The manuscript entitled “Joint Resource Allocation and UAV Scheduling with Ground Radio Station Sleeping” has been accepted for publication in IEEE Access.

AKHILESWAR CHOWDARY (Student Member, IEEE), YOGHITHA RAMAMOORTHI (Member, IEEE), ABHINAV KUMAR (Senior Member, IEEE), AND LINGA REDDY
CENKERAMADDI (Senior Member, IEEE),” Joint Resource Allocation and UAV Scheduling with Ground Radio Station Sleeping,” IEEE Access, 2021.

Keywords:Unmanned aerial vehicles,NOMA,Signal to noise ratio, Interference, Throughput, Quality of service, Resource management

Abstract: 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-point (CoMP) with joint transmission (JT) and non-orthogonal multiple access (NOMA) along with GRSS to increase the coverage and data rates, respectively. Through exhaustive simulation results, we show that the proposed CoMP along with GRSS results in up to 10% higher energy savings and 24% increase in coverage. Further, NOMA along with GRSS results in up to 9% enhancement in throughput of the system.

More details:DOI: 10.1109/ACCESS.2021.3111087

The paper titled, “Point Cloud Instance Segmentation for Automatic Electric Vehicle Battery Disassembly” has been accepted for publication in Intelligent Technologies and Applications: 4th International Conference, INTAP 2021.

Henrik Bradland, Martin Choux and Linga Reddy Cenkeramaddi, “Point Cloud Instance Segmentation for Automatic Electric Vehicle Battery Disassembly”, Intelligent Technologies and Applications: 4th International Conference, INTAP 2021.

Keywords:Graph CNN,Part segmentation,Large point clouds,Structured-light camera

Abstract:This paper describes a novel design based on recent 3D perception methods for capturing point clouds and segmenting instances of cabling found on electric vehicle battery packs. The use of cutting-edge perception algorithm architectures, such as graph-based and voxel-based convolution, in industrial autonomous lithium-ion battery pack disassembly is investigated. The proposed approach focuses on the challenge of getting a desirable representation of any battery pack using an industrial robot in conjunction with a high-end structured light camera, with “end-to-end” and “model-free” as design constraints. The proposed design employs self-captured datasets comprised of several battery packs that have been captured and labeled. Following that, the datasets are used to create a perception system. Based on the results, graph-based deep-learning algorithms have been shown to be capable of being scaled up to 50, 000 inputs while still exhibiting strong performance in terms of accuracy and processing time. The results show that an instance segmenting system can be implemented in less than two seconds. Using off-the-shelf hardware, we demonstrate that a 3D perception system is industrially viable and competitive as compared to a 2D perception system (The different algorithms studied in this article are implemented in Python and can be obtained through the following link: https://github.com/HenrikBradland-Nor/intap21).

More details:DOI: 10.1007/978-3-031-10525-8_20

The paper titled, “Classification of Targets using Statistical Features from Range FFT of mmWave FMCW Radars”, has been accepted for publication in the MDPI Electronics (Artificial Intelligence Circuits and Systems (AICAS)), 2021.

Jyoti Bhatia, Aveen Dayal, Ajit Jha, Santosh Kumar Vishvakarma, Soumya J., Srinivas M. B., Phaneendra K. Yalavarthy, Abhinav Kumar, V. Lalitha, Sagar Koorapati, and Linga Reddy Cenkeramaddi, “Classification of Targets using Statistical Features from Range FFT of mmWave FMCW Radars”, has been accepted for publication in the MDPI Electronics (Artificial Intelligence Circuits and Systems (AICAS)), 2021.

Keywords:mmWave radar,FMCW radar, Autonomous systems,Machine learning,Ground station radar,Targets classification,Range FFT features

Abstract: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 pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classification technique extends the potential of mmWave FMCW radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.

More details:https://doi.org/10.3390/electronics10161965

The paper titled, “LTE-based Passive Radars and Applications: A Review,” has been accepted for publication in the International Journal of Remote Sensing, 2021.

