Power quality issues and demand-power supply requirements are the two major problems that worry power sector. Non-linear loads deteriorate the power quality inducing harmonics into the source currents and dynamic load nature causes threat to the power system and insists to meet power demand. This paper presents the power quality issue addressing along with feeding active power from renewable source to power distribution system using dual inverter configuration in distribution system. In the proposed parallel (dual) inverter configuration in distribution system, one inverter acts as DSTATCOM filter to suppress source current harmonics injecting compensating signals and the other as DG inverter which converts DC supply (from renewable source - DG) to AC type to feed active power to distribution system. DSTATCOM is controlled from triggering pulses generated from SRF (synchronous reference frame) control theory and the Distributed Generation Integrated converter is controlled with simple ‘Id-Iq’ based control. The SRF theory for DSTATCOM and DG inverter control strategies are explained. Proposed system is developed and result analysis is presented using MATLAB/SIMULINK software.
The traffic management system is one of the important areas in any country for easy travel. In India, communication innovation in the social insurance sector has not yet been sufficiently updated to improve its administrative nature. With the increasing use of communication technology, many countries have implemented electronic card systems. Currently, there is no electronic traffic management system in India other than Digi locker. The goal of this proposed smartcard framework is to improve the efficiency and accessibility of driver and government document verification services. Using the smart card, all driver data, vehicle details, vehicle history, and past vehicle violations can be viewed by authorized parties through one website. Our proposed concept communicates the benefits of secure shipping to India using advances in data and correspondence. Digital profiling enables better tracking and more standardized documentation of drivers and vehicles, potentially reducing errors
The roadways in our country are continuously developing and expanding and many highways are in proximity to the forests, which increases the possibility of human-animal collision making deadly accidents imminent. This paper proposes an application that uses the ‘YOLO’ algorithm to efficiently recognize and classify the animals in the images that are passed to it as input and alert the user via the map interface along with the location of the animal on Google Maps. Deep Learning method is used here for animal detection and classification. Using Deep Learning, a detection and notification system is proposed. Our application trained for two datasets, tiger and elephant.
The suggested system is constructed using a line voltage of 220V/50Hz as input, which is stepped down, rectified, then admitted through a filter capacitor to produce an unregulated DC voltage. This uncontrolled voltage is stepped down to provide a steady 15v supply to the IC, which is controlled by a PWM signal to control the output voltage level to the various applications. To isolate the DC output from the input source, a Separation Transformer is employed. The transformer output is rectified again by the high frequency Diode bridge rectifier and filtered with a capacitor to provide the regulated DC output. To provide correct voltage output, a voltage regulator is installed. The feedback network generates a high frequency PWM signal that drives the MOSFET switch. The DC voltage at the output is determined by the width of the switching pulse. The pulse width varies in response to fluctuations in the DC output voltage level; this variation in the pulse width eliminates the output voltage variation, and the SMPS output stays constant independent of load dissimilarities
The developing need to supply evaluated quality palm oil items inside a brief timeframe has given high need to Automated Grading of Agricultural Products. There have been numerous endeavors by scientists around the globe to create arranging machines equipped for reviewing natural products by size, color yet in addition fit for perceiving extra highlights and different deformities utilizing various systems. Since color of fruit fluctuates from one locale to another as a result of geological areas, extra component can been added to help the choice cycle of evaluating utilizing fuzzy logic. We present a productive technique for choosing significant information factors when fabricating a fuzzy model from information. Earlier techniques for feature selection required producing various models while looking for the ideal blend of factors; our strategy requires creating just one model that utilizes all conceivable information factors. To decide the significant factors, premises in the fuzzy rules of this underlying model are efficiently eliminated to look for the best worked on model without really creating any new models. This expert system will without a doubt eliminate the vulnerability in decision making and lower the mistakes presented utilizing human reviewing. The proposed technique additionally improves the viability when contrasted with the traditional algorithms and strategies.
The Organic Light Emitting Diodes (OLEDS) is limited by several losses. OLEDs have emissive display that do not require a backlight and they are thinner and more efficient than LCD displays. OLED displays are not only thin but also efficient and they provide the best image quality ever and they can also be made transparent, flexible, foldable, rollable and even stretchable in the future. In any light emitting devices, losses are the major drawbacks. In the same way OLEDS also have some losses which includes waveguide losses, substrate losses and organic losses. In this paper, the organic losses caused by OLEDS were addressed by using Nano photonic structures. FDTD simulation software called Lumerical FDTD is used for simulating the structures. The improvement in light extraction by substrate modification allows for optimization of the optical design with different Cathode materials using FDTD. In this work the OLED with Nano spheres on Cathode layer is presented. The efficiency depends upon the radius, inter sphere separation and RI of Nano Spheres.
