Compression of data is a common practice in the signal - processing & digital image analysis fields, and is often employed for multimedia & image processing purposes. Approximate computation is a prominent design approach in arithmetic. New high-speed areas may be opened up by high-speed multimedia applications. Error-tolerant circuits that use approximate computations. At the same time, these applications give great production at a reduced cost of accuracy In addition, the system's complexity is reduced as a result of their implementations. Power consumption and latency in the system's architecture are the main factors. It is proposed that two compressors be designed and analysed that have smaller size, less delay, and more power than the present systems, all while maintaining precision that is equivalent to the current systems.

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we propose a low-power high-speed pipeline multiply-accumulate (MAC) architecture. In a conventional MAC, carry propagations of additions (including additions in multiplications and additions in accumulations) often lead to large power consumption and large path delay. To resolve this problem, we integrate a part of additions into the partial product reduction (PPR) process. In the proposed MAC architecture, the addition and accumulation of higher significance bits are not performed until the PPR process of the next multiplication. To correctly deal with the overflow in the PPR process, a small-size adder is designed to accumulate the total number of carries. Compared with previous works, experimental results show that the proposed MAC architecture can greatly reduce both power consumption and circuit area under the same timing constraint.

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In this article, we present a simple, yet energy- and area-efficient method for tolerating the stuck-at faults caused by an endurance issue in secure-resistive main memories. In the proposed method, by employing the random characteristics of the encrypted data encoded by the Advanced Encryption Standard (AES) as well as a rotational shift operation, a large number of memory locations with stuck-at faults could be employed for correctly storing the data. The technique may be employed along with other error correction methods, including the error correction code (ECC) and the error correction pointer (ECP). To assess the efficacy of the proposed method, it is implemented in a phase-change memory (PCM)- based main memory system and compared with three error tolerating methods. The results reveal that for a stuck at fault occurrence rate of 10−2 and with the method is similar to that of the state-of-the-art method.

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An exportable application-specific instruction-set elliptic curve cryptography processor based on redundant signed digit representation is proposed. The processor employs extensive pipelining. Furthermore, an efficient modular adder without comparison and a high through put modular divider, which results in a short data path form a ximiNISTreductionscheme.Thepropoprocessorerforms single point multiplication employing points in affine coordinates in 2.26 ms and runs at a maximum frequency of 160 MHz in Xilinx Virtex 5 (XC5VLX110T) field-programmable gate array.

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The most popular communications encoding algorithm, the iterative decodin requires an exponential increase in hardware project focuses on the Keywords- encoding, decoder trellis o technique.The turbo codes are designed with the help of Recursive Systematic Convolutional and are separated by inter leaver, which (component used to rearrange the bit sequence) plays a vital role in the encoding process. This the design paper provides of a Turbo E

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To increase system composability and facilitate timing closure, fully synchronous clocking is replaced by more relaxed clocking schemes, such as mesochromous clocking. Under this regime, the modules at the two ends of a monochromous interface receive the same clock signal, thus operating under the same clock frequency, but the edges of the arriving clock signals may exhibit an unknown phase relationship. In such cases, clock synchronization is needed when sending data across modules. In this brief, we present a novel mesochromous dual-clock first-input– first-output (FIFO) buffer that can handle both clock synchronization and temporary data storage, by synchronizing data implicitly through the explicit synchronization of only the flow-control signals. The proposed design can operate correctly even when the transmitter and the receiver are separated by a long link whose delay cannot fit within the target operating frequency. In such scenarios, the proposed mesochronous FIFO can be extended to support multicycle link delays in a modular manner and with minimal modifications to the baseline architecture. When compared with the other state-of-the-art dual-clock mesochromous FIFO designs, the new architecture is demonstrated to yield a substantially lower cost implementation

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A Bose-Chaudhuri-Hocquenghem (BCH) code decoder with high decoding efficiency and low power for error correction in developing memories is provided in this research as DEC-TED, or double-error-correcting and triple error-detecting. We suggest an adaptive error correction method for the DEC-TED BCH code to improve decoding efficiency. This method counts the number of mistakes in a codeword right after syndrome creation and uses a different error correction algorithm based on the error conditions the syndrome vectors in the error-finding block in order to further reduce the power consumption. The suggested decoders for the (79, 64, 6) BCH code achieve more than 70% power reduction compared to the standard fully parallel decoder under the 104-102 raw bit-error rate, according to synthesis findings with an industry-compatible 65-nm technology library.

