The article titled, “Depth Camera based Dataset of Hand Gestures” has been accepted for publication in the Data in Brief Journal (2022).

Sindhusha Jeeru, Arun Kumar Sivapuram, David Gonzalez Leon, Gröli Jade, Sreenivasa Reddy Yeduri, Linga Reddy Cenkeramaddi, “Depth Camera based Dataset of Hand Gestures,” has been accepted for publication in the Data in Brief Journal (2022).

Keywords: Video hand gestures, RGB image, Depth image, RGB-D Camera, Machine learning

Abstract: 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 total of 662 sequences corresponding to each gesture in the dataset. To capture all the variations in the dataset, the hand is oriented in various ways while capturing.

More details: https://doi.org/10.1016/j.dib.2022.108659

Workshops

INCAPS – 2nd Indo-Norway Workshop on Smart Sensing, Communication and Machine Learning for Autonomous and Cyber Physical Systems (IN-SSCOM) 14-16 October 2022

INCAPS – 2nd Indo-Norway Workshop on Smart Sensing, Communication and Machine Learning for Autonomous and Cyber Physical Systems (IN-SSCOM) from Oct 14, 2022 to Oct 16, 2022 (3 day workshop) is organized at Indian Institute of Technology Hyderabad (IITH) in Hybrid mode. This workshop is an outcome of the multiple ongoing collaborations between Indian Institute of Technology Hyderabad (IITH), India, and University of Agder (UiA), Norway. This includes the Department of Science and Technology (International Bilateral Cooperation Division), Government of India and the Norwegian Research council funded project, Low Altitude UAV Communication and Tracking (LUCAT), and the Norwegian Research council and the Diku (the Norwegian agency for international cooperation and quality enhancement in higher education) funded project, the Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS). There exists an MoU between IITH and UiA to further promote this research and collaboration. Through IN-SSCOM 2022, we aim to bring together researchers, industries, and practitioners to present and discuss their latest achievements and innovations in the areas of Smart Sensing, Communications and Machine Learning for Autonomous Cyber-Physical Systems. The workshop will be an experience sharing forum and we expect to attract a good mix of academic, industry researchers, practitioners, and students working in this area.

The Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS) aims to establish long-term collaboration between highly reputed Indian universities which include Indian Institute of Science (IISc), Bangalore, Indian Institute of Technology Hyderabad (IITH), International Institute of Information Technology Hyderabad (IIITH) and Birla Institute of Technology and Science (BITS), Hyderabad and Norwegian universities, University of Agder (UiA), Norwegian University of Science and Technology (NTNU) and Norwegian Institute for Water Research (NIVA) in world-class research and education. INCAPS considers broad areas of research which include smart sensing for autonomous cyber physical systems, mmWave sensor-based system design, de-centralized wireless communications, in-network processing and intelligence for heterogeneous wireless sensor and communication networks, machine learning and deep learning for autonomous systems, data analytics, energy harvesting based smart electronic systems, smart water networks and inference methods for timely detection and prediction, cognitive control and adaptive learning in autonomous cyber-physical systems.

The key objectives of the INCAPS project are to: I). Strengthen collaborative network between industry (both public and private enterprises, small and medium-sized enterprises, and multi-national companies) and academia. II). Increased value creation and enhanced innovation by using smart sensing, machine, and deep learning techniques in autonomous cyber-physical systems. III). Facilitate education and knowledge sharing through better mobility for students and researchers. IV). Create an arena for the generation of research and innovation projects. V). Increased utilization of research and educational infrastructure both in Norway and India. VI). Integrate professionals from industries and academics through workshops, seminars, webinars, and summer/winter schools.

