arXiv subjects

Shovan Maity

Publications and source records attributed to Shovan Maity.

17 recordsLinked to original sources

arXiv2025

Near Field Electric (NFE): Energy-efficient, High-speed Communication at Decimeter-range

Near-field technologies enable contactless payments, building access, automotive keyless entry, and supply chain tracking. Existing approaches face fundamental trade-offs: magnetic-based methods (NFC/NFMI) achieve low power but are limited to sub-megabit rates, while millimeter-wave techniques provide gigabits/sec connectivity at higher power consumption and only centimeter-scale ranges. We demonstrate that near-field electric (NFE) communication breaks this trade-off via capacitive coupling enabled by confined electric fields. NFE simultaneously achieves ultra-low power ($<$1 mW per transceiver), high-speed data throughput ($>$3 Mbps), and configurable decimeter-range (5-30 cm) capabilities previously considered mutually exclusive. Systematic measurements across multiple orientations and configurations show NFE can support decimeter communication coverage. The power consumption of 0.4 mW at the transmitter (Tx) and 0.6 mW at the receiver (Rx), when combined is up to $\sim$24$\times$ lower than NFC and $\sim$3$\times$ lower than NFMI while achieving significantly higher data rates, and a couple of orders of magnitude lower power than mm-wave based technique. Testing with symmetrical electrodes across eight orientations validated consistent performance and robustness for practical deployments. Extended-range experiments achieved stable 2 Mbps throughput at 3.5 meters using conductive media, demonstrating NFE's unique ability to leverage environmental conductors. Optimized device design can facilitate achieving an extended range for Body-assisted NFE up to 1 m. Results establish NFE as foundational for next-generation wireless applications where security, low power, and throughput converge, enabling dense IoT deployments, secure payment systems, and high-speed device-to-device communication previously limited by the power-performance trade-off.

eess.SP↗

arXiv2025

Effect of nearby Metals on Electro-Quasistatic Human Body Communication

In recent decades Human Body Communication has emerged as a promising alternative to traditional radio wave communication, utilizing the body's conductive properties for low-power connectivity among wearables. This method harnesses the human body as an energy-efficient channel for data transmission within the electro-quasistatic frequency range, enabling advancements in human-machine interaction. While prior work has noted the role of parasitic return paths in such capacitively coupled systems, the influence of surrounding metallic objects on these paths, which are critical for EQS wireless signaling, has not been fully explored. This paper fills that gap with a structured study of how various conducting objects, from non-grounded (floating) metals and grounded metals to enclosed metallic environments such as elevators and cars, affect the body-communication channel. We present a theoretical framework supported by finite element method simulations and experiments with wearable devices. Results show that metallic objects within 20 cm of devices can reduce transmission loss by about 10 dB. When a device ground connects to a grounded metallic object, channel gain can increase by at least 20 dB. Contact area during touch-based interactions with grounded metals produces contact-impedance dependent high-pass channel characteristics. Proximity to metallic objects introduces variability within a critical distance, with grounded metals producing a larger overall effect than floating metals. These findings improve understanding of body-centric communication links and inform design for healthcare, consumer electronics, defense, and industrial applications.

eess.SP↗

arXiv2024

dAJC: A 2.02mW 50Mbps Direct Analog to MJPEG Converter for Video Sensor Node using Low-Noise Switched Capacitor MAC-Quantizer with Auto-Calibration and Sparsity-Aware ADC

