0000000000767775
AUTHOR
Linga Reddy Cenkeramaddi
Depth camera based dataset of hand gestures
The dataset contains RGB and depth version video frames of various hand movements captured with the Intel RealSense Depth Camera D435. The camera has two channels for collecting both RGB and depth frames at the same time. A large dataset is created for accurate classification of hand gestures under complex backgrounds. The dataset is made up of 29718 frames from RGB and depth versions corresponding to various hand gestures from different people collected at different time instances with complex backgrounds. Hand movements corresponding to scroll-right, scroll-left, scroll-up, scroll-down, zoom-in, and zoom-out are included in the data. Each sequence has data of 40 frames, and there is a tot…
Flexible Spare Core Placement in Torus Topology based NoCs and its validation on an FPGA
In the nano-scale era, Network-on-Chip (NoC) interconnection paradigm has gained importance to abide by the communication challenges in Chip Multi-Processors (CMPs). With increased integration density on CMPs, NoC components namely cores, routers, and links are susceptible to failures. Therefore, to improve system reliability, there is a need for efficient fault-tolerant techniques that mitigate permanent faults in NoC based CMPs. There exists several fault-tolerant techniques that address the permanent faults in application cores while placing the spare cores onto NoC topologies. However, these techniques are limited to Mesh topology based NoCs. There are few approaches that have realized …
Phase-noise Impact on the Performance of mmWave-radars
The impact of phase noise in Frequency Modulated Continuous Wave (FMCW) millimeter-wave (mmWave) radars is investigated in this paper. A FMCW signal is transmitted from the radar, reflected off a moving object and processed at a receiver in the radar. The impact of random phase noise/jitter on the performance parameters of estimated distance, speed and angle of arrival of an object is studied. Our studies show that there exists a threshold at about fifteen percent of the period of the carrier frequency, over which errors substantially manifest in the estimations. Distance estimation is less affected than speed and angle, which rely directly on the phase information for the estimations. Angl…
Experimental validation for spectrum cartography using adaptive multi-kernels
This paper validates the functionality of an algorithm for spectrum cartography, generating a radio environment map (REM) using adaptive radial basis functions (RBF) based on a limited number of measurements. The power at all locations is estimated as a linear combination of different RBFs without assuming any prior information about either power spectral densities (PSD) of the transmitters or their locations. The RBFs are represented as centroids at optimized locations, using machine learning to jointly optimize their positions, weights and Gaussian decaying parameters. Optimization is performed using expectation maximization with a least squares loss function and a quadratic regularizer. …
Feedback Biasing Based Adjustable Gain Ultrasound Preamplifier for CMUTs in 45nm CMOS
As CMOS technology is scaled down, supply voltages are decreasing and intrinsic gain of the nanoscale CMOS transistors is dropping while the threshold voltages of transistors are remaining relatively constant. In such scaled down nanoscale CMOS technologies, conventional vertical stacking architectures (for example. cascode architectures) for high-gain becomes no more attractive. In this paper we present the analysis and design of a feedback biasing based adjustable gain ultrasound preamplifier which is capable of amplifying signals from 15 MHz to 45 MHz from Capacitive Micromachined Ultrasound Transducers (CMUTs) in 45nm CMOS technology for medical ultrasound imaging applications. From the…
Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices
Due to the high demands of tiny, compact, lightweight, and low-cost photoplethysmogram (PPG) monitoring devices, these devices are resource-constrained including limited battery power. Consequently, it highly demands frequent charge or battery replacement in the case of continuous PPG sensing and transmission. Further, PPG signals are often severely corrupted under ambulatory and exercise recording conditions, leading to frequent false alarms. In this paper, we propose a unified quality-aware compression and pulse-respiration rates estimation framework for reducing energy consumption and false alarms of wearable and edge PPG monitoring devices by exploring predictive coding techniques for j…
Fault-Tolerant Application Mapping on to ZMesh topology based Network-on-Chip Design
This paper proposes Particle Swarm Optimization (PSO) based fault-tolerant application mapping on to ZMesh topology based Network-on-Chip (NoC) design. Permanent faults in application cores has been considered and performed application mapping using PSO. The major contribution of this paper is to find out the best position for the spare core to be placed in the network using PSO. Experimentations have been carried out by scaling the ZMesh network size and percentage of network faults. The results show that the proposed approach leads to minimum overhead in communication cost over fault-free result.
Design and implementation of a long-range low-power wake-up radio and customized DC-MAC protocol for LoRaWAN
In this paper, we present the design and implementation of a long-rage wake-up radio (WuR) and customized duty cycled (DC) MAC protocol for wireless IoT devices. The WuRx achieves a sensivity of −70 dBm by consuming just 0.032 mA, thereby optimizing the energy consumption of battery powered long-range wireless IoT devices. Reducing the power consumption of these devices minimizes the overall costs when deployed in large scale.
Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars
Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including p…
Fault Tolerant Routing Methodology for Mesh-of-Tree based Network-on-Chips using Local Reconfiguration
Increase in the processing elements in a System-on- Chip (SoC) has led to an increasing complexity between the cores in the entire network. This communication bottleneck led to rise in the new paradigm called Network-on-Chip (NoC). These NoC are very much susceptible to various types of faults which can be transient, intermittent or permanent. This paper presents a fault-tolerant routing technique which can route the packets from a source to a destination in presence of permanent faults in the leaf routers of Mesh-of-Tree topology where cores are connected. This is achieved by using reconfiguration in the local ports of the leaf routers by inserting multiplexers as a layer between the leaf …
Autonomous Mooring towards Autonomous Maritime Navigation and Offshore Operations
Bollard is a vital component of mooring system. It is the anchor point for mooring ropes to be fixed in order to secure the vessel or ship. An algorithm that translates the segmented mask of bollard output from masked R-CNN along with bounding box and associated class probability to its corresponding edge coordinate and finally to the single reference point for efficient detection and classification of bollard towards autonomous mooring is presented. At first stage, Mask R-CNN framework is trained with custom built bollard. The model obtained from the training is inferred with real data resulting in instance segment of bollard. The segmented mask obtained contains relatively large amount of…
Target Classification by mmWave FMCW Radars Using Machine Learning on Range-Angle Images
In this paper, we present a novel multiclass-target classification method for mmWave frequency modulated continuous wave (FMCW) radar operating in the frequency range of 77 - 81 GHz, based on custom range-angle heatmaps and machine learning tools. The elevation field of view (FoV) is increased by orienting the Radar antennas in elevation. In this orientation, the radar focuses the beam in elevation to improve the elevation FoV. The azimuth FoV is improved by mechanically rotating the Radar horizontally, which has antenna elements oriented in the elevation direction. The data from the Radar measurements obtained by mechanical rotation of the Radar in Azimuth are used to generate a range-angl…
Fault-Tolerant Network-on-Chip Design for Mesh-of-Tree Topology Using Particle Swarm Optimization
As the size of the chip is scaling down the density of Intellectual Property (IP) cores integrated on a chip has been increased rapidly. The communication between these IP cores on a chip is highly challenging. To overcome this issue, Network-on-Chip (NoC) has been proposed to provide an efficient and a scalable communication architecture. In the deep sub-micron level NoCs are prone to faults which can occur in any component of NoC. To build a reliable and robust systems, it is necessary to apply efficient fault-tolerant techniques. In this paper, we present a flexible spare core placement in Mesh-of-Tree (MoT) topology using Particle Swarm Optimization (PSO) by considering IP core failures…
A Self-Powered Long-range Wireless IoT Device based on LoRaWAN
In this article, we propose a self-powered long-range wireless Internet-of-Things (IoT) device based on Long Range Wide Area Network (LoRaWAN) with various sensing capabilities. The nodes are designed based on ambient energy harvesting in such a way that these are self-sustainable throughout the components’ lifespan. Also, these nodes can be deployed on a large scale and are maintenance-free. In addition, these nodes can be deployed in remote places where the accessibility is limited, and maintenance is difficult. The wireless sensor nodes can be deployed both in indoor and outdoor environments with sufficient light levels for the solar panel, such as indoor lights in the indoor environment…
Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19
Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with …
A Survey on Sensors for Autonomous Systems
This paper presents a survey on state-of-the-art sensors for autonomous systems. The key performance parameters along with the operating principle of sensors used in autonomous systems are thoroughly explored. Practical aspects such as performance parameters, sensor output data format, sensor interfaces, size, power consumption, compatible hardware platforms, data analysis, and signal processing complexities are summarized. Such information serves as a practical guide for designing smart sensing systems for autonomous systems.
Message from the Technical Program Chairs iSES 2020
It is with distinct pleasure that we welcome you to the 6th IEEE International Symposium on Smart Electronic Systems (IEEE-iSES) to be virtually held in Chennai, India from 14–16th December 2020. Over the last few years, iSES has evolved into a leading research forum for the academic and industrial community to share their innovative ideas and research results. The primary objective of IEEE-iSES is to provide a platform for both hardware and software researchers to interact under one umbrella for further development of smart electronic systems.