The requirement for low-power high-thickness gadgets has added to consistent MOSFET scaling. Nonetheless, assortment of troubles owing to the semiconductor size scaling, for example, SCEs and edge voltage move off concerns. FinFET is one of the options in contrast to MOSFET's numerous entryway FET to settle SCEs and improve effectiveness. As it is consistent with the continuous MOSFET producing innovations and disentangled in structure, the vast majority of the applications supplant conventional MOSFET. Recollections Specifically Static Random Access Memories One of the manners by which FinFETs can be utilized is (SRAMs). The force dissemination investigation utilizing FinFETs and MOSFET for 6T SRAM is talked about in this article. Since high thickness and low spillage recollections are required, the utilization of FinFETs for SRAM configuration is generally fitting. The channel is wrapped with a three-sided entryway that diminishes the corruption of versatility because of the cross over electric field. Since the door has awesome channel control, we can utilize low voltage supply for SRAM administration, bringing about force scattering reduction.90% of the force dispersal is diminished utilizing FinFET based 6T SRAM.
The network protocol BFD (Bidirectional Forwarding Detection) that detects issues between duplet forwarding engines. With minimum overhead, it detects flaws in physical media that doesn't allow for any kind of finding defects, including Ethernet, MPLS label switched channels, tunnels, and virtual circuits. One of the features is to verify the functionalities of static BFD. The work modes in BTS are Asynchronous mode, Demand mode, Echo mode. Currently only Asynchronous mode is supported in WBTS. BFD can be configured in two ways: Single hop and multi hop. Configured and commissioned the BTS & TRS setup in SEM tool and configured BFD in SEM (Site Element Manager) tool. BTS has come ON AIR and CS (Circuit Switch) PS (Packet Switch) calls are processing. Here we are checking the functionality of BFD whether it detects faults properly or not and removing routing connection from router side and check BFD alarm show up or not in SEM. Several calls are successfully placed after functionality check. Complete BFD process setting up and functionality check is automated. Where its check switch and router configuration as precondition and then perform BTS commissioning, checks BTS RNC BFD alarms and also placed call automatically. For switch, router configuration check and Wireshark log collection several key words are designed in Robot framework format so that script can be easily understandable by anyone. Configuring and verifying BIDIRECTIONAL FORWARDING DETECTION functionality in WCDMA Base Transceiver station (WBTS) is done manually and automated the complete process.
With the increasing use of the internet for transferring data, the security of this data has been a serious concern since the very beginning. There has been an ever-increasing number of cyber-attacks happening all over the internet. Hackers, after getting access to the end user's personal computer, have complete control over all the data flowing in and out of the computer. In this case, if any sensitive data gets in the hands of the hacker, it might create a great catastrophe for that person and the party he wants to communicate with. Hence, there is a need for creating an encryption system for data transfer that is extremely sensitive such as Criminal Data, Banking data and it can extend to a person's private details such as banking details and account passwords. For any such sensitive data transfer, we need a very strong encryption system, which ensures that the data being transferred is safe and is only accessible to the person who is authenticated to view that data. This paper discusses the various methodologies, algorithms, and proposes a solution to securely transfer sensitive data over the internet.
Traditional methods for classification of soil types are time consuming, invasive and expensive. A non-invasive method like ground penetrating radar (GPR) provides a suitable way to classify soil types based on its electromagnetic properties. Deep learning algorithms have proven to be an effective tool for features extraction of GPR data. A deep convolutional neural network (CNN) model for automatic classification of soil types is proposed. A synthetic dataset is created using gprMax and used to train and validate the proposed CNN model. The proposed model shows good performance in classifying 7 different soil types from GPR B-Scan images. Upon testing the model on new and unseen data, its accuracy is found to be 97%.
