The article titled “Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review” is one of the top 50 most popular articles in IEEE Sensors Journal in March 2022. https://ieeexplore.ieee.org/xpl/topAccessedArticles.jsp?punumber=7361

Keywords:Sensors,Thermal sensors,Machine learning algorithms,Cameras,Autonomous aerial vehicles,Software,Hardware

Abstract:Unmanned aerial vehicles (UAVs) are increasingly becoming popular due to their use in many commercial and military applications, and their affordability. The UAVs are equipped with various sensors, hardware platforms and software technologies which enable them to support the diverse application portfolio. Sensors include vision-based sensors such as RGB-D cameras, thermal cameras, light detection and ranging (LiDAR), mmWave radars, ultrasonic sensors, and an inertial measurement unit (IMU) which enable UAVs for autonomous navigation, obstacle detection, collision avoidance, object tracking and aerial inspection. To enable smooth operation, UAVs utilize a number of communication technologies such as wireless fidelity (Wi-Fi), long range (LoRa), long-term evolution for machine-type communication (LTE-M), etc., along with various machine learning algorithms. However, each of these different technologies come with their own set of advantages and challenges. Hence, it is essential to have an overview of the different type of sensors, computing and communication modules and algorithms used for UAVs. This paper provides a comprehensive review on the state-of-the-art embedded sensors, communication technologies, computing platforms and machine learning techniques used in autonomous UAVs. The key performance metrics along with operating principles and a detailed comparative study of the various technologies are also studied and presented. The information gathered in this paper aims to serve as a practical reference guide for designing smart sensing applications, low-latency and energy efficient communication strategies, power efficient computing modules and machine learning algorithms for autonomous UAVs. Finally, some of the open issues and challenges for future research and development are also discussed.

More details:DOI: 10.1109/JSEN.2021.3139124

The article titled, “Updating Thermal Imaging Dataset of Hand Gestures with Unique Labels” has been accepted for publication in Data in Brief Journal.

Sreenivasa Reddy Yeduri, Daniel Skomedal Breland, Om Jee Pandey, Linga Reddy Cenkeramaddi, “Updating Thermal Imaging Dataset of Hand Gestures with Unique Labels,” has been accepted for publication in Data in Brief Journal.

Keywords:Thermal imaging,Hand Gestures,Thermal Camera,Machine learning models, Sensor

Abstract: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 160×120 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 images captured under different orientations of the hand under different lighting conditions.

The article titled, “Low Resolution Thermal Imaging Dataset of Sign Language Digits” has been accepted for publication in Data in Brief Journal.

Sreenivasa Reddy Yeduri; Daniel Skomedal Breland; Simen Birkeland Skriubakken; Om Jee Pandey; Linga Reddy Cenkeramaddi, “Low Resolution Thermal Imaging Dataset of Sign Language Digits” has been accepted for publication in Data in Brief Journal (2022).

Keywords:Thermal imaging,Sign language digits,Thermal camera,Machine learning models,Sensor,Temperature

Abstract: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 32×32 pixels. Because of the low resolution of the images captured by this camera, machine learning models for detecting and classifying sign language digits face additional challenges. Furthermore, the sensor’s position and quality have a significant impact on the quality of the captured images. In addition, it is affected by external factors such as the temperature of the surface in comparison to the temperature of the hand. The dataset consists of 3200 images corresponding to ten sign digits, 0–9. Thus, each sign language digit consists of 320 images collected from different persons. The hand is oriented in various ways to capture all of the variations in the dataset.

