are accurately read by a scanner outside the line-of-sightAs shown in Figure 1 the application scenes of FerroTag includemanufacturing factories warehouses and retail offices

Specifically FerroTag is a paper-based mmWave-scannable tag-ging infrastructure A pattern printed by naturally degradable inkis served as an identity [61] Thus it is highly economical environ-mentally harmless and fully passive in its origin In order to realizeFerroTag we need to address two technical challenges in this work(a) design and implement a paper-based mmWave-scannable tagginginfrastructure for the inventory management system The foundationof FerroTag rests on the FerroRF effects When RF signals meetsurfaces and objects a responsive RF signal will be generated [78]In our application when there is an RF signal passing through a fer-rofluidic ink print will generate a recognizable response (hereafterthe FerroRF response) which is associated with the ferrofluidic printpattern (ie the tag identity) To retrieve and recognize the identitya fine-grained response analysis protocol is developed First of allto protect the FerroRF response from distortions a range resolutionanalysis and an envelope correlation function are applied Secondlywe extract a set of critical scalar features (n = 25) including tenmost impactful ones based on Mel-Frequency Cepstral CoefficientsAt the end the selected features are fed into a classifier containinga cluster of decision trees (m = 150) for identification In additionby analyzing the angle of arrival multiple tags can be simultane-ously detected and identified (b) Model and optimization of theFerroRF effects for high capacity We first investigate and establish amathematical model of the FerroRF effects The FerroRF responseis generated by the magnetic nanoparticles within the ferrofluidicink Since the quantity and the arrangement (ie three-dimensionallocations) of particles are varying along with any variation in aferrofluidic ink printed pattern a unique pattern can be served as anon-contact retrievable identity as it can generate a unique FerroRFresponse Secondly with the in-depth modeling and understanding

of the FerroRF effects and response we study a systematic approachto optimize the variation of the FerroRF effects by designate tag pat-terns such that it contains only succinct geometric features but canbe used to room high-capacity identities in the tagging infrastruc-ture More specifically we investigate and evaluate an innovativenested pattern for tagging design in FerroTag which can providea large number of identities with regulations to a vast amount ofproducts in inventories

Our contribution can be summarized in three-fold

bull We investigate the FerroRF effects and discover the mag-netic nanoparticles within the ferrofluidic ink modulatesprobing mmWave with classifiable characteristics Moreoverwe model and advance the FerroRF infrastructure with aninnovative nested tag patternbull We design and implement FerroTag with one working proto-type machine Specifically we develop a new hardware andsoftware stack support and retrofit a commodity printer intothe application of FerroTag fabrication It is a paper-basedmmWave-scannable tagging infrastructure Each tag is madewith low-cost naturally degradable paper and ferrofluidicink Outside the line-of-sight multiple tags can be remotelyscanned by a cost-efficient mmWave probe to extract theidentitiesbull We evaluate FerroTag from three aspects First we study thespec of our infrastructure including cost time budget sens-ing accuracy and more Second we test the reliability withvariances in sensing distances orientations environmentsbarriers and more Finally we investigate the performanceof FerroTag and measure the stability in a case study oncomplex scenes

2 BACKGROUND AND PRELIMINARIES21 FerroRF EffectsFerrofluidic ink is colloidal liquids whose core components aremagnetic nanoparticles (eg ferrite compound-Magnetite powder)carrier fluid that suspends the nanoparticles (eg organic solvent)and the surfactant that coats each magnetic nano-particles [77]The quantity and the arrangement of these magnetic nanoparticlesreflect into the unique characteristic frequency responses whentags are probed by broadband radio frequency (RF) signals as shownin Figure 2 When a fundamental tone is passed through the fer-rofluidic ink magnetic nanoparticles modulate the response signaland generate additional frequency tones besides the fundamentalone as formulated in Section 22

Figure 2 The ferrofluidic pattern generates a FerroRF re-sponse under the RF beam The FerroRF response is associ-ated with intrinsic physical characteristics of tag pattern

hf (t ) (1)