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As per the requirement of a design with minimal power has been a cardinal matter for the systems based on digital technology & greater performance like microprocessors, DSPs & various applications apart. The rise in market of mobile & electronic products powered by portable batteries needs chips which intake minimal power. SRAM incorporates around 60% of VLSI circuitries. Also memories are considered as the major flaw for decadence of power in a circuitry but no digital circuitry is accomplished by nor using memories. The absorption of power & SRAM’s speed are major concern which followed several designs in accordance to the minimal absorption of power. The main concern of this document is on decadence of power while operation of Write is executed in 6-T CMOS SRAM. In this paper we mainly focus on decadence of power during short circuits also the fluctuating decadence of power which can also be termed as power which is dynamic. The tool of Tanner is deployed to evaluate the circuitry, the schema of cell of SRAM is formulated on S Edit & simulation of net list is furnished by making use of T Spice & also assessment of waveforms is done by W Edit.

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In order to meet the growing demand for construction materials, there is a pressing need to use alternative building materials that are environmentally friendly and promote long-term growth. One of the primary goals of this research is to determine the efficacy of different types of robo sand and bentonite (calcium) on the strength and durability of concrete. Calcium bentonite is used to partly replace cement, while robo sand is used to partially replace fine aggregates in the mix. At 28 days, a rapid chloride permeability test is carried out to determine the chloride resistance. Durability tests, such as acid assault (with sulphuric acid and hydrochloric acid), are performed at 28 and 56 days after the first application. It is not only for the goal of increasing strength that additives such as bentonite are used, but it is also for the purpose of increasing durability. Bentonite is a clay that interacts with cement to produce a gelling compound, while robo sand is an environmentally friendly and cost-effective replacement for fine aggregates in concrete.

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Redundant Binary Partial Product Generator technique are used to reduce by one row the maximum height of the partial product array generated by a radix16 Modified Booth Encoded multiplier, without any raise in the delay of the partial product creation Block. In this paper, we describe an optimization for binary radix-16 (modified) Booth recoded multipliers to reduce the maximum height of the partial product columns to [n/4] for n = 64-bit unsigned operands. This is in contrast to the conventional maximum height of [(n + 1)/4]. Therefore, a reduction of one unit in the maximum height is achieved. These Arithmetic multipliers increase the performance of ALU and Processors. We evaluate the proposed approach by comparison with Normal Booth Multiplier. Logic synthesis showed its efficiency in terms of delay and power consumption when the word length of each operand in the multiplier is 64bits.

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Development of communication technologies and ecommerce has made the credit card as the most common technique of payment for both online and regular purchases. So, security in this system is highly expected to prevent fraud transactions. In this. Many people are trying novel techniques to detect and prevent such frauds. This paper uses a Neural network based unsupervised learning technique.Credit card fraud detection is the process of identifying purchase attempts that are fraudulent and rejecting them rather than processing the order. Payment cards are easy to use because you only need to transmit a few simple numbers to the bank in order to identify your account and authorize the transaction. It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Such problems can be tackled with Data Science and its importance, along with Machine Learning, cannot be overstated. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analysing and pre-processing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data.

