Impact of Direction Parameter in Performance of Modified AODV in VANET

A vehicular ad hoc network (VANET) is a technology in which moving cars are used as routers (nodes) to establish a reliable mobile communication network among the vehicles. Some of the drawbacks of the routing protocol, Ad hoc On-Demand Distance Vector (AODV), associated with VANETs are the end-to-end delay and packet loss. We modified the AODV routing protocols to reduce the number of route request (RREQ) and route reply (RREP) messages by adding direction parameters and two-step filtering. The two-step filtering process reduces the number of RREQ and RREP packets, reduces the packet overhead, and helps to select the stable route. In this study, we show the impact of the direction parameter in reducing the end-to-end delay and the packet loss in AODV. The simulation results show a 1.4% reduction in packet loss, an 11% reduction in the end-to-end delay, and an increase in throughput

A Real-Time Wireless Sweat Rate Measurement System for Physical Activity Monitoring

There has been significant research on the physiology of sweat in the past decade, with one of the main interests being the development of a real-time hydration monitor that utilizes sweat. The contents of sweat have been known for decades; sweat provides significant information on the physiological condition of the human body. However, it is important to know the sweat rate as well, as sweat rate alters the concentration of the sweat constituents, and ultimately affects the accuracy of hydration detection. Towards this goal, a calorimetric based flow-rate detection system was built and tested to determine sweat rate in real time. The proposed sweat rate monitoring system has been validated through both controlled lab experiments (syringe pump) and human trials. An Internet of Things (IoT) platform was embedded, with the sensor using a Simblee board and Raspberry Pi. The overall prototype is capable of sending sweat rate information in real time to either a smartphone or directly to the cloud. Based on a proven theoretical concept, our overall system implementation features a pioneer device that can truly measure the rate of sweat in real time, which was tested and validated on human subjects. Our realization of the real-time sweat rate watch is capable of detecting sweat rates as low as 0.15 µL/min/cm2, with an average error in accuracy of 18% compared to manual sweat rate readings

Co-optimization of energy and reserve capacity considering renewable energy unit with uncertainty

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability

Applications of Complex Network Analysis in Electric Power Systems

This paper provides a review of the research conducted on complex network analysis (CNA) in electric power systems. Moreover, a new approach is presented to find optimal locations for microgrids (MGs) in electric distribution systems (EDS) utilizing complex network analysis. The optimal placement in this paper points to the location that will result in enhanced grid resilience, reduced power losses and line loading, better voltage stability, and a supply to critical loads during a blackout. The criteria used to point out the optimal placement of the MGs were predicated on the centrality analysis selected from the complex network theory, the center of mass (COM) concept from physics, and the recently developed controlled delivery grid (CDG) model. An IEEE 30 bus network was utilized as a case study. Results using MATLAB (MathWorks, Inc., Nattick, MA, USA) and PowerWorld (PowerWorld Corporation, Champaign, IL, USA) demonstrate the usefulness of the proposed approach for MGs placement

Early Detection and Continuous Monitoring of Atrial Fibrillation from ECG Signals with a Novel Beat-Wise Severity Ranking Approach

Irregularities in heartbeats and cardiac functioning outside of clinical settings are often not available to the clinicians, and thus ignored. But monitoring these with high-risk population might assist in early detection and continuous monitoring of Atrial Fibrillation(AF). Wearable devices like smart watches and wristbands, which can collect Electrocardigraph(ECG) signals, can monitor and warn users of unusual signs in a timely manner. Thus, there is a need to develop a real-time monitoring system for AF from ECG. We propose an algorithm for a simple beat-by-beat ECG signal multilevel classifier for AF detection and a quantitative severity scale (between 0 to 1) for user feedback. For this study, we used ECG recordings from MIT BIH Atrial Fibrillation, MIT BIH Long-term Atrial Fibrillation Database. All ECG signals are preprocessed for reducing noise using filter. Preprocessed signal is analyzed for extracting 39 features including 20 of amplitude type and 19 of interval type. The feature space for all ECG recordings is considered for Classification. Training and testing data include all classes of data i.e., beats to identify various episodes for severity. Feature space from the test data is fed to the classifier which determines the class label based on trained model. A class label is determined based on number of occurences of AF and other arrhythmia episodes such as AB(Atrial Bigeminy), SBR(Sinus Bradycardia), SVTA(Supra Ventricular Tacchyarrhythmia). Accuracy of 96.7764% is attained with Random Forest algorithm, Furthermore, precision and recall are determined based on correct and incorrect classifications for each class. Precision and recall on average of Random Forest Classifier are obtained as 0.968 and 0.968 respectievely. This work provides a novel approach to enhance existing method of AF detection by identifying heartbeat class and calculates a quantitative severity metric that might help in early detection and continuous monitoring of AF

