Device-free localization methods allow users to benefit from location-aware services and smart environments without the need to wear a transponder or carry a mobile device continuously. However, conventional radio tomographic imaging approaches that place active wireless sensor nodes around the perimeter of a living space for localization require wired power or continual battery maintenance, limiting usability and deployability. We present a real-time multi-user UHF RFID tomographic localization system that employs a novel signal processing pipeline that uses communication channel parameters such as RSSI, RF Phase, and Read Rate to create tomograms which are processed by our custom-designed convolutional neural network to predict user's locations. Results show that our system is highly accurate, with an average mean error of 17.0 cm for a stationary user and 20.2 cm when walking and moving. We also demonstrate multi-user tracking with an average mean error of 39.4 cm. Overall, the method empowers a minimally intrusive, scalable, and deployable system for locating un-instrumented users in indoor environments.

A welcome address to open the 2nd annual Workshop on Wireless Motion Capture and Fine-scale Localization as well as an overview for the newly-formed IEEE CRFID Technical Committee on Motion Capture and Localization. A summary of TC-MoCap's mission and future plans are included. These include additional workshops at two more CRFID-sponsored conferences later this year (IEEE RFID-TA and IEEE WiSEE) as well as a special issue call for papers for IEEE Journal on RFID in the area of Motion Capture and Localization.

Systems integrators often struggle to understand and interpret radiation patterns of reader antennas to set up RFID read points to efficiently detect intended tags and avoid reading unwanted or stray tags. This poster presents a guide to make RFID deployments easier.

Investigating the identification probability of passive acoustoelectronic sensors and identification tags needs to carry out a comparative assessment of the characteristics of signal detectors under different distribution. The paper compares noise with a Gaussian distribution and a non-Gaussian distribution that has heavier tails than the normal. Thus, the paper illustrates a comparative estimation of the detector's characteristics. The results obtained provide adjustment tools for the detection thresholds Changement. This feature allows us to fix the probability of a false alarm.

Differential RCS characterizes the aptitude of a tag to modulate the backscattered power and is classically estimated based on the variation of the in-phase and quadrature channels in the time domain. This paper presents the results detailed in [1] which introduces a generalization of the RCS backscattered by a tag and a new definition of the delta RCS in the frequency domain. The associated measurement is compared to the classical time domain based methods. Results show a good agreement with the different approaches with an error less than 0.5 dB and are only at 3.5 dB from the introduced bound.

Of the more than 2200 satellites orbiting the earth, most are positioned in Low Earth Orbits (LEO) with extremely large angular velocities. The International Space Station, for example, orbits the earth in 90 min. Such an object stays within the Field of View of a particular point on earth for merely 3-5 min. In order to communicate with such satellites, the position of the satellite is tracked using certain algorithms such as the "Signal Based" and the "Program Based" methods [1]. The Signal and Program based methods of tracking are dead reckoning methods with a very high computational complexity and large margins of error. This creates challenges in the efficient communication or relay of messages and commands to LEO satellites. The rapid speed with which the satellites move across a ground RADAR's Field of View makes it extremely difficult to accurately predict and track a satellite as it moves along the sky, and present day trackers usually lag or show significant deviations from the actual satellite position. Satellite trackers typically compensate by spreading a high power beam over a larger solid angle, which leads to huge power wastage as well. The present effort proposes a Control Architecture for a system comprising of antenna arrays, motor controls and computer systems with fast interconnects for efficient and quick prediction of the position of a satellite along with sensors to handle feedback in order to reduce sensitivity to disturbances. Rotational Reconfigurable Antennas (RRAs) [2] prove to be extremely efficient in both producing narrow band beams in specific directions, and in simplifying the hardware required for transmitting and receiving signals. They serve as efficient abstractions of the traditional Phased Antenna Arrays. A time varying trajectory tracker algorithm has been proposed for the plant comprising of servo motors actuating RRA cells to produce beams having accurate azimuth and elevation throughout the time a satellite stays in the RRA array's field of view. The system was found to be controllable, observable and commandable and the proposed trajectory tracker behaves as though the disturbance is the only input to the overall system. Further prospects for optimal control using the Pareto Optimal Curve and Tichonov Regularization will also be discussed.

This project proposes a highly efficient, novel and low cost method for tracking fast moving satellites using RF transmission and reception with minimum number of moveable parts and hence involves minimal energy dissipation compared to the traditional tracking systems. It also demonstrates a proof of concept and a basic embedded system set up with Servo Motors and RRA cells fabricated at the Interdisciplinary Design Commons. It is therefore highly relevant as an exploratory research field at the IEEE RFID Conference. This poster outlines work conducted as part of the year long Opportunity Research Scholars Program at Georgia Tech in collaboration with Viasat. It delineates the prototype of the satellite tracker and the underlying principles for optimal positioning of the beam. It also demonstrates potential optimization algorithms we shall be implementing as part of future work involving deploying a working prototype for Viasat. The authors believe that this novel method for efficient low cost LEO Satellite tracking can benefit from such a platform as the conference.

References [1] A. Khanlari and F. MansourKiaie, "A new efficient algorithm for tracking LEO satellites," 2013 IEEE International Systems Conference (SysCon), Orlando, FL, pp. 587-590, 2013. [2] X. Yang et al., "A Broadband High-Efficiency Reconfigurable Reflectarray Antenna Using Mechanically Rotational Elements," IEEE Trans. Antennas Propagat., vol. 65, no. 8, pp. 3959-3966, Aug. 2017.

Recent industries have tried to use Radio Frequency Identification (RFID) to improve their services. The direction estimation scheme for tag movements is the recent interest to track objects with an RFID tag. This paper proposes a new direction estimation scheme of moving tags with an angled single antenna that is inexpensive and general antenna. The idea of the proposed scheme is to focus on asymmetric radio radiation with an angled antenna. The proposed scheme can estimate the direction of a tag movement based on the Received Signal Strength Indicator (RSSI) features and the phase value. The evaluation results showed that the proposed scheme could realize an accurate estimation for tag movements.

RFID and backscatter allow for extremely low-power or battery-free tags by outsourcing the generation of the radio carrier wave to an external device such as an RFID reader. Recent advances in backscatter communication enables tags to both transmit and receive standards-compliant packets with sub-milliwatt power consumption. The ability to receive packets makes multi-hop tag-to-tag networking possible, a task that current backscatter networks currently provide only for limited topologies. Tag-to-tag networks further allow for novel applications such as wireless robotic materials that inherently require dense networks of such tags. In contrast to conventional networks, the tags' communication range in such networks depends heavily on the signal strength of the carrier wave at the transmitter and the receiver. In this paper, we demonstrate for the first time on real hardware multi-hop in backscatter-based networks using standards-based protocols. We present analytical and simulation results that show that both the output power and the position of the carrier generator impact the reliability of the network. We finally demonstrate that simple flooding with a random forwarding delay is an efficient solution for transfering data in such networks.

