GANGIREDDY NARENDRA KUMAR REDDY, M. SABARIMALAI MANIKANDAN, N V L NARASIMHA MURTY, AND LINGA REDDY CENKERAMADDI, “Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices” has been accepted for publication in IEEE Access (2023).
The article titled, Hardware Acceleration of a CNN-based Automatic Modulation Classifier has been accepted for publication in the 2023 Southern Conference on Programmable Logic- SPL2023.
Sravanth Chebrolu, Srinivas Boppu and Linga Reddy Cenkeramaddi, “Hardware Acceleration of a CNN-based Automatic Modulation Classifier,” has been accepted for publication in the 2023 Southern Conference on Programmable Logic- SPL2023.
Abstract: Automatic modulation classification (AMC) has found its place in numerous applications, ranging from cognitive radio and adaptive communication to electronic reconnaissance and spectrum interference detection. Several attempts have been made to develop a high-accuracy modulation classifier using machine learning based convolutional neural networks (CNNs). This paper considers one such model, which uses a fixed boundary range empirical wavelet transform and deep CNN, and accelerates the model on the ZCU104 FPGA board to achieve fast classification times. The proposed accelerator can achieve a maximum classification accuracy of 96% for +8 dB signal-to-noise ratio (SNR) radio signals. Compared to similar works, the accelerator performs reasonably well for low SNR ratios (≤ +6 dB). Furthermore, the model is implemented on an edge CPU device (Raspberry Pi), and our accelerator is 50× faster than the CPU implementation. Our design achieves a reasonable throughput of 1.8K classifications/sec and a classification time of 550 µs per sample.
Keywords: Modulation Classification, Hardware Acceleration, Deep Learning, Convolutional Neural Networks, Vitis AI
The article titled, “Adversarial Unsupervised Domain Adaptation for Hand Gesture Recognition using Thermal Images,” has been accepted for publication in the IEEE Sensors Journal (2023).
Aveen Dayal, Aishwarya M., Abhilash S., C. Krishna Mohan, Abhinav Kumar, Linga Reddy Cenkeramaddi, “Adversarial Unsupervised Domain Adaptation for Hand Gesture Recognition using Thermal Images,” has been accepted for publication in the IEEE Sensors Journal (2023).
Abstract: Hand gesture recognition has a wide range of applications, including in the automotive and industrial sectors, health assistive systems, authentication, and so on. Thermal images are more resistant to environmental changes than red–green–blue (RGB) images for hand gesture recognition. However, one disadvantage of using thermal images for the aforementioned task is the scarcity of labeled thermal datasets. To tackle this problem, we propose a method that combines unsupervised domain adaptation (UDA) techniques with deep-learning (DL) technology to remove the need for labeled data in the learning process. There are several types and methods for implementing UDA, with adversarial UDA being one of the most common. In this article, the first time in this field, we propose a novel adversarial UDA model that uses channel attention and bottleneck layers to learn domain-invariant features across RGB and thermal domains. Thus, the proposed model leverages the information from the labeled RGB data to solve the hand gesture recognition task using thermal images. We evaluate the proposed model on two hand gesture datasets, namely, Sign Digit Classification and Alphabet Gesture Classification, and compare it to other benchmark models in terms of accuracy, model size, and model parameters. Our model outperforms the other state-of-the-art methods on the Sign Digit Classification and Alphabet Gesture Classification datasets and achieves 91.32% and 80.91% target test accuracy, respectively.
DOI: 10.1109/JSEN.2023.3235379
Keywords: Task analysis, Gesture recognition, Adaptation models, Feature extraction, Thermal imaging sensors, Thermal imaging camera, Data models, Training
The article titled, “Recent Advances in Thermal Imaging and It’s Applications using Machine Learning: A Review,” has been accepted for publication in the IEEE Sensors Journal (2023).
