Miracle-dz/UniFAR-FTUs huggingface dataset:
A High Gain and High CMRR Chopper Amplifier Using Current Splitting OTA for Biomedical Applications

This paper proposes a capacitively-coupled current splitting OTA designed specifically for biomedical recording applications, focusing on achieving power efficiency, low noise, and high swing. The utilization of contemporary current splitting and current scaling techniques in the current mirror has resulted in a highly favorable balance between power consumption and noise levels. The design being discussed is characterized by its simplicity since it does not have a cascode transistor. Despite this simplicity, it can achieve an open-loop gain of over 60 dB without incurring any additional power consumption. Consequently, the suggested configuration exhibits a superior power efficiency factor (PEF) and an enhanced output swing. To mitigate flicker noise and optimize the balance between power consumption and noise performance, PMOS transistors operating in the sub-threshold region have been employed, utilizing an optimal size. The amplifier has been built and simulated using the Cadence Virtuoso tool with 0.18 µm CMOS technology. Corner simulations have been conducted to analyze the effects of process variations and mismatch. The amplifier under consideration has a gain and CMRR of 57.06 dB, and 161.7 dB, within its specified frequency range of 0.5 Hz to 10 kHz respectively. The total input-referred noise (IRN) is 14.45 ${\bf{\mu Vrms}}$ within the frequency range of 1 Hz to 10 kHz. The amplifier's power consumption is measured to be 6.17 ${\bf{\mu W}}$ when operating with a supply voltage of $ \pm \;0.8\;{\bf{V}}$. The proposed technique is suitable for a variety of biomedical applications due to its high gain and high CMRR.
Millimeter-Wave Miniaturized Planar Dual-Reflector Antenna via Transformation Optics

A novel millimeter-wave wideband multibeam planar dual-reflector antenna with a three-layered parallel plate waveguide (PPW) configuration is proposed in this paper. To miniaturize the dual-reflector beam-former, a transformation optic (TO) approach is used to flatten the main-reflector contour, which achieves a reduction of 50% on the longitudinal size. The periodic dielectric hole units compose the transformation material filled in the PPW beam-former. Moreover, multiple waveguide feeds following a two-feed-per-beam scheme and a metallic flare acting as the radiator are combined with the dual-reflector beam-former to fulfill the Ka-band multibeam radiation. A wide bandwidth of 32%, stable multibeam radiation with a scanning range of ±42° and a gain of up to 15.3 dBi are realized, which is attractive for future millimeter-wave applications.
Design of Self-Biased Microstrip Double-Y Junction Circulator at Ka Band

In this paper, a self-biased double- Y-junction circulator at Ka band is designed based on the circulator theory. The circulator can achieve the circular effect without an external bias magnetic field. By comparing with the traditional circulator with biased magnetic field, this design of the circulator is significantly smaller and lighter, and easy to be integrated. The simulation software results indicate that the operating frequency is at Ka band, the insertion loss is below 1.5 dB, VSWR is below 1.2, the isolation is greater than 18 dB, relative bandwidth is greater than 5%.
Development of Grid Connected Solar PV Power Management Controller with Internet of Things-Based Real Time Data Checking

The power supply is the de-facto standard of any electrical and electronic appliances that require operation and control, and it is all dependent on it. Batteries have been extensively used for many years and decades as a leading source of energy storage for diversified electrical and electronics systems such as electric vehicles, electronics systems and devices, communication systems, electric trains and ships, power systems, and most notably renewable energy systems. Nonetheless, when the energy storage is wearied out, the external AC power source is essential. In recent years, external energy dilemmas and increasing environmental concerns have pushed stakeholders and policy makers to invest in renewable energy systems, such as solar PV systems. Further, the advent of the Internet of Things achieved great attention from developers, programmers, and engineers in recent years. In this work, the development and design of grid-tied solar PV power management controller with IoT–based real-time data checking is presented. The whole prototype model is portrayed by the battery energy storage connected to the source utility AC power line through the AC/DC converter, and a solar photovoltaic panel buttoned with a DC/DC power converter. The intelligent energy management system for the overall prototype operation, administered by the ESP32 microcontroller, and the Internet of Things platform was used for the real-time health condition monitoring with the aid of the Android-based mobile phone. The experimental results reveal that the prototype and system model are compelling.
Integrated Energy System Energy Conversion Based on Data Analysis to Promote Optimal Scheduling of Wind Power Consumption