With the ever increasing requirement of compact and lightweight RF devices, there is a need to miniaturize the antennas which are one of the largest RF components, particular at lower frequencies, such as RFID band. It is well known that the radiation performance of antennas degrades as their size becomes smaller. For compact designs, the radiating element may be very close to the ground plane or there may be a phase difference of 180° between adjacent radiating elements, particularly for meandered structures. The challenge, therefore, lies on the compromise between size and performance. Antenna-in Package concept, where the antenna is realized on the package of the driving electronics, can be beneficial in such cases. In this work, a half-wave lambda dipole, which is part of a UHF RFID tag, has been miniaturized by combining three techniques, namely, by folding the antenna on the RFID chip package, by realizing the antenna on a relatively high dielectric constant material, and by loading the antenna with slow-wave structures. Firstly, the dipole is carefully folded over a 20 x 18 x 10 cm box, in a manner that the currents on different segments of the antenna do not add in a destructive fashion. Secondly, a substrate with a dielectric constant of 5.3 has been chosen to slow down the electromagnetic waves. Next, slow wave structures, comprising of inductors and capacitors, have been used to achieve further miniaturization. The artificial transmission line approach has been utilized to determine the required values of the series inductors and shunt capacitors. The location of the slow wave components has also been studied in order to achieve a high level of miniaturization while maintaining a decent radiation efficiency. The capacitive RFID chip has been conjugately matched with the antenna without an external matching network, through the values of the lumped components as well as the trace width of the antenna. The package has been realized with a low loss filament and a high-resolution 3D printer, and the conductor is cut by using laser patterning. At an operational frequency of 866 MHz, a ka of 0.26, a read range of well above 1 m, and a radiation efficiency of approximately 32% has been achieved, which is one of the best tradeoffs between size and performance in open literature.

In the related paper we explore two machine learning approaches to improve RFID tag localization in the highly reflective environment imposed by the International Space Station. We propose P-RFIDNet (Passive RFID RNN), a residual neural network (He, et al., 2015) [1] for localizing passive RFID tags in high multipath environments with fixed antennas. Furthermore, we show how transfer learning can be used to generalize P-RFIDNet to new RFID environments with limited training data. In addition to P-RFIDNet, we present REALMRFC, a random forest (Breiman, 2001) [2] model with feature engineering performed by an RFID localization expert. We benchmark P-RFIDNet and REALMRFC using data from the RFID Enabled Autonomous Logistics Management (REALM) RFID system on the International Space Station (ISS).

In this poster we hope to convey the challenges of localization within spacecraft and discuss the techniques we've used to model the data. We'll break down the data environment and discuss the modeling techniques used by P-RFIDNet and REALMRFC.

Image processing can help humans learn more about various objects and phenomena that cannot be easily perceived by the naked eye. This project takes a closer look at Jupiter through the lens of the JunoCam, a wide-angle camera mounted on the Juno spacecraft, which travels around the planet's polar orbit. The camera offers the best spatial resolution of Jupiter's features, including its storms and cloud tops [1]. This project intends to use object detection methods to track and automatically detect Jupiter's features found in Junocam images in order to assist scientists in understanding the planet's atmospheric dynamics. Currently, there are no similar processes to track storms automatically using images; this research complements the current methods of detection, such as Juno's Microwave Radiometer (MWR) and Jovian InfraRed Auroral Mapper (JIRAM) [1], by providing a description of storm movement in the visual spectrum. The research group leverages an expansive online database of JunoCam images to test the effectiveness of three different object detection methods in identifying and tracking storms on Jupiter. The raw images from the database are first preprocessed to create a dataset of full RGB images which is split into testing and training sets. The team has developed a Python package with efficient and automated ways to detect the planet's features, specifically white storms on the planet's surface. The object detection methods implemented in the Python package are template matching, feature-based image matching, and eigenimages. The team found that using eigenimages resulted in the best accuracy, followed by template matching, then feature-based image matching.

[1] Hansen, C.J., Caplinger, M.A., Ingersoll, A. et al. Space Sci Rev (2017) 213: 475. [Online]. Available: https://doi.org/10.1007/s11214-014-0079-x. [Accessed Oct. 7, 2020].

The research focuses on the implementation of Genetic Algorithms (GAs) in solving efficiency concerns with different types of circuits. Part of this project focuses on Ternary Content Addressable Memory (TCAM) circuits and how to maximize power and area efficiency of these circuits, while the other part focuses on improving cache timing efficiency. The TCAM circuit was described in HSPICE and its area and energy parameters were explored by a python script containing the GA. TCAM circuits with defined area and energy constrained in literature are compared with the results of GA-defined constraints to measure the performance of the GA. Our GA for cache optimization was implemented in C and utilized an LRU stack data structure and algorithm to evict physical memory frames. With a given memory trace, the implementation allowed us to view relationships between cache parameters and the effects they have on the miss rates and writebacks of a cache. The parameters outputted by the GA are then implemented in Verilog before the timing of the cache implementation is tested. Therefore, by using GA's, more efficient cache implementations that will minimize miss rate and other forms of cache inefficiency can be enacted, and as a result GA's can eventually be used to optimize more complex cache implementations.

Improved precision in autonomous navigation is a research focal point in computer vision and mobile robotics, significantly impacting the advancement of industries such as autonomous vehicles, surgical technology, and industrial automation. Autonomous navigation techniques fall under two main categories: allocentric and egocentric spatial planning. In contrast to the allocentric approach, which involves the robot encoding information of the location of an object in relation to other objects, an egocentric approach involves the robot exploring its own relation to other objects and the environment. Egocentric approaches within autonomous navigation have been shown to yield more efficient pathfinding and minimize the distance traveled between two locations. Currently, there is significant testing and benchmarking in indoor environments for egocentric navigation approaches using egoTEB and PiPS collision checking. Our team presents an approach in simulation to benchmark egocentric navigation frameworks with various path planners in an outdoor environment, using GPS sensors to find ground truth. We develop a methodology to benchmark the egocentric algorithms via an Extended Kalman Filter utilizing 2 GPS sensors and an Odometry Sensor. In general, implementing testing strategies in an indoor environment is the standard, leaving these path-planning algorithms unproven in the outdoors. The additional data from evaluating navigation testing strategies in outdoor environments allows for the evaluation of the shortcomings and successes of the algorithms in unique, previously untested conditions. Additionally, there are many potential applications for outdoor autonomous navigation, such as in rugged terrain vehicles, in which ground truth and outdoor testing over long distances would be extremely valuable.