1st Indo-Norway Workshop on unmanned Aerial VEhicles (IN-WAVE): April 30th, 2021 – May 2nd, 2021

The 1st Indo-Norway Workshop on unmanned Aerial VEhicles (IN-WAVE) from April 30, 2021 to May 2, 2021 (3 day workshop) is organized in a virtual mode. This workshop is an outcome of the multiple ongoing collaborations between Indian Institute of Technology Hyderabad (IITH), India, and University of Agder (UiA), Norway. This includes the Department of Science and Technology (International Bilateral Cooperation Division), Government of India and the Norwegian Research council funded project, Low Altitude UAV Communication and Tracking (LUCAT), and the Norwegian Research council and the Diku (the Norwegian agency for international cooperation and quality enhancement in higher education) funded project, the Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS). There exists an MoU between IITH and UiA to further promote this research and collaboration. Through IN-WAVE 2021, we aim to bring together researchers, industries, and practitioners to present and discuss their latest achievements and innovations in the area of UAV Communication and Tracking. The workshop will be an experience sharing forum and we expect to attract a good mix of academic, industry researchers, practitioners, and students working in this area.

The Indo-Norwegian collaboration in Autonomous Cyber-Physical Systems (INCAPS) aims to establish long-term collaboration between highly reputed Indian universities which include Indian Institute of Science (IISc), Bangalore, Indian Institute of Technology Hyderabad (IITH), International Institute of Information Technology Hyderabad (IIITH) and Birla Institute of Technology and Science (BITS), Hyderabad and Norwegian universities, University of Agder (UiA), Norwegian University of Science and Technology (NTNU) and Norwegian Institute for Water Research (NIVA) in world-class research and education. INCAPS considers broad areas of research which include smart sensing for autonomous systems, mmWave sensor-based system design, de-centralized wireless communications, in-network processing and intelligence for heterogeneous wireless sensor and communication networks, machine learning and deep learning for autonomous systems, data analytics, energy harvesting based smart electronic systems, smart water networks and inference methods for timely detection and prediction, cognitive control and adaptive learning in autonomous cyber-physical systems.

The key objectives of the INCAPS project are to: I). Strengthen collaborative network between industry (both public and private enterprises, small and medium-sized enterprises, and multi-national companies) and academia. II). Increased value creation and enhanced innovation by using smart sensing, machine, and deep learning techniques in autonomous cyber-physical systems. III). Facilitate education and knowledge sharing through better mobility for students and researchers. IV). Create an arena for the generation of research and innovation projects. V). Increased utilization of research and educational infrastructure both in Norway and India. VI). Integrate professionals from industries and academics through workshops, seminars, webinars, and summer/winter schools.

The article titled, “Automatic Contact-less Monitoring of Breathing Rate and Heart Rate utilizing the Fusion of mmWave Radar and Camera Steering System,” has been accepted for publication in the IEEE Sensors Journal (2022).

Khushi Gupta, Srinivas M. B., Soumya J, Om Jee Pandey, Linga Reddy Cenkeramaddi, “Automatic Contact-less Monitoring of Breathing Rate and Heart Rate utilizing the Fusion of mmWave Radar and Camera Steering System,” has been accepted for publication in the IEEE Sensors Journal (2022).

Keywords: Radar, Heart rate, Monitoring, Sensors, Cameras, Radar measurements, Personnel

Abstract: The demand for noncontact breathing and heart rate measurement is increasing. In addition, because of the high demand for medical services and the scarcity of on-site personnel, the measurement process must be automated in unsupervised conditions with high reliability and accuracy. In this article, we propose a novel automated process for measuring breathing rate and heart rate with mmWave radar and classifying these two vital signs with machine learning. A frequency-modulated continuous-wave (FMCW) mmWave radar is integrated with a pan, tilt, and zoom (PTZ) camera to automate camera steering and direct the radar toward the person facing the camera. The obtained signals are then fed into a deep convolutional neural network to classify them into breathing and heart signals that are individually low, normal, and high in combination, yielding six classes. This classification can be used in medical diagnostics by medical personnel. The average classification accuracy obtained is 87% with precision, recall, and an F1 score of 0.93.