In the integrated energy system, there is a problem of insufficient new energy consumption caused by the constraint of "determining electricity based on heat" in cogeneration. A comprehensive energy system source load coordination and optimization scheduling method that takes into account the conversion of supply and consumption energy to promote wind power consumption is proposed. Under the guidance of multiple energy Price signal, a comprehensive demand response mechanism with energy conversion is formed to coordinate the grid connected consumption of wind power. Finally, a comprehensive energy system source load coordination optimization model is constructed that takes into account user satisfaction with comprehensive energy consumption, with the goal of minimizing system operating costs and optimizing unit output at each time period. The calculation results show that this method can promote wind power consumption while reducing system operating costs; The comprehensive demand response mechanism with energy conversion has a smaller impact on user satisfaction and is more in line with the actual energy consumption of users.
Microcavity with Output Waveguide by Different Connection Angles

The present paper does a theoretical and numerical investigation of a circular microcavity connected with an output waveguide using different connection angles. The simulation results indicate that, the proportions of the WGM components, which constitute the high-Q lasing mode, change with the different connection angle between the microcavity and the output waveguide. By finely adjusting the connection angle, the field in the cavity can have one dominant propagation direction leading to increment of the output efficiency and improvement of the far-field pattern. The propagation constant of the lasing mode and waveguide fundamental mode can achieve the same direction under certain connection angle resulting in stronger coupling between the lasing mode and the waveguide fundamental mode, so that the circular microcavity can achieve unidirectional output emission with narrow angular divergence.
Super Deep Learning Ensemble Model for Sentiment Analysis

Sentiment analysis, the automated task of discerning and categorizing sentiments conveyed in text, has seen remarkable progress recently, primarily owing to the emergence of deep learning techniques. However, conventional deep learning models continue to grapple with accuracy and resilience limitations, stemming from their inherent deficiencies and biases. To address these constraints, we propose an innovative Super Deep Learning Ensemble Model (SDL-EM) tailored for sentiment analysis. The SDL-EM is crafted to exploit the complementary strengths of various deep learning architectures and harness ensemble learning's potency to enhance sentiment classification accuracy. This model comprises an assortment of base learners, encompassing Convolutional Neural Networks (CNNs) and varieties of Recurrent Neural Networks (RNNs), each trained on distinct feature representations of the input text. To construct the ensemble, we introduce a fusion mechanism that dynamically amalgamates predictions from individual base learners using a Multi-Layer Perceptron Model. Subsequently, we subject the resulting model to testing using the evaluation dataset, yielding discernible outcomes. To substantiate the efficacy of our SDL-EM proposal, we conduct thorough experiments on a widely recognized sentiment analysis dataset. The results unequivocally substantiate our model's superiority over state-of-the-art deep learning models, manifesting in elevated accuracy, recall, precision and Fl-score metrics. This endeavor contributes to the advancement of sentiment analysis, providing pragmatic solutions for scrutinizing sentiments within extensive textual datasets, characterized by enhanced performance and generalization capabilities.
Profanity Detector on Twitter Using Naive Bayes and Decision Tree

In modern times, profanities can be found all over the internet including social media platforms like Twitter. The study will compare the performance of Naive Bayes and Decision Tree algorithms, which are widely used in text classification tasks. The dataset for training and testing the profanity detector will consist of an extensive collection of tweets labeled as offensive or not offensive. The dataset will be preprocessed to be used to train the models of classification. The Decision Tree achieves an accuracy of 0.94, while Multinomial Naïve Bayes achieves an 0.92 accuracy.
Finger Rehabilitation Training Robot Mechanism Design