With the advancement in the field of the Internet of Things(IoT) and Internet of Bodies(IoB), video camera applications using Video Sensor Nodes(VSNs) have gained importance in the field of autonomous driving, health monitoring, robot control, and security camera applications. However, these applications typically involve high data rates due to the transmission of high-resolution video signals, resulting from high data volume generated from the analog-to-digital converters (ADCs). This significant data deluge poses processing and storage overheads, exacerbating the problem. To address this challenge, we propose a low-power solution aimed at reducing the power consumption in Video Sensor Nodes (VSNs) by shifting the computation from the digital domain to the inherently energy-efficient analog domain. Unlike standard architectures where computation and processing are typically performed in digital signal processing (DSP) blocks after the ADCs, our approach eliminates the need for such blocks. Instead, we leverage a switched capacitor-based computation unit in the analog domain, resulting in a reduction in power consumption. We achieve a $\sim4X$ reduction in power consumption compared to digital implementations. Furthermore, we employ a sparsity-aware ADC, which is enabled only for significant compressed samples that contribute to a small fraction ($\le5\%$) of the total captured analog samples, we achieve a $\sim20X$ lower ADC conversion energy without any considerable degradation, contributing to the overall energy savings in the system.

eess.SP↗

arXiv2022

A Quantitative Analysis of Physical Security and Path Loss With Frequency for IBOB Channel

Security vulnerabilities demonstrated in implantable medical devices have opened the door for research into physically secure and low power communication methodologies. In this study, we perform a comparative analysis of commonly used ISM frequency bands and human body communication (HBC) for data transfer from in-body to out-of-body (IBOB). We develop a figure of merit (FoM) that comprises of the critical parameters to quantitatively compare the communication methodologies. We perform finite-element method (FEM)-based simulations and experiments to validate the FoM developed.

eess.SP↗

arXiv2020

Theoretical Analysis of Multi Integrating RX Front-Ends for Lossy Broad-Band Channels

In this paper, we present a theoretical analysis of different integrating front-ends employed in broad-band communications through \textit{lossy} channels. Time-domain receivers for broad-band communication typically deal with large integrated noise due to its high bandwidth of operation. However, unlike traditional wireline systems that are typically not noise-limited, channels with high channel-loss render the input signal swing to be very small imposing several challenges in RX design as the circuits operate in the noise-limited regime. This simultaneous high integrated noise and low signal-swing limits the maximum achievable data-rate for a target bit-error-rate (BER) and deteriorates the energy-efficiency of the RX. In this work, transient, noise and gain performance of different standard signaling blocks have been obtained with closed-form expressions and are validated through spice-simulations. Multi-integrator cascade has been proposed which provides significant gain with relatively lower power consumption than the standard gain elements. Also, maximum achievable data-rate and optimum energy efficiency for different channel losses have been obtained theoretically for different architectures revealing their advantages and limitations. All the pertaining circuits have been designed in 65 nm CMOS process with a 1 V supply voltage.

eess.SP↗

arXiv2020

Inter-Body Coupling in Electro-Quasistatic Human Body Communication: Theory and Analysis of Security and Interference Properties

Radiative communication using electromagnetic fields is the backbone of today's wirelessly connected world, which implies that the physical signals are available for malicious interceptors to snoop within a 5-10 m distance, also increasing interference and reducing channel capacity. Recently, Electro-quasistatic (EQS) human body communication was demonstrated which utilizes the human body's conductive properties to communicate without radiating the signals outside the body. Previous experiments showed that an attack with an antenna is unsuccessful, more than 1 cm of the body surface and 15 cm of an EQS-HBC device. However, since this is a new communication modality, it calls for investigation of new attack modalities - that can potentially exploit the physics utilized in the EQS-HBC to break the system. In this study, we present a novel attack method for EQS-HBC devices, using the body of the attacker itself as a coupling surface and capacitive inter-body coupling between the user and the attacker. We develop theoretical understanding backed by experimental results for inter-body coupling, as a function of distance between the subjects. We utilize this newly developed understanding to design EQS-HBC transmitters to minimize the attack distance through inter-body coupling as well as minimize the interference among multiple EQS-HBC users due to inter-body coupling. This understanding allows us to develop more secure and robust EQS-HBC based body area networks in the future.

eess.SP↗

arXiv2018

Characterization and Classification of Human Body Channel as a function of Excitation and Termination Modalities

