Reviewerly/exHarmony huggingface dataset:
HEAP-SORT on Dual Port RAM Based FPGA

Often, sorting is a crucial step in the data processing process. It is possible to employ software implementations in numerous apps that are generally accessible in both paid and unpaid versions. But in some high-speed applications, sorting should be done in hardware, making use of the possibility of parallel processing. The Heapsort is one of the algorithms with the best complexity O(n*log(n)). Our approach is based on the implementation of the Heapsort algorithm, which makes use of dual port random access memory (DP RAM), a hardware feature of contemporary Field-Programmable Gate Array (FPGA) processors, to implement effective sorting of a data stream. The complexity of the sorter is inversely correlated with the sorter capacity due to effective use of FPGA resources.
Detecting Handwritten digits using fully connected Artificial Neural Network in FPGA which will act like a replacement for GPU

AI/ML has grown significantly in the last few years, and slowly and steadily it is starting to impact the semiconductor industry as well. The world is moving towards more application specific technologies. We first saw CPU s, then GPUs came along for image specific applications, now, a new revolution is trying to take place by implementing Neural Networks in FPGA. In this paper, a 5-layer fully connected artificial neural network has been built using the MNIST dataset to recognize handwritten digits. Software like Vivado 2022.1 and in built Zynq 7000 board has been used to realize the implementation. An accuracy of 98% has been achieved through this implementation, and very minimal power and memory usage has also been seen. Regarding the paper, it has been clearly seen that ANN based implementation of FPGA is far superior to the workings of a GPU because of its efficient memory and power usage, also it creates less latency issue as FPGA works in a clock cycle of nanoseconds.
Court Ledger-Decentralized and Tamper-Proof Solution for Storing Evidence

Court Ledger is an open-source blockchain application created to modernize and protect the administration of court data. The blockchain's immutability and invulnerability are put to use here to keep court documents incorruptible and easily accessible to legitimate users. Court Ledger eliminates the need for time-consuming and prone-to-error manual record-keeping by keeping track of and updating court records in real time. Furthermore, the system protects sensitive information while allowing authorized users access to court documents. Court Ledger has the ability to completely change the way courts handle their case files by providing a streamlined, easy-to-use platform for doing so.
Text And Language Independent Speaker Recognition System for Robust Environment

Forensic speaker recognition is required when an individual voice is presented as evidence, such as in telephone recordings, wiretaps, audio surveillance, or informant recordings. Speech is the primary means of communication in our daily lives, with numerous languages used for this purpose. Speech technologies are widely utilized and have countless applications, allowing machines to respond accurately and dependably to human voices and provide helpful and valuable services. The goal of this project is to create a model that can identify speakers regardless of the language they speak and the volume of their voice, and can even recognize speakers when there is noise present in the speech. In our proposed work, Discrete wavelet transform (DWT) is used for feature extraction and Back Propagation Network (BPN) and Adaptive Resonance Theory (ART) is used for classification. This speaker recognition system can be applied in various fields, particularly in forensics, security, and biometrics where speech can be used as a password or for other purposes.
Camobot - Thermal Image Processing Surveillance Rover with GPS Tracking and Human Detection Using OpenCV

Criminal sniping incidents are a prevalent and ongoing issue that can occur without warning, and in any location. Despite being most commonly observed at the line of contact (LOC), these incidents often go unreported and unnoticed, with their victims remaining unknown until mass casualties occur. There are typically two types of sniper criminals, with the hit-and-run sniper being the most common. These criminals are challenging to apprehend, as they can quickly disappear after firing a few shots and relocating to another position. To address this issue, a Rover has been developed, equipped with a thermal camera and object detection technology to warn and prevent such scenarios. The Rover can also provide real-time GPS location information of disguised individuals, which is relayed to authorities for further action. Additionally, artificial intelligence (AI) cameras have been developed that can detect people and capture their photos, along with their exact location, enabling swift action and identification of suspects. These technologies have the potential to significantly improve public safety and prevent further criminal sniping incidents in the future.
An Enhanced Image Colorization using Modified Generative Adversarial Networks with Pix2Pix Method

The science of deep learning has undergone a revolution thanks to the invention of generative adversarial networks (GANs), a kind of deep learning design that can produce synthetic data that is equivalent to real-world data. GANs have recently been employed for a number of images processing operations, including photo colorization. The storing of grayscale photos, which require less storage space and can then be colored, and the restoration of old photographs are two of the key uses for image colorization. Although there are numerous color choices that can be employed with a specific grayscale image, this problem is challenging. Deep learning has been used in recent advances to make an effort to overcome this issue. In addition to the generator network, the Pix2Pix architecture also has a discriminator network that tries to tell bogus from real color images. Many datasets, including the CIFAR-10 and COCO datasets, have shown that the suggested method produces high-quality colorized images that closely resemble the ground truth images. Overall, this study shows how well GANs–and specifically the Pix2Pix architecture–can be used to colorize images. According to the research, this technology might be applied to a number of activities, including the processing of images and videos, digital restoration, and aesthetic representation.
Design and optimization of an on-chip Artificial neural network on FPGA for recognizing handwritten digits

