This research work proposes a hybrid ultra-capacitor-battery energy storage technology for electric cars. The Quasi Z-source inverters (qZSIs) buck/boost feature allows the Hybrid ESS(HESS) to be integrated into the traction-inverter-system (TIS). The switch can be activated for a quasi-Z-source network with Zero Current Switching (ZCS) process. To automatically turn off all free-wheeling diodes, the inductor currents in the quasi-Z-source network run in Boundary-Conduction-Mode (BCM) or Discontinuous-Conduction-Mode (DCM). It is possible to eliminate the battery converter and lower the rated potential for the battery part and Ultra-capacitors. In different operation modes, the stable power distribution theory is explained. On a short time, scale, a frequency diving frequency diving coordinated control technique is intended to maximize the parameters like battery current stress and dynamic- power regulation.
A blockchain is an ever-growing list of records that are linked to each other in a distributed network. These linked records called ledgers are immutable in nature providing resistance to change. Blockchain provides a secure way of processing the data in a distributed environment. It was widely involved in crypto currencies in the earlier days and however its application in bit coin motivated and inspired other applications to adapt its concepts. Its application in healthcare requires blockchain to be highly secure, provide a more trusted environment than the traditional blockchain, that is by design should be an enterprise level blockchain by restricting access to the public. Hyperledger Fabric caters to all these requirements in providing a secure and distributed environment for healthcare systems. In healthcare there are a lot of fields where Hyperledger Fabric can be adopted, but the focus here is given to management of patient's medical records. Traditionally the medical records are either stored centrally in a database that is accessible to only the hospitals owning it, this creates a several of problems for patients. The aim is to consider the records are handled, how the patient will interact in the real world and design a system using hyper ledger Fabric to tackle major problems using smart contract.
Obesity, a serious chronic disease, is on the rise as a result of how easily food can be brought to our door steps. People's need for food grew, and at the same time, their anxiety about their nutrition also grew. This study offers an image-based calorie estimation system that asks the user to upload an image of a food item in order to calculate the estimated number of calories in the image. It is a multitasking system that displays weekly information on a user's calorie consumption and the number of calories that must be ingested to prevent obesity-related illnesses like cancer, heart attack, etc. To recognize complex pictures, a collection of food images with 20 classes and 500 images are built in each class. This study has developed a six-layer Convolutional Neural Network (CNN) architecture for the purpose of extracting the traits and classifying the images. The proposed food identification trials had an accuracy of 78.7% during testing and 93.29% throughout training. By using software designed to accurately estimate food calories from still images, users and healthcare experts may be able to more rapidly detect dietary practices and food choices connected to health and health concerns. Calorie calculation has been done by using photographs, however, it is difficult, and there is presently no publicly available program that can conduct both food estimation using images and provide health information about the individual.
This project uses software to generate forensic facial art by obtaining information directly from the human brain via a BCI headband. We can quickly cut the time necessary to design the victim's face by automatically picking the pre drawn structure. The above suggested approach will not only sketch the victim's face, but it will also search the criminal database at random to see if the victim's face has previously been recorded. First, we use the Brain Computing Interface Band to get the EEG signal from the witness's brain. The EEG data is then processed in bit Brain to categorise it into each instruction, and the classified signal is then moved to the next phase to choose the face portion. This study includes the previously collected pre-drawn facial components and categorized the images by this point. The CNN algorithm is significantly more accurate in classifying the images, and the classified images are saved with the trail in BCI computing to select the image in an accurate way. A categorized image data collection is used to generate the processed EEG signal. to discover the face region that is equivalent to an EEG signal. Drawing software was used to choose the selected face portion, which was then placed at the fundamental facial structure. When the painting is 40% complete, the face structure is compared to an existing criminal database to check whether the facial structure matches any previous crimes. This initiative aids in the identification of criminals and the creation of forensic art in considerably less time than the traditional method.
Nowadays the use of inverters is increasing tremendously in many applications. Multilevel inverters give the accurate output waveform as a nearly sinusoidal waveform. This paper gives an overview of different types of multilevel inverters i.e., Diode-Clamped, Flywheel, and Cascaded H-Bridge inverters. The proposed topology deals with the REDUCED DEVICE COUNT 9-LEVEL INVERTER, its operation, switching sequence, and control technique used, and gives a review of output waveforms and THD. It has many advantages over the conventional 9-level inverter such as a lesser number of switches, low THD, high efficiency, and low price.