Prabhat Kumar Rai, Abhinav Kumar, Mohammed Zafar Ali Khan, and Linga Reddy Cenkeramaddi, “LTE-based Passive Radars and Applications: A Review,” has been accepted for publication in the International Journal of Remote Sensing, 2021. 

Abstract: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 on cross- and self-ambiguity functions are all discussed. The paper investigates related standard development proposals, with a focus on performance evaluation criteria for existing passive radar technologies. As a result, this survey paper serves as a starting point for evaluating the performance of current and future passive radar innovations, including an emerging 5 G radar.

More details:https://doi.org/10.1080/01431161.2021.1959669

The paper titled, “Recent Advances and Future Directions of Microwave Photonic Radars: A Review,” has been accepted for publication in the IEEE Sensors Journal, 2021.

S. S. S. Panda, T. Panigrahi, S. R. Parne, S. L. Sabat, and L. R. Cenkeramaddi, “Recent Advances and Future Directions of Microwave Photonic Radars: A Review,” has been accepted for publication in the IEEE Sensors Journal, 2021.

Keywords:Radar,Microwave photonics,Microwave oscillators,Laser radar, Masers, Microwave imaging, Radar imaging

Abstract: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 different components of microwave photonic radar, different design challenges, and issues pertaining to it. We present a comparative study of different MWP radars on different applications. It also discusses possible future research directions of MWP radar.

More details:DOI: 10.1109/JSEN.2021.3099533

The paper titled, “Bollard Segmentation and Position Estimation from Lidar Point Cloud for autonomous mooring” has been accepted for publication in the IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021.

Mehak Jindal, Ajit Jha, and Linga Reddy Cenkeramaddi, “Bollard Segmentation and Position Estimation from Lidar Point Cloud for autonomous mooring” has been accepted for publication in the IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021.

Keywords:Laser radar,Three-dimensional displays,Feature extraction, Robots,Global Positioning System, Solid modeling,Manipulators

Abstract: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 of classification, localization, and representing it using a unique coordinate in the 3-D world. The proposed algorithms are tested and validated by implementing upon an experimental dataset of 105 scenes where the bollard is at different positions and orientations with respect to lidar mounted on the robotic arm. Statistical and probabilistic-based approaches are taken into account to determine the performance of proposed algorithms. Model parameters’ estimation implies that errors resulting from the 3-DFM algorithm follow homoscedastic bimodal Gaussian distribution with individual Gaussian components having mean 0.03 and 0.09 m, and both have an equal standard deviation of 0.01 m. Furthermore, the posterior component assignment probability is used to identify and cluster the scenes that contribute to relatively larger errors. Finally, an improved algorithm, MFCM, is proposed, whose errors follow unimodal Gaussian distribution with a mean and a standard deviation of 0.03 and 0.01 m, respectively, thus mitigating the shortcomings of the former.

More details:DOI: 10.1109/TGRS.2021.3097134

The article titled, “Architectural Implementation of a Reconfigurable NoC Design for Multi-Applications” has been accepted for presentation in the 24th Euromicro Conference on Digital System Design (DSD) Palermo, Sicily, Italy, Sept. 1st – Sept. 3rd, 2021.

Aparna Nair M K, Soumya J, and Linga Reddy Cenkeramaddi, “Architectural Implementation of a Reconfigurable NoC Design for Multi-Applications” has been accepted for presentation in the 24th Euromicro Conference on Digital System Design (DSD) Palermo, Sicily, Italy, Sept. 1st – Sept. 3rd, 2021.

Keywords:Multiplexing,Power demand,Network topology,Digital systems, Network-on-chip,Topology,Reconfigurable architectures

Abstract: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 presented in the paper are developed in Verilog HDL, applied to the NoC router platform and simulated for functional verification. The synthesis results show that the proposed tri-state buffer-based reconfiguration logic has better performance in terms of area, power and speed compared with the multiplexer-based reconfiguration logic.