Early detection of brain tumors helps the specialists to take care of the affected persons, also reduce the threat, and enhance the possibility of existence. Brain tumor detection was once performed manually, which required a great deal of expertise and was time-consuming. Magnetic resonance imaging (MRI) modality used to identify tumors at early. The aim this research paper is to develop an automated segmentation method toward efficiently segmenting and extracting brain tissues from T1w MRI images. The proposed medical image segmentation method involves three main phases in the presented work: pre-processing, clustering, and validation. The presented KIFCM integrates the K-means with fuzzy c-means (FCM) techniques to takes advantage of them to overcome its limitations. To verify the output of each method, all the experiments are run on three brain tumor datasets: BRATS 2015, Brain Web simulated, and Harvard datasets. The computational time, segmentation accuracy, and the number of iterations were used to determine the algorithms results. From all the responses, the K-means clustering technique faster to detect the tumor from large datasets than FCM clustering but suffers from inadequate tumor detection. FCM algorithm detects the tumor location accurately and retains more details from the abnormal image than the K-means algorithm, but it takes more processing time. As per all responses, the KIFCM is an efficient approach to detect tumors with less time and high segmentation accuracy of 98.78%than other clustering methods. In comparison to existing approaches, the presented brain tumor segmentation system outer performs well in detecting the brain tumors with high segmentation accuracy and reduced processing time.
3D multi-object detection and tracking is an essential constituent for many applications in today's world. Object detection is a technology related to computer vision and image processing that allows us to detect instances of certain classes. There are numerous applications like robotics, autonomous driving and augmented reality. A bounding box often defines the region of interest and then is used to classify into respective categories. Due to identical appearance and shape of various objects and the interference of lighting and shielding, object detection has always been a challenging problem in computer vision. Conventional 2D object detection yields four degrees of freedom axis-aligned bounding boxes with centre (x, y) and 2D size (w, h), the 3D bounding boxes generally have 6 Degrees of freedom: 3D physical size (w, h, l), 3D centre location (x, y, z). 2D object detection and tracking methods do not provide depth information to perform essential tasks in various computer vision applications. One among them is the Autonomous driving. 3D object detection includes depth information that provides more information on the structure of detected object. More information is required to make decisions accurately in different fields where 3D object detection and tracking can be applied. In this paper various 3D object detection and tracking methods are elaborated for various computer vision applications, this includes various fields such as robotics, driving, space field and also in the military.
Numerous cattle calves do not return to the sheds after grazing because they become disoriented, resulting in the loss of that specific cattle. If the cattle calves are not counted before and after grazing, it is impossible for the person to manually count them. Cattle calves are sensitive to a range of illnesses/diseases, most of them may reduce the production and quality of milk products and, if not discovered early, could even result in the cattle's death. Diseases have an influence on-farm production, therefore ends up in low production, low income, and low quality. This research work provides a methodology where different sensors and image processing techniques are used to monitor the health of the cattle and alert the user regarding its health condition, also an automated counting system is implemented to count the total number of cattle in the shed.
World energy consumption is expanding at a breakneck pace. This rise in demand raises concerns about the global energy crisis and its associated environmental risks. Sustainable energy sources hold the key to resolving these concerns. Solar energy is widely regarded as one of the most abundant sources of sustainable energy, as it is both abundant and cost-free. Photovoltaic (PV) cells on solar panels are utilized to transform solar energy into uncontrolled electrical energy. These nonlinear solar photovoltaic cells have very poor efficiency. As a result, it becomes critical to optimize the output of solar photovoltaic cells by tracking their Maximum Power Point Tracking (MPPT). One of these MPPT approaches is Perturb & Observe (P&O). MPPT schemes' performance under rapidly changing weather and climate circumstances is crucial. This occurs in two circumstances: fast changes in solar irradiance and partial shading from clouds, etc. Additionally, it becomes necessary to study the performance of MPPT systems under different loading conditions. The objective of this article is to discuss the standard P&O MPPT scheme's behavior under increased solar irradiation circumstances and under different load conditions. The enhanced MPPT system is incorporated in a DC-DC converter's control circuit. MATLAB/Simulink is used for the simulated investigation.
The speech data is usually a degraded speech data, which is a combination of clean speech and various noises like vocal, animal noise, instrumental noise and speech from other speakers. The noisy speech data collected and studied in detail for various kinds of noises. The spectral subtraction one of the best algorithms proposed for the enhancement of single channel speech. The speech enhancement is based on two aspects i.e., Noise estimation and Speech estimation, where noise estimation is critical part which has major impact on the quality of enhanced speech. Speech enhancement is used for many applications such as mobile phones, communication, speech recognition etc. The aim of this paper is to provide a comparison study of the different forms of speech enhancement techniques and algorithms by using filters like Kalman filter and Weiner filter on the output of SS- VAD which gives improved enhanced speech signals. Coding techniques like Code Excited Linear Predictive (CELP) Coding and Algebraic Code Excited Linear Predictive (ACELP) Coding algorithm, is also discussed the performance of these are compared with LPC algorithm for speech enhancement.
