Miracle-dz/UniFAR-FTUs huggingface dataset:
Surveillance to Detect and Classifying of Chicken Poultry Diseases from Fecal Images Using CNN

The growing demand for livestock products among people exerts pressure on the agriculture sector, especially in chicken poultry industry, to produce more. Expanded poultry farming can result in elevated spread of various kind of infectious diseases in chickens, that can cause extensive chicken mortality and large financial damages. In order to identify various diseases in chickens, a variety of techniques are utilized, such as measuring the number of oocytes in the feces or intestinal tract, looking for viruses, and performing polymerase chain reaction (PCR) tests, which call for various diagnoses. Farmers lose a lot of domesticated birds due to delayed diagnosis or a lack of reliable specialists. By analyzing images of chicken droppings, utilizing machine learning as well as artificial intelligence techniques based on computer screening and image analysis, it is possible to quickly identify the most prevalent illnesses afflicting hens. In this paper, through the classification of healthy and harmful fecal pictures various deep convolutional neural network (CNN) model was discussed to identify poultry diseases.
ResNet/ResNetV2 Supported Framework for Rice-Plant Disease Detection Using Leaf Data

Computer algorithm supported data-analysis is one of the common practices to solve the chosen data-evaluation tasks. Recently, the computer algorithm assisted image-evaluation is emerged as one of the capable research field. The purpose of this research is to use leaf information to create a deep learning scheme to investigate rice plant disease (RD). This approach consists of three stages: (i) gathering and resizing leaf images; (ii) extracting deep features using selected DS; and (iii) using SoftMax based binary classification with 5-fold cross validation. In this work, 1000 photos from each class are examined, and the categorization result that is obtained is confirmed. This study considered the ResNet and ResNetV2 variants for the examination and the achieved result is separately verified for 50, 101 and 152 layered schemes. This investigation task confirms that the ResNet variants provided >91% accuracy and the ResNetV2 variants provided an accuracy of >94%. This demonstrates that the proposed method performs satisfactorily on the selected leaf data, and going forward, real-time data may be taken into consideration to validate the technique's effectiveness for RD detection.
Time Series Prediction Integrated RNN-DRL for Energy-Emission Management in HEVs

Learning-based strategies have gained widespread adoption in the field of energy management strategy (EMS) for hybrid electric vehicles (HEVs). However, existing algorithms often struggle to maintain the effectiveness in different driving scenarios. Additionally, achieving a fully observable Markov decision process in real-world situations is impractical due to the limitation in sensor sensitivity and noise. Therefore, it is crucial to design a stable EMS for HEVs that can adapt to various driving scenarios. This paper proposes a novel approach called the twin-delayed deep deterministic policy gradient with a long-short-term combined algorithm. It aims to enhance the generalization performance and EMS capability of HEVs’ learning-based EMS by integrating temporal predicted information using a recurrent neural network. Comparing this algorithm with other established deep reinforcement learning algorithms, the results demonstrate significant improvements in EMS performance for HEVs, including state of charge maintenance, energy conservation, and emission reduction. Notably, the algorithm achieves these improvements while ensuring robust generalization across diverse driving cycles and scenarios.
Full-Range Thermal-Optimal Modulation for Reactive Capacity Enhancement of Wind Power Converter During LVRT

Wind power converters need to abide by the Low Voltage Ride-Through (LVRT) demands of grid code during faults, and a reactive-dominated current output is needed according to the sag depth. The output capacity of converter is mainly restricted by junction temperatures of IGBT, for which a temporary enhancement of the reactive capacity is possible. However, the existing LVRT strategies merely consider the rated capacity rather than actual thermal constraints, and only the deepest sag with pure reactive power factor is studied. This paper proposes a full-range thermal-optimal modulation for wind power converter, which is implemented by clamping the vicinity switching intervals to the current peak. Thus, the thermal loss is minimized within the full range of sag depths and power factors. The reactive enhancement of the proposed modulation is also demonstrated by theoretical analysis, and further verified by simulation results.
Functionalized ZnO Nanowires for Biosensing Applications

In this paper, we demonstrate the immobilization of carboxylic acid moieties on the surface of ZnO and silicon nanowires and its potential for biosensing applications. We functionalize ZnO and Si nanowires that were synthesized with the CVD technique. We use spectroscopic techniques to determine the nature of bonding and orientation of oleic acid molecules at the nanowire surface, which is used as a model system. Furthermore, based on photoluminescence data on modified and unmodified ZnO nanowires with oleic acid, we propose a generic approach to sense biomolecules along with a mechanism of sensing.
Optimizing Real- Time Threat Detection with Feature Engineering Strategies for Intelligent NIDS