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Throughout the evolution of the Internet and social networks, forums and online platforms have a vital role in sharing information, along with the creation and engagement of virtual communities. Such websites represent great resources, and they are the first step in the adoption of e-health services. When the persons are ill, many of them use search engines for self-diagnosis and gather possible treatment ideas before asking for a doctor's opinion. This takes a lot of time because the information is scattered across various forums and websites. In this project is presented an application that aims to provide an online self-diagnosis and drug recommendation tool and online delivery of drugs that are required and this project also helps the users in curing its disease by giving the list of fruits and herbs that the user should consume in order to get rid of its disease. Thus, the platform automates the search process, and provides the user with the most relevant information, eliminating the need for manual data interpretation. The results are ranked according to the confidence score obtained after the execution of the fuzzy search algorithm. The platform provides medical advice and fruits to recover, through online. Thus, it is intended for informational purposes only. The developed platform is not a substitute for professional medical advice, diagnosis, or treatment. Another feature of the platform is that it enables users to identify hospitals and clinics around them, so that they can receive professional healthcare service.

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Crying is a form of communication for children to express their feelings. A baby's cry is characterized by its natural, periodic tone and vocal changes. This project provides an overview of current research on the analysis of infant cry signals and classification tasks. Detecting baby cries in audio signals is an important step in applications such as remote baby monitoring, and is also important for scientists studying the relationship between baby cry signal patterns and other developmental parameters. This tone detection study involves feature extraction and classification by determining tone patterns. We use MFCC as feature extraction method and K-Nearest Neighbor (K-NN) for classification. K-Nearest Neighbour (KNN) is a classification technique commonly used for audio data. The ANN classifier shows significantly better results compared to other classifiers.

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To avoid rigging completely. Electronic voting systems have come into picture to prevent rigging up to the maximum extent. But even there may be some malfunctions during elections. Thus, fingerprint based electronic voting system has been designed. According to ancient Greek scripts BIOMETRICS means study of life. Biometrics studies commonly include fingerprint, face, iris, voice, signature, and hand geometry recognition and verification. Many other modalities are in various stages of development and assessment. Among these available biometric traits, Finger Print proves to be one of the best traits providing good mismatch ratio and also reliable. To provide perfect security and to make our work easier, we are taking the help of two different technologies viz. EMBEDDED SYSTEMS and BIOMETRICS. Firstly, discussing about Biometrics, we are concentrating on Fingerprint scanning. For this, we are using FIM 3030N high voltage module as a scanner. This module has in-built ROM, DSP and RAM. In this, we can store the fingerprints of up to 100 users. This module can operate in 2 modes i.e., Master mode and User mode. We will be using Master mode to register the fingerprints which will be stored in the ROM present on the scanner with a unique id.

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Avalanche forecasting is an iterative process, where forecasters use weather data and snow observations in addition to previous assessments to conclude what forecast to publish. This project investigates how the forecasting process could be automated, using three seasons worth of data from 23 of Norway’s avalanche forecasting regions. Three scenarios were considered, using different amounts of input parameters based on what data would be available to the model in each respective scenario. For each scenario a machine learning model was trained, and a separate naïve model was constructed. The machine learning model could only beat the naïve model in the simplest scenario, using only weather data. In the other scenarios it was found that the data representation was lacking; highly intermittent snow observation data was structured as time series when a more pre-processed representation may have been more fruitful.

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The purpose of this Project is to detect humans based their Walking styles. Identification of a person based on gait has created a sphere of curiosity in computer vision domain due to its high recognition capability even at a far distance. The main aim of the project is to develop automatic biometric system to identify a person based on his Gait. Biometric identification like fingerprints, retina, palm and voice recognition needs subject’s permission and physical attention and it takes long time to complete the process. But for Human Gait recognition it works on the gait of walking subjects to identify people without them knowing or without their permission, because each gait is unique, the recognition algorithms encounter new data every time they are used. The more gait variants the system sees, the better it analyses future data. Here, Gait recognition technology uses several sources or capture devices — video cameras, motion sensors, and so on — to acquire data, with the development of Computer Vision techniques, there are many approaches to human identification by movement in video, using natural biometric characteristics (the human skeleton, silhouette, change while walking) and abstract features. we first extract the gait features from image sequences using the Feature Module. Features are then trained based on the frequencies of these feature trajectories, from which recognition is performed. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance.