With the increasing push towards smaller benchtop and point-of-care biomedical devices, lab-on-chip systems utilizing microfluidics are at the frontier of biomedical device innovation. Currently, these systems lack access to readily available embeddable sensors common to larger in-line processes such as liquid pressure, density, and viscosity sensors. It is critical to the development of such embedded sensors that appropriate, inexpensive, and efficient microfluidic testing apparatuses exist that enable rapid prototyping of these devices. In this application, the device is a micro-resonator-based fluidic property sensor developed in the iSenSys Lab, which utilizes microelectromechanical systems (MEMS) based technology. An appropriate testing apparatus incorporates the design of an inexpensive flow cell controlled by syringe pumps and enclosed fluidic channels interfacing with the exposed MEMS sensor device and the associated readout circuitry. The thermally excited and piezoresistively sensed signal from the device interfaces with an open feedback loop that includes the re-evaluated excitation and readout circuitry along with the necessary signal conditioning. The testing apparatus incorporates the automation of the data acquisition (DAQ) systems for the probing of several sensor devices in parallel using LabVIEW to improve testing throughout and accelerate prototyping. This parallelization of device characterization required the design of an intensive multiplexing system capable of communicating among sixteen devices in multi-device and single-device operation and with the appropriate instruments (network analyzer, syringe pump, laboratory oven, etc.). Data processing and analysis of the frequency spectrum is conducted in MATLAB to extract and subsequently relate peak amplitude, peak frequency, and the peak Q factor to density and viscosity. The designed testing apparatus effectively decoupled the liquid-exposed microsensors from its electronic readout with minimal signal distortion, eventually enabling accurate characterization of liquid fluidic properties for both validation and potential redesign of the MEMS device.

As application of medical implant expand in complexity and position of usage, thermal regulation become a major issue with the device power expansion which led to elevating thermal energy. With brain implant, even a slight increase in device temperature may cause irreversible damage to the brain cell. Therefore, the goal of our project is to implement an online version of the LSTM system, building of a off-line LSTM system, with control scheme that can predict the temperature rise and regulate the device when temperature rises above threshold. Long Short-term memory system (LSTM), with use of the past data and prediction along with the forgetting mechanism to allocate computational resources to more valuable data, yield a high prediction accuracy while keeping the computational cost low.

The COMSOL set up for the project contains six temperature sensors and two input sources, which are used for one-step-prediction and control scheme testing. To achieve low MSE with low computational cost, additional parameters and weight factor are included into the existing modules, while control scheme is introduced and tested to enable response from bioimplant when prediction reach above a defined threshold. In particular, a suspicion ratio function is implemented into the LSTM code which incorporates both a 1D suspicion ratio array for W[x-10] sliding window and adaptive suspicion point replacement into the training algorithm. To obtain suspicion ratio array, a sliding windows with length W[x-10] is generated and input into a normal distribution function to identify outliers and their associated suspicion ratio. The extrapolated information is then feed into a logistic sigmoid function to normalize its weight on the training model and tested via the different datasets. The resulting MSE will be calculated for each sensor reading and prediction. In addition, for the outliers identified, suspicion ratio prior to the point is extracted and rated for its frequency to determine whether the data point need to be dropped or replaced to prevent undesired fluctuation in the weight gradient curve. A matrix will also be added to the input and output arrays to incorporate the covariance parameters into the algorithm, while a non-linear predictive control is implemented onto the LSTM via SQP optimization.

In summary, a low computational cost online LSTM system will be achieved through additional training parameters and control scheme which aim to make temperature prediction and control medical bioimplant to prevent potential tissue damages. The result will provide data for future analysis and improvement, giving researchers directions for possible optimization through the control of training factors of the model.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects an individual's communication skills and social interactions. Currently, the only way to determine if a child is autistic, is to send them to several different doctors and specialist. This can be very expensive, time consuming, and very often, doctors reach different conclusions on whether the child has autism. Previous research has shown that analyzing a day's audio recording is sufficient for autism diagnosis. The object of this research is developing a tool to automatically analyze audio recordings and reach conclusion on (ASD). The first step in this research is speaker diarization. This goal of diarization is to partition the audio to determine when the child is talking versus when parents are talking. Currently, we have gathered data from Voxceleb to create a large-scale dataset of human speech. Extracting the speech from an audio sample required a concept known as the Mel-Frequency Cepstral Coefficients (MFCC) to be used. The data was then organized using a speaker diarization tool known as Kaldi. This semester, we plan to create an autism spectrum disorder transfer learning model to train our data using the Voxceleb dataset. With that we will be able to accurately come closer to understanding the algorithm for detecting ASD. Additionally, we will also identify questions segments and determine the child's rate to question from parents.

Range-only Simultaneous Localization and Mapping (SLAM) considers the problem of tracking a moving target along with estimating locations of fixed landmark points in an environment. Most prior works solve the problem based on the direct knowledge of a sequence of distance measurements between the target and the landmarks. We consider the scenario where the exact distance measurements are either unavailable or unreliable due to high noise levels. Instead, we assume the knowledge of a sequence of relative distance measurements and initial location estimates for the landmarks.

Specifically, we assume that the measurements are available in the form - t is closer to a than b - where t refers to the target and a, b is a pair of landmarks. We propose a particle filter-based Bayesian method to simultaneously localize the target and map the landmarks using such paired comparisons. The preliminary results of the proposed method are presented by performing simulations. A potential application of this work includes RFID-based object localization where a prior exact knowledge of the map is not available.

While digital-only hardware-software co-design is an established, although unsolved and currently researched, discipline, it is difficult to replicate these methods in the analog domain. Although digital processing research is widely used in the modern world, the computation is sufficiently costly in terms of computational energy and power efficiency to limit its portability. Current research is exploring the opportunities in the analog domain, particularly programmable and configurable analog domain.

This research uses a Field Programmable Analog Array (FPAA), which has significant advantages over its digital counterpart, the Field Programmable Gate Array (FPGA), in terms of power consumption, parameter density, and data flow handling. Working with the FPAA yields an efficiency of nearly 1000 times greater than that of digital systems. The switching of thousands of transistors in the digital domain is replaced by only a few in analog, regulated by a power rail between two voltages. The FPAA is programmed using an open-source tool set that converts block designs into switch lists for compilation. FPAA devices demonstrate many diverse signal processing applications and enables r the ongoing body of research in analog computation.

