Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and implementation of carrier aggregation and secure communication in distribution field network

The communication network in the distribution station area has many pain points and difficult problems, such as a wide range of points, a large number of equipment, a large amount of data, and slow response speed. Improving the field network performance and communication security reliability is an urgent problem to be solved in the construction of the power distribution Internet of Things. In this paper, a new algorithm of power line carrier and wireless communication carrier aggregation is designed, which mainly includes subcarrier optimization calculation, carrier aggregation based on clustering, carrier aggregation update, etc. The combination of subcarrier reliability optimization and network clustering is used for carrier aggregation of power line carrier and wireless integrated communication. At the same time, a field area network security communication method based on the combination of Physical Unclonable Function (PUF) message security encryption and relay node security verification is proposed to improve the security and reliability of power equipment body area network deployment. The test results show that the method proposed in this paper can give consideration to both the acquisition success rate and the acquisition security, and has the promotion value.
A Rapid Reset 8-Transistor Physically Unclonable Function Utilising Power Gating

Physically Unclonable Functions (PUFs) need error correction whilst regenerating Secret Keys in cryptography. The proposed 8-Transistor (8T) PUF, which coordinates with the power gating technique, can significantly accelerate a single evaluation cycle 1000 times faster than $\mathbf{6}\mathbf{T}$ -SRAM PUF does with a 12.8% area increase. This design enables multiple evaluations even in the key regeneration phase in field, hence greatly reducing the number of errors and the hardware penalty for error correction. The $\mathbf{8T}$ PUF derives from the $\mathbf{6}\mathbf{T}$ SRAM. It is built to eliminate data retention swiftly and maximise physical mismatches. And a two-phase power gating module is designed to provide controllable power-on/off cycles rapidly for the chosen PUF clusters in order to facilitate statistical measurements and curb the in-rush current, thereby enhancing PUF entropy and security. An architecture of the power-gated PUF is developed to accommodate fast multiple evaluations. Post-layout Monte Carlo simulations were performed with Cadence, and the extracted PUF Responses were processed with Matlab to evaluate the 8T PUF performance and statistical metrics for subsequent inclusion into PUF Responses.
Comparative Study of Data Driven Methods for State of Charge Estimation of Li-ion Battery

The battery storage system is an inevitable component in an electric vehicle. A precise state of charge is critically notable to evaluate the level of battery aging and ensure electric vehicle reliability and security. Recently, data driven methods have procured much popularity in many research fields. Data-driven methods are found to be a promising approach to provide a high accuracy solution to battery state of charge estimation problem. Research works focus on the state of charge estimation under a fixed operating temperature. Apparently, the maximum deliverable charge of a battery degrades with charge-discharge cycle and temperature. Thus, it is important to consider the operating temperature as one of the input features. This paper identifies the best data driven model for state of charge estimation among linear regression, random forest, CatBoost and XGBoost models. The data driven models are developed via training, validating, and testing stages deploying Li-ion (LG 18650HG2) battery data set under -10°C, 0°C, 10°C and 25°C. The best model is identified using performance indices. The results show the superior performance of XGBoost model under all temperatures and best performance at 25°C with mean absolute error of 0.68%, mean square error of 0.01% and root mean square error of 1.10%, mean absolute percentage error of 1.78%, and R2 value of 99.86%, compared to linear regression, random forest, CatBoost models.
Design Of Wallace Multiplier Using Novel Approximate 4:2 Compressors

In this study, we suggested an approximation multiplier that employs an approximate 4-2 compressor and is energy-efficient. When compared to the current designs, the suggested compressor has a small area. The results of simulations reveal that the suggested approximation multipliers display a reasonable decrease in Mean Error Distance, Mean Relative Error Distance, Normalized Mean Error Distance, compared to multiplier that is designed with exact compressors. The Power, Delay and Area of multipliers developed with this approximate compressor is superior to that obtained with previously suggested approximate compressors, according to implementation results in 90nm CMOS technology.
Indian Sign Language Recognition Using Classical And Machine Learning Techniques – A Review

Humans need to communicate in order to emphasize their thoughts and feelings, collaborate with others, and elevate society as a whole. A hearing-impaired person uses sign language to communicate and this language develops naturally within them. However, the non-signer community doesn’t somehow acknowledge it and hence this remains as a significant barrier that negatively impacts living quality. To bridge the gap, effective sign-language recognition (SLR) system is required and is still an unsolved research issue. New technologies have been developed for the past few years to counter the problem of recognition and were mainly developed using sensors and hardware equipment based on gloves. As contrary to earlier technology, this review presents that, there is no need for expensive and complex hardware in order to recognize sign language, only a modern device with a camera is sufficient. This is accomplished by using Google’s MediaPipe framework and machine learning techniques. In this paper, we had presented various techniques developed for Indian sign language and our future goal is to deliver a reliable SLR system with computer vision and AI due to its self-learning capabilities and increased accuracy.
Study on human pose estimation based on channel and spatial attention