Cyber-Physical Systems for Smart Water Networks: A Review
Author's accepted manuscript. There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scienti…
Single-channel speech enhancement using implicit Wiener filter for high-quality speech communication
AbstractSpeech enables easy human-to-human communication as well as human-to-machine interaction. However, the quality of speech degrades due to background noise in the environment, such as drone noise embedded in speech during search and rescue operations. Similarly, helicopter noise, airplane noise, and station noise reduce the quality of speech. Speech enhancement algorithms reduce background noise, resulting in a crystal clear and noise-free conversation. For many applications, it is also necessary to process these noisy speech signals at the edge node level. Thus, we propose implicit Wiener filter-based algorithm for speech enhancement using edge computing system. In the proposed algor…
Multi-application Based Fault-Tolerant Network-on-Chip Design for Mesh Topology Using Reconfigurable Architecture
In this paper, we propose a two-step fault-tolerant approach to address the faults occurred in cores. In the first stage, a Particle Swarm Optimization (PSO) based approach has been proposed for the fault-tolerant mapping of multiple applications on to the mesh based reconfigurable architecture by introducing spare cores and a heuristic has been proposed for the reconfiguration in the second stage. The proposed approach has been experimented by taking several benchmark applications into consideration. Communication cost comparisons have been carried out by taking the failed cores as user input and the experimental results show that our approach could get improvements in terms of communicati…
LTE-based passive radars and applications: a review
This paper provides an overview of the most recent passive radars based on long-term evolution (LTE). To begin, this paper investigates the various characteristics and requirements of 4 G LTE signals for radar, taking performance aspects such as range, velocity, range resolution, and velocity resolution into account. An ambiguity function analysis is performed on a measured LTE signal using the synchronization and reference signal components to evaluate key performance parameters such as Doppler and range characteristics. We also discuss how LTE passive radar can be used in a variety of applications. The detailed analysis of the LTE downlink signal, its structural overview, and the effect o…
Design and implementation of a long-range low-power wake-up radio for IoT devices
In this paper, we present the design and implementation of an on-demand wake-up radio (WuR) for long-range wireless IoT devices to reduce the power consumption, thereby increasing the life time of the devices. A custom narrow-band (NB) low noise amplifier is designed and implemented for WuR. The low-noise amplifier achieves a gain of 31 dB at 1 mA current consumption from a 6 V power supply. The WuR achieves a sensivity of -80 dBm by consuming just 1 mA, thereby optimizing the energy consumption of battery powered long-range IoT devices, hence reducing the power consumption and overall costs when deployed in large scale.
Current modulation induced stability in laser diode under high optical feedback strength
The back-reflection of emitted laser beam (optical feedback, also know as selfmixing) from various external interfaces are sufficient to cause instability, and prohibiting its use in various fields such as communication, spectroscopy, imaging to name a few. So it is desirable to study the laser dynamics and the conditions causing it to be stable in spite of strong optical feedback. With the aid of mathematical formulation, simulation and backed by experimental evidences, it is demonstrated that the frequency deviation of the laser emission due to current (intensity) modulation alters the dynamic state and boundary conditions of the system such that even under large optical feedback strength…
Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks
Identifying vital nodes is important in disease research, spreading rumors, viral marketing, and drug development. The vital nodes in any network are used to spread information as widely as possible. Centrality measures such as Degree centrality (D), Betweenness centrality (B), Closeness centrality (C), Katz (K), Cluster coefficient (CC), PR (PageRank), LGC (Local and Global Centrality), ISC (Isolating Centrality) centrality measures can be used to effectively quantify vital nodes. The majority of these centrality measures are defined in the literature and are based on a network’s local and/or global structure. However, these measures are time-consuming and inefficient for large-scale netwo…
Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images.
Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images. The proposed Anam-Net has 7.8 times fewer parameters …
Localization of Multi-Class On-Road and Aerial Targets Using mmWave FMCW Radar
mmWave radars play a vital role in autonomous systems, such as unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), ground station control and monitoring systems. The challenging task when using mmWave radars is to estimate the accurate angle of arrival (AoA) of the targets, due to the limited number of receivers. In this paper, we present a novel AoA estimation technique, using mmWave FMCW radars operating in the frequency range 77–81 GHz by utilizing the mechanical rotation. Rotating the radar also increases the field of view in both azimuth and elevation. The proposed method estimates the AoA of the targets, using only a single transmitter and receiver. The measurements are…
Detection of Depression Using Weighted Spectral Graph Clustering With EEG Biomarkers
The alarming annual growth in the number of people affected by Major Depressive Disorder (MDD) is a problem on a global scale. In the primary scrutiny of depression, Electroencephalography (EEG) is one of the analytical tools available. Machine Learning (ML) and Deep Neural Networks (DNN) methods are the most common techniques for MDD diagnosis using EEG. However, these ML methods heavily rely on manually annotated EEG signals, which can only be generated by experts, for training. This also necessitates a large amount of memory and time constraints. The requirement of huge amounts of data to foresee emerging tendencies or undiscovered alignments is enforced. This article develops an unsuper…
Novel Fault-Tolerant Routing Technique for ZMesh Topology based Network-on-Chip Design
This paper proposes a novel fault-tolerant routing technique for ZMesh topology based Network-on-Chip (NoC) design. The proposed algorithm caters the link faults and routes the data packets seamlessly to the destination. This algorithm has been compared with the existing techniques proposed for mesh topology counterparts. The experimentations have been carried out by increasing ZMesh network size and percentage of link faults. The results show that in the event of link failures the proposed algorithm routes the data from source to destination flawlessly.
