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A self-excited induction generator (SEIG) is very simple and robust, has a reduced unit size, is easy to implement and simple to control, and requires very little maintenance compared to other types of generators. In variable operating conditions, the SEIG requires a power electronics interface to transform from the variable frequency voltage output of the generator to a battery voltage output or the related applications. In our study, we tied the SEIG to the power electronics system comprising a diode rectifier and DC/DC converter, and then a final DC load for fuel cell applications was connected. An example of such an application is an electrolyzer where an equivalent circuit is modeled for use in this study. To accomplish the proposed system, we utilized PSCAD and MATLAB for its simulation, control, and analysis. A new system configuration considering three different wind speeds and breaker conditions is modeled and analyzed. The results show that the suggested strategies in this study would contribute to designing and analyzing a more practical power electronics interface system for a wind turbine generator with a DC load 

Computer vision techniques always had played a salient role in numerous medical fields, especially in image diagnosis. Amidst a global pandemic situation, one of the archetypal methods assisting healthcare professionals in diagnosing various types of lung cancers, heart diseases, and COVID-19 infection is the Computed Tomography (CT) medical imaging technique. Segmentation of Lung and Infection with high accuracy in COVID-19 CT scans can play a vital role in the prognosis and diagnosis of a mass population of infected patients. Most of the existing works are predominately based on large private data sets that are practically impossible to obtain during a pandemic situation. Moreover, it is difficult to compare the segmentation methods as the data set are obtained in various geographical areas and developed and implemented in different environments. To help the current global pandemic situation, we are proposing a highly data-efficient method that gets trained on 20 expert annotated COVID-19 cases. To increase the efficiency rate further, the proposed model has been implemented on NVIDIA - Jetson Nano (System-on-Chip) to completely exploit the GPU performance for a medical application machine learning module. To compare the results, we tested the performance with conventional U-Net architecture and calculated the performance metrics. The proposed state-of-art method proves better than the conventional architecture delivering a Dice Similarity Coefficient of 99%. 

Battery-powered computing solutions have grown in importance and utility across a wide range of applications in the technology industry, including both consumer and industrial uses. Devices that are not attached to a stable and constant power source must ensure that all power consumption is minimized while necessary computation and communications are performed. WiFi networking is ubiquitous in modern devices, and thus the power consumption necessary to transmit data is of utmost concern for these battery powered devices. The Ad hoc OnDemand Distance Vector (AODV) routing algorithm is a widely adopted and adapted routing system for path finding in wireless networks. AODV’s original implementation did not include power consumption as a consideration for route determinations. The Energy Aware AODV (EA-AODV) algorithm was an attempt to account for energy conservation by varying broadcast power and choosing paths with distance between nodes as a consideration in routing. Lightning Strike AODV (LS-AODV) described in this paper is a proposed routing algorithm that further accounts for energy consumption in wireless networking by balancing energy in a network. Quality of service is maintained while energy levels are increased through networks using the LS-AODV algorithm. 

As technology advances and cities become more innovative, the need to harvest energy to power intelligent devices at remote locations, such as wireless sensors, is increasing. This paper focuses on studying and simulating an energy management system (EMS) for energy harvesting with a battery and a supercapacitor for low power applications. Lithium-ion batteries are the primary energy storage source for low power applications due to their high energy density and efficiency. On the other hand, the supercapacitors excel in fast charge and discharge. Furthermore, supercapacitors tolerate high currents due to their low equivalent series resistance (ESR). The supercapacitor in the system increases the time response of the power delivery to the load, and it also absorbs the high currents in the system. Moreover, the supercapacitor covers short-time load demand due to the fluctuation of the renewable source. The EMS monitors the proposed system to maintain power to the load either from the renewable source or the energy storage. The power flow of the energy storage is controlled via DC-DC bidirectional converters. The lithium-ion battery is charged via a constant current (CC) using a sliding mode controller (SMC) and a constant voltage (CV) via a typical PI controller. The response of the SMC current controller is compared with PI and Fuzzy current controller. Furthermore, the performance of a system having and not having a supercapacitor is compared. Finally, MATLAB modeling system simulation and experimental implementation results are analyzed and presented. 

It is vital to consider the energy usage of motes when designing a Wireless Sensor Network (WSN). Protocols can be altered to their application to enhance a system's performance. This project modifies the Routing Protocol for Low-Power and Lossy Networks (RPL) protocol using a S-MAC algorithm to increase its energy efficiency. The project began with the application and the focus of the WSN. The proposed protocol was developed within the Cooja simulator, then implemented on TelosB motes using the Contiki-NG operating system. Lastly, the WSN was tested with the proposed system and compared against its original counterpart. In conclusion it was found that the proposed method provides a significant increase in energy efficiency, extending the life of a WSN. 

A Mobile Ad Hoc Network (MANET) is a decentralized wireless network that does not rely on pre-existing infrastructure. Instead, it is each node's responsibility to forward data according to its specified routing protocol. Although these protocols perform the same task, their performance in a variety of scenarios differ. This paper simulates four different routing protocols in NS-3 at a variety of movement speeds and area sizes, comparing their Packet Delivery Ratio (PDR) and Average End-To-End Delay (AETED). The performance results reflect what would be expected if a system would be implemented in a similar environment. Therefore, it is crucial selecting a protocol to best suit a system. 

A mobile ad-hoc network is a set of mobile nodes in which data is transmitted wirelessly amongst all nodes. Due to the mobility of wireless nodes, network topology changes frequently. Consequently, routing protocols used in mobile ad-hoc networks must be adaptive. The routing protocols enabling data transfer within MANETs are classified into reactive, proactive, and hybrid protocols. Proactive routing protocols, such as the Destination Sequenced Distance Vector, are table-driven protocols that use stale paths in case of broken links which causes loss of data in the network. This research study will explore A Neighbor Coverage Multipath DSDV as a potential solution for data loss by finding alternate routes to the destination when a link is broken. Simulations have been carried out for the three routing protocols: DSDV, FSR, and the proposed NCMDSDV. Results showed that the proposed routing protocol has better efficiency compared to DSDV and FSR routing protocols. 

In this research, a Kalman filter-based Z-source inverter is proposed with an enhanced control algorithm for Maximum Power Pointer Tracking(MPPT) and this capacitor voltage stabilization. By implementing Unified Linear Kalman Filter Algorithm with Capacitor Voltage Control (CVC) algorithm for the Z-source inverter, the Kalman Filter can track Maximum Power Point (MPP) faster than traditional algorithm such as Perturb and Observation (P&O) algorithm, that has a minimum impact on rapidly changing atmospheric conditions. Thus, by using the Integrated Kalman Filter and CVC algorithm we can achieve faster, effective and capacitor voltage regulation at the same time. The effectiveness of this proposed Kalman Filter with CVC Algorithm for Z-source inverter is validated in MATLAB/Simulink and a hardware prototype has been built to verify the simulation and theoretical results.