More details:https://doi.org/10.1016/j.dib.2022.107977

The article titled “Target Classification by mmWave FMCW Radars Using Machine Learning on Range-Angle Images” is one of the top 50 most popular articles in IEEE Sensors Journal in February 2022. https://ieeexplore.ieee.org/xpl/topAccessedArticles.jsp?punumber=7361

Keywords:Radar,Chirp,Automobiles,Radar antennas,Drones,Azimuth,Heating systems

Abstract: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-angle heatmap. The measurements are taken in a variety of real-world scenarios with various objects such as humans, a car, and an unmanned aerial vehicle (UAV), also known as a drone. The proposed technique achieves accuracy of 97.6 % and 99.6 % for classifying the UAV and humans, respectively, and accuracy of 98.1 % for classifying the car from the range-angle FoV heatmap. Such a Radar classification technique will be extremely useful for a wide range of applications in cost-effective and dependable autonomous systems, including ground station traffic monitoring and surveillance, as well as control systems for both on-ground and aerial vehicles.

More details: DOI: 10.1109/JSEN.2021.3092583

Two articles have been accepted for publication in the IEEE Wireless Communications and Networking Conference (IEEE WCNC), 10–13 April 2022 // Austin, TX, USA.

1. Sreenivasa Reddy Yeduri, T. Uday, Sindhusha Jeeru, Abhinav Kumar, Ankit Dubey, and Linga Reddy Cenkeramaddi, “SIC-RSRA for Massive Machine-to-Machine Communications in 5G Cellular IoT,” IEEE Wireless Communications and Networking Conference (IEEE WCNC) 10–13 April 2022 // Austin, TX, USA.

Keywords:Performance evaluation,Energy consumption,Machine-to-machine communications,Interference cancellation,Costs,Closed-form solutions,5G mobile communication

Abstract:Inclusion of massive machine-type-communication (mMTC) devices in 5G cellular Internet of Things (IoT) has significantly raised the issue of network congestion. To address this challenge, a successive interference cancellation-rate splitting random access (SIC-RSRA) mechanism is proposed in this paper. Unlike traditional mechanisms, all selected mMTC devices are allowed to make a finite number of repeated message requests in randomly selected time slots within a radio frame. The gNodeB, on the other hand, applies both intra-slot SIC (utilizing RSRA) and inter-slot SIC to decode messages from a larger number of devices. For the proposed mechanism, the impact of increasing the number of devices as well as the received power difference is investigated. Through extensive simulations, we show that the proposed mechanism outperforms the other mechanisms in terms of number of RACH successes and number of supported devices.

More details:DOI: 10.1109/WCNC51071.2022.9771752

2. Akhileswar Chowdary, Garima Chopra, Abhinav Kumar, and Linga Reddy Cenkeramaddi, “Impact of NOMA and CoMP Implementation Order on the Performance of Ultra-Dense Networks,” IEEE Wireless Communications and Networking Conference (IEEE WCNC) 10–13 April 2022 // Austin, TX, USA.

Keywords:NOMA,Base stations,Spectral efficiency, System performance, Simulation, Conferences,Benchmark testing

Abstract:Non-orthogonal multiple access (NOMA) is a next-generation multiple access technology to improve users’ throughput and spectral efficiency for 5G and beyond cellular networks. Similarly, coordinated multi-point transmission and reception (CoMP) is an existing technology to improve the coverage of cell-edge users. Hence, NOMA with CoMP can potentially enhance the throughput and coverage of the users. However, the order of implementation of CoMP and NOMA can significantly impact the system performance of Ultra-dense networks (UDNs). Motivated by this, we study the performance of the CoMP and NOMA-based UDN by proposing two kinds of user grouping and pairing schemes that differ in the order in which CoMP and NOMA are performed for a group of users. Detailed simulation results are presented, comparing the proposed schemes with the state-of-the-art systems with varying user and base station densities. Through numerical results, we show that the proposed schemes can be used to achieve a suitable coverage-throughout trade-off in UDNs.

More details:DOI: 10.1109/WCNC51071.2022.9771552

IEEE Sensors Journal Paper Has Been Selected to be a Feature Article!

The paper titled “Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review” has been selected to be published in the IEEE Sensors Journal as a Featured Article in Issue 3. This honor has been extended to all authors since the paper has been considered of high interest to the scientific community in the multi-disciplinary domain.