Implementation of a two stage fully-blind self-adapted spectrum sensing algorithm
In this paper, an experimental validation of a combined two-stage detector called 2EMC is carried out. The detector is proposed in [1]. The 2EMC is composed of energy detector as a primary stage and maximum-minimum eigenvalue detector as a secondary stage. The 2EMC outperforms the two individual detectors in terms of the probability of detection for the same probability of false alarm. Regarding the complexity measured in the sensing time, the 2EMC sensing time is bounded by the sensing times of the two individual detectors. 2EMC incorporates noise estimation that is used by the energy detector, which makes it fully-blind and self-adapted detector. The noise estimator performance is express…
Low resolution thermal imaging dataset of sign language digits.
The dataset contains low resolution thermal images corresponding to various sign language digits represented by hand and captured using the Omron D6T thermal camera. The resolution of the camera is
Spectrum cartography techniques, challenges, opportunities, and applications: A survey
The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the pe…
A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars
Perception in terms of object detection, classification, and dynamic estimation (position and velocity) are fundamental functionalities that autonomous agents (unmanned ground vehicles, unmanned aerial vehicles, or robots) have to navigate safely and autonomously. To date, various sensors have been used individually or in combination to achieve this goal. In this paper, we present a novel method for leveraging millimeter wave radar’s (mmW radar’s) ability to accurately measure position and velocity in order to improve and optimize velocity estimation using a monocular camera (using optical flow) and machine learning techniques. The proposed method eliminates ambiguity in optical flow veloci…
Design and Fabrication of Liquid Pressure Sensor Using FBG Sensor Through Seesaw Hinge Mechanism
Pressure sensors are used in various industrial applications assisting in preventing unintended disasters. This paper presents the design and fabrication of a novel Seesaw device incorporating a diaphragm and Fiber Bragg Grating (FBG) sensor to measure the pressure of liquids. The designed sensor has been tested in a static water column. The proposed design enables the user to easily make and modify the diaphragm based on the required pressure range without interfering with the FBG sensor. The developed pressure sensor produces improved accuracy and sensitivity to applied liquid pressure in both low and high-pressure ranges without requiring sophisticated sensor construction. A finite eleme…
Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey
In the modern-day era of technology, a paradigm shift has been witnessed in the areas involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Specifically, Deep Neural Networks (DNNs) have emerged as a popular field of interest in most AI applications such as computer vision, image and video processing, robotics, etc. In the context of developed digital technologies and the availability of authentic data and data handling infrastructure, DNNs have been a credible choice for solving more complex real-life problems. The performance and accuracy of a DNN is a way better than human intelligence in certain situations. However, it is noteworthy that …
Mixed signal system design (A project based course)
This paper describes an undergraduate 10 ECTS course in the design of analog and digital microelectronic circuits based on a project. This is offered for the students of Electronics engineering in their 3 rd semester of the 6-semester bachelor-programme. The emphasis is given on the mixed signal aspects of the system design. From the project, students get practical experience in the mixed signal system design.
Long-range & Self-powered IoT Devices for Agriculture & Aquaponics Based on Multi-hop Topology
This article presents the prototype design and testing of a long-range, self-powered IoT devices for use in precision agriculture and aquaponics. The devices are designed using the ultra-low power nRF52840 microcontroller with Bluetooth 5 support and ambient energy harvesting. A power of 942µW is harvested in an indoor environment. The devices are therefore suitable for both indoor and outdoor use, as natural sunlight will provide far more energy compared to artificial indoor lights. A line-of-sight range of up to 1.8km is achieved with the use of coded transmissions. However, the coverage area and range can be extended significantly by deploying the devices in multi-hop network topology. T…
Fault-Tolerant Network-on-Chip Design for Mesh-of-Tree Topology Using Particle Swarm Optimization
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Design and Implementation of an Ultra-Low Power Wake-up Radio for Wireless IoT Devices
In this paper, we present the design and prototype implementation of an ultra-low power wake-up radio for wireless IoT devices. The prototyped wake-up radio consumes only 580nA from 3V power supply, covers distance range of up to 55 meters and achieves a sensitivity of -49.5dBm. This wakeup radio module can easily be integrated into wireless IoT devices and thereby reducing the overall power consumption of the battery powered and energy harvesting based devices. The prolonged life time of the devices can reduce the overall costs when deployed in large scale.