Today's requirement for massive computation power and the inefficiency of deep learning applications are the significant drawbacks holding back the seamless integration of machine learning and deep learning in embedded systems. This paper aims to implement and optimize the neural network for handwritten digit recognition on low-power efficient FPGA devices. The comparative analysis of artificial neural architectures by varying the number of neurons in each layer is performed. This analysis comprises the accuracy of recognition with respect to the resource utilization of FPGAs. MNIST (Modified National Institute of Standards and Technology database) is used to test the neural network. For this implementation, both hardware and software platforms are used. Also, python libraries such as Zynet, TensorFlow, and NumPy are used to train the neural network. Studies have indicated that implementing deep neural networks purely in software can encounter significant constraints in their hardware implementation, specifically in the utilization of critical parameters such as memory, computational resources, and power. Therefore, to tackle these constraints, the Vivado high-level synthesis (Vivado HLS) tool is used, which leverages the advantages of hardware implementation, such as pipelining and parallelism, to improve the performance of FPGAs.
VLSI Architectures for Security Analysis with Dual-Key LFSR Using Barrel Shifter and S-Box

As our reliance on digital information increases, its security has become extremely important. Sectors such as IT, healthcare, and telecommunications rely on digital data. This data is therefore encrypted to prevent unauthorized access. Current encryption standards such as AES, DES, and RSA are complex to implement and performance sensitive, making them unusable in low-power devices such as RFID and mobile medical devices. This paper focuses on building cryptographic systems using a scrambler circuit constructed using reversible logic gates, S-Box, a barrel shifter, and an LFSR that generates a 4-bit random key which is then used as a dual key for XOR and a barrel shifter. Three circuits are proposed in which the architecture is changed and the output is observed. The results led to an increase in encryption by 20%.
RAEEUCCI2023-Comparative study of Hate Speech detection using different Machine Learning Classifiers

Social media involves people getting closer to different parts of the world. The Creation of networks among different types and regions of the world consists of a clash of opinions and interests. This may lead to sharing of hate amongst people. Many social media platforms involve the concept of ‘Freedom to speech.’ But this also incorporates some restrictions as it can lead to huge problems of spreading hate messages. To have a peaceful environment around and prevent any kind of misinformation, there is a need of having a mechanism to automatically detect hate speech. So, this research paper solves this problem by using five different classifiers with a dataset with three classes. The classes are classified as offensive speech, hate speech, or offensive speech. The classifiers involved are Gaussian Naive Bayes, Random Forest, Support Vector Machine classifier, Logistic Regression, Decision Tree Classifier, and a TF IDF -based feature engineering technique. Maximum accuracy was achieved through RandomForest which was over 90%. Moreover, accuracy can be increased by using other feature engineering techniques and a combination of classifiers.
Deep Learning based Sign Language Recognition robust to Sensor Displacement

In this study, a wearable sensor-based sign language recognition system comprised of a single tri-axis accelerometer placed on the user's right forearm is used to classify signs from the Indian sign language. In practical usage, however, the placement of the sensor during testing may vary from that used to acquire data that was used to train the classifier. It is experimentally determined that slight sensor displacement could drastically affect the model's performance unless the model is fine-tuned for the new sensor placement. Hence, transfer learning is proposed as a solution to enable sign classification every time the user places the sensor assembly on his forearm for sign language translation. The sign language classifier is trained on the large, labelled data collected for a certain sensor placement and then fine-tuned on few observations of data collected for the displaced sensor location. The classification accuracy of a deep learning model improves from 88.2% without transfer learning to 92.5% with transfer learning when only limited categorized data is available from the displaced sensor location for classification of the considered signs.
Effective Facial Emotion Recognition Using Bi-wavelet Bi-directional Gated Recurrent Unit Neural Network

When communicating with others, one of the best ways to express human emotions is through facial expression. Emotion recognition through face expression find good place in researchers mind in the field of affect computing. For various interactive applications facial expressions must be recognized automatically for better performance. Here an efficient emotion recognition system using face images is presented reckoned on wavelet transform. The effective extraction of spectral and spatial domain features makes wavelet transform seems to a major technique used in extracting localized features for facial emotion recognition. In this work the extraction of features is accomplished using proposed Bi-wavelet transform method. The temporal correlation in the extracted features is learned using bi-directional gated recurrent unit. The SoftMax classifier is utilized to get the classified output. Experiments were carried out on JAFEE, CK+ and SFEW database which provides a promising result with better accuracy.
A Robust and Fast Symmetric Text Encryption Algorithm Based on Fermat's Two Squares Theorem

