Miracle-dz/UniFAR-FTUs huggingface dataset:
The Bovine Major Histocompatibility Complex and Its Role in Tick and Tick-borne Disease Resistance in Indigenous Cattle Breeds in Sub-Saharan Africa: A Review

Ticks present a major challenge in livestock production, given the increased demand for animal protein worldwide. In addition to being vectors of numerous diseases in livestock, ticks cause blood loss, worry and damage hides. Anaplasmosis, Theileriosis, Babesiosis and cowdriosis are amongst the critical tick-borne diseases causing havoc across the world in the livestock industry. Global economic losses caused by these ectoparasites amount to billions of dollars annually. Although there are different methods of tick control, they all have shortcomings which frustrate the efforts of farmers in controlling ticks and tick-borne diseases. This has motivated researchers to search for sustainable alternative methods of tick control which include the genetic selection of naturally resistant breeds. The Major Histocompatibility complex (MHC) also known as the Bovine Leukocyte Antigen (BoLA) in cattle has been associated with tick-borne disease resistance in some cattle breeds. Different breeds have different levels of resistance to ticks and tick-borne diseases. The Bos indicus breeds and their crosses are known to be more resistant to ticks and tick-borne diseases than the Bos Taurus . Given the downsides of acaricides, vaccines and other tick-control methods, the use of tick and tick-borne disease-resistant cattle breeds is a promising choice for tick and tick-borne disease control. This review summarizes the role of the MHC in resistance to ticks and tick-borne diseases in indigenous cattle breeds of Sub-Saharan Africa.
Design and Comparative Analysis of THz Antenna through Machine Learning for 6G Connectivity

The rise of sixth-generation (6G) technology has become increasingly necessary to meet the growing demand for high-speed internet and the continuous advancements in technology. The development of an optimal antenna design is crucial to attain the required performance and capabilities. Traditional electromagnetic modeling approaches for antenna design are, however, time-consuming and computationally intensive requiring long simulation time and high-end computing systems. Therefore, Machine Learning (ML) technology can be utilized to deal with these limitations in the context of Terahertz (THz) antenna design, which has not been done before. The main objective of this work is to develop an antenna that operates in the THz Band, which is the essential 6G band for the future infrastructure revolution, and to predict and optimize the antenna's return loss using ML models like K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XG-Boost), Decision Tree, and Random Forest and Mean Squared Error (MSE) of 3.816. The findings show that all of these models perform accurately, particularly Random Forest having the highest accuracy of 82% in predicting the return loss. ML offers novel possibilities for the development of optimized and efficient 6G antennas for high-speed communication.
A low ripple Frequency-Feedback PFM-PWM Buck converter with seamless mode transition

A low ripple frequency feedback Buck converter is proposed. And a frequency feedback loop control method instead of hysteresis comparator is adopted in the Buck converter to implement PFM modulation, which eliminates the output ripple limitation caused by hysteresis comparator's hysteresis limit. In addition, the PFM mode of frequency feedback is modeled, and it is analyzed that a type II compensation can ensure stability of the PFM mode. Furthermore, an analog control method is also utilized for smooth switching between PWM and PFM modes. The simulations result shows that proposed Buck converter achieves an output ripple of only 4mV under a 0.1mA load.
Vehicle Trajectory Prediction Based on Spatial-aware Transformer

In recent years, deep learning-based vehicle trajectory prediction methods have gained significant attention in the field of transportation. However, most existing approaches rely on RNN-based models, which suffer from issues such as vanishing and exploding gradients when dealing with long sequences. These limitations hinder their performance in practical applications. To overcome these challenges, this paper proposes the application of the Transformer model for vehicle trajectory prediction. By incorporating the multi-headed self-attention mechanism and position encoding mechanism, the Transformer model can effectively capture the temporal relationships within vehicle trajectories and handle long sequence data. Moreover, considering that vehicle trajectories are influenced by surrounding neighboring vehicles, this paper introduces a novel attention mechanism-based spatial information awareness method to extract the impact of neighboring vehicles on the target vehicle. Additionally, the vehicle behaviors are categorized into six maneuvers, and multimodal trajectory prediction is achieved by generating corresponding trajectories and their probability distributions based on these six maneuvers. To evaluate the proposed method, experiments are conducted on the real dataset NGSIM and compared against existing approaches. The experimental results demonstrate that the proposed method surpasses the performance of benchmark methods in terms of prediction accuracy, highlighting its potential and practical value in vehicle trajectory prediction.
A Sustainable Development Goal for a Campus: LED Vertical Illumination for a Classroom