Serverless computing is one of the most recent additions to the long list of services provided by cloud computing. Improving upon the attributes of scalability, affordability, and granularity present in earlier services offered by cloud computing, serverless computing is the next major development. Function as a Service has further modularized applications and enabled the individual execution of functions using triggers which has led to reduced costs, improved scaling of applications and almost no configuration expenses. Several cloud computing providers have developed their serverless computing services, each having its advantages and disadvantages. This paper aims to describe the need for serverless computing, its working, its economics, applications, its pros and cons in the current state, and the developments over existing technologies. Various serverless computing providers and their services have also been compared and studied.
Covid-19 is a pandemic disease that is affecting people all over the world. The Novel Corona virus spreads mostly through human contact, which includes coughing, sneezing, and even coming into contact with materials used by an infected person. Hence this paper presents a solar powered kiosk for contactless temperature sensor for human and automatic hand sanitizer dispenser with flip gates to monitor and control the spread of covid-19 disease. The main aim is to aid in the prevention and control of corona virus infection, as well as to maintain and improve community health by lowering the infection's detrimental impact on the economy and society. The paper presents the design and development of a kiosk that helps in screening human body temperature and dispense the hand sanitizer with automatic opening of flip gates upon the temperature of human in kiosk is under limits. The temperature of human body is sensed automatically with the help of sensor placed in the kiosk and the dispenser will dispense the sanitizer when the person keeps his/her hand under the dispenser. Once, these two task completes, a flip gate will open automatically, which helps people to come out of the kiosk. A 10 Watt solar PV (Photovoltaic) placed on the roof of the kiosk is used to power up the kiosk. An Arduino Uno is used to control the operations of various actuators used in the system. The kiosk is portable and can be placed at the entrance of malls, offices, educational institute and etc.
In this work, the introduction to the traditional magnetic high frequency transformers is provided along with the trending piezoelectric transformers. The working principle of magnetic transformer is explained in comparison with the piezoelectric transformer. This paper provides a glimpse of existing architectures in the field of piezoelectric transformers. Furthermore, a simulation model is developed using computational tool on the existing specific: single layer thickness mode polarized Rosen transformer. The mathematical model is developed using Mason's lumped parameter modelling. The equivalent circuit parameters, key electrical parameters are derived from the Mason's lumped parameter modelling and the simulation results are compared with the peer reviewed journals. The objective of the paper is to carry out the review on piezoelectric transformer for analyzing its performance and to further emphasize on possibility to improve the performance of PT's with novel geometrical structures.
Object identification and tracking is one of the most significant and demanding disciplines in computer vision, and have been\swidely utilised in many domains, such as health-care monitoring, autonomous driving, anomaly detection, and so on. With\sthe fast growth of Deep Learning (DL) networks and GPU's computational capacity, the performance of object detectors\sand trackers has been considerably enhanced. In this study, we have critically examined the existing DL network-based techniques of object identification\sand tracking and detailed several benchmark datasets. We present a practical, simple, more convertible, and structurally generic architecture for performing tasks such as picture segmentation. The approach we employ identifies and masks objects in images using high-quality picture segmentation for each item. We utilise Faster RCNN to identify the item and add an additional layer to the architecture at the conclusion of the localization layer (Faster RCNN), i.e. The segmentation layer conceals the object, resulting in Mask RCNN. Proposed Mask RCNN is a simple modification to the existing Faster RCNN in which we segment the detected Bounding Boxed item and provide Prediction concurrently at a rate of 5 frames per second. In general, Mask RCNN is easy to distinguish from other types of detection algorithms, for example, it enables us to quickly determine the actual structure of an item in an image.
This paper presents development of a Centralized Management System (CMS) software that helps in facilitating smooth charging of Electric Vehicles (EV). CMS encompasses a web application, developed using PHP and HTML; connected to the database, implemented in MySQL, through a local server and also a mobile application, developed in Android Studio. The communication abilities of Electric Vehicle Supply Equipment (EVSE) and EV are realized using Arduino, ESP8266 Wi-Fi modem and CAN modules. The comprehensive communication between EV and EVSE is executed over CAN/Wi-Fi as part of the charging system and EVSE - server data exchange transpires over an Ethernet-based network. This work showcases a software framework and its proficiencies that are needed for a smooth EV charging. The work will help in comprehending the needs and capabilities of a full-fledged communication link and software facilities to realize smooth and flexible EV charging.
