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A novel methodology for designing Chipless Radio Frequency Identification (RFID) tags realized using the Additive Manufacturing technologies and exhibiting a high number of identification states is proposed and investigated. An encoding scheme based on the resonances and anti-resonances of the input impedance of the resonator is employed for identifying the tag, which is achieved by exploiting the set of zero-crossing points of the tag reactance. A tolerance analysis is also performed to avoid any ambiguity in the reading process and to minimize the redundancy, thus providing unique electromagnetic signatures which are also suitable for anti-counterfeiting applications. Several prototypes have been fabricated and tested to assess the practical implementation of the proposed encoding scheme.

This article summarizes the Detection of Pond Fish Challenge (DePondfi’23 Challenge), held during the National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2023). The main goal of the challenge was to find the most effective methods for detecting pond fish in underwater images, overcoming obstacles such as poor visibility, variations in turbidity, and environmental shifts. Sixty participants registered, with 15 teams submitting results for phase 1. The challenge concluded with four teams earning top honors based on mAP (mean Average Precision) score and time complexity. The mAP scores achieved by toppers are as follows: DETECTRON - 38.93%, DMACS SAI - 36.65%, PondVision - 31.63%, and Sahajeevis - 29.06%. This article describes the toppers method and discusses the detection results. Our challenge event is in line with Sustainable Development Goal 14, which focuses on the conservation and sustainable utilization of ponds and marine resources for sustainable development.

While resistance training promotes muscle hypertrophy and strength, accessibility of equipment is a barrier. This study evaluated a wearable VAriable Resistance Suit (VARS) as a novel and alternative method to achieve muscle strength improvement. It was hypothesized that by providing adjustable, bi-directional and speed dependent resistance, VARS can target specific muscles to improve muscle strength via an accessible and portable device. Twelve untrained healthy male adults (22.08 ± 4.1 years old) participated in an 8-week long resistance training using VARS to strengthen four muscles (biceps brachii, triceps brachii, biceps femoris, rectus femoris) of their non-dominant arm and leg using VARS. The results showed significant improvements in the muscle strength measured by isokinetic dynamometer – 49.9±9.6% increase in isokinetic force and 30.6±7.6% increase in isometric force. Muscle size and body composition were also assessed using ultrasound imaging and bioelectrical impedance analysis, which did not show significant changes. The study demonstrates the efficacy and feasibility of VARS as a resistance training tool to achieve muscle strength improvement and its potential extension to clinical populations.

Multistatic phaseless synthetic aperture radar (SAR) is a novel imaging modality that offers advantages in reduced hardware complexity, operability at high frequencies, robustness, jamming resistance, and improved accuracy and resolution. Illumination diversity is a key facilitator in designing novel phaseless imaging systems for applications in optics, and with growing interest in radio-frequency sensing. In this article, we present a novel multistatic phaseless SAR imaging method using stochastic waveforms and the nonconvex Wirtinger flow (WF) framework that provides performance guarantees under sufficient conditions known to hold for certain random forward models. We present multiple variations of the WF algorithm including different initialization and regularization methods and study the tradeoffs between the performance of our algorithms with respect to the resources needed for phaseless multistatic imaging. Our extensive numerical simulations show that the waveform-diverse random illumination approach coupled with optimization-based reconstruction provides near-exact imaging with a limited number of transmitters and a single receiver, promoting our method for practical realization of phaseless multistatic SAR.

In urban traffic, a Dynamic Traffic Light System (DTLS) is an important aspect of automatic driving. DTLS estimates the time of the light signal from images of dynamically changing road traffic. In conventional traffic light systems, light signals are enabled at predefined or fixed time intervals without having information on the current traffic density on the road. This static behavior of the traffic light system increases unnecessary waiting time on the road, eventually creating traffic jams, environmental pollution, and other health emergencies. The smart traffic light system addresses these issues with self-learning algorithms and dynamically allows traffic to pass by learning current traffic density. In this paper, a vision-based DTLS is proposed using the YOLO (You Only Look Once) object detection algorithm that detects and counts the total number of vehicles on the roads of a traffic signal junction. The traffic signals are tuned based on the computed traffic to minimize the overall delay at that junction. Moreover, the traffic junctions are facilitated to communicate with the adjacent junctions to transmit the cumulative traffic delay observed. This delay is used to prioritize traffic passing through salient blocks like schools, offices, hospitals, etc. The paper aims to minimize the overhead incurred in both computations of traffic (using approximate computing) and in communication networks (using low-power technologies of IEEE 802.15.4 standard, specifically DSME MAC and/or LoRaWAN). The proposed system accomplishes its objective of smart city infrastructure by optimizing the traffic flow. Further, the paper provides a mechanism for green traffic corridors for emergency vehicles.

