Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Review on Deep Learning Algorithms and its Applications

A very active expansion of data and convenience in modern period accept to motivate extremely significant automation tasks with the advanced algorithmic models with the technologies such as Artificial Intelligence, Deep Learning (Portion belonging to Machine Learning) to overcome the challenges in speech synthesis and brain tumor analysis. This research study explores different deep learnign algorithms and its applications. This review paper investigates the incoporation of Deep Learning techniques in signal processing, communications, medical image processing, particularly the models, which manage through effective activation of Artificial Intelligence. At this moment, many researchers attempt to obtain novel solutions to several issues that occur in different domains. This investigation focuses on anticipating threats that arise in different domains, which will be clarified through Deep Learning and it unquestionably be an upcoming generation of certain issues that are valuable and may help the analysts.
Novel chlorine doped graphene electrodes for positive electrodes of a vanadium redox flow battery

One‐step electrochemical preparation of chlorine doped graphene coated electrodes was succeed by cyclic voltammetric method for the first time in the literature in this work. Because hetero atom and oxygen functional groups doped graphene‐based electrodes can improve the electrochemical performance of the battery with formed defects sides on the main structure of the electrodes, the modification of graphene‐based electrodes by chlorine and oxygen including functional groups such as ―Cl, ―ClO4, ―ClO3, ―OH, and ―C═O have been done by arranging of scanned potential in cyclic voltammetric treatment. The structural features of chlorine doped graphene electrodes were analyzed by scanning electron microscopic analysis. Chemical structures of the surface of the electrodes were elucidated by X‐ray photoelectron spectroscopy. Raman analyses were carried out to determine the optical properties of the electrodes. Chlorine doped graphene‐based electrodes were also used as positive electrode component of a vanadium redox battery for the first time in the literature. The electrodes showed great electrochemical performance as positive electrode materials of the battery.
Exploration on Electroencephalogram Controlled Haptic Humanoid Arm for amputees

The World Health Organization (WHO) estimated over 40 million amputees throughout the world, out of 30% are upper limb amputees. In recent times, the prosthetic devices play a major role in technological development. A plenty of amputees are among us who are not able to compete with others only because some part of their body is not fully functional. There are prosthetic limbs available for amputees as a commercial solution in economically developing nations, to overcome the drawbacks in prosthetic limbs like finger movements and wrist movements. Electroen cephalography (EEG) signals offers tremendous influence on the progression of assistive haptic rehabilitation devices. These signals are highly utilized by the academic world to ingress the functions and behavior of human motion. Though EEG controlled supporting/assistive devices have gained the consideration of people, few studies and comprehensive reviews have been done on this. Hence the goal of this paper is to examine a variety of promising recent related works to offer research gap that should be looked into further.
A 65 nm CMOS Quadrature Balanced Switched-Capacitor Power Amplifier for Full- and Half-Duplex Wireless Operation

This article proposes a multi-mode wireless transmitter for full-duplex (FD) and half-duplex (HD) operation based on a quadrature balanced switched-capacitor power amplifier (QB-SCPA) architecture in 65 nm CMOS. The QB-SCPA provides inherent passive transmit–receive (TX–RX) isolation along with an embedded digital self-interference cancellation (SIC) signal injection mechanism and exhibits excellent TX and RX linearity characteristics. To minimize noise figure degradation to the RX path, local oscillator (LO) signal sharing between the two SCPAs comprising the transmitter and a retiming circuit that generates the 25% clock signals is employed to obtain phase noise suppression at the LNA port. In the FD mode, 50 dB of SIC is demonstrated across the instantaneous bandwidth of a 20 MHz orthogonal frequency-division multiplexing (OFDM) signal with 11 dB peak-to-average power ratio (PAPR), along with −35 dB TX error vector magnitude (EVM) with SIC ON and without digital predistortion. The transmitter achieves a maximum output power of 23 dBm with 26.5% power-added efficiency (PAE) and 21 dBm with 17.4% PAE in HDTX and FD modes, respectively, and has a wide operating bandwidth between 1.5 and 3 GHz.
A Novel Approach Based on a Hierarchical Multiresolution Analysis of Optical Time Series to Reconstruct the Daily High-Resolution Snow Cover Area