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In the current scenario, the data on the web is growing to a larger extent. Social Media is generating a large amount of data such as reviews, comments and customer’s opinions on a daily basis. This huge amount of user generated data is worthless unless some mining t e c h n i q u e s are applied to it. Nowadays, there are several people using social media reviews to order anything through online. Online spam detection is one of the herculean problems since there are many faux or fake reviews that are created by organizations or by the people themselves for various purposes. Such organizations tend to write fake reviews to mislead readers or automated detection systems by promoting or demoting the targeted product services. Fake reviews detection has recently become a limelight that’s capturing attention. Fake reviews are generated intentionally to mislead readers to believe false data that makes it tough and non-trivial to discover supported content. Hence, it is highly necessary to create a monitoring system which thoroughly checks for fake reviews among various product websites and removes them promptly

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Online shopping has received very crucial role within the twenty first century as the general public are busy, loaded with nerve-racking time table. In this sort of scenario online shopping became the very best and maximum appropriate mode for their shopping. Internet has modified the manner of consumer’s save, and has unexpectedly developed right into an international angle. An online store arouses the physical similarity of buying merchandise as well as services from net store and this manner of purchasing is known as business-to-consumer online shopping. The gift paper is based totally on assumption of classical model conduct. This paper examines the conduct and perception of online customers in Aizawl.

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With the exponential increase in information, it has become imperative to design mechanisms that allow users to access what matters to them as quickly as possible. The recommendation system (RS) with information technology development is the solution, it is an intelligent system. Various types of data can be collected on items of interest to users and presented as recommendations. RS also play a very important role in e-commerce. The purpose of recommending a product is to designate the most appropriate designation for a specific product. The major challenge when recommending products is insufficient information about the products and the categories to which they belong. In this paper, we transform the product data using two methods of document representation: bag-of-words (BOW) and the neural network-based document combination known as vector-based (Doc2Vec). We propose threecriteria recommendation systems (product, package and health) for each document representation method to foster online grocery shopping, which depends on product characteristics such as composition, packaging, nutrition table, allergen, and so forth. For our evaluation, we conducted a user and expert survey. Finally, we compared the performance of these three criteria for each document representation method, discovering that the neural network-based (Doc2Vec) performs better and completely alters the results.

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Developing tools to support students and learning in a traditional or online setting is a significant task in today's educational environment. The initial steps towards enabling such technologies using machine learning techniques focused on predicting the student's performance in terms of the achieved grades. The disadvantage of these approaches is that they do not perform as well in predicting poor-performing students. The objective of our work is two-fold. First, in order to overcome this limitation, we explore if poorly per- forming students can be more accurately predicted by formulating the problem as binary classification. Second, in order to gain insights as to which are the factors that can lead to poor performance, we engineered a number of humaninterpretable features that quantify these factors. These features were derived from the students' grades from the University of Minnesota, an undergraduate public institution. Based on these features, we perform a study to identify different student groups of interest, while at the same time, identify their importance.

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Depression is a psychological disorder that affects over three hundred million humans worldwide. A person who is depressed suffers from anxiety in daytoday life, which affects that person in the relationship with their family and friends, leading to different diseases and in the worst-case death by suicide. With the growth of the social network, most of the people share their emotion, their feelings, their thoughts in social media. If their depression can be detected early by analyzing their post, then by taking necessary steps, a person can be saved from depression-related diseases or in the best case he can be saved from committing suicide. In this research work, a hybrid model has been proposed that can detect depression by analyzing user's textual posts. Deep learning algorithms were trained using the training data and then performance has been evaluated on the test data of the dataset of reddit which was published for the pilot piece of work, Early Detection of Depression in CLEF eRisk 2019. In particular, Bidirectional Long Short Term Memory (BiLSTM) with different word embedding techniques and metadata features were proposed which gave good results.