More details: DOI: 10.1109/JSEN.2022.3210256

The article titled, “Design and Fabrication of Liquid Pressure Sensor using FBG Sensor through Seesaw Hinge Mechanism,” has been accepted for publication in IEEE Photonics Journal (2022).

Venkata Satya Chidambara Swamy Vaddadi, Saidi Reddy Parne, Vijeesh V. P., Suman Gandi, Saran Srihari Sripada Panda and Linga Reddy Cenkeramaddi, “Design and Fabrication of Liquid Pressure Sensor using FBG Sensor through Seesaw Hinge Mechanism,” has been accepted for publication in IEEE Photonics Journal (2022).

Keywords: Fiber gratings, Temperature Measurement, Temperature sensors, Sensitivity, Strain, Pressure measurement, Optical fiber sensors

Abstract: 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 element analysis has been performed on the diaphragm and on the entire structure at 10 bar pressure. The deformation of the diaphragm is comparable to theoretical deformation levels. A copper diaphragm with a thickness of 0.25 mm is used in the experiments. All experiments are performed in the elastic region of the diaphragm. The sensor’s sensitivity as 19.244 nm/MPa with the linearity of 99.64% is obtained based on the experiments. Also, the proposed sensor’s performance is compared with recently reported pressure sensors.

More details: DOI: 10.1109/JPHOT.2022.3210146

The article titled, “Design of Synthesis-time Vectorized Arithmetic Hardware for Tapered Floating-point Addition and Subtraction,” has been accepted for publication in ACM Transactions on Design Automation of Electronic Systems (2022).

Ashish Reddy Bommana, Susheel Ujwal Siddamshetty, Dhilleswararao Pudi, Arvind T. K. R, Srinivas Boppu, M Sabarimalai Manikandan, Linga Reddy Cenkeramaddi, “Design of Synthesis-time Vectorized Arithmetic Hardware for Tapered Floating-point Addition and Subtraction,” has been accepted for publication in ACM Transactions on Design Automation of Electronic Systems (2022).

Abstract: Energy efficiency has become the new performance criterion in this era of pervasive embedded computing; thus, accelerator-rich multi-processor system-on-chips are commonly used in embedded computing hardware. Once computationally intensive machine learning applications gained much traction, they are now deployed in many application domains due to abundant and cheaply available computational capacity. In addition, there is a growing trend toward developing hardware accelerators for machine learning applications for embedded edge devices where performance and energy efficiency are critical. Although these hardware accelerators frequently use floating-point operations for accuracy, reduced-width floating-point formats are also used to reduce hardware complexity; thus, power consumption while maintaining accuracy. Vectorization concepts can also be used to improve performance, energy efficiency, and memory bandwidth. We propose the design of a vectorized floating-point adder/subtractor that supports arbitrary length floating-point formats with varying exponent and mantissa widths in this article. In comparison to existing designs in the literature, the proposed design is 2.57× area- and 1.56× power-efficient, and it supports true vectorization with no restrictions on exponent and mantissa widths.

The article titled, “Performance Analysis of Deep Neural Networks for Covid-19 Detection from Chest Radiographs,” has been accepted for publication in the 15th International Conference on Machine Vision(ICMV 2022).

B. H. Shekar, Shazia Mannan, Habtu Hailu, C. Krishna Mohan and C. Linga Reddy, “Performance Analysis of Deep Neural Networks for Covid-19 Detection from Chest Radiographs,” has been accepted for publication in the 15th International Conference on Machine Vision(ICMV 2022).

The article titled, “Classification of UAVs utilizing Fixed Boundary Empirical Wavelet Subbands of RF Fingerprints and Deep Convolutional Neural Network,” has been accepted for publication in IEEE Sensors Journal (2022).

Kenneth Bremnes, Rebecca Moen, Sreenivasa Reddy Yeduri, Rakesh Reddy Yakkati, and Linga Reddy Cenkeramaddi, “Classification of UAVs utilizing Fixed Boundary Empirical Wavelet Subbands of RF Fingerprints and Deep Convolutional Neural Network,” has been accepted for publication in IEEE Sensors Journal (2022).