A Novel Fault-Tolerant Routing Technique for Mesh-of-Tree based Network-on-Chip Design
Due to the increase in the number of processing elements in System-on-Chips (SoCs), communication between the cores is becoming complex. A solution to this issue in SoCs gave rise to a new paradigm called Network-on-Chips (NoCs). In NoCs, communication between different cores is achieved using packet based switching techniques. In the deep sub-micron technology, NoCs are more susceptible to different kinds of faults which can be transient, intermittent and permanent. These faults can occur at any component of NoCs. This paper presents a novel Fault-Tolerant Routing (FTR) technique for Mesh-of-Tree (MoT) topology in the presence of router faults. The proposed technique is compared with routi…
Torus Topology based Fault-Tolerant Network-on-Chip Design with Flexible Spare Core Placement
The increase in the density of the IP cores being fabricated on a chip poses on-chip communication challenges and heat dissipation. To overcome these issues, Network-onChip (NoC) based communication architecture is introduced. In the nanoscale era NoCs are prone to faults which results in performance degradation and un-reliability. Hence efficient fault-tolerant methods are required to make the system reliable in contrast to diverse component failures. This paper presents a flexible spare core placement in torus topology based faulttolerant NoC design. The communications related to the failed core is taken care by selecting the best position for a spare core in the torus network. By conside…
Bollard Segmentation and Position Estimation From Lidar Point Cloud for Autonomous Mooring
This article presents a computer-aided object detection and localization method from lidar 3-D point cloud data. This topic of interest is in the framework of autonomous mooring, where the ship is tied to the rigid structure on-shore (bollard) for autonomous maritime navigation. Using shape and features priors, unlike matching the whole object template to the experimental 3-D point cloud representation of the scene, two customized algorithms: 1) 3-D feature matching (3-DFM) and 2) mixed feature-correspondence matching (MFCM) are presented. The proposed algorithms discriminate and extract the 3-D points corresponding to the noncooperative bollard's surface from the background, thus capable o…
Radio measurements on a customized software defined radio module: A case study of energy detection spectrum sensing
In this paper, we developed a software defined radio (SDR) system for implementing energy detection spectrum sensing. The SDR module can be used for a wide range of applications. The use of the SDR module is motivated by its high interoperability, availability for relatively cheaper prices and being software independent. Energy detection for cognitive radios is chosen for its simplicity and popularity. However, it is chosen as a representative for a very wide range of measurements and algorithms that can be implemented in the SDR. We have used probabilities of detection and false alarm with the receiver operating characteristics (ROC) curves as performance metrics for the implemented energy…
A Novel Angle Estimation for mmWave FMCW Radars Using Machine Learning
In this article, we present a novel machine learning based angle estimation and field of view (FoV) enhancement techniques for mmWave FMCW radars operating in the frequency range of 77 - 81 GHz. Field of view is enhanced in both azimuth and elevation. The Elevation FoV enhancement is achieved by keeping the orientation of antenna elements in elevation. In this orientation, radar focuses the beam in vertical direction there by enhancing the elevation FoV. An Azimuth FoV enhancement is achieved by mechanically rotating the radar horizontally, which has antenna elements in the elevation. With the proposed angle estimation technique for such rotating radars, root mean square error (RMSE) of 2.5…
Self-powered IoT Device based on Energy Harvesting for Remote Applications
In this paper, we present the design and prototype implementation of self-powered Internet of Things (IoT) device based on energy harvesting from a small solar panel of size 63mm x 63mm and 0.36W for remote applications. These IoT devices can be deployed in remote places within the range of a gateway. A complete proof of concept IoT device based on ambient energy harvesting is designed, prototyped and tested with super capacitors and Lithium cells in star topology. Based on the measurements, the IoT device can potentially last for one year with 55 seconds transmission interval with the fully charged 120mAh coin cell battery. On the other hand, a fully charged single 5F supercapacitor lasts …
A Novel Fault-Tolerant Routing Algorithm for Mesh-of-Tree Based Network-on-Chips
Use of bus architecture based communication with increasing processing elements in System-on-Chip (SoC) leads to severe degradation of performance and speed of the system. This bottleneck is overcome with the introduction of Network-on-Chips (NoCs). NoCs assist in communication between cores on a single chip using router based packet switching technique. Due to miniaturization, NoCs like every Integrated circuit is prone to different kinds of faults which can be transient, intermittent or permanent. A fault in any one component of such a crucial network can degrade performance leaving other components non-usable. This paper presents a novel Fault-Tolerant routing Algorithm for Mesh-of-Tree …
The Modular X- and Gamma-Ray Sensor (MXGS)of the ASIM Payload on the International Space Station