This project aims to explore these capabilities through cells customized to perform such functions on the FPAA board in order to develop a set of standard analog cells. To create new analog standard cells, such as in a 350nm CMOS process, current abstraction of programmable analog circuits enables formulating a manageable number of analog cells. In order to ultimately create new analog standard cells, existing standard analog cells need to be understood on a schematic basis. Starting with a standard 350nm Integrated Circuit (IC) process, the programmable analog library requires the constraint of having a standard pitch for the analog cells. This standard cell library will be fabricated and tested, further illustrating the potential place and route capabilities of a mixed structure of analog and digital components.

Developments in the medical Internet of Things (mIoT) field have led to an increase in the need for wearable sensors with small energy and physical footprints. In this paper, RFID backscatter communication is introduced as an alternative wireless communication technique to existing systems. RFID backscatter communication is known for its extreme low power consumption and small physical footprint, potentially ideal for long-term wearables. This presented feasibility study aims to scope the range and bit-error-rate (BER) of the proposed cross-polarized RFID reader system. The proposed system managed to main an acceptable BER of 10^-3 at a range of 2.0 m and a symbol rate of 50 ms, while consuming an order of magnitude less power than conventional systems. This work aims to scope utilizing RFID backscattering systems for future ubiquitous wearable health monitoring systems.

SSP is an expensive technology to deploy due to the costs associated with launching a satellite into geostationary orbit, as well as the cost of such a large ground station and rectennas. The local ground stations necessary to receive and distribute the transmitted power currently employ high-performing but expensive conventional silicon wafer circuit boards. The purpose of this project is to minimize the cost of the ground station to make SSP a more commercially attractive option. This can be accomplished by using inexpensive materials such as copper tape and acrylic to create the rectifying antenna circuit (rectenna). The goal of this project is to simulate and optimize a low-cost and transparent rectenna with decent energy conversion efficiency that can harvest microwave power from space solar power satellites (which may happen in the future). Our team also aims to perform a demonstration of the simulated rectenna with microwave signal sources to show the potential of using such a system for supplying power.

Welcome to IEEE RFID 2021 from the IEEE Council of RFID. This plenary segment provides a state-of-the-council update from CRFID and our IEEE community.

Welcome to IEEE RFID 2021! Here is an overview of IEEE Council of RFID's growing conference portfolio and "eco-system" of events and workshops.

This article describes an alternative procedure for measuring the impedance of an ultra high frequency (UHF) radio frequency identification (RFID) chip and finding its turnon-point. The proposed method is based on measuring the balanced impedance of the RFID chip using a standard vector network analyzer (VNA) and a custom fabricated printed circuit board (PCB) test fixture. The test fixture uses a transmission line transformer to (1) provide a balanced signal to the ports of the RFID chip and (2) achieve a pre-matching to avoid the inaccuracies resulting from measuring high Q components with a VNA. No additional RFID reader is needed, as the turnon-point is extracted from the measured voltage reflection coefficient as a function of frequency and input power. A matching network is designed using a Smith chart approach to prove the applicability of the method by measuring the resulting reflection coefficient. A comparison with the typically used single-ended technique is provided.

This paper describes the miniaturization process of a sequentially rotated circular array antenna implemented with three curved planar inverted-F antenna (PIFA) elements. The antenna miniaturization obtained by bending curved PIFA elements (resulting in closer antennas) makes the impedance matching more complex and, for this reason, a capacitive coupling mechanism is implemented to reduce the antenna impedance. An antenna with diameter 0.16λ (calculated at 915 MHz) and height 0.05λ has been numerically optimized to work within the radio frequency identification (RFID) ultra-high frequency (UHF) bandwidth, showing a joint S11≤-10 dB, axial ratio (AR) ≤3 dB, and right hand circular polarization (RHCP) gain≥2 dBic bandwidth of 908-927 MHz.

This paper presents a method for remotely and dynamically determining the execution schedule of long-running tasks on intermittently powered devices such as computational RFID. Our objective is to prevent brown-out events caused by sudden power-loss due to the intermittent nature of the powering channel. We formulate, validate and demonstrate that the read-rate measured from an RFID reader (number of successful interrogations per second) can provide an adequate means of estimating the powering channel condition for passively powered CRFID devices. This method is attractive because it can be implemented without imposing an added burden on the device or requiring additional hardware. We further propose ReaDmE, a dynamic execution scheduling scheme to mitigate brown-out events to support long-run execution of complex tasks, such as cryptographic algorithms, on CRFID. Experimental results demonstrate that the ReaDmE method can improve CRFID's long-run execution success rate by 20% at the critical operational range or reduce time overhead by up to 23% compared to previous execution scheduling methods.

This paper presents a compact and largely digital UHF EPC Gen2-compatible RFID implemented using digital IP blocks that are easily portable. This is the first demonstration of a digital Gen2-compatible RFID tag chip with an area of 125μmx245μm and -2 dBm sensitivity operating in the 860-960 MHz band. It is enabled by a) largely standard cell-based digital implementation using dual-phase RF-only logic approach, b) near-threshold voltage operation, and c) elimination of area intensive, complex, and less scalable rectifiers, storage capacitors, and power management units used in conventional RFID tags. In this demonstration, all but six cells were directly used from the standard cell library provided by the foundry. This makes it suitable for cost-sensitive applications, and as embedded RFIDs for tagging counterfeit Integrated Circuits (ICs).

In a vector frequency-domain chipless radio frequency identification (RFID) reader, a wideband mixer down-converts the backscattered signal from frequency-coded tags. The strong leakage signal from the LO port into the IF port of the mixer is a severe limitation of the vector reader. The leakage reduces the dynamic range of the receiver, adversely affecting the reading range. Herein, a wideband directive filter (DF) is designed to absorb the leakage signal, and as a result, improve the reading range of the system dramatically. A prototype filtering scheme is implemented for the stopband region of 4-8 GHz, where a suppression level of 50 dB is achieved.

