MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Electronics & Communication Engineering

In todays world Industry 4.0 is an emerging trend towards automation of manufacturing processes and technologies which include internet of things, cognitive and cloud computing, artificial intelligence and machine learning. Our main objective is to develop a method which increases the efficiency and reliability of machines using machine learning.

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Engineering and Technology

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International Journal of Engineering and Technical Research

Industry 4.0 - Optimization of Dressing Cycle Frequency and Power Requirements of Grinding Machine using Neural Networks

There is increase in the number of automobiles ,hence the air pollution is also increasing. It is very important to resolve theproblem of air pollution resulting from automobile exhaust gases.In this paper a system is proposed which uses Radio frequencyidentification (RFID) technology as a low-cost and mature wireless communication method to collect and transmit emission information ofvehicles and Internet of Things (IoT) concept is used. .Hence with the help of notification via the SMS the owner will be notified and furthermeasures will be taken.By applying the system, it is possible to smoothly realize a green traffic network.As the usage of vehicles is increasing drastically, the hazards due to vehicles is also increased. The main cause for accidents is high speed,drunk and drive, diverting minds, over stress and sometimes due to electronic gadgets. This paper also deals with accident detection systemthat occurs due to carelessness of the person who is driving the vehicle.This introduces accident alerting system which alerts the person whois driving the vehicle. If the person is not in a position to control the vehicle then the accident might occur. Once the accident occurs, thissystem will send information and location to the person’s emergency contact number.

IJCRT

Detection of Vehicle Emission and accident using IOT

With the technological advancements there have been advancements in ways of a displaying marketing and advertising of information. LED matrix display boards are used for displaying advertisements and notices. These Display boards have become a primary thing in educational institutes, shop floors (workstations) & various public places for displaying information regarding public transport timings, platforms, various advertisements regarding products, or important notices. People are now adapted to the idea of the world at its fingertips. The older version of display boards made use of wired technology for communication. Here wireless technology wi-fi is used for communication.Information is entered through the keyboard of transmitter or as speech signal and send through wi-fi & nodemcu or wifi module at receiver end will receive it and send it to matrix display due to which the desired information will be displayed on LED matrix display.

INTERNATIONAL JOURNAL OF ADVANCE SCIENTIFIC RESEARCH AND ENGINEERING TRENDS

IoT BASED LED SCROLLING DISPLAY

International Journal of Scientific Research and Engineering Trends

INTELLIGENT TRAFFIC MONITORING SYSTEM USING IOT

Wireless automation in electricity and billing is the new innovation in terms of technology. Retrieving the data of units consumedby the consumers is not efficient. The current process of electricity billing is susceptible to errors. It is time and resource and laborconsuming as well. The problem of the billing system becomes crucial when consumers try to tamper the meter, thereby pay less amountthan what actually should be paid. In our proposed system, already installed energy meters at our houses are not replaced, but an additionalsmall circuit design along with IoT on the already installed meters can change the existing meters into smart meters. Smart Electricity meterusing Internet of Things (IoT) system present an efficient and cost-effective way to transfer the information of energy consumed to theconsumer wirelessly and automatically without any human intervention, as well as it provides facilities to detect the illegal usage of theelectricity. Aim of this project is to calculate the electricity consumption, formulate the bill automatically without any labor and transfer itusing IoT. Bill amount is SMSed through IoT platform to the consumer at the start of every month with the help of RTC. Also, this studyaims to detect and control the energy theft. A passive infrared (IR) sensor is engaged with the system to detect when any illegal alterationhappens in the metering system. In such case, the system itself will notify it to the server, as well as it has the facility to disconnect and reconnectthe electricity supply automatically using a Solid-State Relay, also the power supply can be disconnected and reconnected remotelyin case of pending bills.

IOT based smart energy meter

The brain tumor is a cluster of the abnormal tissues, and it is essential to categorize brain tumors for treatment using Magnetic Resonance Imaging (MRI). The segmentation of tumors from brain MRI is understood to be complicated and also crucial tasks. It can be further use in surgery, medical preparation, and assessments. In addition to this, the brain MRI classification is also essential. The enhancement of machine learning and technology will aid radiologists in diagnosing tumors without taking invasive steps. In this paper, the method to detect a brain tumor and classification has been present. Brain tumor detection processes through pre-processing, skull stripping, and tumor segmentation. It is employing a thresholding method followed by morphological operations. The number of training image influences the feature extracted by the CNN, then CNN models overfit after some epoch. Hence, deep learning CNN with transfer learning techniques has evolved. The tumorous brain MRI is classified using CNN based AlexNet architecture. Further, the malignant brain tumor is classified using GooLeNet transfer learning architecture. The performance of this approach is evaluated by precision, recall, F-measure, and accuracy metrics.