In response to the urgent need of finger rehabilitation medical treatment for finger injury or stroke patients, an exoskeleton finger rehabilitation training robot driven by pneumatic tendon was designed. Firstly, the exoskeleton finger actuator was designed and the three-dimensional model of the whole machine was established; then, the dynamics and static analysis of the finger exoskeleton mechanism was carried out, and finally, the mathematical model established was solved by Matlab and simulation verification was carried out by using Ansys, and the results showed that the designed robot could drive the finger flexion and extension well to achieve the effect of finger rehabilitation training.
Autism Prediction Using CNN VGG16 and T-SNE Visualisation

A pervasive disorder, autism is typically described as such. Individuals with Autism spectrum disorder (ASD) generally experience challenges in social communication. Also they exhibit various behavioral problems, hyperactivity, and various challenges while responding to actions. By using different facial images, a new architecture was developed for the diagnosis of developmental disorders. Here in this architecture Deep Convolutional Neural Networks (DCNN) used for extracting the various facial features from the images. The aim of this study is to develop a real-time neurological structure model which uses facial image recognition technology to diagnose autism spectrum disorder (ASD). ).
Enhancing PROBA-V Satellite Imagery for Vegetation Monitoring Using FSRCNN-Based Super-Resolution

Super-resolution techniques play a crucial role in improving the quality of photographs, especially those taken with lesser resolution or from a distance using cameras or sensors. Improving these images brings a huge improvement in their clarity and fine details, which directly affects the usefulness and worth of remote sensing data products. Based on the available input data, image super-resolution techniques can be divided into two main types. The goal of single-image super-resolution is to convert one low-resolution picture to its equivalent high-resolution image. Multi-image super-resolution, on the other hand, uses a set of low-resolution photos that capture the same object or scene in order to produce a higher-resolution image by mapping the original low-resolution photos. Both techniques make use of generative adversarial networks (GANs) to create enhanced or completely new images and convolutional neural networks (CNNs) to extract important image attributes, playing pivotal roles in this enhancement process.
An Antenna Element and Subarray Design for 5G Massive MIMO Systems

This paper designs a base station antenna element which is easy to manufacture. Through metal plate stamping manufacturing that is a cost-effective way, the element greatly simplifies the manufacturing process and reduces costs while maintaining high performance standards. With differential feeding, the element achieves excellent cross-polarization characteristics, maintaining axial cross-polarization (X-pol) levels below -20 dB over the entire frequency range, resulting in improved signal reception and transmission efficiency. Moreover we developed a one-to-three feed network for the designed antenna element. This network ensures amplitude and phase balance for each subarray and delivers excellent performance within its operating range (2515MHz-2815MHz), with return loss below -10 dB and isolation exceeding -20 dB.
Energy-Efficient CNTFET-RRAM Based Ternary Logic Design

The ever-increasing demand for portable electronic items has led to new innovations in ultra-low power VLSI design. The Multivalued Logic (MVL) is a very potential alternative to the energy consumption due to interconnect issue in binary logic design. This paper proposes new ternary circuits using Carbon-nanotube-field-effect transistors (CNTFET) and resistive random-access memory (RRAM). This paper aims at optimizing their energy efficiency by mitigating the power delay product (PDP). The proposed circuits include the standard ternary inverter (STI) and 1-bit ternary half adder (THA). The proposed circuits are simulated at a VDD of 0.9V and their energy efficiency is compared with some recent designs in the literature. The simulations are carried in Synopsis HSPICE tool using standard 32nm CNTFET and Stanford/ASU RRAM model files. The power consumption and PDP of the proposed STI is 42.62% and 56.57% less than the least power consumption and PDP of other designs under considerations. The power consumption and PDP of the proposed THA is 32.88% and 66.44% less than the least of these two metrics of other designs under consideration.
Urban Daily Water Supply Forecasting Based on the LSTM-AM Model