Wilson A N, Khushi Gupta, Balu Harshavardan Koduru, Abhinav Kumar, Ajit Jha, and Linga Reddy Cenkeramaddi, “Recent Advances in Thermal Imaging and It’s Applications using Machine Learning: A Review,” has been accepted for publication in the IEEE Sensors Journal (2023).
Keywords: Imaging, Cameras, Sensors, Temperature sensors, Optical sensors, Machine learning, Thermal sensing, cameras, data privacy, image colour analysis, image sensors, infrared imaging, learning (artificial intelligence), reviews, machine-learning techniques, RGB imaging, thermal cameras, thermal images, thermal imaging sensor technology, thermal imaging technology, thermal imaging-based applications
Abstract: Recent advancements in thermal imaging sensor technology have resulted in the use of thermal cameras in a variety of applications, including automotive, industrial, medical, defense and space, agriculture, and other related fields. Thermal imaging, unlike RGB imaging, does not rely on background light, and the technique is nonintrusive while also protecting privacy. This review article focuses on the most recent advancements in thermal imaging technology, key performance parameters, an overview of its applications, and machine-learning techniques applied to thermal images for various tasks. This article begins with the most recent advancements in thermal imaging, followed by a classification of thermal cameras and their key specifications, and finally a review of machine-learning techniques used on thermal images for various applications. This detailed review article is highly useful for designing thermal imaging-based applications using various machine-learning techniques.
More details: DOI: 10.1109/JSEN.2023.3234335
The article titled “Performance Analysis of Spectrum Sharing Radar in Multipath Environment” has been accepted for publication in the IEEE Open Journal of the Communications Society (2023).
Gunnery Srinath, Bethi Pardhasaradhi, Ashoka Chakravarthi Mahipathi,
Prashantha Kumar H, Pathipati Srihari, and Linga Reddy Cenkeramaddi, “Performance Analysis of Spectrum Sharing Radar in Multipath Environment,” has been accepted for publication in the IEEE Open Journal of the Communications Society (2023).
Keywords: Radar, Communication systems, Measurement, Wireless sensor networks, Wireless communication, Receivers, Interference cancellation
Abstract: Radar based sensing and communication systems sharing a common spectrum have become a potential research problem in recent years due to spectrum scarcity. The spectrum sharing radar (SSR) is a new technology that uses the total available bandwidth (BW) for both radar based sensing and communication. Unlike traditional radar, the SSR divides the total available BW into radar-only and mixed-use bands. In a radar-only band, only radar sensor signals can be transmitted and received. In contrast, radar and communication signals can both be transmitted and received in the mixed-use band. Taking such BW sharing into account, this paper investigates the performance of SSR in an information-theoretic sense. To evaluate performance, mutual information (MI), spectral efficiency (SE) and capacity (C) metrics are used. Initially, this paper considered a clean environment (no multipath) in order to evaluate performance metrics in the mixed-use band with and without successive interference cancellation. Following that, this paper addresses the performance of BW allocation by allocating low to high BW in mixed-band. Furthermore, the performance metrics are extended to account for the multipath environment, and the same analogy as in a clean environment is used. In addition, the MI and SE of traditional radar system is taken into account when comparing the performance of SSR with and without the use of the SIC. Finally, MI and capacity results show that using the SIC scheme in a mixed-use band yields performance comparable to traditional radar and communication system. In terms of SE, the SSR with SIC scheme outperforms traditional radar and communication system.
More details: DOI: 10.1109/OJCOMS.2023.3240116
The article titled, “Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks,” has been accepted for publication in IEEE Access (2022).
KODURU HAJARATHAIAH, MURALI KRISHNA ENDURI, SATEESHKRISHNA DHULI, SATISH ANAMALAMUDI1, AND LINGA REDDY CENKERAMADDI, “Generalization of Relative Change in a Centrality Measure to Identify Vital Nodes in Complex Networks,” has been accepted for publication in IEEE Access (2022).