A. N. Wilson, Abhinav Kumar,Ajit Jha,and Linga Reddy Cenkeramaddi

Keywords:Sensors,Thermal sensors,Machine learning algorithms,Cameras,Autonomous aerial vehicles,Software,Hardware

Abstract:Unmanned aerial vehicles (UAVs) are increasingly becoming popular due to their use in many commercial and military applications, and their affordability. The UAVs are equipped with various sensors, hardware platforms and software technologies which enable them to support the diverse application portfolio. Sensors include vision-based sensors such as RGB-D cameras, thermal cameras, light detection and ranging (LiDAR), mmWave radars, ultrasonic sensors, and an inertial measurement unit (IMU) which enable UAVs for autonomous navigation, obstacle detection, collision avoidance, object tracking and aerial inspection. To enable smooth operation, UAVs utilize a number of communication technologies such as wireless fidelity (Wi-Fi), long range (LoRa), long-term evolution for machine-type communication (LTE-M), etc., along with various machine learning algorithms. However, each of these different technologies come with their own set of advantages and challenges. Hence, it is essential to have an overview of the different type of sensors, computing and communication modules and algorithms used for UAVs. This paper provides a comprehensive review on the state-of-the-art embedded sensors, communication technologies, computing platforms and machine learning techniques used in autonomous UAVs. The key performance metrics along with operating principles and a detailed comparative study of the various technologies are also studied and presented. The information gathered in this paper aims to serve as a practical reference guide for designing smart sensing applications, low-latency and energy efficient communication strategies, power efficient computing modules and machine learning algorithms for autonomous UAVs. Finally, some of the open issues and challenges for future research and development are also discussed.

More details:DOI: 10.1109/JSEN.2021.3139124

The paper titled “Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review,” has been accepted for publication in IEEE Sensors Journal (2021).

Wilson A N, Abhinav Kumar, Ajit Jha and Linga Reddy Cenkeramaddi, “Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review,” has been accepted for publication in IEEE Sensors Journal (2021).

Keywords:Sensors,Thermal sensors,Machine learning algorithms,Cameras,Autonomous aerial vehicles,Software,Hardware

Abstract:Unmanned aerial vehicles (UAVs) are increasingly becoming popular due to their use in many commercial and military applications, and their affordability. The UAVs are equipped with various sensors, hardware platforms and software technologies which enable them to support the diverse application portfolio. Sensors include vision-based sensors such as RGB-D cameras, thermal cameras, light detection and ranging (LiDAR), mmWave radars, ultrasonic sensors, and an inertial measurement unit (IMU) which enable UAVs for autonomous navigation, obstacle detection, collision avoidance, object tracking and aerial inspection. To enable smooth operation, UAVs utilize a number of communication technologies such as wireless fidelity (Wi-Fi), long range (LoRa), long-term evolution for machine-type communication (LTE-M), etc., along with various machine learning algorithms. However, each of these different technologies come with their own set of advantages and challenges. Hence, it is essential to have an overview of the different type of sensors, computing and communication modules and algorithms used for UAVs. This paper provides a comprehensive review on the state-of-the-art embedded sensors, communication technologies, computing platforms and machine learning techniques used in autonomous UAVs. The key performance metrics along with operating principles and a detailed comparative study of the various technologies are also studied and presented. The information gathered in this paper aims to serve as a practical reference guide for designing smart sensing applications, low-latency and energy efficient communication strategies, power efficient computing modules and machine learning algorithms for autonomous UAVs. Finally, some of the open issues and challenges for future research and development are also discussed.

More details:DOI: 10.1109/JSEN.2021.3139124

The paper titled, “Enhanced User Grouping and Pairing Scheme for CoMP-NOMA-based Cellular Networks” has been accepted for publication in IEEE COMSNETS 2022.

Akhileswar Chowdary, Garima Chopra, Abhinav Kumar and Linga Reddy Cenkeramaddi, “Enhanced User Grouping and Pairing Scheme for CoMP-NOMA-based Cellular Networks” has been accepted for publication in IEEE COMSNETS 2022.