Metaverse refers to the intersection of parallel virtual worlds with their physical counterparts by allowing users to interact with virtual people, objects, and environments. Resource allocation in various aspects of Metaverse domains, called as MetaSlices hereinafter, is a crucial optimization research problem. To serve this purpose, we consider a MetaSlice framework with the notion of sharing resources among common functions and enable placing time-sensitive services at the edge of multi-tier architecture in proximity to users. Unfortunately, the classical Integer Linear Programming is inappropriate for such heavily constrained optimization problem due to the extensive running time and memory. Hence, we model a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation to simultaneously optimize resources and secure Quality of Service for MetaSlices as a paradigm shift towards quantum computing. Furthermore, we propose to employ two hybrid classical-quantum strategies, Warm Start and Cold Start Quantum Annealing to optimize resource under bandwidth uncertainty, offer ultra-low running time, and increase service acceptance rate/scalability in a resource-hungry and dynamic Metaverse system.

In the technology driven era, automation and communication have attained new heights, which paved the way for an improved human lifestyle. The Internet of Things (IoT) is one of the key aspects of this domain that promises to connect real-world objects embedded with sensors, enable them to communicate, and aid in making informed decisions. However, the growth of technology for convenience has also attracted various attacks on privacy and confidentiality. Numerous classical public-key cryptography based security solutions have been adopted to evade the problems in group communication. Unfortunately, most of the solutions fail to meet computationally lightweight requirements. In this paper, we propose a lightweight NTRU (Nth Degree Truncated Polynomial Ring Units) and Secret Sharing Based Secure Group Communication Scheme that is suitable for low bandwidth communication over the Internet of Medical Things (IoMT), Vehicular Adhoc Networks (VANET) and Precision Agriculture. Theoretical analyses and illustrations demonstrate that the proposed scheme is superior in comparison to the existing schemes.

Violence, in any form, is a disgrace to our civilized world. Nevertheless, even in modern times, violence is an integral part of our society and causes the deaths of many innocent lives. One of the conventional means of violence is using a firearm. Firearm-related deaths are currently a global phenomenon. It is a threat to society and a challenge to law enforcement agencies. A significant portion of such crimes happen in semi-urban areas or cities. Governments and private organizations use CCTV-based surveillance extensively today for prevention and monitoring. However, human-based monitoring requires a significant amount of person-hours as a resource and is prone to mistakes. On the other hand, automated smart surveillance for violent activities is more suitable for scale and reliability. The paper’s main focus is to showcase that deep learning-based techniques can be used in combination to detect firearms (particularly guns). This paper uses different detection techniques, such as Faster Region-Based Convolutional Neural Networks (Faster RCNN) and the latest EfficientDet-based architectures for detecting guns and human faces. An ensemble (stacked) scheme has improved the detection performance to identify human faces and guns at the post-processing level using Non-Maximum Suppression, Non-Maximum Weighted, and Weighted Box Fusion techniques. This paper has empirically discussed the comparative results of various detection techniques and their ensembles. It helps the police gather quick intelligence about the incident and take preventive measures at the earliest. Also, the same technique can be used to identify social media videos for gun-based content detection. Here, the Weighted Box Fusion-based Ensemble Detection Scheme provides mean average precisions 77.02%, 16.40%, 29.73% for the mAP0.5, mAP0.75 and mAP[0.500.95], respectively. The results achieve the best performance among all the experimented alternatives. The model has been rigorously tested with unknown test images and movie clips. The obtained ensemble schemes are satisfactory and consistently improve over primary models.