In the digital age, the integrity and privacy of information transmitted, communicated over computer networks or stored on computer systems are crucial. This paper suggests a novel and robust symmetric encryption scheme based on Fermat's theorem to facilitate seamless and secure communication while preserving the confidentiality and integrity of data sent over a public channel. The knowledge of the public key does not reveal any valuable information to an intruder for computing the encryption/decryption keys used here. An essential feature of this scheme is that no additional information is passed again through the public channel or a private courier in the computation of the encryption/decryption keys. Also, the keys are dynamic for each data transmission, and the Plaintext and Ciphertext are not the same lengths. Consequently, the proposed encryption-decryption technique is safer against all possible standard attacks and more efficient than the existing encryption algorithms.
An innovative Image Cryptosystem Sequences based on Biological Protein

Image cryptosystem is mandatory to maintain images in a secure format, whether on users IOT devices, the cloud, or fog drives. Many trustworthy image cryptosystems have been used previously to keep image in encrypted safe formats from illegal decryption by adversaries. This article introduces Image-as-Protein Sequence (IaPS), a genuine image cryptosystem depend on disguising image pixels by replacing them with sequences of characters analogous to biological protein sequences and permuting these sequences into different k-mers (frequent and non-frequent), which are then binary encoded into the encrypted file. The proposed cryptosystem scheme was tested with various grayscale and RGB colour images, and its robustness, performance, and security were evaluated.
Analysis of Various Machine Learning and Deep Learning Algorithms for Bitcoin Price Prediction

Bitcoin (BTC) is a cryptocurrency meaning that it is a virtual asset that works on encryption systems to control production of units and verify the funds transfer. Bitcoin's operation is independent of central bank and can be used as a decentralized medium of exchange. Being an emerging domain, the blockchain systems on which these cryptocurrencies are based, are a great source of investment among enthusiasts. However, there is high volatility in the price of Bitcoin and thus it paves the way for its prediction so as to get a clear insight on its trend. Forecasting Bitcoin prices can provide valuable insights for investment portfolio management, risk assessment, and the identification of profitable trading or arbitrage opportunities. Furthermore, Bitcoin price predictions may assist firms that accept Bitcoin payments in enhancing their financial management practices by improving their revenue and cash flow forecasting. Additionally, governmental entities and regulatory bodies could leverage Bitcoin price forecasting as a tool for monitoring and regulating the cryptocurrency industry. This paper explores the effectiveness of ML models in predicting Bitcoin price by analyzing a diverse set of historical data. We evaluate several ML algorithms, and compare their performance in terms of accuracy to find out best algorithms for short term and long-term Bitcoin price prediction.
An Efficient Down Sampling Scheme Based on a Matching Factor for Federated Learning

Federated learning (FL) is a novel distributed machine learning paradigm used to solve data sharing and privacy security in today's academic community. As FL edge devices increasingly participate in collaborative computing, improving communication efficiency while ensuring the accuracy of the model turns out to be a pressing challenge. However, it is difficult for existing FL frameworks to make full use of the contribution of edge devices onto the global model in each model aggregation process since computing capacity and communication environment of edge devices have diverse characteristics. In this paper, we propose an efficient down sampling strategy based on a proposed matching factor for FL edge devices selection to achieve better global model accuracy with less selected FL edges. In our scheme, we make use of the observation that the model can converge, and the data characteristics within the same data source have autocorrelation implicitly. Our down sampling scheme based on the matching factor is evaluated on a popular data set CIFAR10. The experimental results show that this scheme can improve the prediction accuracy, significantly save the communication cost and computing cost, and improve the utilization of edge devices.
Zynq SoC-based IPs for Deep Learning Neural Networks

Deep Learning Neural Networks (DLNNs) involve computationally intensive operations and are associated with a collection of interconnected artificial neurons. DLNNs can provide intelligent information systems that find a place in many areas from healthcare to robotics. High-speed and low-latency performance are important constraints of these networks. VGG-16 architecture used for object detection is based on the idea of Convolutional Neural Network (CNN) and applies the technique of deepening the network to extract the best use of CNNs. Its architecture consists of 16 layers making its training process extremely slow. It highly depends on multiplication and addition operations over the inputs and weights data. This introduces high computational complexity. This work aims to design an IP for the convolutional layer along with the Rectified Linear Activation Unit (ReLU) function and zero padding operation, a max pooling IP, and an IP for a low-latency implementation of the final fully connected layer with ReLU activation followed by softmax activation layer of VGG-16 architecture. High level hardware-based code-optimization methods for VGG-16 are presented to be accelerated in an FPGA based environment. Vitis HLS (High-Level Synthesis) tool was used to describe the proposed systems through a High-Level Language (HLL) C++ programming.
Automatic Segmentation of Underwater Images with Shannon's Thresholding and UNet Variants