Rf t (τ ) =Rf t (τ )1Rf t (τ )2

γ 2(τ ) =π 2

χmprime(τ ) =

γ τML(σ )ωHn

(1 + η2)2Hn4 + (η2 minus 1)Hn

(1 + η2)2Hn4 + 2(η2 minus 1)Hn

ηHn2(1 + Hn

2(1 + η2))(1 + η2)2Hn

4 + 2(η2 minus 1)Hn2 + 1

(Nminus1)

Category Features

Temporal Feature

Others MFCC-10 [64]

Bm [k ] =

fmminus1 le k le fm

fm le k le fm+1

Y [m] = log(fm+1sum

k=fmminus1

|X [k ] |2Bm [k ]) (10)

c[n] =1M

Msumm=1

) (11)

λ (12)

ν∆dmax cosθ

represented as

ξ =Pr

PtGtGr

(4π )3d4

λ2 (14)

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

hf (t ) (1)

Rf t (τ ) =Rf t (τ )1Rf t (τ )2

γ 2(τ ) =π 2

χmprime(τ ) =

γ τML(σ )ωHn

(1 + η2)2Hn4 + (η2 minus 1)Hn

(1 + η2)2Hn4 + 2(η2 minus 1)Hn

ηHn2(1 + Hn

2(1 + η2))(1 + η2)2Hn

4 + 2(η2 minus 1)Hn2 + 1

(Nminus1)

Category Features

Temporal Feature

Others MFCC-10 [64]

Bm [k ] =

fmminus1 le k le fm

fm le k le fm+1

Y [m] = log(fm+1sum

k=fmminus1

|X [k ] |2Bm [k ]) (10)

c[n] =1M

Msumm=1

) (11)

λ (12)

ν∆dmax cosθ

represented as

ξ =Pr

PtGtGr

(4π )3d4

λ2 (14)

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

(Nminus1)

Category Features

Temporal Feature

Others MFCC-10 [64]

Bm [k ] =

fmminus1 le k le fm

fm le k le fm+1

Y [m] = log(fm+1sum

k=fmminus1

|X [k ] |2Bm [k ]) (10)

c[n] =1M

Msumm=1

) (11)

λ (12)

ν∆dmax cosθ

represented as

ξ =Pr

PtGtGr

(4π )3d4

λ2 (14)

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

(Nminus1)

Category Features

Temporal Feature

Others MFCC-10 [64]

Bm [k ] =

fmminus1 le k le fm

fm le k le fm+1

Y [m] = log(fm+1sum

k=fmminus1

|X [k ] |2Bm [k ]) (10)

c[n] =1M

Msumm=1

) (11)

λ (12)

ν∆dmax cosθ

represented as

ξ =Pr

PtGtGr

(4π )3d4

λ2 (14)

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

(Nminus1)

Category Features

Temporal Feature

Others MFCC-10 [64]

Bm [k ] =

fmminus1 le k le fm

fm le k le fm+1

Y [m] = log(fm+1sum

k=fmminus1

|X [k ] |2Bm [k ]) (10)

c[n] =1M

Msumm=1

) (11)

λ (12)

ν∆dmax cosθ

represented as

ξ =Pr

PtGtGr

(4π )3d4

λ2 (14)

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

Category Features

Temporal Feature

Others MFCC-10 [64]

Bm [k ] =

fmminus1 le k le fm

fm le k le fm+1

Y [m] = log(fm+1sum

k=fmminus1

|X [k ] |2Bm [k ]) (10)

c[n] =1M

Msumm=1

) (11)

λ (12)

ν∆dmax cosθ

represented as

ξ =Pr

PtGtGr

(4π )3d4

λ2 (14)

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

λ (12)

ν∆dmax cosθ

represented as

ξ =Pr

PtGtGr

(4π )3d4

λ2 (14)

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

represented as

ξ =Pr

PtGtGr

(4π )3d4

λ2 (14)

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

Abstract

1 Introduction

21 FerroRF Effects


















83 Tag Uniqueness




11 Discussion

12 Related Work

13 Conclusion

Acknowledgments

References

Documents

FerroTag: A Paper-based mmWave-Scannable Tagging ...wenyaoxu/papers/conference/xu-sensys2019.pdfFerroTag: A Paper-based mmWave-Scannable Tagging Infrastructure SenSys ’19, November