In this work, a mmID tag and reader system operating in the 60 GHz ISM band is proposed. The sticker-form factor-mmID-tag has a total footprint of only 3.5 cm by 1.5 cm and is fabricated utilizing ultra-low cost inkjet-printed PCB technology and components. For the first time, a low-cost and ultra-compact antenna-on-package radar module is utilized for the reader system, to provide accurate microlocalization of the mmID tags in 2D space. One proof-of-concept tag was successfully localized at various steps of 5 cm from a range of 10 cm and 50 cm with maximum deviation of 4.46 cm. The reader system also demonstrated the ability to detect the Angle-of-Arrival at a range of 15 cm in angular steps of 20 ◦ providing an angular coverage of ±40 ◦. Lastly, the total power consumption of the mmID tag was 1.43 mW, allowing for long term usage of the tag in autonomous settings. This work potentially introduces a paradigm shift in the application of the backscatter systems by introducing an affordable mmID-enabled real-time-microlocalization solution compatible with inconspicuous ubiquitous and wearable implementations.

The paper proposes a wearable miniaturized antenna array suitable to be adopted in 3.6 GHz body-centric backscattering communications. The array is a modified version of the comb-line antenna, whose horizontal segments are bent such to place the radiating dipoles near and increase the radiation efficiency of the structure by exploiting the vertical component of the transmission line currents. The antenna footprint is hence sensibly improved. Parametric analysis are performed and an optimal configuration is derived capable to provide a theoretical read distance of more than 4m.

Ambient Scatter Communications (ASC) is an emergent field of scatter-based-radio which promises low-power, low-cost, and 'regulation-free' wireless communications. Ambient Scatter Communication Systems (ASCS) uniquely employ previously modulated and transmitted ambient RF energy as its RF carrier, creating non-trivial complexity in matters of signal processing and RF propagation. This paper provides a comprehensive analysis of past ASC works, demonstrating how Primary User Interference (PUI) -- the non-scattered signal which travels directly from the ambient source to the ambient receiver -- limits the operating range for current ASCS's. First-time theoretical descriptions are developed for the PUI Power Differential in ambient bistatic links and for describing bistatic radio topologies. A large survey of current devices in the field of ASC's is compiled and compared to each other using these new metrics as reference, illustrating ways to greatly enhance the range of ASCs in the future.

RFID and backscatter allow for extremely low- power or battery-free tags by outsourcing the generation of the radio carrier wave to an external device such as an RFID reader. Recent advances in backscatter communication enables tags to both transmit and receive standards-compliant packets with sub-milliwatt power consumption. The ability to receive packets makes multi-hop tag-to-tag networking possible, a task that current backscatter networks currently provide only for limited topologies. Tag-to-tag networks further allow for novel applications such as wireless robotic materials that inherently require dense networks of such tags. In contrast to conventional networks, the tags' communication range in such networks depends heavily on the signal strength of the carrier wave at the transmitter and the receiver. In this paper, we demonstrate for the first time on real hardware multi-hop in backscatter-based networks using standards-based protocols. We present analytical and simulation results that show that both the output power and the position of the carrier generator impact the reliability of the network. We finally demonstrate that simple flooding with a random forwarding delay is an efficient solution for transfering data in such networks.

This paper studies the potential of multistate vector scattering using configurable general purpose input/output (GPIO) pins commonly present on digital devices. A low-cost EFM8BB1 microcontroller and its development board is studied to explore the possible signaling constellations. This specific microcontroller architecture is a modeled after the Intel 8051 and presents a variety of configuration options for digital GPIO pins. This work measures impedances of 32 possible GPIO configurations, and designs a quaternary over-the-air signaling scheme that transmits two bits per symbol period. This study and experimental proof-of-concept is performed at the UHF RFID band (915 MHz). This work demonstrates that multistate scattering without modification is possible, although with performance limitations that are discussed.

The commodity passive RFID system employs slotted ALOHA protocol to interrogate the tags within the reader's communication range. So, at each time slot, there is only one RFID tag communicating with the reader. This degrades the network throughput, especially in large-scale RFID deployments such as warehouses. Recently, parallel decoding techniques are proposed, which can only read less than ten tags at each time slot. So, it is not applicable for warehouse applications, where there are thousands of RFID tags.

In this paper, we propose to achieve parallel decoding with compressive sensing technique for multi-reader large-scale RFID system. Since it is difficult to decode the collisions from multiple tags at one reader, we distributively deploy multiple readers. However, we have to consider the inter-reader interference. Even though there are thousands of tags deployed in the large warehouse, they may not backscatter the signals at each time slot simultaneously due to the heterogeneity. Therefore, this sparsity property of backscattering signals can allow us to leverage compressive sensing to decode multiple tags simultaneously with multiple readers. Our simulation results reveal that compressive sensing can efficiently achieve parallel decoding in multi-reader large-scale RFID system.

Yoga is popular in our daily lives for body fitness practice. We can practice yoga on a specific mat for body fitness. It's important to have the smart yoga mat, which can adjust the surrounding environment based on user's physical activities on the yoga mat. For example, we can adjust the ambient light, music and temperature based on the yoga practitioner's pose on the yoga mat.

In this paper, we present RFitness, a system that can detect the fitness posture on the yoga mat. To do so, we can attach multiple commodity passive RFID tags on the yoga mat, such that the different fitness postures can affect different RFID tags. Based on the signal strength readings from all the tags, we can estimate the yoga fitness posture using the model-driven deep neural networks. We implement the prototype of RFitness using commodity passive RFID tags and USRP reader. Our extensive experiments show that RFitness can achieve the median accuracy of 0.96.

Orthopedic implants could be subjected to infections. Conventional diagnostic tools involve X-Rays, MRI, CT imaging or, more commonly, the onset of the patient's pain. Monitoring the health state of a prosthesis from the outside the limb can be accomplished by through-the-body wireless communication link. However, techniques for integrating a wireless sensor into orthopedic implants require a structural modification of the prosthesis. To overcome this limitation, a non-invasive way to augment a prosthesis with wireless monitoring capability is here proposed for the early detection of local tissue infection. The idea can be applied to an orthopedic fixation plate provided with holes, with no changes to its structure. The fixation bar is transformed into an RFID tag by exciting voltage gap into an unused screw hole. The electrical and geometrical parameters of the exciter enable a convenient two-steps tuning mechanism (coarse and fine) to adjust the working frequency. Preliminary simulations predicted a read range of more than 50 cm outside the body that is suited to an early and non-collaborative diagnosis in the emerging Personalized Healthcare.