Human Body Communication (HBC) has recently emerged as an alternative to radio frequency transmission for connecting devices on and in the human body with order(s) of magnitude lower energy. The communication between these devices can give rise to different scenarios, which can be classified as wearable-wearable, wearable-machine, machine-machine interactions. In this paper, for the first time, the human body channel characteristics is measured for a wide range of such possible scenarios (14 vs. a few in previous literature) and classified according to the form-factor of the transmitter and receiver. The effect of excitation/termination configurations on the channel loss is also explored, which helps explain the previously unexplained wide variation in HBC Channel measurements. Measurement results show that wearable-wearable interaction has the maximum loss (upto -50 dB) followed by wearable-machine and machinemachine interaction (min loss of 0.5 dB), primarily due to the small ground size of the wearable devices. Among the excitation configurations, differential excitation is suitable for small channel length whereas single ended is better for longer channel.

cs.HC↗

arXiv2018

RF-PUF: Enhancing IoT Security through Authentication of Wireless Nodes using In-situ Machine Learning

Traditional authentication in radio-frequency (RF) systems enable secure data communication within a network through techniques such as digital signatures and hash-based message authentication codes (HMAC), which suffer from key recovery attacks. State-of-the-art IoT networks such as Nest also use Open Authentication (OAuth 2.0) protocols that are vulnerable to cross-site-recovery forgery (CSRF), which shows that these techniques may not prevent an adversary from copying or modeling the secret IDs or encryption keys using invasive, side channel, learning or software attacks. Physical unclonable functions (PUF), on the other hand, can exploit manufacturing process variations to uniquely identify silicon chips which makes a PUF-based system extremely robust and secure at low cost, as it is practically impossible to replicate the same silicon characteristics across dies. Taking inspiration from human communication, which utilizes inherent variations in the voice signatures to identify a certain speaker, we present RF- PUF: a deep neural network-based framework that allows real-time authentication of wireless nodes, using the effects of inherent process variation on RF properties of the wireless transmitters (Tx), detected through in-situ machine learning at the receiver (Rx) end. The proposed method utilizes the already-existing asymmetric RF communication framework and does not require any additional circuitry for PUF generation or feature extraction. Simulation results involving the process variations in a standard 65 nm technology node, and features such as LO offset and I-Q imbalance detected with a neural network having 50 neurons in the hidden layer indicate that the framework can distinguish up to 4800 transmitters with an accuracy of 99.9% (~ 99% for 10,000 transmitters) under varying channel conditions, and without the need for traditional preambles.

cs.CR↗

arXiv2018

Exploiting Inherent Error-Resiliency of Neuromorphic Computing to achieve Extreme Energy-Efficiency through Mixed-Signal Neurons

Neuromorphic computing, inspired by the brain, promises extreme efficiency for certain classes of learning tasks, such as classification and pattern recognition. The performance and power consumption of neuromorphic computing depends heavily on the choice of the neuron architecture. Digital neurons (Dig-N) are conventionally known to be accurate and efficient at high speed, while suffering from high leakage currents from a large number of transistors in a large design. On the other hand, analog/mixed-signal neurons are prone to noise, variability and mismatch, but can lead to extremely low-power designs. In this work, we will analyze, compare and contrast existing neuron architectures with a proposed mixed-signal neuron (MS-N) in terms of performance, power and noise, thereby demonstrating the applicability of the proposed mixed-signal neuron for achieving extreme energy-efficiency in neuromorphic computing. The proposed MS-N is implemented in 65 nm CMOS technology and exhibits > 100X better energy-efficiency across all frequencies over two traditional digital neurons synthesized in the same technology node. We also demonstrate that the inherent error-resiliency of a fully connected or even convolutional neural network (CNN) can handle the noise as well as the manufacturing non-idealities of the MS-N up to certain degrees. Notably, a system-level implementation on MNIST datasets exhibits a worst-case increase in classification error by 2.1% when the integrated noise power in the bandwidth is ~ 0.1 uV2, along with +-3σ amount of variation and mismatch introduced in the transistor parameters for the proposed neuron with 8-bit precision.

cs.ET↗

arXiv2018

BioPhysical Modeling, Characterization and Optimization of Electro-Quasistatic Human Body Communication