Despite the vast research in Sustainable Development (SD), little is known about providing “sustainable Light Emitting Diode (LED) lighting” for a campus. The overall image that emerges from advertisements is upgrading to LED’s will last a long time. This is a simplistic view of understanding a Sustainable Development Goal (SDG). This misunderstanding is the motivation for this study is to help new graduate students build knowledge about sustainable lighting. Establishing sustainable LED lighting can increase student’s wellbeing, which is a gap in most past research [14]. The primary objective of this study was proven after evaluating the lighting in two classrooms for: (1) economic growth, (2) environmental benefits, (3) and improve students well-being. A LEED certified classroom met all objectives with the lighting levels, mood, and reduced cost. Whereas the non-LEED certified classroom did not meet any objectives or compliances. The larger implications of the findings are that a sustainable LED lighting can benefit any size campus.
A Lung Electrical Impedance Tomography (EIT) Reconstruction Method using Deep Learning

Due to the inherent soft-field characteristics of Electrical Impedance Tomography (EIT) and the nonlinearity of the inverse problem, the lung contour and lesion structure cannot be effectively reconstructed by conventional model-based algorithms. In this study, a direct estimation model based on, Residual Network (ResNet) is proposed to improve the reconstruction accuracy of lung contour and lesion structure. The imaging results indicate that the contour of the lung and the position and size of the lesion can be reconstructed more accurately than the Tikhonov Regularization (TR) algorithm, Conjugate Gradient (CG) algorithm and 1D-CNN.
A hardware-efficient FPGA cochlear model for next generation nonlinear cochlear implant

We proposed a hardware efficient unified non-linear model of the cochlea based on ergodic sequential logic. It is shown that the proposed cochlear model is able to reproduce the distortion product of a cochlea of mammals, which is one of the most famous non-linear sound processing functions of the cochlea of mammals. Furthermore, the proposed unified model of cochlea and an ordinary differential equation (ODE) model based on Hopf bifurcation are implemented on FPGAs and it is shown that the proposed cochlear model uses a smaller number of circuit elements for the FPGA implementation and has a lower power consumption property than the ODE cochlear model.
Affirmative action and long-run changes in group inequality in India

Research on caste-based inequalities in India has generally focused on differences between large categories such as the Scheduled Castes, the Scheduled Tribes, and the remainder of the population. We contribute to the literature on horizontal inequalities in India by looking within these groupings, and studying differences between the individual jatis that comprise these categories. Using census data, we find evidence of persistent inequalities in educational outcomes between the jatis, suggesting that socio-economic hierarchies have proved to be stable throughout the post-Independence period. Indeed, the evidence points to divergence: communities with more education in 1961 also had higher educational attainment in 2001. Also, while numerically larger Scheduled Caste communities witnessed greater improvements in educational levels compared to smaller ones, this was not true for the Scheduled Tribes. This may be the result of their greater political mobilization.
Millimeter-Wave Channel State Information Acquisition on PXI-Based Testbed

This paper introduces mmWave channel state information (CSI) acquisition testbed for the demonstration of several algorithms in mmWave communications research fields. The testbed comprises multiple field-programmable gate arrays (FPGA), a radio frequency (RF) converter, and a phased array antenna for mmWave signal generation and high-speed data processing. We also propose system design and implementation for the control of mmWave CSI acquisition testbed by NI LabView programming software. The experimental results show that received mmWave pilot signal data is adequately accomplished via the proposed testbed, which is an important prerequisite for channel estimation.
FlsGraph: A Parallel Architecture for Large-scale Graph Processing