Accurate pose estimation is crucial for understanding human behavior in images or videos. Given an RGB image, we want to be able to accurately locate some important keypoints on the body. Understanding human pose and body structure is important for high-level tasks such as human-computer interaction. Human pose estimation usually has problems such as low discrimination between human body and background, and human pose estimation based on HRnet network does not make full use of important feature information. To solve these problems, a human pose estimation method MCSA-hrnet (Multi-scale Channel and Spatial Attention) based on multi-scale channel and spatial attention is improved by using channel attention mechanism and spatial attention mechanism. Starting from the channel domain and spatial domain, MCSA-HRnet integrates the multi-level attention mechanism into the high-resolution network structure, and designs the channel attention block and spatial attention block. This enables the network to focus on the regions of the image that are highly associated with the human body and not on other regions. MCSA-HRnet uses 1×1 convolutions for information extraction in the core part of the ca block (channel attention block) and parallel $\boldsymbol{3\mathrm{x}3}$ and $\boldsymbol{5\mathrm{x}5}$ convolutions in the sa block (spatial attention block). Different sizes of parallel convolutions can derive spatial attention maps of different scales, which makes the ability of the network to distinguish human features from background features more significant. Thus, the human body region and its key points can be accurately located. The improved method is verified on COCO keypoint dataset, and the results show that MCSA-HRnet can effectively improve the accuracy of human pose estimation joint point localization.
Unconditional Generation of Scalable Chinese Characters as Bézier Curve Sequences

Neural generative modelling of sketches has been an active research direction. SketchRNN set a milestone with their sequence-to-sequence variational autoencoder architecture being able to generate hand drawn sketches in various classes by modelling them as sequences of displacements between consecutive stroke points. The diversity and variety in the set of handwritten Chinese characters makes them a good candidate for such generative modelling enabling their unconditional generation. However, modelling them as sequences of points causes coarse looking strokes and much longer sequences, consequently requiring use of polygonal approximation algorithms to cut down on points. Instead, we propose and investigate the modelling of Chinese characters as sequences of Bézier curves using the SketchRNN architecture with a few modifications, to allow the model to directly generate smooth curves. This way the encoded representation is smaller while more of the stroke’s characteristics are retained and the generated characters are truly scalable. We also suggest the appropriate preprocessing strategy for the KanjiVG dataset to make it suitable for this purpose. Qualitative evaluation of the results suggests the model demonstrates generation of characters with mostly well-structured and ordered strokes. This was substantiated by quantitative evaluation based on the FrFréchetchet Inception Distance score.
Multidimensional Features Helping Predict Failures in Production SSD-Based Consumer Storage Systems

As SSD failures seriously lead to data loss and service interruption, proactive failure prediction is often used to improve system availability. However, the unidimensional SMART-based prediction models hardly predict all drive failures. Some other features applied in data centers and enterprise storage systems are not readily available in consumer storage systems (CSS). To further analyze related failures in production SSD-based CSS, we study nearly 2.3 million SSDs from 12 drive models based on a dataset of SMART logs, trouble tickets, and error logs. We discover that SMART, Firmware Version, WindowsEvent, and BlueScreenof Death (SFWB) are closely related to SSD failures. We further propose a multidimensional-based failure prediction approach (MFPA), which is portable in algorithms, SSD vendors, and PC manufacturers. Experiments on the datasets show that SFWB-based MFPA achieves a high true positive rate (98.18%) and low false positive rate (0.56%), which is 4% higher and 86% lower than the SMART-based model. It is robust and can con-tinuously predict for 2–3 months without iteration, substantially improving the system availability.
Machine Learning Technique to Monitor Heartbeat using Amalgamated Data of Multi-Sensor Stream

The use of wearable devices in healthcare has become increasingly prevalent, with a focus on monitoring chronic diseases such as heart rate. Researchers have employed various methods, including machine learning, to analyze and predict personal activities. This study aims to predict abnormal behavior patterns within a FitBit dataset to anticipate potential diseases. The methodology involves using classification models such as Long Short-Term Memory (LSTM), Deep Neural network (DNN), and Support Vector Memory (SVM), as well as feature selection techniques such as Recursive Feature Elimination (RFE) and Random Forest (RF). The proposed system achieved a high accuracy rate of 85% using Random Forest. This research can aid in predicting and preventing future diseases, as well as providing behavior advice to help individuals achieve their fitness goals.
Investigation of the Direct Source to Drain Tunneling in 5 nm Nanotube Junctionless Field Effect Transistor