More details:DOI10.1109/DSD53832.2021.00030

The paper titled “Target Classification by mmWave FMCW Radars using Machine Learning on Range-Angle Images,” has been accepted for publication in the IEEE Sensors Journal.

Siddharth Gupta, Prabhat Kumar Rai, Abhinav Kumar, Phaneendra K. Yalavarthy, and Linga Reddy Cenkeramaddi “Target Classification by mmWave FMCW Radars using Machine Learning on Range-Angle Images,” has been accepted for publication in the IEEE Sensors Journal, 2021.

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

Paper Accepted in IEEE PIMRC 2021.

The paper titled Rate-Splitting Random Access Mechanism for Massive Machine Type Communications in 5G Cellular Internet-Of-Things” has been accepted for presentation in 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 13-16 September 2021.

Sreenivasa Reddy Yeduri, Garima Chopra, Ankit Dubey, Abhinav Kumar, Trilochan Panigrahi and Linga Reddy Cenkeramaddi, “Rate-Splitting Random Access Mechanism for Massive Machine Type Communications in 5G Cellular Internet-Of-Things” has been accepted for presentation in 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 13-16 September 2021.”

Keywords:Performance evaluation,5G mobile communication,Massive machine type communications, Receivers,Decoding,Land mobile radio

Abstract:The cellular Internet-of-Things has resulted in the deployment of millions of machine type communication (MTC) devices under the coverage of a single gNodeB (gNB). These massive number of devices should connect to the gNodeB (gNB) via the random access channel (RACH) mechanism. Moreover, the 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 the RACH success rate. The proposed mechanism divides the message into common and private messages and enhances the decoding performance. We demonstrate, using extensive simulations, that the proposed RSRA mechanism significantly improves the success rate of MTC in cellular IoT networks. We also evaluate the performance of the proposed mechanism with increasing number of devices and received power difference.

More details:DOI: 10.1109/PIMRC50174.2021.9569424

The paper titled “Fault-Tolerant Application-Specific Topology based NoC and its Prototype on an FPGA” has been accepted for publication in IEEE Access Journal.

P. Veda Bhanu, Rahul Govindan, Rajat Kumar, Vishal Singh, Soumya J, and Linga Reddy Cenkeramaddi, Fault-Tolerant Application-Specific Topology based NoC and its Prototype on an FPGA, Accepted for publication in IEEE Access Journal (2021).

Keywords:Topology,Fault tolerant systems,Fault tolerance,Field programmable gate arrays, Network topology, Routing,Measurement

Abstract: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 application-specific topology-based NoC design and its prototype on an FPGA. To place spare links in the ASNoC topology, a meta-heuristic algorithm based on Particle Swarm Optimization (PSO) is proposed. By taking link faults into account in ASNoC design, we also propose an application mapping heuristic and a table-based fault-tolerant routing algorithm. Experiments are carried out for a specific link and any link fault in fault-tolerant topologies generated by our approach and approaches reported in the literature. For the experimentation, we used the multi-media applications Picture-in-Picture (PiP), Moving Pictures Expert Group (MPEG) – 4, MP3Encoder, and Video Object Plane Decoder (VOPD). Experiments are run on software and hardware platforms. The static performance metric communication cost and the dynamic performance metrics network latency, throughput, and router power consumption are examined using software platform. In the hardware platform, the Field Programmable Gate Array (FPGA) is used to validate proposed fault-tolerant topologies and analyze performance metrics such as application runtime, resource utilization, and power consumption. The results are compared with the existing approaches, specifically Ring topology and its modified versions on both software and hardware platforms. The experimental results obtained from software and hardware platforms for a specific link and any link fault show significant improvements in performance metrics using our approach when compared with the related works in the literature.

More details:DOI: 10.1109/ACCESS.2021.3082852