Keywords: Volume measurement, Time measurement, Time complexity, Object recognition, Machine learning, Laplace equations, Market research
Abstract: 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 networks. Also, these measures cannot study the effect of removal of vital nodes in resource-constrained networks. To address these concerns, we propose the six new centrality measures namely GRACC, LRACC, GRAD, LRAD, GRAK, and LRAK. We develop these measures based on the relative change of the clustering coefficient, degree, and Katz centralities after the removal of a vertex. Next, we compare the proposed centrality measures with D, B, C, CC, K, PR, LGC, and ISC to demonstrate their efficiency and time complexity. We utilize the SIR (Susceptible-Infected-Recovered) and IC (Independent Cascade) models to study the maximum information spread of proposed measures over conventional ones. We perform extensive simulations on large-scale real-world data sets and prove that local centrality measures perform better in some networks than global measures in terms of time complexity and information spread. Further, we also observe the number of cliques drastically improves the efficiency of global centrality measures.
More details: DOI: 10.1109/ACCESS.2022.3232288
The article titled, “Multi-target Angle of Arrival Estimation using Rotating mmWave FMCW Radar and Yolov3,” has been accepted for publication in the IEEE Sensors Journal (2022).
Wilson A N, Abhinav Kumar, Ajit Jha, and Linga Reddy Cenkeramaddi, “Multi-target Angle of Arrival Estimation using Rotating mmWave FMCW Radar and Yolov3,” has been accepted for publication in the IEEE Sensors Journal (2022).
Keywords: Estimation, Radar, Millimeter wave communication, Sensors, Receiving antennas, Chirp, Radar detection
Abstract: It is still challenging to accurately localize unmanned aerial vehicles (UAVs) from a ground control station (GCS) using various sensors. The mmWave frequency-modulated continuous wave (FMCW) radars offer excellent performance for target detection and localization in harsh environments and low lighting conditions. However, the estimated angle of arrival (AoA) of targets in the captured scene is quite poor. This article focuses on improving AoA estimation by combining the cutting-edge machine learning (ML) algorithms with a mechanical radar rotor setup. An mmWave FMCW radar system is mounted on a programmable rotor to capture range–angle maps of targets at various locations. The range–angle images are then labeled and trained further with the Yolov3 algorithm. Subsequent testing reveals that for detected target objects, the centroid of the bounding boxes from the detected objects provides accurate AoA estimation with very low root mean square error (RMSE). The results show that the proposed approach outperforms traditional methods in terms of performance and estimation accuracy.
More details:DOI: 10.1109/JSEN.2022.3231790
The article entitled, “Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey” has been accepted for publication in IEEE Access (2022).
PUDI DHILLESWARARAO, SRINIVAS BOPPU, M. SABARIMALAI MANIKANDAN, LINGA REDDY CENKERAMADDI, “Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey” has been accepted for publication in IEEE Access (2022).
Keywords: Field programmable gate arrays, Computer architecture, Deep learning, AI accelerators, Hardware acceleration, Graphics processing units, Feature extraction.
Abstract: 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 way better than human intelligence in certain situations. However, it is noteworthy that the DNN is computationally too cumbersome in terms of the resources and time to handle these computations. Furthermore, general-purpose architectures like CPUs have issues in handling such computationally intensive algorithms. Therefore, a lot of interest and efforts have been invested by the research fraternity in specialized hardware architectures such as Graphics Processing Unit (GPU), Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), and Coarse Grained Reconfigurable Array (CGRA) in the context of effective implementation of computationally intensive algorithms. This paper brings forward the various research works on the development and deployment of DNNs using the aforementioned specialized hardware architectures and embedded AI accelerators. The review discusses the detailed description of the specialized hardware-based accelerators used in the training and/or inference of DNN. A comparative study based on factors like power, area, and throughput, is also made on the various accelerators discussed. Finally, future research and development directions, such as future trends in DNN implementation on specialized hardware accelerators, are discussed. This review article is intended to guide hardware architects to accelerate and improve the effectiveness of deep learning research.