Effective forecasting of urban water supply is the premise and basis for urban water supply management. The long short-term memory (LSTM) network tends to ignore the importance of data information, so this paper proposes an urban daily water supply forecasting model based on the combination of LSTM and attention mechanism (AM). The original water supply data of a water plant in Zhuzhou City, Hunan Province is used as the study object. First, the raw data is pre-processed using the Savitzky–Golay filter for smooth denoising, and then the LSTM network is used to pick up the trajectory features from the water supply data. After, the AM network is introduced to extract the crucial feature information and automatically fit the weights to strengthen the ability of the model to obtain the key feature information of the data on water supply. Experimental results display that the root mean square error indexes of the proposed LSTM–AM model are reduced by 38.87% and 12.25% compared to the typical LSTM and CNN–LSTM models, respectively.
Intervention of Internet of Things in Home Automation Applications

The Internet of Things (IoT) has been widely regarded as the driving force behind the transformation of our daily lives. Home automation, which is a key application of IoT, is becoming more prominent with the advent of smart homes. This trend is particularly evident as a growing number of households are seeking to integrate smart home automation systems into their daily routines. The concept behind IoT is simple, it involves the connection of daily life devices to the internet and vice versa, thus enabling them to communicate and carry out tasks autonomously or through remote commands from the user. IoT's intervention homes is vast and has several aspects of applications. In this study we will be discussing a few of those applications including home automation for control of home appliances without physical presence, monitoring of garden plants and autonomous or semi-autonomous irrigation systems, home security and surveillance using sensors for real-time alerts and alarms in case of unauthorized access.
Trajectory tracking of intelligent vehicle systems based on sampled data and adaptive dynamic programming

With the development of intelligent driving technology, the trajectory tracking of intelligent driving has been studied by many researchers. And the actual intelligent vehicle system is a hybrid system with sampled data. Consequently, a controller with sampled data based on the framework of adaptive dynamic programming (ADP) for trajectory tracking of the intelligent vehicle is proposed. The structure of this paper is as follows. First, the intelligent vehicle model is established in accordance with the frenet coordinate frame. Second, the closed-loop stability of the intelligent vehicle system with sampled data is analyzed by a new technique, and then the maximum sampling period can be obtained. Third, a controller composed of a forecasting module, a feedforward controller, and an actor-critic controller based on the framework of ADP is proposed. Last, the simulation result based on Carsim illustrates the validity of the control strategy.
U-Net-based Semi-supervised learning Transformer for the segmentation of Epicardial Adipose Tissue (EAT)

A small amount of fat called epicardial adipose tissue (EAT) surrounds the heart muscle. Since differences in volume and size are closely related to cardiovascular problems, EAT segmentation is extremely important among those who work in the cardiovascular field. The complexity of this segmentation process, however, makes it more difficult. Several deep-learning techniques have been utilized successfully to allay this worry. However, to segment an image effectively, one must have a thorough understanding of its global information as well as its position and spatial information. The unique U-Net semi-supervised transformer network presented in this paper specifically aims to accomplish this. The segmentation procedure was much improved with the method. The segmentation procedure was much improved with the method. Using Coronary Computed Tomography Angiography(CCTA) data, the suggested model is assessed with average dice scores of above 92%.
A Printed Antenna Array for Vehicular Applications