In recent years, graph processing of large-scale data sets becomes a challenge issue to be solved in data center applications. In this work, we propose a method for subgraph partitioning of graph data sets based on grouping evenly by edge (referred to as GEE), which can significantly reduce the waiting time for data synchronization in parallel processing of multiple subgraphs. Based on this partitioning method, we propose an architecture called FlsGraph. In FlsGraph, the speed of data access from DRAM is improved by optimizing the internal structure of processing elements. The increase in the total number of iterations caused by the inability to synchronize vertex updating between subgraphs is minimized by optimizing the way how synchronous update is carried out. Our simulation shows that compared to the state-of-art FPGA accelerator, FlsGraph uses only 43.8% size of on-chip SRAM while achieving the BFS speedup 1.04x to 3.12x, the PageRank speedup 0.89x~2.86x, the WCC speedup 1.37x to 4.13x.
Research on face expression recognition with subdivision structure based on depth learning

As an important part of emotion analysis and computation, facial expression recognition has been widely studied and applied in many fields, such as education, medical treatment and so on. At present, the method of facial expression recognition based on depth learning has the advantages of automatic extraction of facial expression features, fast computation speed and high recognition accuracy, and has become the main research direction of facial expression recognition. However, current convolutional neural network can not accurately extract facial features in key areas. Another difficulty in the task of facial expression recognition is that facial expressions change slightly, and different expressions are similar and difficult to distinguish, and different individuals express expressions in different ways, this makes it difficult to improve the accuracy of facial expression recognition. Based on the theory of deep learning, the methods and applications of convolutional neural network in facial expression recognition are studied in this paper. The main research contents of this paper are as follows: aiming at the polymorphism of facial expression images in the real world, the different expression modes among individuals, the similarity of different expressions and the difficulty in distinguishing them, etc., a method of facial expression recognition based on subdivision structure is proposed. This method first makes a rough classification of the facial expression images on the REXNET-F network, and then uses the subdivision structure to compute the class center of each class during the training process of the convolutional neural network, using the information of the context, therefore, the facial expression images which were misclassified before were corrected and the effect of facial expression polymorphism on recognition task was weakened. On RAF-DB and FERPLUS data sets, the accuracy of facial expression recognition was 88.00% and 88.50%, respectively.
A Smart Speaker Lamp for Assisting Bedtime Smartphone Non-Use: A Feasibility Study

Smartphone use before and during bedtime has been pointed out as one of the major contributing factor of sleep disorders. In this paper, we designed a smart speaker lamp to support smartphone non-use at bedtime. It uses two devices with three delivery channels (smartphone screen notification, smart speaker sound & light) for intervention delivery at three different time periods (pre-bedtime preparation, pre-bedtime, bedtime). In our feasibility study with 30 participants, we measured perceived behavioral adherence, awareness, and appropriateness of different intervention periods/methods. The result showed the perceived adherence and awareness from the intervention as around moderate, and the deliveries are above moderately suitable for behavioral change. The sound from the smart speaker lamp was indicated acceptable compared to the sound from the smartphone and assessed the light-encoded information as highly interpretable. Five participants were randomly selected and interviewed for further implications from the results and to suggest the system’s future directions.
Multiple Pedestrian Object Recognition to Correct User Direction of Vision-Impaired Assistive Device

Pedestrian symbols inherently have specific relative positions concerning one another, as perceived by individuals without visual impairments. Developing a vision-impaired assistive device capable of detecting these relative positions of pedestrian objects could effectively guide users in the correct direction when crossing pedestrian roads. For instance, it is commonly observed that pedestrian lights are positioned above pedestrian lanes. This study demonstrated this concept by employing multiple object recognition architectures, such as YOLOv8 and Faster RCNN, to detect the presence of pedestrian lights in green or “go” status and the existence of pedestrian lanes. YOLOv8 performed better with 88.8% precision and 90.8% recall than Faster-RCNN with only 83.6% precision and 86.2%, respectively. Furthermore, the relative positions of these elements are estimated using the centers of the generated proposals or bounding boxes. Several facing direction scenarios are illustrated using the developed difference method that systematizes the recognition of object cues for a person with normal vision. By integrating this into a visually-impaired assistive device, it is now possible to adjust the user's facial direction, thus ensuring safe pedestrian crossing.
A SPAD Relative Address Coding for Lateral Resolution Improvement in Coincidence Detection