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Sentiment analysis is the analysis of emotions and opinions from any form of text. Sentiment analysis is also termed as opinion mining. Sentiment analysis of the data is very useful to express the opinion of the mass or group or any individual. This technique is used to find the sentiment of the person with respect to a given source of content. Social media and other online platforms contain a huge amount of the data in the form of tweets, blogs, and updates on the status, posts, etc. In this paper, we have analyzed the Movie reviews using various techniques like Naïve Bayes, K-Nearest Neighbor and Random Forest

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Conversing to a person with hearing disability is always a major challenge. Sign language has indelibly become the ultimate panacea and is a very powerful tool for individuals with hearing and speech disability to communicate their feelings and opinions to the world. It makes the integration process between them and others smooth and less complex. However, the invention of sign language alone, is not enough. There are many strings attached to this boon. The sign gestures often get mixed and confused for someone who has never learnt it or knows it in a different language. However, this communication gap which has existed for years can now be narrowed with the introduction of various techniques to automate the detection of sign gestures. In this paper, we introduce a Sign Language recognitionusing American Sign Language. In this study, the user must be able to capture images of the hand gesture using web camera and the system shall predict and display the name of the captured image. We use the HSV colour algorithm to detect the hand gesture and set the background to black. The images undergo a series of processing steps which include various Computer vision techniques such as the conversion to grayscale, dilation and mask operation. And the region of interest which, in our case is the hand gesture is segmented. The features extracted are the binary pixels of the images. We make use of Convolutional Neural Network (CNN) for training and to classify the images. We can recognise 10 American Sign gesture alphabets with high accuracy. Our model has achieved a remarkable accuracy of above 90%.

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Online Shopping is increasing day by day and more people are interested in buying the products of their need from the online stores. This type of shopping takes less time and easy for customer. Customer searches the item of his/her need through online store and place the order. Only by looking at the rating and by reading the reviews related to the particular product customer places the order. Customer takes comments of other people as the source of satisfaction for the new product buyer. Here there is a possibility that the single negative review changes the angle of the customer not to buy that product. So it is possible that one review among multiple reviews is fake. This creates the difficult situation for the customer to read fake reviews and to make a decision whether to buy or not the product. In order to remove this type of fake reviews a, we proposes a Fake Product Review Finding and Reducing System to provide the users with the original reviews and rating for the products. In our proposed system, we can find the given review is genuine or fake so that User can buy a genuine product.

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Blockchain technologies have gained interest over the last years. While the most explored use case is financialtransactions, it has the capability to agitate other markets. Blockchain remove the need for trusted intermediaries, can facilitate faster transactions and add more transparency. This paper explores the possibility to deflate counterfeit using blockchain technology. This paper provides an overview of different solutions in the anti-counterfeit area, different blockchain technologies and what characteristics make blockchain especially interesting for the use case. We havedeveloped three different concepts and the expansion of an existing system concept, is pursued further. It is shown, thatreducing counterfeits cannot be achieved by using technological means only. Increasing awareness, fighting counterfeiterson a legal level, a good alert system, and having tamper-proof packaging are all important aspects. These factors combined with blockchain technology can lead to an efficient and comprehensive approach to reduce counterfeiting

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Compression of data is a common practise in the signal - processing & digital image analysis fields, and is often employed for multimedia & image processing purposes. Approximate computation is a prominent design approach in arithmetic. New high-speed areas may be opened up by high-speed multimedia applications. Error-tolerant circuits that use approximate computations. At the same time, these applications give great production at a reduced cost of accuracy In addition, the system's complexity is reduced as a result of their implementations. Power consumption and latency in the system's architecture are the main factors. It is proposed that two compressors be designed and analyzed that have smaller size, less delay, and more power than the present systems, all while maintaining precision that is equivalent to the current systems. All designs were tested and forecasted on area, delay, power (PDP), Margin Of error (ER), Range of Error (ED), & Accurate Output Count (AOC) before being implemented in 45 nm CMOS technology (the AOC). In comparison to an exact 4:2 compressor, the suggested 4:2 compressor with approximation has an overall decrease of 56.80 percent and 57.20 percent Power and delayed reduction of 73.30 percent Dadda multipliers of 8 × 8 and 16 x 16 are used in the suggested compressors. There's a comparable level of precision in these multipliers compared to current technology. Image smoothing & propagation are two examples of error-tolerant applications that will benefit from the architecture now under consideration.