The Modular X- and Gamma-ray Sensor (MXGS) is an imaging and spectral X- and Gamma-ray instrument mounted on the starboard side of the Columbus module on the International Space Station. Together with the Modular Multi-Spectral Imaging Assembly (MMIA) (Chanrion et al. this issue) MXGS constitutes the instruments of the Atmosphere-Space Interactions Monitor (ASIM) (Neubert et al. this issue). The main objectives of MXGS are to image and measure the spectrum of X- and γ-rays from lightning discharges, known as Terrestrial Gamma-ray Flashes (TGFs), and for MMIA to image and perform high speed photometry of Transient Luminous Events (TLEs) and lightning discharges. With these two instruments sp…
Multi-application Based Network-on-Chip Design for Mesh-of-Tree Topology Using Global Mapping and Reconfigurable Architecture
This paper outlines a multi-application mapping for Mesh-of-Tree (MoT) topology based Network-on-Chip (NoC) design using reconfigurable architecture. A two phase Particle Swarm Optimization (PSO) has been proposed for reconfigurable architecture to minimize the communication cost. In first phase global mapping is done by combining multiple applications and in second phase, reconfiguration is achieved by switching the cores to near by routers using multiplexers. Experimentations have been carried out for several application benchmarks and synthetic applications generated using TGFF tool. The results show significant improvement in terms of communication cost after reconfiguration.
Updating thermal imaging dataset of hand gestures with unique labels.
An update to the previously published low resolution thermal imaging dataset is presented in this paper. The new dataset contains high resolution thermal images corresponding to various hand gestures captured using the FLIR Lepton 3.5 thermal camera and Purethermal 2 breakout board. The resolution of the camera is with calibrated array of 19,200 pixels. The images captured by the thermal camera are light-independent. The dataset consists of 14,400 images with equal share from color and gray scale. The dataset consists of 10 different hand gestures. Each gesture has a total of 24 images from a single person with a total of 30 persons for the whole dataset. The dataset also contains the image…
Low-Power Wide-Area Networks: A Broad Overview of its Different Aspects
Low-power wide-area networks (LPWANs) are gaining popularity in the research community due to their low power consumption, low cost, and wide geographical coverage. LPWAN technologies complement and outperform short-range and traditional cellular wireless technologies in a variety of applications, including smart city development, machine-to-machine (M2M) communications, healthcare, intelligent transportation, industrial applications, climate-smart agriculture, and asset tracking. This review paper discusses the design objectives and the methodologies used by LPWAN to provide extensive coverage for low-power devices. We also explore how the presented LPWAN architecture employs various topol…
Methodology for Structured Data-Path Implementation in VLSI Physical Design: A Case Study
State-of-the-art modern microprocessor and domain-specific accelerator designs are dominated by data-paths composed of regular structures, also known as bit-slices. Random logic placement and routing techniques may not result in an optimal layout for these data-path-dominated designs. As a result, implementation tools such as Cadence’s Innovus include a Structured Data-Path (SDP) feature that allows data-path placement to be completely customized by constraining the placement engine. A relative placement file is used to provide these constraints to the tool. However, the tool neither extracts nor automatically places the regular data-path structures. In other words, the relative placement f…
Architectural Implementation of a Reconfigurable NoC Design for Multi-Applications
With the increasing number of applications running on a Network-on-Chip (NoC) based System-on-Chip (SoC), there is a need for designing a reconfigurable NoC platform to achieve acceptable performance for all the applications. This paper proposes a novel architecture for implementing a reconfiguration logic to the NoC platform executing multiple applications. The proposed architecture reconfigures SoC modules to the routers in the NoC with the help of tri-state buffers based on the applications running. The overhead in implementing the reconfiguration circuitry is significantly less, approximately 0.9% of the area and 1% of the total power consumed by the router network. The architectures pr…
Reinforcement Learning based Fault-Tolerant Routing Algorithm for Mesh based NoC and its FPGA Implementation
Network-on-Chip (NoC) has emerged as the most promising on-chip interconnection framework in Multi-Processor System-on-Chips (MPSoCs) due to its efficiency and scalability. In the deep submicron level, NoCs are vulnerable to faults, which leads to the failure of network components such as links and routers. Failures in NoC components diminish system efficiency and reliability. This paper proposes a Reinforcement Learning based Fault-Tolerant Routing (RL-FTR) algorithm to tackle the routing issues caused by link and router faults in the mesh-based NoC architecture. The efficiency of the proposed RL-FTR algorithm is examined using System-C based cycle-accurate NoC simulator. Simulations are c…
Angle and Height Estimation Technique for Aerial Vehicles using mmWave FMCW Radar
In this article, we present a novel angle and height estimation technique for aerial vehicles using mmWave frequency modulated continuous wave (FMCW) Radar. In the proposed method, Radar’s antennas are oriented vertically to measure the elevation angle of arrival of the aerial vehicle from ground station. Height of the aerial vehicle and horizontal distance of the aerial vehicle from Radar station on ground are estimated using the measured radial range and the elevation angle of arrival.