International Journal of Advanced Computer Science and Applications(IJACSA)

A framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm

Cryptanalysis is used to analyze the strength of a cryptographic algorithm. Various cryptanalytic attacks against AES algorithm target reduced-round variants. These attacks are theoretical and are generally considered infeasible due to the demand for a large number of computations. To meet this high-computational requirement, an FPGA-based High-Performance Computing (HPC) platform is presented in this paper. FPGAs are advantageous for implementing cryptanalytic attacks, as the modular arithmetic is implemented more efficiently in FPGAs as compared to GPUs. The proposed HPC platform consists of four Spartan6 FPGAs connected in a mesh topology. A brute force cryptanalytic attack on the AES algorithm with a 128-bit key is implemented on the proposed HPC platform. Four-AES key search engines are designed in each FPGA. Thus, 16-AES key search engines are instantiated in parallel to perform AES cryptanalysis using different keys in parallel. To allocate distinct the key space to the 16 AES key search engines, an efficient key generator is also proposed in this work. The proposed architecture achieves a computational complexity of 2124 for an attack against 10-rounds AES algorithm. © 2020, Springer Nature Singapore Pte Ltd.

Computing in Engineering and Technology. Advances in Intelligent Systems and Computing

FPGA-Based High-Performance Computing Platform for Cryptanalysis of AES Algorithm

Cadmium Zinc Telluride Imager (CZTI) is one of five payloads of AstroSat, a multi-wavelength satellite, which has observed multiple stellar phenomena using coded mask imaging. This mask convolves with the observation before being detected. Thus, some preprocessing of this detected data is required before it can be analysed. Deconvolution is often implemented in such situations where we try to recover the original source by removing the effect of convolution on the observed data. Deconvolution is often seen in filtering or for removal of interfering phenomena such as point spread functions. This paper proposes a new method for deconvolution of two multi-dimensional discrete signals, resulting in the recovery of the original source intended for capture. The discrete signals are represented as matrices and then deconvolved in the Z-domain. This has been verified using two and three dimensional matrices and has been thus been extended for m-dimensional signals.

Iterative Deconvolution for Multidimensional Signals

The high security necessities in work and management motivated prospering of biometric technologies. Currently iris recognition is largely endorsed system. An expanding biometric identification technique which offers distinct verification based on distinguishing feature or characteristic possessed by the individual. Here, a fast methodology for classification and identification is suggested. The proposed system advances the effectiveness of iris recognition system. A joint tactic of SVM-Distance matching along with HAAR wavelet packet transform for feature extraction is used. For iris detection, Hough transform and Doughman's rubber sheet model for normalization is used. As in multi-faceted iris pattern, most of the information lies within zigzag collarette area, it is chosen for feature extraction. The proposed method shows a trade-off between two approaches SVM-HD and SVM-MD in terms of recognition accuracy and execution time. Maximum accuracy rate of 99.72% is achieved on CASIAV1.0 database. © 2020 IJSTR.

International Journal of Scientific and Technology Research

A SVM based iris recognition using wavelet packet transform and manhattan distance

A method is proposed, which uses the digital image processing technique to identify cloud boundaries from scanning Ka-band (~35.29 GHz) radar imagery dataset. In this method, a cloud is considered as an uninterrupted region of radar echoes with radar reflectivity higher than -34 dBZ and area greater than 3 km 2 . The proposed algorithm involves 1) conversion of radar RGB image to grayscale by removing white background and noise, 2) identification of cloud boundaries by canny edge detection, and 3) estimation of cloud cross-section area (CCSA) and cloud top height (CTH) based on the pixel width. This method is effectively applied to Ka-band radar images collected over Mandhardev, a high altitude scanning station in the Western Ghats (WGs), India to derive CTH and CCSA. CTH distribution shows three peaks, one at about 2 km with others at about 7-8 km, and 12 km. The cloud occurrence shows an apparent diurnal variation with a maximum in the afternoon hours while a semidiurnal variation is observed in CCSA. The proposed method shows consistent statistics with Global Precipitation Measurement Ka-band radar observations and thus suitable to build robust cloud climatology over the WGs. Such cloud statistics are essential to validate the representation of clouds in weather and climate models.