Keywords: RF signals, Drones, Fingerprint recognition, Convolutional neural networks, Autonomous aerial vehicles, Radio frequency, Feature extraction

Abstract: Unmanned aerial vehicle (UAV) classification and identification have many applications in a variety of fields, including UAV tracking systems, antidrone systems, intrusion detection systems, military, space research, product delivery, agriculture, search and rescue, and internet carrier. It is challenging to identify a specific drone and/or type in critical scenarios, such as intrusion. In this article, a UAV classification method that utilizes fixed boundary empirical wavelet sub-bands of radio frequency (RF) fingerprints and a deep convolutional neural network (CNN) is proposed. In the proposed method, RF fingerprints collected from UAV receivers are decomposed into 16 fixed boundary empirical wavelet sub-band signals. Then, these sub-band signals are then fed into a lightweight deep CNN model to classify various types of UAVs. Using the proposed method, we classify a total of 15 different commercially available UAVs with an average testing accuracy of 97.25%. The proposed model is also tested with various sampling points in the signal. Furthermore, the proposed method is compared with recently reported works for classifying UAVs utilizing remote controller RF signals.

More details:DOI: 10.1109/JSEN.2022.3208518

The article titled, “Methodology for Structured Data Path Implementation in VLSI Physical Design: A Case Study” has been accepted for publication in MDPI Electronics (Advanced Design Techniques and EDA Methodologies for Analog, RF and MM-Wave Circuit Design) 2022.

Dhilleswararao Pudi, Samuel Harrison, Dimitrios Stathis, Srinivas Boppu, Ahmed Hemani, Linga Reddy Cenkeramaddi, “Methodology for Structured Data Path Implementation in VLSI Physical Design: A Case Study” has been accepted for publication in MDPI Electronics (Advanced Design Techniques and EDA Methodologies for Analog, RF and MM-Wave Circuit Design) 2022.

Keywords: data-path; placement; routing; innovus; electronic design automation; physical design

Abstract: 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 file is not automatically generated. In this paper, we propose a semi-automated method for extracting bit-slices from the Innovus SDP flow. It has been demonstrated that the proposed method results in 17% less density or use for a pixel buffer design. At the same time, the other performance metrics are unchanged when compared to the traditional place and route flow.

More details: https://hdl.handle.net/11250/3031245

Two articles titled, “1. A GNSS Position Spoofing Mitigation Algorithm using Sparse Estimation and 2. Robust Positioning and Grubbs Outlier Test for Navigation in GPS Spoofing Environment,” have been accepted for publication in IEEE INDICON 2022.

  1. Bethi Pardhasaradhia, Gunnery Srinatha, Ashoka Chakravarthi Mahipathia, Pathipati Sriharia, and Linga Reddy Cenkeramaddi, “A GNSS Position Spoofing Mitigation Algorithm using Sparse Estimation,” has been accepted for publication in IEEE INDICON 2022.
  2. Bethi Pardhasaradhi, Purushottama Lingadevaru, Balarami Reddy BN, Pathipati Srihari, and Linga Reddy Cenkeramaddi, “Robust Positioning and Grubbs Outlier Test for Navigation in GPS Spoofing Environment,” has been accepted for publication in IEEE INDICON 2022.

Keywords:Global navigation satellite system, Satellites, Receivers, Mathematical models, Trajectory, Kalman filters, State estimation

Abstract:The Global Navigation Satellite Systems (GNSS) are widespread for providing Position, Velocity, and Time (PVT) information across the globe. The GNSS usually employs the Extended Kalman Filter (EKF) framework to estimate the PVT information of the receiver. The GNSS receivers PVT information is falsified by using a mimic GNSS signals is called a spoofing attack. This paper focuses mainly to combat the spoofing attack using sparse estimation theory. A generalized mathematical model is proposed for authentic and spoofed pseudoranges at the GNSS receiver. After that, a generalized pseudorange measurement model is presented by combining the authentic and spoofed pseudorange measurements. It is assumed that, only a part of satellite signals are spoofed. Further, the GNSS receiver’s state is estimated by mitigating the spoofed pseudoranges and it is formulated as a Least Absolute Shrinkage and Selection Operator (LASSO) optimization problem. The simulated results, compares the proposed LASSO based EKF algorithm with traditional EKF framework. It is observed that, the proposed algorithm suppresses the spoofing effect. Moreover, the Position Root Mean Square Error (PRMSE) of the proposed algorithm decreases by increasing the number of spoofed measurements.