Human Body Communication (HBC) has emerged as an alternative to radio wave communication for connecting low power, miniaturized wearable and implantable devices in, on and around the human body which uses the human body as the communication channel. Previous studies characterizing the human body channel has reported widely varying channel response much of which has been attributed to the variation in measurement setup. This calls for the development of a unifying bio physical model of HBC supported by in depth analysis and an understanding of the effect of excitation, termination modality on HBC measurements. This paper characterizes the human body channel up to 1MHz frequency to evaluate it as a medium for broadband communication. A lumped bio physical model of HBC is developed, supported by experimental validations that provides insight into some of the key discrepancies found in previous studies. Voltage loss measurements are carried out both with an oscilloscope and a miniaturized wearable prototype to capture the effects of non common ground. Results show that the channel loss is strongly dependent on the termination impedance at the receiver end, with up to 4dB variation in average loss for different termination in an oscilloscope and an additional 9 dB channel loss with wearable prototype compared to an oscilloscope measurement. The measured channel response with capacitive termination reduces low frequency loss and allows flat band transfer function down to 13 KHz, establishing the human body as a broadband communication channel. Analysis of the measured results and the simulation model shows that (1) high impedance (2) capacitive termination should be used at the receiver end for accurate voltage mode loss measurements of the HBC channel at low frequencies.

cs.ET↗

arXiv2018

In-field Remote Fingerprint Authentication using Human Body Communication and On-Hub Analytics

In this emerging data-driven world, secure and ubiquitous authentication mechanisms are necessary prior to any confidential information delivery. Biometric authentication has been widely adopted as it provides a unique and non-transferable solution for user authentication. In this article, the authors envision the need for an in-field, remote and on-demand authentication system for a highly mobile and tactical environment, such as critical information delivery to soldiers in a battlefield. Fingerprint-based in-field biometric authentication combined with the conventional password-based techniques would ensure strong security of critical information delivery. The proposed in-field fingerprint authentication system involves: (i) wearable fingerprint sensor, (ii) template extraction (TE) algorithm, (iii) data encryption, (iv) on-body and long-range communications, all of which are subject to energy constraints due to the requirement of small form-factor wearable devices. This paper explores the design space and provides an optimized solution for resource allocation to enable energy-efficient in-field fingerprint-based authentication. Using Human Body Communication (HBC) for the on-body data transfer along with the analytics (TE algorithm) on the hub allows for the maximum lifetime of the energy-sparse sensor. A custom-built hardware prototype using COTS components demonstrates the feasibility of the in-field fingerprint authentication framework.

eess.SP↗

arXiv2018

A 6.3pJ/b 30Mbps -30dB SIR-tolerant Broadband Interference-Robust Human Body Communication Transceiver using Time Domain Signal-Interference Separation

Human Body Communication (HBC) provides a low power communication medium for energy constrained wearable/ implantable devices in and around the human body. This paper presents a broadband HBC transceiver implemented in 65nm CMOS that achieves 6.3pJ/b energy efficiency at 30Mbps with -30dB interference-tolerance. Capacitive termination at the receiver end is used to achieve a wideband HBC channel, and Time Domain Signal-Interference Separation (TD-SIS) using Integrating DDR (I-DDR) receiver allows a tolerance of -30 dB Signal to Interference Ratio (SIR) with a BER <10-3. The transceiver achieves 18X improvement in energy-efficiency compared to the State-of-the-Art HBC transceivers while being simultaneously broadband (carrier-less, low-energy) and interference-robust. Such order-of-magnitude improvement in energy-efficiency and private communication through the human body may enable applications like closed-loop neuromodulation, health-monitoring, secure authentication among many others.

eess.SP↗

arXiv2017

An Energy-Efficient Mixed-Signal Neuron for Inherently Error-Resilient Neuromorphic Systems