Spectrum cartography using adaptive radial basis functions: Experimental validation
In this paper, we experimentally validate the functionality of a developed algorithm for spectrum cartography using adaptive Gaussian radial basis functions (RBF). The RBF are strategically centered around representative centroid locations in a machine learning context. We assume no prior knowledge about neither the power spectral densities (PSD) of the transmitters nor their locations. Instead, the received signal power at each location is estimated as a linear combination of different RBFs. The weights of the RBFs, their Gaussian decaying parameters and locations are jointly optimized using expectation maximization with a least squares loss function and a quadratic regularizer. The perfor…
Robust Hand Gestures Recognition Using a Deep CNN and Thermal Images
Medical systems and assistive technologies, human-computer interaction, human-robot interaction, industrial automation, virtual environment control, sign language translation, crisis and disaster management, entertainment and computer games, and so on all use RGB cameras for hand gesture recognition. However, their performance is limited especially in low-light conditions. In this paper, we propose a robust hand gesture recognition system based on high-resolution thermal imaging that is light-independent. A dataset of 14,400 thermal hand gestures is constructed, separated into two color tones. We also propose using a deep CNN to classify high-resolution hand gestures accurately. The propose…
Rate-Splitting Random Access Mechanism for Massive Machine Type Communications in 5G Cellular Internet-of-Things
The cellular Internet-of-Things has resulted in the deployment of millions of machine-type communication (MTC) devices. These massive number of devices must communicate with a single gNodeB (gNB) via the random access channel (RACH) mechanism. However, existing RACH mechanisms are inefficient when dealing with such large number of devices. To address this issue, we propose the rate-splitting random access (RSRA) mechanism, which uses rate splitting and decoding in rate-splitting multiple access (RSMA) to improve RACH success rates. The proposed mechanism divides the message into common and private messages and enhances the decoding performance. We demonstrate, using extensive simulations, t…
Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping
Applications of Unmanned aerial vehicles (UAVs) have advanced rapidly in recent years. The UAVs are used for a variety of applications, including surveillance, disaster management, precision agriculture, weather forecasting, etc. In near future, the growing number of UAV applications would necessitate densification of UAV infrastructure (ground radio station (GRS) and ground control station (GCS)) at the expense of increased energy consumption for UAV communications. Maximizing the energy efficiency of this UAV infrastructure is important. Motivated by this, we propose joint resource allocation and UAV scheduling with GRS sleeping (GRSS). Further, we propose the use of coordinated multi-poi…
Self-Powered IoT Device for Indoor Applications
This paper presents a proof of concept for selfpowered Internet of Things (IoT) device, which is maintenance free and completely self-sustainable through energy harvesting. These IoT devices can be deployed in large scale and placed anywhere as long as they are in range of a gateway, and as long as there is sufficient light levels for the solar panel, such as indoor lights. A complete IoT device is designed, prototyped and tested. The IoT device can potentially last for more than 5 months (transmission interval of 30 seconds) on the coin cell battery (capacity of 120mAh) without any energy harvesting, sufficiently long for the dark seasons of the year. The sensor node contains ultra-low pow…
Fault-Tolerant Application-Specific Topology-Based NoC and Its Prototype on an FPGA
Application-Specific Networks-on-Chips (ASNoCs) are suitable communication platforms for meeting current application requirements. Interconnection links are the primary components involved in communication between the cores of an ASNoC design. The integration density in ASNoC increases with continuous scaling down of the transistor size. Excessive integration density in ASNoC can result in the formation of thermal hotspots, which can cause a system to fail permanently. As a result, fault-tolerant techniques are required to address the permanent faults in interconnection links of an ASNoC design. By taking into account link faults in the topology, this paper introduces a fault-tolerant appli…
Object Classification Technique for mmWave FMCW Radars using Range-FFT Features
In this article, we present a novel target classification technique by mmWave frequency modulated continuous wave (FMCW) Radars using the Machine Learning on raw data features obtained from range fast Fourier transform (FFT) plot. FFT plots are extracted from the measured raw data obtained with a Radar operating in the frequency range of 77- 81 GHz. The features such as peak, width, area, standard deviation, and range on range FFT plot peaks are extracted and fed to a machine learning model. Two light weight classification models such as Logistic Regression, Naive Bayes are explored to assess the performance. Based on the results, we demonstrate and achieve an accuracy of 86.9% using Logist…
UDP flows in Cognitive Radios with Channel Aggregation and Fragmentation: A Test-bed Based Evaluation
Channel aggregation (CA) and channel fragmentation (CF) have been studied extensively in cognitive radios (CRs) for many years. However, a test-bed evaluation for such techniques at flow level is still open. In this study, employing National Instruments devices, a test-bed is set up to evaluate the performance of UDP flows for CRs with CA and CF, considering the aspects of blocking, preemption, and throughput in probability. The measurements clearly show that there are performance improvements in applying CA and CF in CRs for UDP flows.