In this paper, The performance of a nanotube (NT) junctionless field effect transistor (JLFET) is studied in the sub-5 nm range. We demonstrate the effect of quantum confinement effects which leads to direct source to drain tunneling in the 5nm NT JLFET. Lateral band to band tunneling(L-BTBT) along with Direct source to drain tunneling which affects the OFF state performance of the device is also studied in this paper. The inclusion of high dielectric(high-k) spacers and the core gate to the device improve the performance. Lastly we have shown the effect of diameter of core gate on the device and inclusion of the hetero structures such Si-Ge also helps in achieving better performance in the device.
A Passive Indoor Population Detection Method Based on WiFi Channel Status Information

Due to the existing technology for indoor population detection has disadvantages, such as the need for special hardware equipment or camera technology. This paper proposes a fine-grained passive indoor personnel detection method, the method to construct a single transmitter - receiver number recognition, using amplitude information can be received sub-carrier structure state of its fluctuation characteristics, depicting the indoor number change, use of machine learning algorithm is concluded that the stability of the indoor personnel changes and counting. The results of three classifiers (K-nearest neighbor algorithm (KNN), Support Vector Machine (SVM) and BP Neural Network) are compared respectively. The results show that KNN has a better performance when the number of people is small, and the accuracy rate is up to 99.7%. Among them, the CSI signal collection method is more flexible than the traditional method, and the number of received signal subcarriers is more than the traditional method. The Multi Input Multi Output technology is fully used to achieve multiple and multiple collection, and the accuracy rate is improved.
Voice Controlled Humanoid Robot 3-D Printing Industrial Application

At this time this rapid evaluation industrial 5.0 age each and each company necessities rapidity in manufacturing to cope up with the customer’s requirements. Each and each industrialist cannot meet the expense to revolutionize its unit from manual to semi-automatic or fully automatic, automation uneconomical in India. the essential objective of our project is to develop a flexible and low-cost humanoid that may be utilized in any industry to eliminate this problem. Our humanoid is utilized in the quantity of applications by changing the program of the controller and also the structure is intended in such how that it’s proficient to gauge suitable loads but are often able to lift intermediate loads. Our humanoid has infinite possibilities of applications after implementing computing in it. it’ll also work borders in situ of kinsmen so it’ll help such a lot to our country.
Developing an Ultra-low Power RISC-V Processor for Anomaly Detection

This paper aims to develop an ultra-low power processor for wearable devices for anomaly detection. To this end, this paper proposes a processor architecture that divides the architecture into a part for general applications running on wearable devices (day part) and a part that performs anomaly detection by analyzing sensor data (night parts), and each part operates completely independently. This day-night architecture allows the day part, which contains the power-hungry main-CPU and system interconnect, to be turned off most of the time except for intermittent work, and the night part, which consists only of the sub-CPU and minimal IPs, can run all the time with low power. By developing a processor based on the proposed processor architecture, the design verification of the proposed technology and the superiority of power saving are demonstrated.
Spectrogram Generation for Music Game Based on Attention-BiLSTM

Music game has been popular among digital entertainment since 1960s, and it is significant for children’s musical interests and the inculcation of music. The most important part of music game, charts, however are rarely created by professional staff. In many music games, charts are created by players themselves, resulting in uneven quality. The tedious and labor-intensive production of charts, and the negative impact it can have on the game experience, make them an important and difficult aspect of music game. Most research on music games has conducted from a sociological or design perspective, and there is little on automatic spectrogram for music games. Moreover, BiLSTM neural network mode has achieved remarkable success in processing temporal problems such as speech recognition. Meanwhile, Attention excels in enabling models the ability to discriminate. In this paper, we proposed an automatic generation model combined Attention mechanism with BiLSTM neural network for music game.
A Novel Speech Emotion Model Based on CNN and LSTM Networks

LSTM is a sequential model containing the long short-term memory cells gated recurrent units. Compared to the traditional RNN, LSTM introduces three gates which solve the exploding and vanishing gradient problems of RNN. In this paper, we propose a new speech emotion model by combining CNN and LSTM. The model is implemented based on the CASIA data sets, the Python librosa library and the opensmile tool to get the speech emotion features by extracting the multi-feature of the fusion acoustics which would then be compared to the features based on different configurations to evaluate the recognition accuracy. The experimental results show that the features extracted from the emobase2010 configuration can achieve 84% recognition accuracy based on the CASIA dataset. Compared with other models, the recognition accuracy of the model introduced in1 this paper is 3.3% higher than that of the SVM model, but 6.3% lower than that of the ConvLSTM model.
Hyperplastic and Adenoma polyp classification using Deep networks