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There are many applications where multiplication is essential, including multimedia processing and artificial neural network (ANN. Multiplier is a substantial contribution to the energy usage, critical path latency, and resource usage in these applications. In FPGA-based designs, these impacts are more severe. ASIC-based systems, on the other hand, are the most up-to-date designs. Furthermore, the few Device designs that do exist are usually restricted to unsigned integers, requiring additional circuitry to provide signed operations. This work proposes an area-optimized, low-latency, and energy-efficient design for an accurate signed multiplier for FPGA-based solutions of applications that use signed integers. In order to speed up a multiplier, the best strategy is to limit the amount of incomplete products. With a modified Booth multiplier, adjustable arithmetic capacity and trade-offs in output accuracy are achieved.

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Ternary Content Addressable Memories, or TCAMs, are often used by network devices in order to conduct packet categorization. For example, they are used in the construction of software-defined networks, the management of security, and the transmission of packets (SDNs). TCAMs are often used either as standalone devices or as a component embedded into networking application-specific integrated circuits. TCAMs may also be used in either capacity simultaneously. When working with memory, one of the problems that might arise is the possibility of soft errors destroying the bits that have been saved. The memories might be protected by using an error-correcting code or a parity check to locate any errors; however, doing so would require an increase in the number of memory bits used each word. This approach takes into consideration the need of maintaining the integrity of the memory while simulating TCAMs. This technique gives protection against soft faults and the error correcting strategy that provides rapid response time, inexpensive cost, and excellent search performance in order to deliver an error-free SRAM-Based TCAM Design. In addition, this method offers protection against hard faults.

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While renewable energy is a viable alternative energy source, when connected to the grid, it may offer additional challenges and problems. Likewise, wind turbines must deliver high-quality power in order to maintain the grid's stability & reliability Increased wind turbine connections to the electrical grid are being made to lessen the negative environmental impact of traditional electricity generating. In order to connect a wind turbine to the grid, it is necessary to understand how disturbances affect the electricity quality. There must be as much stability as possible in voltage and frequency. FACTS devices can provide this stability. Voltage-source or current-source inverters have been employed to dampen power system oscillation in recent years. In addition, several of them are employed to increase wind power generation system's transient and dynamic stability (WPGS). A wind turbine connected to a grid system generates active and reactive energy, voltage sag and swell, flickering and harmonic emissions, and electrical switching behaviour. There are several ways to compensate for the reactive power requirement of a three-phase grid linked wind driven induction generator, including employing UPQC to compensate for harmonics created by a non-linear load connected to the PCC and using instantaneous pq theory. To increase power quality, the FACTS Device UPQC control method is simulated using MATLAB/SIMULINK

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This research paper suggests both the design and the execution of wearable antennas for use in wireless applications. The primary objective of this research is to concentrate on the design and hardware implementation of various types of antennas using various techniques, such as the addition of Meta-materials, flexible substrates, formation of EBG structures, antenna arrays, and antenna slots, as well as to analyze the performance of antennas for wearable applications. In addition, the research will look at how well antennas perform in situations where they are worn on the body. There are three unique designs for a wearable microstrip patch antenna that operate at dual bands (2.5 and 5.2 GHz). The first antenna is based on a regular ground plane, but the other two antennas are both based on different forms of two-dimensional electromagnetic band gap (EBG) structures. Numerous aspects were taken into consideration during the design of these two-alternative dual-band EBG structures employing wearable substrates in order to improve the performance of the suggested conventional ground plane (dual band) wearable antenna. This was done in order to enhance the overall performance of the wearable antenna. The first EBG in the form of a mushroom is about 22.7% less in size than the second EBG, which has slots in the shape of a plus sign. It has been demonstrated that the EBG with plus-shaped slots and the mushroom-shaped EBG are superior.

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