A two-element array of miniaturized printed radiators for vehicular communication has been presented in this article. The IEEE $\boldsymbol{802.11 p}$ standard specifies that the operational range is within the 5.85 to 5.925 GHz frequency. An 18% miniaturized antenna has been derived from a concentric-ring-based architecture, where the outside ring is directly excited while the core circle is stimulated parasitically. Subsequently, four bow-tie-shaped structures are carved from the outside circles to augment the antenna gain. Moreover, a cross-rectangular slotted structure has been precisely carved from the parasitic circle to achieve resonance of the structure at the intended frequency. The use of the Wilkinson power divider is applied in the design of the feed network of an array to get a significant degree of isolation. The development of antennas in CST Microwave Studio involves the usage of a single-layer PCB made of FR-4 laminate material with a relative permittivity of 4.4. The antenna and the array provide a gain of 6.75 and 10.00 dBi, respectively, and exhibit bandwidth coverage exceeding 100%. Both antennas exhibit a low amount of cross-polarization along the primary beam direction. Both antennas exhibit significant radiation levels and have acceptable aperture efficiencies, respectively. Due to its minimum correction factor values, the antenna exhibits a reduced electromagnetic interference (EMI) and electromagnetic compatibility (EMC) impact. Both antennas are viable options for vehicle applications.
H∞ State-Observer Controller Synthesis for Multiple Time-Delay Systems

In order to achieve the desired operational state using a state feedback controller for nonfully measured states, it is necessary for the controller to possess information regarding all states. The unmeasured states are estimators and inputs to the controller. This article aims to provide a synthesis of the controller and observer, ensuring closed-loop stability and fulfilling performance requirements in the presence of disturbances. The closed loop asymptotic stability of multiple state delayed systems can be ensured by a sufficient condition stated in a linear matrix inequality (LMI). This condition satisfies the H∞ norm bound constraint. The proposed technique is demonstrated numerically to design observer-based controllers for commonly used dynamic models in literature. The simulation results demonstrates that the controllers have the capability to drive the states towards the origin.
Study On the Application of Xenogeneic Acellular Dermal Matrix Combined with Human Epidermal Growth Factor in Repairing Oral Superficial Soft Tissue Defects Based on Big Data Analysis Technique

Objective: This study aims to assess the efficacy of using xenogeneic acellular dermal matrix in combination with human epidermal growth factor for the repair of oral superficial soft tissue defects. Methods: Natural language processing techniques were employed to extract curative effect indices from the unstructured medical records of 560 patients with oral superficial soft tissue defects. A comprehensive analysis and comparison of the therapeutic effects of utilizing xenogeneic acellular dermal matrix combined with human epidermal growth factor versus iodoform gauze ball were conducted through big data integration and an Analytic Hierarchy Process (AHP) evaluation model. Results: The treatment involving xenogeneic acellular dermal matrix combined with human epidermal growth factor outperformed the application of iodoform gauze ball compression suture for repairing oral superficial soft tissue defects. Conclusion: The use of xenogeneic acellular dermal matrix combined with human epidermal growth factor represents a safe, minimally invasive, and effective approach for repairing oral superficial soft tissue defects.
Hopfield Neural Network Love It or Leave It for Classification

Classification problems play an important role in machine learning and data mining. It is used in many areas such as classification problems, disease diagnosis, customer forecasting in companies, drug research, emotional evaluation. Its applications span various fields and contribute to advancements in both theory and practice. For tasks like character recognition, postal code sorting, and handwriting recognition in optical character recognition (OCR) systems, it may be necessary to achieve higher classification accuracy with less processing. In this study, the classification of two distinct classification features-handwritten digits and skin cancer lesions-is done in order to evaluate the effectiveness of the Hopfield neural network (HNN). HNN is the one of the recurrent neural network consists of single layer where every neuron is connected to all other neurons except itself. The HNN architecture's simplicity causes the complexity to decrease. The HNN model conducts the investigation using the MNIST database. The dataset contains handwritten digits from 0 to 9 in 60,000 training photos and 10,000 testing order to classify skin cancer lesions, an ISIC combination database made up of seven different classes produced an overall average accuracy of the training and validation accuracy of 16.3 percent and 15.1 percent, respectively because it captures the pattern's structure, the performance study of the HNN model yields greater accuracy for digit classification than it does for skin cancer, which lacks any predetermined patterns. The study of HNN is expanded using this technique in order to produce better categorization methods and outcomes.