High-resolution (HR) snow cover maps derived by remotely sensed images are an asset for data assimilation in hydrological models. However, the current satellite missions do not provide daily HR multispectral observations suitable for an accurate snow monitoring in alpine environments. On the contrary, low-resolution (LR) sensors acquire daily information of snow cover fraction (SCF) but at an inappropriate spatial scale. This article proposes a novel approach that combines multisource and multiscale acquisitions to infer the daily HR snow cover area (SCA) for mountainous basins. The approach builds on the assumption that interannual snow patterns are both affected by the local geomorphometry and meteorology. We derive these patterns through a hierarchical multistep approach based on historical statistical analyses on a long and sparse HR time-series. At each step, we obtain HR snow cover maps with higher number of reconstructed pixels but decreasing level of confidence. Historical data are used to estimate the probability that a HR pixel is covered by snow according to two possible multiscale strategies: 1) HR gap-filling, or 2) LR downscaling. These analyses lead to the identification of the patterns that regularly appear given certain conditions. When no systematic patterns are observed, we reinforce the inference of the pixel class by a generalized additive model that exploits not only the historical data, but also explicit geomorphometric, global snow, and multitemporal properties. The proposed approach has been validated on a catchment in the Sierra Nevada, USA, for three hydrological years (2017–2019) showing an average overall accuracy of 92%.
Improvement of affected Intellectual behavior student's by social media using collaboration learning method

Social Media (SM) is a web-based service that gives a unique option for creating public or private profile in a limited system. It helps to build community sense through networking, knowledge co-construction through cooperation, and room for individualization through individualization while learning has been differentiated to accumulate for diverse learning requirements. According to SM application, there are negative aspects which get related over increment of connectivity but there are several positive characteristics present in the SM that have positional for engaging students with an environment of quality learning. These applications of SM have created an online environment, the user present in the application has performed as creator instead of passive users whereas it allowed users to share and exchange their idea in terms of read and write. In the case of education, the advancement of SM is to recognize there collaboration and communication. Hence, this paper is performed based on the examining student intellectual behavior impact performance by SM in college present over Chennai and focused the affected students as Research Learner (RL) to build up their learning behavior using the same SM with the methodology of Collaborative Learning (CL). Moreover, the identification of analyzing the positive and negative aspects of students are done through Google form survey done to 837 students presented with questionnaires based on variable like SM improve academic performance, SM build good relationship, SM create introvert, SM cause health problem and time spending in SM are observed while using SM to the student of its institute. This research has revealed that use of SM sites have affected an academic performance for the students by engagement and collaborative learning through SM which can be predicted by exact dimension reduction using Sparse Principal Components Analysis (SPCA).
DCR: A Unified Framework for Holistic/Partial Person ReID

According to the occlusions, Person reidentification (ReID) can be divided into holistic person ReID and partial person ReID tasks. Occlusions commonly exist in the partial person ReID task but pose or observation perspective changes often occur in the holistic person ReID task; thus, many different network architectures have been designed for each task. However, this approach increases the cost in practice and hinders the development of ReID techniques. To solve this problem, in this work, a unified framework is proposed for holistic/partial person ReID, which can effectively and efficiently address changes in pose or observation perspective and the occlusions in both tasks. In detail, we first employ a fully convolutional network to generate feature maps for an arbitrarily sized image and then use spatial pyramid pooling to obtain its spatial pyramid feature. Thereafter, to efficiently solve the matching problem between the query image and gallery images, we build a deep spatial pyramid feature collaborative reconstruction model (DCR). Experimental results on two partial person ReID datasets and three holistic person ReID datasets demonstrate that DCR outperforms state-of-the-art approaches on both tasks and all datasets. Specifically, it outperforms all competitors with a large margin and achieves an improvement of 9.07% and 5.95% over DSR on Partial ReID and Partial-iLIDS datasets with Rank-1, respectively. Similarly, it also achieves an improvement of 5.08% over VPM on the DukeMTMC-ReID dataset with Rank-1. Additionally, the running time of our method for each query is more than 7× faster than that of DSR or DuATM methods.
Computer Aided Landscape Design and Image Recognition of Ornamental Grass Based on Ecological Aesthetics

The concept of landscape architecture has a long history, which existed before the computer appeared, such as the Western Palace landscape architecture and Chinese classical landscape architecture. But in the past, landscape designers could only rely on paper and pen to complete their work, so the work efficiency was very low. Quickly and effectively classifing and recognizing images is of great significance. This paper proposes an improved multi-layer perceptron model and introduces dropout to solve the over fitting problem. Experimental results show that Parzen classifier can effectively classify images, and the algorithm has good generalization performance.
Design of an Automatic System of an Accelerated Biogas Biodigester for Rural Areas in Peru

This research presents the design and control of a biodigester with mechatronic systems for control and a closed system of controlled compression to reduce the times in the generation of biogas. The development of the project shows that the application of level sensors, electro valves, and exhaust ducts are feasible in each tank through a control panel in order to monitor each process. The acceleration stage to obtain biogas consists of a closed system type cylinder-piston, to perform a controlled compression reducing fermentation times, additionally it was considered that the drive of this cylinder is mechanical type due to the limited access of electrical energy in rural areas. The control and design of the proposed system will provide multiple improvements in homes in rural Peruvian areas that require solutions based on better use of natural resources with the waste generated by livestock, use of energy reducing pollution to their environment, creating access to energy generation, and the possibility of reducing diseases due to the consumption of polluting fuels during the preparation of their food.
Secured Natural Language Processing for Conversion of Unstructured Text Into Structured Intelligence