Hand Gesture Classification Using Grayscale Thermal Images and Convolutional Neural Network
Accepted manuscript.
Design and Implementation of Density Sensor for Liquids Using Fiber Bragg Grating Sensor
In this paper, an optical fiber sensor based density sensor is proposed and demonstrated experimentally. The sensor is formed by fiber Bragg grating (FBG) sensor. The proposed sensor design is very simple and versatile for density measurements of liquids. The FBG strain sensor has one end mounted to a 3D printed rigid support, and the other end connected to a 3D manufactured clamp in this sensor design. A metal ball is suspended from this clamp by a non-stretchable cord. When it is completely immersed in liquid, the liquid buoyancy force acts on it. As a result, the strain in FBG varies depending on the force applied to the ball. This results in a wavelength shift in the FBG sensor. The pro…
Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures
Hand gestures based sign language digits have several contactless applications. Applications include communication for impaired people, such as elderly and disabled people, health-care applications, automotive user interfaces, and security and surveillance. This work presents the design and implementation of a complete end-to-end deep learning based edge computing system that can verify a user contactlessly using &lsquo
Localization and Activity Classification of Unmanned Aerial Vehicle Using mmWave FMCW Radars
In this article, we present a novel localization and activity classification method for aerial vehicle using mmWave frequency modulated continuous wave (FMCW) Radar. The localization and activity classification for aerial vehicle enables the utilization of mmWave Radars in security surveillance and privacy monitoring applications. In the proposed method, Radar’s antennas are oriented vertically to measure the elevation angle of arrival of the aerial vehicle from ground station. The height of the aerial vehicle and horizontal distance of the aerial vehicle from Radar station on ground are estimated using the measured radial range and the elevation angle of arrival. The aerial vehicle’s activ…
Recent Advances and Future Directions of Microwave Photonic Radars: A Review
Microwave photonic (MWP) radar has the advantages of generating and processing wide bandwidth microwave signals, reconfigurability, high immunity to electromagnetic interference compared to microwave electronic radar. It has the potential to be used in applications such as intelligent autonomous and cyber-physical systems. Recent advances in microwave photonic technology led to the generation, fast processing, and control of broadband signals. Because of the advancements in photonic technologies, next-generation microwave photonic radar is becoming more prominent. This article reviews the most recent advancements and future directions in MWP radars. This review article overviews the differe…
Deep Learning-Based Sign Language Digits Recognition From Thermal Images With Edge Computing System
The sign language digits based on hand gestures have been utilized in various applications such as human-computer interaction, robotics, health and medical systems, health assistive technologies, automotive user interfaces, crisis management and disaster relief, entertainment, and contactless communication in smart devices. The color and depth cameras are commonly deployed for hand gesture recognition, but the robust classification of hand gestures under varying illumination is still a challenging task. This work presents the design and deployment of a complete end-to-end edge computing system that can accurately provide the classification of hand gestures captured from thermal images. A th…
Radio Frequency Spectrum Sensing by Automatic Modulation Classification in Cognitive Radio System Using Multiscale Deep CNN
Automatic modulation categorization (AMC) is used in many applications such as cognitive radio, adaptive communication, electronic reconnaissance, and non-cooperative communications. Predicting the modulation class of an unknown radio signal without having any prior information of the signal parameters is challenging. This paper proposes a novel multiscale deep-learning-based approach for the automatic modulation classification using radio signals. The approach considered the fixed boundary range-based Empirical wavelet transform (FBREWT) based multiscale analysis technique to decompose the radio signal into sub-band signals or modes. The sub-band signals computed from the radio signal comb…