Recent Advances and Future Trends in Nanophotonics

Nanophotonics has emerged as a multidisciplinary frontier of science and engineering. Due to its high potential to contribute to breakthroughs in many areas of technology, nanophotonics is capturing the interest of many researchers from different fields. This Special Issue of Applied Sciences on “Recent advances and future trends in nanophotonics” aims to give an overview on the latest developments in nanophotonics and its roles in different application domains. Topics of discussion include, but are not limited to, the exploration of new directions of nanophotonic science and technology that enable technological breakthroughs in high-impact areas mainly regarding diffraction elements, detection, imaging, spectroscopy, optical communications, and computing

Unobtrusive Implementation of Wireless Electronics into Clothing

Research in flexible and stretchable electronics (FSE) has gained significant momentum in recent years due to being mechanically durable without compromising electrical performance. Newer materials and manufacturing methods are studied for efficiently developing FSEs. These materials and methods can be applied to the widespread development of wearable electronics, particularly clothing-integrated electronics. However, seamlessly integrating clothing into electronics has been quite challenging, where achieving an optimal balance between electrical performance and mechanical reliability is a key issue. This thesis aims to find innovative and novel solutions for integrating electronics into clothing, which could be mechanically durable, with limited compromise to their electrical functionality. This thesis combines 3D printing with passive radio frequency identification (RFID) technology to develop wireless platforms integrated into clothing. 3D printing was used to create encapsulants in which electronic components and antennas, designed with conductive yarns and textiles, were embedded. The wireless platforms developed in this study were tested for their mechanical reliability and evaluated for their wireless performance. This study then extended to RFID sensor development, where stimuli responsive materials were 3D printed onto textiles, and wireless performance concerning stimuli response were observed. This study observed that 3D printing encapsulated RFID-based wireless platforms functioned well regarding their wireless performance, despite exposure to moisture and mechanical stress. Although in their preliminary stages, the sensor platforms were also optimally responsive to moisture and temperature changes. Future studies include further evaluating the 3D printing parameters and materials for better mechanical reliability and more extensive studies on the sensor platforms. The wireless platforms developed in this study can be further developed for applications related to health care, logistics, security, and sensing applications

Optimization-based Fast-frequency Support in Low Inertia Power Systems

The future electrical energy demand will largely be met by non-synchronous renewable energy sources (RESs) in the form of photovoltaics and wind energy. The lack of inertial response from these non-synchronous, inverter-based generation in microgrids makes the system vulnerable to large rate-of-change-of-frequency (ROCOF) and frequency excursions. This can trigger under frequency load shedding and cause cascaded outages which may ultimately lead to total blackouts. To limit the ROCOF and the frequency excursions, fast-frequency support can be provided through appropriate control of energy storage systems (ESSs). For proper deployment of such fast-frequency control strategies, accurate information regarding the inertial response of the microgrid is required. In this dissertation, a moving horizon estimation (MHE)-based approach is first proposed for online estimation of inertia and damping constants of a low-inertia microgrid. The MHE also provides real estimates of the noisy frequency and ROCOF measurements. The estimates are employed by a model predictive control (MPC) algorithm that computes control actions to provide fast-frequency support by solving a finite-horizon, online optimization problem. The combined MHE-MPC framework allows an ESS operator to provide near-optimal fast-frequency support as a service. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low pass filter and traditional derivative-based (virtual inertia) controllers with high gains. Through simulation results, it has been shown that the proposed framework can provide near-optimal fast-frequency support while incorporating the physical limits of the ESS. The MHE estimator provides accurate state and parameter estimates that help in improving the dynamic performance of the controller compared to traditional derivative-based controllers. Furthermore, the flexibility of the proposed approach to achieve desired system dynamics based on the desired quality-of-service has also been demonstrated

Contribution of New Digital Technologies to the Digital Building Logbook

According to the European Commission, the Digital Building Logbook (DBL) is a repository of all of the relevant data of a building. It was first introduced at the European scale in the Renovation Wave strategy and was first defined in the proposal for the recast of the energy performance of buildings Directive in December 2021. The European DBL has not been implemented yet, since a common model does not yet exist. Even though great efforts are being made to establish it, some relevant issues need to be addressed first. One of them is the identification of data sources that will feed the DBL. Existing digital data sources have already been explored in some countries and they have been found to be insufficient. In this paper, new digital data sources suitable for the logbook are identified, and their contribution in terms of indicators and interoperability is analysed. The analysis shows that these sources have great potential to contribute to the DBL, because they bring the possibility to collect a great amount of real data on buildings. However, the main barrier for these tools to be incorporated into the DBL is that their linkage still requires further research

Nonlinear control and observation of full-variable speed wind turbine systems.

With increasing concern for the environmental effects of power generation from fossil fuels, wind energy is a competitive source for electrical power with higher efficiency than other clean sources. However, the nature of this power source makes controlling wind turbines difficult. The variability of wind as a source either requires highly accurate measurement equipment or sophisticated mathematical alternatives. In addition to the unknown quantities of the weather itself, the efficiency of power capture at the turbine blades is highly nonlinear in nature and difficult to ascertain. The ability of either determine these troublesome quantities, or control the system despite ignorance of them, greatly increases the overall efficiency of power capture. To this end, a series of nonlinear controllers and observers have been developed for wind turbine systems