Natural language processing(NLP) enables the communication between humans and computers with the help of natural language. Most humans try to act immediately in case of an emergency. It will be in the form of a text message or voice message. This paper is mainly focused on such a situation and along with that more security is ensured to the message being sent. This can be developed further to form a private application that is free from vulnerabilities up to a limit. Sentiment analysis is performed on the text message to identify various information contained in the text and its importance. After checking the polarity, Named Entity Recognition(NER) helps in identifying the location of the person which increases the chance of the person being rescued. Messages are encrypted using Fernet(Symmetric Encryption) which is built on top of Advanced Encryption Standard(AES) in Cipher Block Chaining(CBC) mode and is transmitted to the receiver. Once it reaches the receiver, it is decrypted and converted into a text message. After syntax and semantic analysis, the conversion of unstructured data into a structured form is performed. NLP is such a strong process that can detect the presence of sensitive data.
IoT based Automated Physiotherapy device for Seizure patients using EMG sensor

Around 60% of patients experienced seizures due to the surge of electrical impulses in the brain. Earlier, it involved the use of Electro Neuro therapy and surgeries to overcome the seizure. Later, the patient experiences side effects such as chronic pain for prolonged hour. An automated device is being designed to regain the muscular motions with the help of therapy exercise. Flex sensor and accelerometer gives input to the Microcontroller, as the angle and speed can be controlled manually which in turn assists by surveying the data and multiplying the action. The Servo motor controls the device where the system needs a controlling input and output to improve consistency. This device is inexpensive, laborsaving, and environmental-friendly. The combined data about regain of muscle activity can be transferred to the physiotherapist using the Internet of Things. It is focused on the automatic classification of exercises, within the context of motions, controlling the motor and sensors as a tool which is used to automatically assess the performance of therapy exercises.
Core-Size Dependent Fluorescent Gold Nanoclusters and Ultrasensitive Detection of Pb2+ Ion

Gold nanoclusters (Au NCs) are new class of fluorescent nanomaterials with widespread applications in energy, water and healthcare. Here, we report a green synthesis of Au NCs with tunable emission wavelength from 590 to 510 nm in aqueous medium by core etching and ligand exchange method. Investigation reveals that the number of Au atoms present in the core of nanoclusters controls the emission wavelength. The quantum yield (QY) of nanoclusters increases from 0.57 to 3.15% with changing core from Au12 to Au6. Time resolved spectroscopic study reveals that the emission with higher lifetime (>100 ns) originates from ligand to metal charge transfer (LMCT; S to gold core of NCs). It is demonstrated that the highly green emitting NCs (Au-510) are more sensitive than orange emitting NCs (Au-590) toward Pb2+. The detection limit of Pb2+ is found to be 10 nM which is much lower than allowed concentration of Pb2+ in drinking water. Thus, Au NCs based optical sensor is promising for the selective detection of Pb2+ .
Data visualization and pre-processing techniques based Diabetes Prediction System

Over time, having too much glucose in blood can cause health issues. Diabetes occurs when your blood glucose or sugar is too high. One in six people with diabetes in the world is from India. With the development of standards of living, diabetes is gradually increasing in people. Therefore, a convenient way to predict diabetics is essential so that necessary protocols can be taken beforehand. With this intention, we developed a web-based diabetes prediction application which helps its users diagnose the possibility of this disease by entering valid data. In this paper, a diabetes prediction system is implemented for predicting diabetes which comprises of some external features for diabetes alongside regular features like Insulin, Glucose, BMI, Age, etc. This application is designed with the help of HTML and CSS. The prediction model, which produces highly accurate results, applied and compared various algorithms like K-nearest neighbors (KNN), Decision Tree, Logistic Regression (LR), Random Forest (RF), Support Vector Classifier (SVC) and Naïve Bayes to determine Diabetes. The database system for our web application is constructed using My SQL with the vision of creating a system where a user can log on to the system for personal use. The entire application is deployed using XAMPP. The system utilizes Flask framework in Python to launch the web application. To achieve improved accuracy on the datasets, Data visualization and pre-processing techniques were taken to use. One of the data attributes to be inputted requires user's BMI record and to calculate that the application also contains an in-build BMI calculator.
SeLaB: Semantic Labeling with BERT