Breath monitoring is critical for multiple applications, ranging from monitoring patients in Intensive Care Units (ICUs) to the design of optimized physical training. Recently proposed Radiofrequency Identification (RFID) tags and systems for breath monitoring only return integrated information on breathing, whereas the air flow through each nostril can provide more useful information. In this paper, a dual-tag temperature-sensing RFID device is introduced for the simultaneous bilateral monitoring of the nostrils' breath. The device comprises two coupled tapered loops each closed to a transmission line probe excited by a smaller loop hosting the Integrated Circuit (IC). The resulting two-ports tag is such that each temperature-sensing IC is placed just below a nostril. Numerical and preliminary experimentations with epidermal prototypes suggest that the two sensors can be simultaneously read along the nose septum's direction up to a distance of 50 cm.

We examine the use of chipless RFID tags for plastic identification in two applications in the recycling chain. First, on a conveyor belt system, where tagged plastics pass by a reader one at a time. We demonstrate that our approach can successfully differentiate between 2 plastic types with over 90% accuracy and is agnostic to tag orientation or contaminants such as oil and water. In doing so, we show that the technology works just as well or is superior to comparable studies using optical methods.Second, for contents estimation, where we estimate the relative fraction of two types of plastics in a bulk mixture. We show that we are able to detect pure or homogeneous bales of plastic with over 90% accuracy and estimate the content of non-homogeneous bales with 65-75% accuracy. This could help recycling plant managers prioritize sorting operations based on composition of incoming shipments with more information than they have currently. Future directions of research are also discussed.

RFID technology not only enables wireless identification, it also provides the platform for battery-less antenna sensing capabilities, i.e., the sensing information is modulated by the antenna impedance, and it can later be extracted from the backscattered signals by the readers. However, Antenna sensing is prone to interferences, any ambient impacts, such as distance variation and noises in the transmission path can cause the amplitude and phase changes of the receiving signals, and deteriorate the quality of the sensing information. In this paper, we propose a deformation sensor that consists of two adjacent-placed RFID antennas. The impedance of the two antennas will be modulated differently under the bending condition, and the deformation information can be extracted from the differential backscattered signals of the two antennas. To be deployed as a wearable body gesture sensor, the two antennas are embroidered on apparels using conductive yarns. Experiments show that differential sensing signals can effectively eliminate the impact from ambient interferences, while tracking the body gesture even under constant distant variations.

This paper presents a novel method to improve the subcarrier frequency accuracy of tag chip for UHF RFID sensing system based on EPC global Gen2 protocol. By changing the tag's response to the Write command, the tag returns the subcarrier before returning the response specified in the protocol. In this way, a frequency calibration loop will be established between reader and tag. This method not only enables a low-power oscillator with a precise frequency to be integrated into a tag chip, but also enables flexible configuration of the frequency. The tag chip containing this idea is fabricated in SMIC 0.18 µm CMOS process with a die size of 850 µm × 850 µm. Measurement results show that the proposed novel method can realize the calibration of the subcarrier frequency, and the subcarrier frequency deviation is less than ±0.385KHz in the range of 40KHz-640KHz. With this accuracy, the tag can be allocated up to 78 subcarrier communication channels.

This paper introduces a wireless power transfer system using a 5-coil asymmetric topology to mitigate range limitations when powering devices requiring small coils. The efficiency of a traditionally coupled wireless power transfer system falls sharply at distances beyond the diameter of the coils used. In order to power devices requiring small packaging at a distance, this limitation must be eliminated. Magnetically coupled coils have demonstrated the ability to extend power transfer distances beyond that of traditional coupling techniques; however, power transfer is still limited by coil size. We utilized the power transfer capabilities demonstrated by a 4-coil magnetic resonant transfer system and introduced a fifth coil to extend the transfer range. Coil asymmetry was then introduced to overcome coils radius limitations. This power transfer technique was demonstrated by charging a cell phone at a distance of 61 cm. The 5-coil transfer system operates at 40% efficiency from amplifier output to rectifier input. We develop an end-to-end power transfer system including a power amplifying source, 5-coil transfer system, and load including rectification to feed the cell phone. Circuit models and equations are introduced for all three stages with an emphasis on the asymmetric 5-coil system. Key parameters characterizing the 5-coil wireless transmission segment are also derived. Individual and system-wide efficiency limitations are discussed, and areas of future work are presented.

This work presents a method for enhanced wireless power transfer using an algorithm to calculate the optimum phases of multiple transmitting antennas in a passive UHF RFID system. The algorithm performs the calculation based on measured backscatter phase value of individual antenna port and the phase rotation caused by each port's receiving channel. Through experimental validations, it is shown that the proposed algorithm can achieve up to 18 dB improvement in the tag RSSI using three transmitting antennas. The proposed algorithm could be used in the next generation sensor tags to optimise power delivery efficiency.

The new algorithm described in this work produces an optimized RF beam-forming network. Based on a sequential optimization technique that has been particularly adapted to microwave circuitry, the technique is well-suited for design of energy-harvesting networks with arrays because it can emphasize compact size and low-loss. This methodology defines a planar area (with a ground plane), ports, and a goal scattering matrix then iterates through various design structures to find an optimal solution. Numerous circuit design applications beyond energy-harvesting would also benefit.

In this paper, new compressive sensing (CS)-based direction of arrival (DOA) estimation technique using the beamspace (BS) processing is proposed. Two techniques have been proposed, namely, full beam-space (FBS) as well as multiple beam-space (MBS), and investigated versus the ordinary element-space (ES) technique in a CS-based framework. More, the rank one update covariance matrix has been combined along with all the investigated techniques. Both of the proposed schemes can identify more source signals than the number of sensors used, without requiring an a priori knowledge of the number of source signals to be estimated. The performance of the proposed schemes is compared to that of the ES-based technique.

This paper presents a RFID-based mobile robot able of self-locating within an indoor scenario and to estimate the position of target UHF-RFID tags. To locate itself, the robot exploits a sensor-fusion method which combines data from an infrastructure of passive reference RFID tags arranged in known locations and data from rotary wheel encoders. Besides, during its motion it is able of measuring the target tag locations through a synthetic-array approach. The knowledge of the reader antenna trajectory is here achieved from the RFID-based sensor-fusion method which exhibits a localization error lower than 0.27 m for 20-m long paths in a real office environment. Then, the estimated trajectory is exploited for target tag localization with high accuracy by using the synthetic-array approach.