Colorectal cancer (CRC) is the world’s third most frequent disease. Polyps which are growths that emerge as lumps on the colon lining are often benign, some may develop into malignant tumours over time, thus it is advisable to have them removed to prevent the risk of colorectal cancer. Early identification and characterization of the kind of polyp are crucial for cancer prevention and treatment. DCNNs have proved to be extremely effective in object categorization over a wide range of object categories. In this study, we experimentally evaluated and compared the effectiveness of the ResNet50 and EfficientNetB0 models in distinguishing Hyperplastic from Adenoma polyps and diagnosing them. Our findings show that cutting-edge DCNN models may correctly characterize the polyps with accuracy equivalent to or greater than that predicted by doctors. As a result, our findings might be valuable for future polyp categorization studies.
Theoretical investigation into the optimized design of a durable OFSPR hydrogen sensor based on a PdY alloy

Hydrogen sensing technology by definition necessitates high accuracy, rapid response time, and durability. Thin film Pd has demonstrated excellent use in this field owing to large sensitivity and fast detection time. Interaction with hydrogen causes a crystallographic phase transition of the Pd lattice resulting in expansion. Subsequently repeated hydrogen loading cycles increases mechanical stress on the Pd lattice and thus leads to delamination of the hydrogen sensitive layer. By alloying Pd with Y, it is possible to mitigate the unwanted phase transition thereby significantly improving durability. We present the first optical fibre surface plasmon resonance (OFSPR) hydrogen sensor based on a multilayer Ag/SiO2/PdY deposited on the unclad core of a silica optical fibre. In this submission, we investigated the spectral influence of fibre numerical aperture in addition to Ag and SiO2 thickness within the multilayer. Sensor sensitivity and figure of merit were found to reach a maximum when a fixed Ag thickness was paired with a set of corresponding SiO2 thicknesses. We demonstrate that changing the thickness of one of these layers alters the optimal thickness of the other. We present a figure by which an array of optimal sensing structures can be determined. The largest sensor figure of merit in this study was found to be 0.062732, and was produced using Ag = 50nm, and SiO2 = 70nm. This sensor operates with sensitivity of 17.57nm to 4% hydrogen, detection accuracy of 0.014282nm-1, and operated at a spectral centre of 524.09nm.
PARTIAL SPREADS AND HYPERBENT FUNCTIONS IN ODD CHARACTERISTIC

We study bent functions which are as different as possible from linear functions. Functions that remain bent under all bijective monomial substitutions are called hyperbent. In Chapter 2 we introduce partial spreads to construct a family of bent functions on vector spaces of even dimension over a finite field. This generalizes the construction given by Dillon for fields of characteristic 2. In Chapter 3 we use finite fields to introduce an explicit family of functions in the trace form whose hyperbentness can be tested using results of Chapter 2. This test is more efficient than using the definition of a bent function. It is an analogue of a result by Charpin and Gong for characteristic 2. The motivation for studying bent functions is their important role in cryptography and coding theory. For example, the CAST cipher constructed using bent functions is approved for use by the Canadian government.
An EHV Transformer Fault Diagnosis Method based on Acoustic Perception and Pseudo-Labels Intensive Training-CNN

The importance of power transformers is more prominent in the context of EHV grid interconnection, and their state health is the basis of grid operation stability. The classic ways of monitoring transformer faults all have some impact on the transformer. Consider incorporating acoustic detection without transformer outage detection and placement of intrusive sensors. Meanwhile, in order to solve the problem of insufficient labeling of test data, an EHV transformer fault diagnosis method based on acoustic perception and pseudo labels intensive training-CNN is proposed in combination with semi-supervised training. First, the convolutional neural network is trained to ensure the classification accuracy of the model using deep learning ideas. Then, the pseudo-labels idea is introduced and the intensive training mechanism is proposed to improve the classification accuracy of the model again. Finally, the transformer acoustic data are processed and integrated as a way to validate the model performance. Experiments show that the proposed model can further improve the classification performance of the model by using the pseudo-labels intensive training mechanism on the basis of ensuring the classification accuracy. The final accuracy rate can reach more than 92.4 percent.
Recent Advancements in Applications of Artificial Intelligence and Machine Learning for 5G Technology: A Review

As the fifth generation (5G) of wireless communication rolls out worldwide, conceptualized use cases and disruptive industry solutions are being deployed to offer smooth, frictionless, and secure connectivity. The landscape of Artificial Intelligence (AI) and Machine Learning (ML) can be seen as potential drivers in the automation and optimization of network performances and management complexities. The shifting network behaviors and complicated modern applications present diverse network performance traffic, which can be exploited by service providers to deal with network demands and provide superior user experiences. The existing research can be divided into the following 5G research areas, which include network traffic, resource allocation, network slicing, mobility management, physical layer security, etc., to name a few. The primary objective of this paper is to provide a comprehensive perspective on the expanding diversity of viable ML-assisted solutions for tackling various 5G network-level issues. The paper concludes with an indepth investigation of the challenges and unexplored directions of future research pertaining to making 5G applications more reliable for future use cases.