More details: DOI: 10.1109/ACCESS.2022.3229767
The article titled, “Energy and Throughput Management in Delay-Constrained Small-World UAV-IoT Network,” has been accepted for publication in the IEEE Internet of Things Journal (2022).
Sreenivasa Reddy Yeduri, Naga Chilamkurthy, Om Jee Pandey, and Linga Reddy Cenkeramaddi, “Energy and Throughput Management in Delay-Constrained Small-World UAV-IoT Network,” has been accepted for publication in the IEEE Internet of Things Journal (2022).
Keywords: Internet of Things, Delays, Throughput, Protocols, Data communication, Wireless networks, Routing
Abstract: Multi-hop data routing over a large scale IoT network results in energy-imbalance and poor data throughput performance. In addition, data transmission using large number of hops causes more delay. In light of this, in this work, a novel method of energy and throughput management in delay-constrained small-world unmanned aerial vehicle (UAV)-IoT network is proposed. The proposed small-world framework optimizes the number of hops required for the data transmission leading to improved energy efficiency and quality of service. The method introduces optimal long range links between device-pairs resulting in low average path length and high clustering coefficient which are called as small-world characteristics. Therefore, in this work, UAVs are deployed to collect the data from IoT devices and forward it to the ground station utilizing the small world framework. It is shown through results that the network delays corresponding to the proposed method, conventional routing method, LEACH protocol, modified LEACH protocol, and canonical particle multi swarm (CPMS) method are 789.39 seconds (s), 1602.53 s, 1000.92 s, 873.63 s, and 999.79 s, respectively. It is also observed that the number of dead UAVs in case of proposed method is reduced when compared to other existing methods. It is also noticed that the proposed method results in 100% packet delivery ratio dominating LEACH and modified LEACH protocols. Thus, it is shown that the proposed method outperforms the other shortest path methods in terms of network latency, lifetime, and packet delivery ratio. Further, the effect of location of ground station, velocities of UAVs, and hovering heights of UAVs is considered for the performance evaluation of the proposed method. The obtained results validate the significance of utilization of proposed method over various network scenarios.
More details:DOI: 10.1109/JIOT.2022.3231644
The article, “Hand Gestures Recognition using Edge Computing System based on Vision Transformer and Lightweight CNN,” has been accepted for publication in the Journal of Ambient Intelligence and Humanized Computing (2022).
Khushi Gupta, Arshdeep Singh, Sreenivasa Reddy Yeduri, M B Srinivas, Linga Reddy Cenkeramaddi, “Hand Gestures Recognition using Edge Computing System based on Vision Transformer and Lightweight CNN,” has been accepted for publication in the Journal of Ambient Intelligence and Humanized Computing (2022).
Keywords: Hand gesture recognition, NUS Hand Posture Dataset I, Turkey Ankara Ayrancı Anadolu High School’s Sign Language Digits Dataset, American Sign Language dataset, Vision transformer, Convolutional Neural Network, Edge computing device, Raspberry Pi, MobileNet, ResNet, VGGNet
Abstract: Human computer interaction, human-robot interaction, robotics, healthcare systems, health assistive technologies, automotive user interfaces, crisis management, disaster relief, entertainment, and contactless communication in smart devices are just a few of the practical applications for hand gesture recognition. In this work, we propose two novel machine learning models for hand gesture recognition using three publicly available datasets, NUS Hand Posture Dataset I, Turkey Ankara Ayrancı Anadolu High School’s Sign Language Digits Dataset, and American Sign Language dataset. The developed models based on vision transformer and lightweight Convolutional Neural Networks are deployed on an end-to-end edge computing system that can accurately provide the classification of hand gestures. The edge computing system presented here utilizes Raspberry Pi. The designed models achieve an accuracy of 90–99% on the test datasets. The performance of the proposed models is also compared to the pre-trained models such as MobileNet, ResNet, and VGGNet.
More details:DOI: https://doi.org/10.1007/s12652-022-04506-4