This work revisits particle filtering RFID localization methods, solely based on phase measurements. The reader is installed on a low-cost robotic platform, which performs autonomously (and independently from the RFID reader) open source simultaneous localization and mapping (SLAM). In contrast to prior art, the proposed methods introduce a weight metric for each particle-measurement pair, based on geometry arguments, robust to phase measurement noise (e.g., due to multipath). In addition, the methods include the unknown constant phase offset as a parameter to be estimated. No reference tags are employed, no assumption on the tags' topology is assumed and special attention is paid for reduced execution time. It is found that the proposed phase-based localization methods offer robust performance in the presence of multipath, even when the tag phase measurements are variable in number and sporadic. The methods can easily accommodate a variable number of reader antennas. Mean absolute localization error, relevant to the maximum search area dimension, in the order of 2% - 5% for 2D localization and 9.6% for 3D localization was experimentally demonstrated with commodity hardware. Mean absolute 3D localization error in the order of 24 cm for RFID tags in a library was shown, even though the system did not exploit excessive bandwidth or any reference tags. As a collateral dividend, the proposed methods also offer a concrete way to classify the environment as multipath-rich or not.

This paper presents two RFID localization methods based on a k-NN algorithm for multiple moving tracking tags attached to a concrete masonry unit (cinder block). This work uses passive RFID tags for localization and seeks to provide rapid wireless analysis for future smart infrastructure projects where precast concrete modular structures are moved during transport and assembly. The RFID localization system uses four reader antennas, four tracking tags, and 28 reference tags in a realistic indoor assembly environment. Results show average error in the direction of movement as low as 10.5 cm. Increasing the number of nearest neighbors in the k-NN algorithm is shown to reduce error in all coordinate directions. Increasing k from 4 to 6 is shown to reduce error by 4 cm or 10%. The localization environment is analyzed, and reference tags 22, 9, 5, and 8 around the moving cinder block are seen most commonly as nearest neighbors. A modified k-NN algorithm, described here as a weighted Euclidian distance k-NN algorithm is presented that reduces total error from 41.1 cm to 32.5 cm.

This paper proposes a new type of real-time decimeter-level radio-frequency identification (RFID) positioning system at 5.8 GHz. The system uses received signal phase (RSP)-based positioning techniques and tunneling tags (TTs). TTs amplify the signal strength of their backscattered signals while preserving the phases allowing for ultra-precise position estimates at long distances. A proof-of-concept RSP-based real-time frequency hopping reader is implemented on Software-Defined Radio (SDR) and Universal Software Radio Peripheral (USRP) platform. Experimental results show an average one dimensional and two-dimensional positioning accuracy of 11 cm and 17 cm, respectively, in outdoor environments.

This paper proposes an ultrahigh-frequency (UHF) radio frequency identification (RFID) based 3D mobile localization system (3DRML) for passive tags and tagged objects. Influenced by factors such as calculation model, grid scale and phase center shift (PCS), prior RFID based 2D and 3D mobile localization methods are subject to certain restrictions in computational time and accuracy. To overcome these limitations, 3DRML has the following features. First, 3DRML achieves grid based mobile localization with low time cost by leveraging the idea of reflection coefficient reconstruction (RCR) which regards each point representing an area as a reflection point and calculates the reflection coefficients from simple matrix operations. Second, a PCS calibration process is performed to compensate the phase shift caused by the antenna phase center change. Third, 3DRML uses the nonlinear optimization algorithm to solve the least square localization model for a quick localization, and then constructs a much smaller grid area to facilitate the grid based real-time accurate localization. The performance of 3DRML is evaluated by simulations with various interferences, and the results show that 3DRML enables fast 3D localization while achieving higher accuracy.

This paper introduces a backscatter channel sounder technique used for a radio-frequency identification (RFID) positioning system at 5.8 GHz. This system applies received signal phase (RSP)-based positioning and channel sounding techniques to a tunneling tag, providing sufficient information to calculate the delay spectrum for accurate positioning in a complicated multipath environment. Ultra-precise (0.45%) position estimates at long distances (100 m) are achieved using the proposed channel sounding techniques.

In this paper we explore two machine learning approaches to improve RFID tag localization in the highly reflective environment imposed by the International Space Station. We propose P-RFIDNet (Passive RFID Net), a neural network with a ResNet50 (He, et al., 2015) [1] backbone for localizing passive RFID tags in high multipath environments with fixed antennas. Furthermore, we show how transfer learning can be used to generalize P-RFIDNet to new RFID environments with limited training data. In addition to P-RFIDNet, we present REALMRFC, a random forest (Breiman, 2001) [2] model with feature engineering performed by an RFID localization expert. We benchmark P-RFIDNet and REALMRFC using data from the RFID Enabled Autonomous Logistics Management (REALM) RFID system on International Space Station (ISS).

Clinicians' work pressure and burnout turned out as an important factor in modern hospitals which urges us to search for rapid and accurate methods to proxy the workload. By using RFID technology, we hope to monitor the physical pressure on the clinical staff of the emergency department (ED) at the Mayo Clinic Saint Mary's Hospital. The data collected will be used to determine the workload and pressure put on various staff members. A real-time location system (RTLS) has been used to monitor and track the movements of the hospital team and compute active vs. sedentary time for staff. Risk thresholds for the sedentary time from a previously published paper were utilized to visualize trends in staff sedentary time. Members of the ED team are under great stress at all times; we use RFID technology to get a better understanding of how much their work environment contributes to the pressure of their jobs.

A tutorial on cryptography for passive UHF RFID tags.

This short tutorial reviews the state-of-the-art of RF energy harvesting, outlines the fundamental limitations from first principles, and explains where future gains will come from. Techniques for achieving -30 dBm sensitivity for RF-driven digital circuits will be discussed, including novel materials and devices and unusual forms of RF excitation.

An introductory tutorial on UHF RFID energy-harvesting that covers charge pump design with insights into practical CMOS implementations.

In the full implementation of Internet of Things (IoT), we may have more than 30 sensors and ID tags in a common room, and more than 300 sensors/tags in a hospital room. In consideration of not only battery recharging but also recycling, the last layer of IoT needs to be passive to support this aggressive number scaling of pervasive tag deployment. This trend will be even more aggravated in the future Internet of Everything (IoET). Hence, energy scavenging of passive tags to support digital ID modulation and sensor readout is critical to realize the eventual IoT and IoET vision. Under the assumption of unspecified tag location within a zone of at least 5m, we will briefly discuss the advantages and limitations of the approaches by RF, thermoelectric, ultrasound/vibration, and solar cells. Following similar arguments, the operational range of modern RFID star network is often limited by the tag sensitivity, where the tag needs to harvest sufficient ambient energy to build the digital communication link. For the RF energy harvesting by charge pumping, we will then investigate the limitation set by the impinging energy and device nonlinearity which render the design tradeoffs between the energy efficiency and peak voltage. Last but not least, we will show how manufacturing variation of the diodes and passive devices can severely influence the harvesting performance and then present possible mitigation methods.

