Major depressive disorder (MDD) contributes the most to human’s functional frailty worldwide. Therefore, its timely diagnosis and treatment is of utmost importance. Conventionally, MDD is diagnosed using subjective evaluation methods, so, it is essential to develop a quantitative biomarker for its automated diagnosis. Accordingly, this study proposes a 2D-CNN network and a new biomarker for automated detection of MDD. The proposed biomarker is developed by estimating wavelet coherence (WCOH) amongst the brain’s default mode network (DMN) regions using EEG signals. This biomarker data from 30 MDD patients and 30 healthy controls (HCs) is randomly divided into training and testing sets for network training and blind testing, respectively. The performance of the network is evaluated via 10-fold cross-validation which is applied to the training data only to avoid learning bias. The blind testing of subjects is performed using two different classification approaches i.e., sample-based and subject-based. The former achieves 98.1% accuracy, 98.0% sensitivity, and 98.2% specificity whereas the latter yields 100% each for accuracy, sensitivity, and specificity. This high classification performance validates that DMN-based WCOH can be used as a potential biomarker and that the proposed 2D-CNN can provide reliable performance assessment for the diagnosis of MDD.

Nowadays vehicles on the roads can communicate using a special type of wireless network called Vehicular Ad-hoc Networks (VANETs). It has been demonstrated by the researchers that because of the unique features such as high density of vehicles and frequent change of network topology, VANETs are not supported by the traditional routing protocols. The routing consistency of such highly dynamic networks must be taken into account in VANETs as communication links are disintegrated in VANETs more often than Mobile ad-hoc Networks (MANETs). The nature of VANET communication can bring extreme routing overhead to the network, therefore to increase network performance, the overhead issue must be tackled. The proposed protocol is focused on reducing the overhead to get the improved PDR performance of the network. The improvement is achieved by permitting communication amongst only those nodes which are considered reliable in terms of availability and geographical position. The reliability factor simply reduces unnecessary nodes from the communication process and selects a set of reliable nodes that are discovered with the help of clustering technique throughout the routing process. Simulation experiments using the network simulator are presented to demonstrate the efficacy of the proposed protocol. The results show that the proposed protocol has enhanced network performance effectively compared to prior approaches.

Everyday many lives are lost due to accidents on roads. Normally death happens due to the injury suffered by the passenger in the road accident but most of the time it also has been seen that the information of the accident, reach the emergency department very late, consequently the injured person could not sustain. So there must be an automatic system in every car which not only detect a road accident efficiently but also notify it to the emergency department very instantly. Researchers have proposed a number of automatic road accident detection methods. These methods typically use airbags, smart-phones, infrared sensors (IR sensor) and mobile application. None of these methods are perfect for the automatic detection of road accident. The filters used in the smart-phones, can cause difficulty in detecting low speed fender bender. Using IR sensor, the threshold distances, on which it will react is not mentioned in the proposed system. The method using airbags are not designed for motorbikes and cycles. If mobile application is used for accident detection, then different smart-phone models have different processors of various speeds so the application may not perform efficiently in all the smart-phone models. So in this paper, a novel approach based on ultrasonic sensor is proposed. Accident detection using ultrasonic sensor provides the facility to detect an accident not only in various street situations but also it might perform well under various natural conditions like rains.

A button sensor antenna for on-body monitoring in wireless body area network (WBAN) systems is presented. Due to the close coupling between the sensor antenna and the human body, it is highly challenging to design sensor antenna devices. In this paper, a mechanically robust system is proposed that integrates a dual-band button antenna with a wireless sensor module designed on a printed circuit board (PCB). The system features a small footprint and has good radiation characteristics and efficiency. This was fabricated, and the measured and simulated results are in good agreement. The design offers a wide range of omnidirectional radiation patterns in free space, with a reflection coefficient (S₁₁) of $-29.30 \left(-30.97\right)$ dB, a maximum gain of $1.75 \left(5.65\right)$ dBi, and radiation efficiency of $71.91 \left(92.51\right)$% in the lower and upper bands, respectively. S₁₁ reaches $-23.07 \left(-27.07\right)$ dB and $-30.76 (-31.12$) dB, respectively, with a gain of $2.09 \left(6.70\right)$ dBi and $2.16 \left(5.67\right)$ dBi, and radiation efficiency of $65.12 \left(81.63\right)\%$ and $75.00 \left(85.00\right)\%$, when located on the body for the lower and upper bands, respectively. The performance is minimally affected by bending, movement, and fabrication tolerances. The specific absorption rate (SAR) values are below the regulatory limitations for the spatial average over 1 g (1.6 W/Kg) and 10 g of tissues (2.0 W/Kg). For both indoor and outdoor conditions, experimental results of the range tests confirm the coverage of up to 40 m.

Due to its heterogeneity, cloud services providers’ (CSPs) rapid expansion presents several challenges, such as optimal service selection and privacy preservation. Multiple users using the cloud service at once increases the delay for service selection and request. Service interruptions result from centralized provisioning and insecurity. Existing work constraints include access control loss, service disruptions, security issues, trust management issues, and delays. Blockchain-based request scheduling and optimal CSP selection in edge-assisted clouds were presented in this research. Five phases—Data User (DS) authentication, sensitivity-aware request scheduling, policy verification, trust management, and optimal CSP selection—are proposed. In the first phase, DU authentication detects and eliminates authorized users. We suggested a chaotic map-based camellia encryption algorithm (CMCE) to boost security. The gateway schedules service requests using Johnson’s rule-based Stochastic Gradient Descent method, considering delay, throughput, and priority, in the second phase. This schedules the request into sensitive and non-sensitive services. Policy verification is done in the third phase utilizing Dynamic Policy-based Access control, which allows only sensitive requests. In phase four, we calculate the CSP trust value to boost security. Based on CSP behavior, we introduced the Multi Behavior Analysis-based Nomadic People Optimizer method. Every CSP’s trust value is modified based on user feedback over time. Finally, the best CSP is chosen for data user service, and suggested Dynamic and non-cooperative Game Theory is to choose the best CSP from a list. CloudSim is used to simulate and assess.

Vehicular ad hoc network (VANET) is a specialized form of wireless network that is solely intended for collaboration between vehicles. Several studies have shown that standard routing protocols cannot be implemented in VANETs because of their unique characteristics such as their significant count of vehicles on the network and the rapid evolution of the network’s design. Because VANET communication links are broken very frequently, it is necessary to address the routing consistency of these highly dynamic networks. The transmission of VANET data may result in a substantial amount of overhead in the routing process; thus, it is vital to address the issue of overhead to enhance the overall network performance. The proposed protocol named compacted area with effective links (CAEL) is designed to focus on decreasing overhead to achieve an enhancement in PDR performance inside the network. The communication between selected nodes that have been judged to be dependable in terms of geographical location and appropriate existing links between vehicles is focused on achieving this goal. With the inclusion of the reliability factor, it is possible to complete the important step of removing extraneous nodes, with the selection of the trustworthy nodes being made based on the link expiration time during the whole routing procedure. When compared to our previously published protocols, i.e., Dynamic Trilateral Enrollment (DyTE) and Reliable Group of Vehicles (RGoV), the results of the simulations demonstrate that CAEL has achieved an overall improvement in the performance of the network.