The escalation of cloud-based network attacks has been observed to be in tandem with the rapid proliferation of cloud-based services worldwide. It has highlighted current network intrusion detection systems' (NIDS) inadequacies in adequately safeguarding against distributed security incidents, particularly within the framework of Internet of Things (loT) networks. The significance of intrusion detection systems in safeguarding computer security cannot be overstated, and considerable research endeavours have been directed toward enhancing NIDS methodologies to counteract network interruptions. Learning-based methods to improve detection accuracy by eliminating correlated, recurring, and irrelevant features are a significant area of emphasis in advancing NIDS. Prior studies have investigated the integration of Machine Learning (ML), Dimensionality Reduction (DR), and Deep Learning (DL) methodologies to enhance the accuracy of classification outcomes on Network Intrusion Detection System (NIDS) datasets. The variability in selected features, design, and kinds of attacks among NIDS datasets requires thoroughly investigating these techniques across diverse datasets. The objective of this article is to furnish an overview of prevailing frameworks through the utilization of benchmark datasets to detect atypical attacks and obtain a comprehensive understanding of the present status of Network Intrusion Detection Systems. The efficacy of current Network Intrusion Detection Systems is assessed by categorizing feature characteristics and applying diverse machine learning methodologies. The study delves into the topic of feature selection techniques as a means of augmenting anomaly detection. Additionally, they investigate the pre-processing of Internet traffic data. Furthermore, the article explores the potential of conceptualizing virtualized real-time Intelligent NIDS using feature engineering techniques. This paper enhances comprehension of NIDS by consolidating varied research findings, thereby furnishing valuable perspectives for creating more resilient and efficient intrusion detection systems.
Reconfigurable Modular PV Converter for Flexible Overcurrent Capacity

With the increase in photovoltaic (PV) generation penetration ratio, PV generation systems have been widely applied. The existing topologies of PV generation units have fixed overcurrent capacity for each conversion stage, making it difficult to respond to various disturbances on PV and grid sides. This paper proposes a family of reconfigurable modular PV converter with flexible modules switching between the PV, grid, and energy storage (ES) sides via flexible switches. We further study an optimal topology configuration for a non-ES case. It control system and flexible module dispatch strategy are proposed to maximize the utilization of hardware resources under various conditions. The proposed topology and its control strategy enhance the flexibility of the PV generation unit and its overcurrent capability by dispatching optimal numbers of flexible modules in different conversion stage to deal with various disturbances on PV and grid sides. The proposed topology and control system are verified by simulation in MATLAB/Simulink.
Design and Performance Analysis of 11-T SRAM Cell with BTI Reliability

The standard 6T SRAM cell stability decreases due to diminished size. The challenges in 6T SRAM are power consumption high, more delay and noise margin in static state low package density. The 11-T cell can be used at low supply voltages but similar operation of writing as of 6T cell and the major disadvantage is its read/write access delay and more area. In order to enhance the write operation of SRAM cells and address the limitations associated with the 11-T cell configuration, two novel 11-T SRAM topologies are currently under development. These topologies involve the incorporation of supply-cut-off and ground-cut-off techniques. However, as the physical distance between transistors decreases, there is a heightened risk of multi-bit soft errors or upsets occurring within the cell. To mitigate this issue, the Bit Interleaving Architecture is being employed. One of the principal reliability concerns in this context is Bit Temperature Instability (BTI). Strategies to mitigate the impact of BTI in MOS transistors include improving the threshold voltage. The implementation of these advanced 11-T SRAM cell designs promises several benefits, including improved write/read margins, reduced leakage power, increased operational speed, and decreased write/read power consumption. Comparative assessments are being conducted with both 6T and conventional 11-T SRAM cells to validate these improvements.
Fine-tuning Transfer Learning for Knock Intensity Modeling of an Engine Fuelled with High Octane Number Gasoline

Despite the potential of high octane number (ON) gasoline in knocking prohibition and fuel efficiency improvement, its application to the existing engines requires recalibration to release its full advantages, where the optimal control parameters, constrained by knock intensity, are re-modification. To reduce experimental efforts for obtaining knock intensity, this paper proposes a knock intensity modeling approach of fine-tuning transfer learning based on multilayer perceptron for the engine fueled with high-ON gasoline. The multilayer perceptron model is pre-trained with the experimental data of the engine fueled with the base gasoline (92#), which have been obtained during the previous engine development and thus consume no more experimental efforts. Then, the model is fine-tuned with the experimental data of the high-ON gasoline (98#) by freezing the first two hidden layers and updating the weights and biases of the last four hidden layers. By a comprehensive study, the results demonstrate that the developed approach can achieve competitive prediction accuracy whilst significantly reducing experimental efforts for 30% of 98# data.
Current Harmonic Compensation Strategy for Series-Shunt Multiport Soft Normally Open Points