Motor vehicle crashes are one of the most common causes of fatalities on the roads. Real-time severity prediction of such crashes may contribute towards reducing the rate of fatality. In this study, the fundamental goal is to develop machine learning models that predict whether the outcome of a collision will be fatal or not. A Canadian road crash dataset containing 5.8 million records is utilized in this research. In this study, ensemble models have been developed using majority and soft voting to address the class imbalance in the dataset. The prediction accuracy of approximately 75% is achieved using Convolutional Neural Networks. Moreover, a comprehensive analysis of the attributes that are important in distinguishing between fatal vs. non-fatal motor vehicle collisions has been presented in this paper. In-depth information content analysis reveals the factors that contribute the most in the prediction model. These include roadway characteristics and weather conditions at the time of the crash, vehicle type, time when the collision happen, road user class and their position, any safety device used, and the status of traffic control. With real-time data based on weather and road conditions, an automated warning system can potentially be developed utilizing the prediction model employed in this study.

Continuous monitoring of the spatio-temporal dynamic behavior of critical infrastructure networks, such as the power systems, is a challenging but important task. In particular, accurate and timely prediction of the (electro-mechanical) transient dynamic trajectories of the power grid is necessary for early detection of any instability and prevention of catastrophic failures. Existing approaches for prediction of dynamic trajectories either rely on the availability of accurate physical models of the system, use computationally expensive time-domain simulations, or are applicable only at local prediction problems (e.g., a single generator). In this paper, we report the application of two broad classes of data-driven learning models – along with their algorithmic implementation and performance evaluation – in predicting transient trajectories in power networks using only streaming measurements and the network topology as input. One class of models is based on the Koopman operator theory which allows for capturing the nonlinear dynamic behavior via an infinite-dimensional linear operator. The other class of models is based on the graph convolutional neural networks which are adept at capturing the inherent spatio-temporal correlations within the power network. Transient dynamic datasets for training and testing the models are synthesized by simulating a wide variety of load change events in the IEEE 68-bus system, categorized by the load change magnitudes, as well as by the degree of connectivity and the distance to nearest generator nodes. The results confirm that the proposed predictive models can successfully predict the post-disturbance transient evolution of the system with a high level of accuracy.

Sensing technology has undergone a revolution as a result of technological advancements, where the wireless sensor network (WSN) is considered as a major research area in recent times. WSN is made up of battery-powered multiple sensor nodes with limited energy, which eventually encourage the researchers in designing an energy efficient routing protocol to prolong the network lifetime. Hierarchical routing protocols can play an important role to improve the network’s energy efficiency utilizing the threshold-based cluster head (CH) selection techniques. In this article, a cluster based proactive routing protocol named as Distance and Energy Aware Stable Election Routing Protocol (DE-SEP) is proposed to ensure optimum energy conservation. Here, both the energy and distance criteria are incorporated in CH selection to give priority for comparatively higher energy nodes and nearby nodes from base station (BS) to be selected as CH. Moreover, the proposed protocol imposes a limit on the number of CHs so that an optimum number of CHs can be utilized to avoid unnecessary cluster formation and reduce energy consumption. Simulation results show that DE-SEP outperforms the existing baseline protocols like P-SEP, M-SEP and SEP in terms of various performance matrices such as network lifetime, stability period, half lifetime, throughput, and normalized residual energy. In particular, the performance of DE-SEP protocol in terms of normalized residual energy is increased by a maximum of 5%, 41% and 41% in comparison to P-SEP, M-SEP and SEP respectively.

The commercialization of automated driving vehicles promotes the development of safer and more efficient autonomous driving technologies including lane marking detection strategy, which is considered to be the most promising feature in environmental perception technology. To reduce the tradeoff between time consumption and detection precision, we propose a real-time lane marking detection method by using LiDAR point clouds directly. A constrained RANSAC algorithm is applied to select the regions of interest and filter the background data. Further, a road curb detection method based on the segment point density is also proposed to classify the road points and curb points. Finally, an adaptive threshold selection method is proposed to identify lane markings. In this investigation, five datasets are collected from different driving conditions that include the straight road, curved road, and uphill, to test the proposed method. The proposed method is evaluated under different performance metrics such as Precision, Recall, Dice, Jaccard as well as the average detection distance and computation time for the five datasets. The quantitative results show the efficiency and feasibility of this proposed method.