Recent advancements in wireless communication technologies have led to the development of practical applications such as infrastructure monitoring, earthquake monitoring, environment monitoring, remote healthcare monitoring etc. In these applications, we may integrate the Wireless Sensor Networks (WSNs) with Internet of Things (IoT), where sensor nodes join the internet dynamically and use it to collaborate and accomplish their tasks. In e-healthcare system, wireless medical sensor nodes continuously monitor and collect the physiological information and transmit the same to healthcare professionals through IoT terminals. To overcome the limitations in communication and computational capabilities in sensors and to attain privacy in healthcare wireless medical sensor networks (HWMSNs), few signature schemes were proposed in literature. However most of these schemes are insecure against various attacks. Hence to ensure privacy and security in medical data, in this paper, we proposed an efficient pairing-free aggregate signature scheme for HWMSNs in certificateless system. We exploit aggregation technique in certificateless system without using pairings to reduce computational complexity and transmission overhead in transferring the data. Our scheme achieves full aggregation to improve communicational efficiency and performance analysis shows that the proposed scheme is more efficient.

The continuous progress of wireless communication technology provides intelligent and efficient transportation system through Vehicular Ad-hoc Networks (VANETS) to mitigate traffic jams and road fatalities, which improves safety of passengers and traffic flow. Many researchers, vehicle manufacturers and telecommunication industries are working on VANETS to construct next generation transport system. In VANETS, vehicles, equipped with wireless devices, exchanges the traffic related information with other vehicles and the fixed Road Side Units (RSUs). The information shared between vehicles and RSUs in VANETS must be secure. For secure communications in VANETS, many cryptographic schemes were proposed in different settings and most of the schemes are using bilinear pairings over elliptic curves. But the computation of a bilinear pairing is very expensive. Also the verification of signatures/messages sent by vehicles increases the computational workload on RSUs. In order to improve computational efficiency and transmission overhead, in this paper, we present an efficient pairing-free certificateless authentication scheme with batch verification for VANETS. We designed the scheme in pairing free environment which improves the communication and computational efficiency. The proposed scheme supports batch verification, which significantly reduces the computational workload on RSUs in VANETS. The proposed scheme is proven secure in the random oracle model and meets the security requirements such as authenticity, integrity, traceability, anonymity, revocation. We compared our scheme with well known existing schemes and efficiency analysis shows that the proposed scheme is more efficient.

In this paper, several simple and efficient sign and error nonlinearity-based adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancellation of noise in electrocardiographic (ECG) signals. The proposed implementation is suitable for applications such as biotelemetry, where large signal to noise ratios with less computational complexity are required. These schemes mostly employ simple addition, shift operations and achieve considerable speed up over the other least mean square (LMS)-based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal-to-noise ratio and computational complexity.

Convolutional Neural network (CNN) based spinal cord disease prediction has emerged as a reliable model in medical imaging applications. Spinal cord injury (SCI) detection is one of the major problems for disorder segmentation and classification. Traditionally, radiologists analyze SCI images manually in order to detect abnormal spinal disorders. Manual interpretation of high dimensional feature space makes it difficult to predict the exact category and level of severity. On the other hand, deep learning framework helps to diagnose accurately and quickly. Deep learning approach is used to classify normal abnormal SCI images automatically. This paper presents a deep learning framework for helping diagnose SCI features based on the segmentation process. In this paper, a novel CNN-deep segmentation based boosting classifier is applied on sensor SCI image data. A real-time wearable sensor is used to capture the spinal cord disorder data with different shapes and orientations. Experimental results show that the present CNN-deep segmentation based boosting classifier has high computational SCI disorder prediction compared to the existing CNN based classifiers. Experimental results proved that the present model has better performance than the existing spinal cord injury detection models in terms of true positive rate; TP=0.9859, Accuracy=0.9894, and Error rate=0.019 are concerned.

This paper presents a novel deep-learning method for detecting and tracking vehicles in autonomous driving scenarios, with a focus on vehicle failure situations. The primary objective is to enhance road safety by accurately identifying and monitoring vehicles. Our approach combines YOLOv8 models with Transformers-based convolutional neural networks (CNNs) to address the limitations of traditional CNNs in capturing high-level semantic information. A key contribution is the integration of a modified pyramid pooling model for real-time vehicle detection and kernelized filter-based techniques for efficient vehicle tracking with minimal human intervention. The proposed method demonstrates significant improvements in detection accuracy, with experimental results showing increases of 4.50%, 4.46%, and 3.59% on the DLR3K, VEDAI, and VAID datasets, respectively. Our qualitative and quantitative analysis highlights the model’s robustness in handling shadows and occlusions in traffic scenes, outperforming several existing methods. This research contributes a more effective solution for real-time multi-vehicle detection and tracking in autonomous driving systems.