Wireless power offers the promise of seamlessly charging our electronic devices as easily as data is transmitted through the air. However, existing solutions are limited to near contact distances or low delivered power levels and thus, do not provide the geometric freedom and ease of use the term "wireless" suggests. This talk presents an overview of work on the use of resonant cavity modes to control magnetic field distribution in order to provide uniform wireless charging in large chambers and rooms. We introduced Quasi-Static Cavity Resonance (QSCR) as a means of enabling large purpose-built structures to generate near-field standing waves that safely deliver kilowatts of power to mobile receivers contained nearly anywhere within. Experimental demonstrations show that our 256 square foot, QSCR enabled room offers a unique charging experience where user's devices can be powered simply by entering the room.

In this talk, we discuss the latest developments in using existing digital devices as power harvesting components. While most RFID devices rely on carefully tuned harvesting components, the underlying technology is already present in the everyday digital devices we use. We show that with the addition of a simple wire antenna to microcontroller, we can turn a device into a fully-functioning wireless sensor. We will discuss how to build an "ex-nihilo" harvester, the characteristics of the ESD protection systems and how we utilize these circuits for power harvesting. We will also present a battery-free Bluetooth sensor that is able to run entirely from harvested RF energy, created from an unmodified Bluetooth sensor.

Digital Low Drop-Out regulators, in contrast to analog counterparts, provide an architecture of sub-1 V regulation with low power consumption, high power efficiency, and system integration. Towards an optimized integration in the ultra-low-power System-On-Chip Internet of Things architecture that is operated through a Radio Frequency energy harvesting scheme, the D-LDO regulator should constitute the main regulator that operates the master-clock and rest loads of the SoC. In this context, we present a D-LDO with linear search coarse regulation and asynchronous fine regulation which incorporates an in-regulator clock generation unit that provides an autonomous, self-start-up, and power-efficient D-LDO design. In contrast to contemporary D-LDO designs that employ ring-oscillator architecture which start-up time is dependent on the frequency, this work presents a fast start-up burst oscillator based on a high-gain stage with wake-up time independent of coarse regulation frequency. The design is implemented in a 55-nm Global Foundries CMOS process. With the purpose to validate the self-start-up capability of the presented D-LDO in the presence of ultra-low input power, an on-chip test-bench with an RF rectifier is implemented as well which provides the RF to DC operation and feeds the D-LDO. Power efficiency and load regulation curves of the D-LDO are presented as extracted from the RF to regulated DC operation. The D-LDO regulator presents 83.6 % power efficiency during the RF to DC operation with 3.65 uA load current and voltage regulator referred input power of -27 dBm. It succeeds 486 nA maximum quiescent current with CL 75 pF, maximum current efficiency of 99.2 %, and 1.16x power efficiency improvement compared to analog voltage regulator counterpart oriented to SoC IoT loads. Complementary, the transient performance of the D-LDO is evaluated under transient droop test and the achieved Figure-Of-Merit is compared with state-of-the-art implementations.

RFID systems often have low-power, low-energy, and low supply voltage constraints while simultaneously requiring ever increasing computation in that constrained environment.

The past two decades have seen considerable amount of energy-efficient computing techniques based on analog techniques validating Carver Mead's 1990 hypothosis that analog computing should be 1000x or more efficient than corresponding digital computation. One approach that enables programmable and configurable approaches are the large-scale Field Programmable Analog Arrays (FPAA), an approach that includes a family of devices, and resulting design tools that are innovating a path for automated analog design.

This talk will review these various approaches, discuss how to adapt these techniques to RFID applications, as well as give a picture of where these techniques could positively impact RFID applications. The talk will discuss how FPAA devices could be adapted to operate in very low voltage applications (e.g. 250-500mV) typical of RFIC voltage supplies.

Scaling has considerably improved the speed of silicon devices, offering transistors with fT and fmax well above 300 GHz. This has opened new activities for the circuit design at upper mmW and sub-mmW frequencies. More importantly, the so-called THz frequencies that have been traditionally less exploited, have found promising applications for sensing, communications, imaging, and security. The abundance of available bandwidth at THz frequencies can enable extremely high data rate communication and improved range resolution for radar applications. The reduced wavelength on the other hand provides the opportunity for high resolution imaging. In addition, it allows integrating the antennas on-chip for large phased-array systems. To improve the communication link budget MIMO phased array systems are very promising candidates for the future 6G and beyond wireless communications. The reduced beam width and directional communication can decrease the free space path loss at THz frequencies compared to lower frequencies. However, the NLoS environment can greatly attenuate the directive THz signals and will requires innovative techniques such as intelligent reflecting surfaces. In this talk challenges, potential solutions, and opportunities of wireless connectivity at sub-mmW frequencies toward next generation communication and radar systems including 6G systems and beyond will be discussed. A few examples of the implemented chip scale fully integrated transmitter array and transceiver circuits will be demonstrated.

The world has been in combat with COVID- 19 since December 2019. Around the world, physicians have been documenting their experiences under the heavy siege of the pandemic, emphasizing the presence of silent hypoxia and hypoxemia. This deceptive side of COVID-19 leads to deficient levels of oxygen even in patients who appear to be breathing normally. Under normal conditions, these levels of oxygen are far below those that cause death. As in the COVID-19 example, respiratory failure is unpredictable in nature and can become life-threatening in a matter of minutes. Changes in vital sign parameters often reveal important markers of the onset of a deterioration in health. Quantifying the real-time dynamics and physiological distributions of blood gas measurements of CO2 and O2 and other respiratory parameters are imperative to both clinicians and researchers. Therefore, it is of the utmost importance to have the ability to sense and measure respiratory parameters in a continuous, reliable, and accurate manner across different conditions. Remote and continuous monitoring of respiratory parameters with a miniaturized, noninvasive, and comfortable device is essential, not only for known respiratory diseases, including COPD and sleep apnea in adults and babies, but for uprising diseases as COVID-19. The talk covers emerging devices for respiration parameters monitoring, transforming bulky benchtop instruments into miniaturized, next-generation medical IoT devices. The talk also explores currently available technologies, with a short glance back to the evolution of respiratory parameter sensing systems.

This talk provides an overview of cellphone location technologies in E911 services today. We'll review location accuracy requirements set by FCC and present technologies currently available to enhance location accuracy further, especially in the vertical dimension, also known as the z-axis.