Keywords:Cellular networks,NOMA,Machine learning algorithms,5G mobile communication,Spectral efficiency,Simulation,Heuristic algorithms

Abstract:Non-orthogonal multiple access (NOMA) has been identified as one of the promising technologies to enhance the spectral efficiency and throughput for the fifth generation (5G) and beyond 5G cellular networks. Alternatively, Coordinated multi-point transmission and reception (CoMP) improves the cell edge users’ coverage. Thus, CoMP and NOMA can be used together to improve the overall coverage and throughput of the users. However, user grouping and pairing for CoMP-NOMA-based cellular networks have not been suitably studied in the existing literature. Motivated by this, we propose a user grouping and pairing scheme for a CoMP-NOMA-based system. Detailed numerical results are presented comparing the proposed scheme with the purely OMA-based benchmark system, NOMA only, and CoMP only systems. We show through simulation results that the proposed scheme offers a trade-off between throughput and coverage as compared to the existing NOMA or CoMP based system.

More details:DOI: 10.1109/COMSNETS53615.2022.9668568

The paper titled “Localization of Multi-class on-Road and Aerial Targets using mmWave FMCW Radar,” has been accepted for publication in MDPI Electronics (2021).

Khushi Gupta, Soumya J., M.B. Srinivas, Srinivas Boppu, M. Manikandan Sabarimalai, LINGA REDDY CENKERAMADDI *, “Localization of Multi-class on-Road and Aerial Targets using mmWave FMCW Radar,” has been accepted for publication in MDPI Electronics (2021).

Keywords: mmWave radar,FMCW radar,localization,Multi-class targets,Angle of arrival (AoA),Azimuth angle, Elevation angle,Range-angle maps,Morphological operators, Unmanned aerial vehicle localization, UAV localization

Abstract: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 carried out in a variety of practical scenarios including pedestrians, a car, and an UAV, also known as a drone. With measured data, range-angle maps are created, and morphological operators are used to estimate the AoA of the targets. We also process radar range-angle images for improved visual representation. The proposed method will be extremely beneficial for practical ground stations, traffic control and monitoring frameworks for both on-ground and airborne vehicles.

More details:https://doi.org/10.3390/electronics10232905

Two journal articles have been accepted. One in the Elsevier Pervasive and Mobile Computing Journal and the other one in the IEEE Photonics Journal.

1.Yeduri Sreenivasa Reddy, Abhinav Kumar , Om Jee Pandey, and Linga Reddy Cenkeramaddi, “Spectrum Cartography Techniques, Challenges, Opportunities, and Applications: A Survey” has been accepted for publication in the Elsevier Pervasive and Mobile Computing Journal.

Keywords:Channel gain map,Channel state information,Interference map,Mean absolute error (MAE), Mean square error (MSE), Normalized MSE (NMSE), Power spectral density map, Power map, Radio frequency (RF) power, RF map, Root MSE (RMSE),Spectrum cartography, Spectrum map, Transmitter locations

Abstract: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 performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.

More details:https://doi.org/10.1016/j.pmcj.2021.101511

2.Venkata satya chidambara swamy Vaddadi, Saidi Reddy Parne, Vijeesh V. P., Suman Gandi and Linga Reddy Cenkeramaddi, “Design and Implementation of Density Sensor for Liquids using Fiber Bragg Grating Sensor,” has been accepted for publication in the IEEE Photonics Journal.

Keywords:Liquids,Fiber gratings,Temperature measurement,Optical fiber sensors,Density measurement, Temperature sensors,Wavelength measurement

Abstract: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 proposed sensor design is tested for four distinct liquids, including water, gasoline, engine oil, and acetone, and the measured density values for each were tabulated. We estimated the density of water by varying the temperature and adding salt. Based on the measurements, the sensitivity of the sensor is 2.584 pm/Kg/m3 when the temperature of liquid changes and 3.375×10−2 pm/Kg/m3 when density varied by adding salt to the liquid is reported.

More details:DOI: 10.1109/JPHOT.2021.3129587