Automatic detection of the Region of Interest (RoI) from the chosen digital image is one of the prime tasks in various domains, and this work considered the underwater images for the study. In order to extract the RoI with better accuracy, the proposed research aims to develop a scheme by integrating the image enhancement approach and the Convolutional Neural Network (CNN) segmentation. The various phases of this scheme involve; (i) Image collection and pre-processing, (ii) RoI extraction using CNN segmentation, and (iii) Comparing the segmented region with Ground-Truth (GT) and confirming the merit of the implemented technique. This work considered Shannon's Entropy and Firefly Algorithm (SE+FA) tri-level thresholding to pre-process the images. Then the necessary RoI from these images are mined using the UNet and its variants, such as UNet+, UNet++, and VGG-UNet. The experimental investigation of this scheme confirms that the VGG-UNet implemented using VGG16 as the backbone (encoder) helped achieve a better result than other schemes. This investigation considered the UFO-120 database for the assessment, and the VGG-UNet offered an RoI mining accuracy of>98%. Other UNet variants offered >96% accuracy, which confirms the merit of the proposed scheme.
AI Model for Bird Species Prediction with Detection of Rare, Migratory and Extinction Birds using ELM Boosted by OBS

In this research study, Extreme Learning Machine (ELM) boosted with new Optimized Bird Swarm Algorithm (OBSA) is used for classification, prediction of birds and identification of migratory, rare and detection of extinction of birds. The model uses spectrogram obtained after preprocessing of the bird sound as input for prediction. A new BSA called OBSA is developed to improve the performance of traditional BSA. The newly developed model, ELM-B-OBSA showed top level performance with less convergence time of 0.333512 seconds, lowest root mean square error (RMSE) of 1.45, high coefficient of determination of 99.65%., highest optimum value of 0.0041 for the chosen fitness function and 99.25% accuracy of prediction in comparison with other swarm optimization algorithms like particle swarm, ant colony and cat swarm optimization algorithms. The ELM-B-OBSA performance measure stood higher than artificial neural network (ANN) and Convolution Neural Network (CNN).
Depthwise Separable Convolutional Neural Network for Multi-class Texture Classification

This study proposed a compact deep learning-based texture classification model with Depth-wise Separable Convolution (DSC) layer. The method helps in extracting inherent texture features from the Convolutional Neural Network (CNN) architecture. The filters in depth-wise separable convolution layer act as filter bank which helps in classifying textures by minimizing the cross-entropy loss function. To speed up convergence and locate a better minimum for the loss function, an Adam optimization approach is utilized. The proposed model is experimented with the texture database: ALOT texture database, kth-tips2-b, kylberg texture dataset v1, kylberg sintorn rotation dataset. The results show that the suggested model achieve 97% accuracy on classifying texture images with lesser parameter.
Active Gamma-Ray Log Pattern Localization With Distributionally Robust Reinforcement Learning

Accurately localizing 1D signal patterns, such as Gamma-ray well-log depth matching, is crucial in the oilfield service industry as it directly affects the quality of oil and gas exploration. However, traditional methods such as well-log curve analysis and pattern hand-picking matching are labor-intensive and heavily rely on human expertise, leading to inconsistent results. Although attempts have been made to automate this process, challenges such as low computational performance, non-robustness, and non-generalization remain unsolved. To address these challenges, we have developed a data-driven AI system that learns an active signal pattern localization strategy inspired by human attention. Our artificial intelligence system uses an offline reinforcement learning (RL) framework as its central component, which solves a highly abstracted Markov decision process problem via offline training on human-labeled historical data. The RL agent uses top-down reasoning to determine the location of target signal fragments by deforming a bounding window using simple transformation actions. To overcome distribution shifts between logged data and real and ensure generalization, we propose a discrete distributionally robust soft actor-critic RL framework (DRSAC-Discrete) to solve the Markov decision process problem under uncertainty. By exploring unfamiliar environments in a restrictive manner, the DRSAC-Discrete algorithm provides a safe solution that can be used when data is limited during the early stage of this industrial application. We evaluated the reinforcement learning-based localization system on augmented field Gamma-ray well-log datasets, and the results showed promising localization capability. Furthermore, the DRSAC-Discrete algorithm demonstrated relatively robust performance guarantees when facing data shortage.