Non-invasive remote health monitoring plays a vital role in epidemiological situations such as SARS outbreak (2003), MERS (2015) and the recently ongoing outbreak of COVID-19 because it is extremely risky to get close to the patient due to the spread of contagious infections. Non-invasive monitoring is also extremely necessary in situations where it is difficult to use complicated wired connections, such as ECG monitoring for infants, burn victims or during rescue missions when people are buried during building collapses/earthquakes. Due to the unique characteristics such as higher penetration capabilities, extremely precise ranging, low power requirement, low cost, simple hardware and robustness to multipath interferences, Impulse Radio Ultra Wideband (IR-UWB) technology is appropriate for non-invasive medical applications. IR-UWB sensors detect the macro as well as micro movement inside the human body due to its fine range resolution. The two vital signs, i.e., respiration rate and heart rate, can be measured by IR-UWB radar by measuring the change in the magnitude of signal due to displacement caused by human lungs, heart during respiration and heart beating. This paper reviews recent advances in IR- UWB radar sensor design for healthcare, such as vital signs measurements of a stationary human, vitals of a non-stationary human, vital signs of people in a vehicle, through the wall vitals measurement, neonate’s health monitoring, fall detection, sleep monitoring and medical imaging. Although we have covered many topics related to health monitoring using IR-UWB, this paper is mainly focused on signal processing techniques for measurement of vital signs, i.e., respiration and heart rate monitoring.

As the world population and its dependency on energy is growing exponentially day by day, the existing energy generating resources are not enough to fulfill their needs. In the conventional grid system, most of the generated energy is wasted because of improper demand side management (DSM). This leads to a difficulty in keeping the equilibrium between the user need and electric power production. To overcome these difficulties, smart grid (SG) is introduced, which is composed of the integration of two-way communication between the user and utility. To utilize the existing energy resources in a better way, SG is the best option since a large portion of the generated energy is consumed by the educational institutes. Such institutes also need un-interrupted power supply at the lowest cost. Therefore, in this paper, we have taken a university campus load. We have not only applied two bio-inspired heuristic algorithms for energy scheduling—namely, the Firefly Algorithm (FA) and the Lion Algorithm (LA)—but also proposed a hybrid version, FLA, for more optimal results. Our main objectives are a reduction in both, that is, the cost of energy and the waiting time of consumers or end users. For this purpose, in our proposed model, we have divided all appliances into two categories—shiftable appliances and non-shiftable appliances. Shiftable appliances are feasible to be used in any of the time slots and can be planned according to the day-ahead pricing signal (DAP), provided by the utility, while non-shiftable appliances can be used for a specified duration and cannot be planned with the respective DAP signal. So, we have scheduled shiftable appliances only. We have also used renewable energy sources (RES) for achieving maximum end user benefits. The simulation results show that our proposed hybrid algorithm, FLA, has reduced the cost excellently. We have also taken into consideration the consumers’ waiting times, due to scheduling of appliances.

Intelligent transportation systems (ITSs) have witnessed a rising interest from researchers because of their promising features. These features include lane change assistance, infotainment, and collision avoidance, among others. To effectively operate ITSs for these functions, there is a need for edge computing. One can install edge computing servers at the roadside units (RSUs). There must be seamless communication between the edge servers and the cars. Additionally, there will be some cars that experience higher delays and thus, are not preferable because they will highly degrade the performance. Therefore, in this work, we consider a vehicular network scenario and define a cost function that takes into account the latency that is determined by the car’s computing frequency, association, and resource allocation while considering fairness constraints. Our cost function is to minimize the total latency (i.e., both local computing latency and transmission latency). The cost of the optimization problem is minimized by optimizing the car’s local frequency allocation, resource allocation, and association. The problem is separable, therefore, we first compute the local frequencies of the cars using a convex optimizer. Then, due to the NP-hard nature of the remaining formulated problem (i.e., joint association and resource allocation), we decompose the main problem into two sub-problems: (a) resource allocation and (b) association. Then, we propose an iterative solution. Finally, we provide numerical results for validating the proposed solution.

It has remained custom to analyze system with standard assumptions on noise. In this work, more realistic scenario has been considered by taking more general noise (colored) that is equally applicable to colored noise and Gaussian noise. A variable factor has been introduced that may switch between both the noise scenarios. Most of the estimation algorithms widely employed are inadequate in case of colored noise. To overcome this limitation, a modified Kalman filtering based estimation algorithm is proposed that processes a real-time data. In order to provide some of the promising results of our proposed observer, a real-time mass-spring damper system case study suffering from colored noise is provided. The simulation results validates the efficiency of proposed scheme wherein the aim is dealing with the estimation problems aroused due to colored noise.