This work presents the design and analysis of a mixed-signal neuron (MS-N) for convolutional neural networks (CNN) and compares its performance with a digital neuron (Dig-N) in terms of operating frequency, power and noise. The circuit-level implementation of the MS-N in 65 nm CMOS technology exhibits 2-3 orders of magnitude better energy-efficiency over Dig-N for neuromorphic computing applications - especially at low frequencies due to the high leakage currents from many transistors in Dig-N. The inherent error-resiliency of CNN is exploited to handle the thermal and flicker noise of MS-N. A system-level analysis using a cohesive circuit-algorithmic framework on MNIST and CIFAR-10 datasets demonstrate an increase of 3% in worst-case classification error for MNIST when the integrated noise power in the bandwidth is ~ 1 μV2.

cs.ET↗

arXiv2017

Design Considerations of a Sub-50 μW Receiver Front-end for Implantable Devices in MedRadio Band

Emerging health-monitor applications, such as information transmission through multi-channel neural implants, image and video communication from inside the body etc., calls for ultra-low active power (<50$μ$W) high data-rate, energy-scalable, highly energy-efficient (pJ/bit) radios. Previous literature has strongly focused on low average power duty-cycled radios or low power but low-date radios. In this paper, we investigate power performance trade-off of each front-end component in a conventional radio including active matching, down-conversion and RF/IF amplification and prioritize them based on highest performance/energy metric. The analysis reveals 50$Ω$ active matching and RF gain is prohibitive for 50$μ$W power-budget. A mixer-first architecture with an N-path mixer and a self-biased inverter based baseband LNA, designed in TSMC 65nm technology show that sub 50$μ$W performance can be achieved up to 10Mbps (< 5pJ/b) with OOK modulation.

cs.NI↗

arXiv2017

Wearable Health Monitoring Using Capacitive Voltage-Mode Human Body Communication

Rapid miniaturization and cost reduction of computing, along with the availability of wearable and implantable physiological sensors have led to the growth of human Body Area Network (BAN) formed by a network of such sensors and computing devices. One promising application of such a network is wearable health monitoring where the collected data from the sensors would be transmitted and analyzed to assess the health of a person. Typically, the devices in a BAN are connected through wireless (WBAN), which suffers from energy inefficiency due to the high-energy consumption of wireless transmission. Human Body Communication (HBC) uses the relatively low loss human body as the communication medium to connect these devices, promising order(s) of magnitude better energy-efficiency and built-in security compared to WBAN. In this paper, we demonstrate a health monitoring device and system built using Commercial-Off-The- Shelf (COTS) sensors and components, that can collect data from physiological sensors and transmit it through a) intra-body HBC to another device (hub) worn on the body or b) upload health data through HBC-based human-machine interaction to an HBC capable machine. The system design constraints and signal transfer characteristics for the implemented HBC-based wearable health monitoring system are measured and analyzed, showing reliable connectivity with >8x power savings compared to Bluetooth lowenergy (BTLE).

cs.NI↗

arXiv2017

High Efficiency Power Side-Channel Attack Immunity using Noise Injection in Attenuated Signature Domain

With the advancement of technology in the last few decades, leading to the widespread availability of miniaturized sensors and internet-connected things (IoT), security of electronic devices has become a top priority. Side-channel attack (SCA) is one of the prominent methods to break the security of an encryption system by exploiting the information leaked from the physical devices. Correlational power attack (CPA) is an efficient power side-channel attack technique, which analyses the correlation between the estimated and measured supply current traces to extract the secret key. The existing countermeasures to the power attacks are mainly based on reducing the SNR of the leaked data, or introducing large overhead using techniques like power balancing. This paper presents an attenuated signature AES (AS-AES), which resists SCA with minimal noise current overhead. AS-AES uses a shunt low-drop-out (LDO) regulator to suppress the AES current signature by 400x in the supply current traces. The shunt LDO has been fabricated and validated in 130 nm CMOS technology. System-level implementation of the AS-AES along with noise injection, shows that the system remains secure even after 50K encryptions, with 10x reduction in power overhead compared to that of noise addition alone.

cs.CR↗

arXiv2012

A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.

cs.LG↗