The soft normally open points (SNOP) is introduced to solve challenges raised by renewable energy in the distribution network. Compared with back-to-back scheme, the novel series-shunt multiport SNOP ($\mathrm{S}^{2}$-MSNOP) is proposed with much lower cost and volume, which is suitable for practical implementation. Considering grid application scenarios, $\mathrm{S}^{2}$-MSNOP should be able to compensate for current harmonics. Therefore, this paper gives current harmonic compensation strategy for $\mathrm{S}^{2_{-}}$ MSNOP. It can supply its connected feeder with certain harmonics through coordinate regulation of all parts’ converters. First, the working principle of harmonic compensation is analyzed. Then the control strategy is presented. Finally, a simulation model is built to test the validity of the proposed strategy.
Evaluating the Performance and Reliability of 5G Networks for Data Communications

This article's goal is to analyse the 5G networks' data communication reliability and performance. The prospective effects of this revolutionary new technology on the sector are evaluated through research and analysis. Faster speeds, wider coverage, and reduced latency are just a few of the many advantages that 5G technology offers. This opens the door for the development of new, more effective solutions that have the potential to completely transform the market. The article assesses the possible effects of 5G technology on the creation, adoption, and general use of items relevant to the industry. The article also examines ways to successfully assess the performance and dependability of 5G technology in the context of data communication as well as related issues and considerations.
Convolution Neural Network for Gastrointestinal Cancer Detection and Classification using Deep Learning

Ailments of the gastrointestinal tract (GIT), including bleeding, ulcers, polyps, Crohn's disease, and cancer, are becoming more prevalent. Ulcers and bleeding in the small and large bowels are two of them that are particularly common. Medical experts find the manual diagnostic process to be both time-consuming and difficult. As a result, experts have suggested computerised techniques for the identification and classification of certain disorders. Medical video endoscopy produces a huge volume of images, thus it takes a long time for specialists to look over them all. Early detection and diagnosis of certain illnesses can result in successful therapy. SqueezeNet, ResNet-101, and DenseNet-169 are three deep learning-based models that we introduce in this paper and assess for their capacity to diagnose a dataset of lower gastrointestinal disorders. There are 5,000 images in the Kvasir dataset, similarly distributed among five different lower gastrointestinal conditions, including ulcerative colitis, polyps, normal cecum, and normal pylorus. The deep feature vector is processed using the softmax activation function, which divides the input images into five groups. Surprisingly, all of the convolutional neural network (CNN) models had exceptional performance, with DenseNet-169 reaching accuracy, specificity, precision, recall, and F1 Score values of 97.8%, 98.07%, 97.5%, 96.02% and 97.6%.
A Wireless Voting System Built With Many Battery-Free Motion-Powered Bluetooth Buttons

The Internet of Things (IoT) has attracted increasing interest from academia and industry. It facilitates many applications, such as the wireless voting system. Yet, current wireless voting terminal devices are mostly powered by batteries. It requires a high maintenance cost for identifying and replacing low-energy batteries. This paper introduces a wireless voting system, which is implemented using many battery-free motion-powered Bluetooth buttons. The tiny power for Bluetooth wireless communication is generated by a local quasistatic electromagnetic mechanical energy harvester and regulated by an ultra-low-cost energy management circuit. The generated voting information is collected and sent to a cloud server for later data visualization. The system has been successfully implemented, tested, and applied in various real scenarios. The battery-free multi-terminal design provides a valuable reference and promising prospect for ubiquitous IoT and pervasive sensing systems.
Influence of the Fluctuating Input on Hydrogen Production by Alkaline Water Electrolysis

Water electrolysis by renewable energy is a popular and promising solution for power-to-hydrogen. Conventional water electrolysis prefers DC voltage which needs converters and filters. However, the fluctuation cannot be completely eliminated and the effect of AC power on electrolyzers may not be entirely negative. In this paper, a multi-physics model for hydrogen production by alkaline water electrolysis has been built. Through the latency of bubble accumulation and the convexity of the I-V curve, it is been proved that the fluctuating input may have a positive effect on the hydrogen production. The transient process of the bubble accumulation has been elaborated. An integrated industrial system for hydrogen production by water electrolysis has been built for experiments. From the results, we conclude that dynamic process of the bubble on the electrode will affect the current dynamic process of the electrolyzer. The fluctuating input promotes the hydrogen production rate and reduces the energy conversion efficiency. In the specific case, a 0.7% loss in efficiency can bring 5.3% generation rate, which may be benefit for the industry.
Ecological effect of Cedar Forest fire on the watershed: A case study