More details: DOI: 10.1109/INDICON56171.2022.10039936

Keywords:Measurement,Navigation,Simulation,Kinematics,Position measurement, information filters, Robustness

Abstract:Global positioning system (GPS) is favored to provide the position, velocity, and time (PVT) details across the globe. This paper proposes an epoch-by-epoch robust positioning algorithm followed by the Grubbs outlier test to address the GPS spoofing problem. We propose to accept both authentic and spoofed GPS signals to compute the robust positions. The robust positioning considers all possible combinations of measurements and generates several position estimates, which contain actual position, spoof position, and biased positions. In this case, the positions evolved due to spoof pseudorange measurements must be removed. Hence, we model eliminating spoof locations as an outlier problem and is addressed using Grubbs outlier test. The median of the processed data after the Grubbs test is the positional information at that epoch. Moreover, this problem is also extended to the Kalman filter’s (KF) framework to address the time-varying kinematics of the target. Simulations are carried out for various numbers of actual and spoofed pseudorange measurements. In order to illustrate the robustness of the proposed technique, position root mean square error (PRMSE) is taken as a metric.

More details:DOI: 10.1109/INDICON56171.2022.10039906

The article titled, “A Socially-Aware Radio Map Framework for Improving QoS of UAV-Assisted Edge Networks” has been accepted for publication in IEEE Transactions on Network and Service Management (2022).

Shraddha Tripathi, Om Jee Pandey, Linga Reddy Cenkeramaddi, and Rajesh M. Hegde, “A Socially-Aware Radio Map Framework for Improving QoS of UAV-Assisted Edge Networks,” has been accepted for publication in IEEE Transactions on Network and Service Management (2022).

Keywords:Three-dimensional displays,Autonomous aerial vehicles,Quality of service,Array signal processing,Interference,Servers,Signal to noise ratio

Abstract:The expeditious growth of the Internet of Things (IoT) has accelerated the evolution of multi-access edge computing (MEC). MEC alleviates the challenges of conventional cloud computing, such as high data latency, poor data gathering reliability, increased network cost, and lack of network robustness. The primary objective of MEC is to facilitate a hierarchy of edge servers to address these quality-of-service (QoS) challenges, especially the information propagation issue due to the mobility of IoT devices (IoD). Further, social-relationship among mobile IoD is a critical parameter used to reduce the data transmission delay and queue size at the MEC. Specifically, in this work, a novel socially-aware radio map generation method is proposed to compute the fine-grained and accurate locations of QoS-deprived areas. Firstly, a novel method to compute the social relationship index (SRI) factor is proposed on the basis of current and future encounters among moving IoDs. Then the obtained SRI factor is used to form clusters of mobile IoD. The clusters’ signal to interference plus noise ratio (SINR) is then used to generate the socially-aware radio map. Following that, unmanned aerial vehicles (UAV) use this radio map, which contains rich and serviceable channel information, for 3D beamforming towards the mobile clusters. Using the obtained radio map, Kalman filter-based offline path planning of UAVs is proposed to minimize the UAVs flying distance from the initial to final locations. Furthermore, an optimization problem is formulated to assess the performance of the proposed method. Finally, the performance of the proposed method is compared with the existing methods, taking into account various network parameters such as optimum number of UAVs needed to cover the deployed area, data transmission delay, and received SINR.

More details: DOI: 10.1109/TNSM.2022.3206473