Summary The goal of this paper is to optimize energy consumption, with minimum consumer interaction, and least impact on his/her life style, comfort and convenience. In the literature different demand side management (DSM) schemes are employed for consumer demand management. Till now, to the best of our knowledge none of researchers considered the consumers preferences, priorities, ease of use, grid stability, deviation minimization, demand curve flattening and implantation cost, collectively in a single management scheme. In this paper we proposed an innovative, cost‐effective solution, to overcome most of the shortcomings of the previous solutions. In addition, it would also provide consumers' privacy as it would mask the energy usage pattern that combines psychological incentives in addition to economic benefits. The proposed Smart Energy‐consumption Management System Considering Consumers' Spending Goal (SEMS‐CCGS) can be used for optimized electricity consumption at any premises (residential, enterprise, commercial, etc.). The preliminary simulations showed 25.6% of electricity cost saving using SEMS‐CCGS. The proposed system is appraised valuable by the utility companies whose focus is to shave peak loads. It also plays an important role in grid stability by improving the diversity factor and deviation of consumers load profile from available load curve. The simulation shows that deviation is minimized up to 45%. Copyright © 2015 John Wiley & Sons, Ltd.

Due to the rising number of heart patients and the apparent need for more robust electrocardiogram (ECG) monitoring of these patients, hospitals are increasingly investing in typical cloud technology or centralized hospital server based remote ECG monitoring systems. However, the deployment these systems in rural communities is limited due to the high cost factor. To counter this challenge, in this paper, we focus on the design and implementation of a low cost real time wireless ambulatory ECG monitoring system. The detected ECG signals are first filtered and amplified and then digitally converted by a microcontroller. The digitized ECG signals are then sent over a ZigBee wireless link to a gateway personal computer (PC) at patient’s premises. The received ECG data from the ZigBee connection is displayed in real time via the National Instruments (NI) Laboratory Virtual Instrument Engineering Workbench (LabVIEW) user interface on the PC for instant personalized evaluation of the ECG data. The ECG data can be saved on the PC and sent via email to a remote cardiologist or a clinician. Additionally, the gateway PC at patient’s end acts as web server for sharing patient’s data over the Internet.  The remote off-site physician (medical staff in a hospital) can use a web browser on a PC, laptop or a mobile phone with Internet connection to access patient’s real time ECG trace for monitoring, expert review and diagnosis. It is shown that the system prototype allows users to acquire reliable ECG signals effectively and simply. The proposed ambulatory ECG system offers an alternative low cost deployment strategy and is especially suited for remote cardiac monitoring of patients in rural communities.

This chapter reviews prevailing methodologies and future techniques to optimize energy consumption. It discerns that smart grid provides better tools and equipment to control and monitor the consumer load, and optimize the energy consumption. Smart grid is essentially composed of smart energy equipment, advance metering infrastructure and Phasor Measurement Units (Synchrophaors) that helps to achieve optimized energy consumption. The chapter also places focus on demand side management and optimized energy consumption scheduling; and establishes that both, the utilities, as well as the users can play a vital role in intelligent energy consumption and optimization. The literature review also reveals smart protection, self-healing systems and off-peak operation result in minimizing transmission and distribution losses, as well as optimizing the energy consumption.

The absence of cardiovascular disease (CVD) diagnostic and management solutions cause significant morbidity among populations in rural areas and the coronavirus disease of 2019 (COVID-19) emergency. To tackle this problem, in this paper, the development of an Internet of things (IoT) assisted ambulatory electrocardiogram (ECG) monitoring system is presented. The system’s wearable single-channel data acquisition device supports 25 h of continuous operation. A right leg drive (RLD) circuit supported analog frontend (AFE) with a high common mode rejection ratio (CMRR) of 121 dB and a digitally implemented notch filter is used to suppress power-line frequency interference. The wearable device continuously sends the collected ECG data via Bluetooth to the user’s smartphone. An application on the user’s smartphone renders real-time ECG trace and heart rate and detects abnormal heart rhythms. This data are then shared in real-time with the user’s doctor via a real-time cloud database. An application on the doctor’s smartphone allows real-time visualization of this data and detection of arrhythmias. Simulations and experimental results demonstrate that reliable ECG signals can be captured with low latency and the heart rate computation is comparable to a commercial application. Low cost, scalability, low latency, real-time ECG monitoring, and improved performance of the system make the system highly suitable for the real-time remote identification and management of CVDs in users of rural areas and in the COVID-19 pandemic.

DC microgrids are set to change the landscape of future energy markets. However, a wide-scale deployment faces three major issues: initial synchronization of microgrid with the utility grid, slip management during its operation, and mitigation of distortions produced by the inverter. This paper proposes a Phasor Measurement Unit (PMU) Assisted Inverter (PAI) that addresses these three issues in a single solution. The proposed PAI continually receives real-time data from a Phasor Measurement Unit installed in the distribution system of a utility company and keeps constructing a real-time reference signal for the inverter. A well-constructed, real-time reference signal plays a vital role in addressing the above issues. The results show that the proposed PAI is 97.95% efficient.