In this study the effect that a large scale forest fire has on the watershed had been studied. For our case study we have taken Cedar fire which started on 25 Oct 2003. A large portion of the fire ravaged area fell under the San Diego river watershed as a result of which it caused an extensive damage to the ecology of the watershed. Due to a forest fire there is a loss of vegetal cover which in turn modifies the processes of interception and evapotranspiration affecting the hydrological properties of the watershed. For the current study we have focused on the effect of the Cedar fire on the suspended sediment concentration at the mouth of San Diego River, and the effect on the Lag time between the precipitation and runoff. Our study shows that the forest fire has led to an increase in concentration of suspended solid concentration after the fire event as compared to the same period during last two years. It also revealed that the fire has decreased the lag time between the precipitation and runoff for the San Diego river watershed. In this paper the ecological impacts of the Cedar forest fire which occurred in California during the year 2003 was taken as a case study. In the current study the effect of the fire on the suspended solid concentration at the mouth of the San Diego River and the changes in the stream flow pattern as a direct result of the fire has been studied. The Cedar fire was one of the worst forest fires in California's history. The total area burnt by the Cedar fire is estimated to be 721,791 acres or about 2921 square kilometers. The study region falls under the San Diego county of California. Forest fires leads to surface erosion which is movement of individual soil particles by removal of material from the soil surface (sheet erosion) or by concentrated removal of material in the down slope direction (rill erosion) or gravity inducted (dry ravel) or by mass movement as landslides and debris flows (Foster 1982). Forested land acts like a sponge, absorbing rainfall and storing the water in the subsurface for a later release. During heavy rainstorms, this slows runoff and
A Design of Intelligent Tire Prototype Based on Multiple Accelerometers

As the only component of the vehicle in contact with the ground, the tire is the key effector to supply the forces involved in vehicle acceleration, deceleration and steering. However, the information about the tire-road condition have always been unknown and could only be estimated from the measurement signals of on-board sensors, which are often accompanied by non-negligible delays and errors. The intelligent tire technology, generally based on accelerometers or strain-gauges glued on the tire, is an effective solution to transform the tire from "passive" to "active" to support the cutting-edge driving assistance systems. Therefore, to fill a gap in the field, this paper proposed a design of intelligent tire prototype based on multiple accelerometers. Further, the details of the construction of the Data Acquisition System are described from both hardware and software. In the end, a specific tire test bench was introduced and utilized to check the validity, and we give the test results in two kinds of experiments to validate the accuracy of the prototype qualitatively and quantitatively, respectively.
OF DISTRACTED DRIVER USING SUPPORT VECTOR MACHINE BASED ON PRINCIPAL COMPONENT ANALYSIS FEATURE

The use of ground transportation in Indonesia especially in major cities like Surabaya has experienced rapid growth. However, this increased usage has also led to a rise in traffic accidents. One of the main contributing factors is driver distraction. Therefore, this study aims to develop a method for detecting distracted drivers using image classification technology with Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) models. In this research, data was obtained from the "State Farm Distracted Driver Detection" dataset, which contains images of drivers who are either distracted or not focused while driving. The initial process involves data preprocessing, such as resizing images to 50 x 50 pixels and dividing the dataset into training and testing data. Next, feature extraction is performed using a CNN model with three convolutional layers, three Maxpooling layers, and one flattened layer. After feature extraction, the Principal Component Analysis (PCA) method is used to reduce the dimensionality of the data. Furthermore, an SVM model was trained using data reduced by PCA with a 60:40 data split. This research conducted a comparison between the use of PCA and not using PCA. Based on the test results, the use of PCA not only improved the classification accuracy to 96.28% compared to 92.46% without PCA but also accelerated the training time to 10.64 seconds from 19.67 seconds without PCA.
A Resonant Switched-Capacitor LLC DCX in Data Center Applications

In this paper, a novel resonant switched-capacitor LLC topology is proposed to serve as the dc transformer (DCX) in data center applications. The proposed DCX is formed by a 2:1 resonant switched-capacitor cell and an LLC converter. To achieve a $\boldsymbol{4n}:\boldsymbol{1}$ voltage step-down ratio, only an $\boldsymbol{n}:\boldsymbol{1}$ turns ratio transformer is required. It presents good soft-charging and soft-switching performances to effectively mitigate charge distribution loss and switching loss. Furthermore, the voltage stresses of primary switches are only half of the input voltage, and the current stresses are also lower than that of conventional LLC converter. The operational principle of steady-state circuit and theoretical analysis are provided to highlight its advantages. To verify the proposed concept, a 300W-rated hardware prototype that converts 48 V to 12 V is designed and tested with 1MHz switching frequency.