Generating schema labels automatically for column values of data tables has many data science applications such as schema matching, and data discovery and linking. For example, automatically extracted tables with missing headers can be filled by the predicted schema labels which significantly reduces human effort. Furthermore, the predicted labels can reduce the impact of inconsistent names across multiple data tables. In this paper, we propose a context-aware semantic labeling method using both data values and contextual information of columns. Our proposed method is based on formulating the semantic labeling task as a structured prediction problem, where we sequentially predict labels for an input table with missing headers. We incorporate both the values and context of each data column using the pre-trained contextualized language model, BERT. To our knowledge, we are the first to successfully adapt BERT to solve the semantic labeling task. We evaluate our approach using two real-world datasets from different domains, and we demonstrate substantial improvements in terms of evaluation metrics over state-of-the-art feature-based methods.
Deep Reinforcement Learning for Energy-Efficient Multi-Channel Transmissions in 5G Cognitive HetNets: Centralized, Decentralized and Transfer Learning Based Solutions

Energy efficiency (EE) constitutes a key target in the deployment of 5G networks, especially due to the increased densification and heterogeneity. In this paper, a Deep Q-Network (DQN) based power control scheme is proposed for improving the system-level EE of two-tier 5G heterogeneous and multi-channel cells. The algorithm aims to maximize the EE of the system by regulating the transmission power of the downlink channels and reconfiguring the user association scheme. To efficiently solve the EE problem, a DQN-based method is established, properly modified to ensure adequate QoS of each user (via defining a demand-driven rewarding system) and near-optimal power adjustment in each transmission link. To directly compare different DQN-based approaches, a centralized (C-DQN), a multi-agent (MA-DQN) and a transfer learning-based (T-DQN) method are deployed to address whether their applicability is beneficial in the 5G HetNets. Results confirmed that DQN-assisted actions could offer enhanced network-wide EE performance, as they balance the trade-off between the power consumption and achieved throughput (in Mbps/Watt). Excessive performance was observed for the MA-DQN approach (>5 Mbps/Watt), since the decentralized learning supports low-dimensional agents to be coordinated with each other through global rewards. In further comparing the T-DQN against MA-DQN solutions, T-DQN presents beneficial usage for very low or very high inter-cell distances, whereas the usage of MA-DQN is preferred (by a factor of ~1.3) for intermediate inter-cell distances (100-600m), where the power savings are feasible towards achieving increased EE. Furthermore, T-DQN scheme guarantees good EE solutions (above 2 Mbps/Watt), even for densely-deployed macro-cells, with effortless training and memory requirements. On the contrary, MA-DQN offers the best EE solutions at the expense of massive training resources and required training time.
SpectroTemporalNet: Automated Sleep Stage Scoring with Stacked Generalization

Polysomnography (PSG) has become a pivotal diagnostic tool in sleep medicine. The scoring obtained on the resultant data from sources including Electroencephalograms (EEG) for these studies are interpreted by Sleep Disorder Specialists (SDS) to identify various sleeping disorders. However this process is tedious and requires highly skilled experts. With the recent advent of Deep Learning techniques, neural networks can be utilized to assist in finding such irregularities in sleep data. This paper proposes a Deep Learning based ensemble model called SpectroTemporalNet for automating the process of sleep stage scoring from single-channel raw EEG data. SpectroTemporalNet uses Stacked Generalization to achieve a better annotation performance as compared to that of existing literature with an overall classification accuracy of 94.31% and a sensitivity of 94% on the test set. The proposed model utilizes both the spectral and the temporal features of EEG data simultaneously thereby improving automated sleep stage scoring.
A Semantic Approach for Automated Rule Compliance Checking in Construction Industry

Automated Compliance Checking (ACC) of building/construction projects is one of the important applications in Architecture, Engineering and Construction (AEC) industry, because it provides the checking processes and results of whether a building design complies with relevant laws, policies and regulations. Currently, Automated Compliance Checking still involves lots of manual operations, and massive time and cost consumption. Additionally, some sub-tasks of ACC have been researched, while few studies can automatically implement the whole ACC process. To solve related issues, we proposed a semantic approach to implement the whole ACC process in an automated way. Natural Language Processing (NLP) is used to extract rule terms and logic relationships among these terms from text regulatory documents. Rule terms are mapped to keywords (concepts or properties) in BIM data through term matching and semantic similarity analysis. After that, according to the mapped keywords in BIM and logic relationships among keywords, a corresponding SPARQL query is automatically generated. The query results can be non-compliance or compliance with rules based on the generated SPARQL query and requirements of stakeholders. The cases study proves that the proposed approach can provide a flexible and effective rule checking for BIM data. In addition, based on the proposed approach, we also further develop a semantic framework to implement automated rule compliance checking in construction industry.