In the readout integrated circuit (ROIC) with coincidence detection, a macro-pixel internal single photon avalanche diode (SPAD) relative address coding scheme is proposed for lateral resolution improvement. Conventional coincidence detection scheme suffers from lateral resolution deterioration due to combining multiple SPADs as one pixel. By distinguishing the triggered SPAD from others, the proposed SPAD relative address coding scheme can obtain independent address of SPADs within macro-pixel. A prototype ROIC with macro-pixels containing 2×2 SPADs was designed by TSMC 180-nm CMOS process with improved lateral resolution of 4 times.
Deep Skin Cancer Lesions Classification Scheme

Skin cancer has risen to be one of the significant forms of cancer worldwide. However, the traditional means of skin cancer detection requires manual examination by an expert, which is often time-consuming. Recently, computer-aided methods have garnered much traction in the research community and among medical practitioners. This paper proposes using a deep-learning model to classify skin lesions into seven classes. The model is a fine-tuned 16-patch vision transformer (ViT) model and a custom classifier. The custom classifier consists of a convolutional computation fitted to the end of the ViT model. Experimental results show that the proposed scheme can classify various (7) skin lesions with an accuracy of 98%.
Spectrum Suppressed Transmission Applied by Higher Coding Rate FEC

Spectrum suppressed transmission is one of the most effective schemes to increase frequency utilization efficiency by making its required bandwidth narrower than the Nyquist bandwidth. In this scheme, ISI(Inter Symbol Interference) caused by spectrum suppression makes its transmission quality worse, so an FEC(Forward Error Correction) must be needed. However, the previous studies of the spectrum suppressed transmission have been limited to the usages of higher coding gain FECs. In this paper, we propose the spectrum suppressed transmission applied by higher coding rate FEC with a coding rate of three-fourth and its frequency utilization efficiency increases are clarified by computer simulations.
A Novel TFET-MOSFET Hybrid SRAM for Ultra-Low-Power Applications

Tunnel Field Effect Transistor (TFET) which has a subthreshold swing lower than 60mV/decade and ultra-low off-current has become one of the most promising candidates to replace MOSFET in ultra-low-power applications, such as artificial intelligence of things. In this paper, we study the TFET-based static random access memory (SRAM) design and propose a novel 8T TFET-MOSFET hybrid SRAM based on the compact model of recently manufactured TFET in 12-inch foundry platform. The proposed design uses a single MOSFET as the write access transistor and the TFET-MOSFET stacked structure as the read access transistor which eliminates the forward p-i-n current fundamentally. Compared with 6T CMOS SRAM, the proposed design demonstrates 3 orders of magnitude lower static and read power, and 6 orders lower write power. Besides, it achieves faster read and write speed than other reported TFET SRAM designs. It also has the maximum hold, read and write static noise margin among all the CMOS and TFET SRAM designs.
Critical Designs for SGCC Standard HRF Receiver Performance Enhancements

The SGCC standard dual-mode communication system that combines advantages of High-speed Power Line Communication (HPLC) and High-speed Radio Frequency (HRF) wireless communication systems is evolving as the mainstream communication scheme for Advanced Metering Infrastructure (AMI) applications, and gradually reveals its potential to fit in Industrial Internet of Things (IIOT) scenarios. Unlike the HPLC section, which has been widely investigated during the last few years, the HRF specification is new and yet to attract enough research attention. In this paper, critical designs for HRF receiver is presented focusing on core baseband processing modules including Synchronization, Carrier Frequency Offset (CFO) estimation and channel estimation. A two-stage CFO estimation method is proposed to improve estimation accuracy under noisy channel conditions. Additionally, performances of different proposed interpolation methods are evaluated, based on which an integrated pilot-aided channel estimation scheme is designed. For numerical verification, comprehensive link-level simulations are conducted to validate the effectiveness of the designs.
Efficient Reduced-Order Algorithm for Transient Simulation of Transformer Thermal Characteristics

Intelligent operation and maintenance of power transformers using digital twin technology is of vital importance for ensuring the safe operation of the power grid. Dynamic simulation and efficient calculation of the thermal characteristics of transformers are key tasks in this area. To achieve fast prediction of temperature rise in transformers under time-varying operating conditions and promote the application of digital twin in substation operation and maintenance, this paper proposes a reduced-order model with full-nodal and multi-time steps output for transient simulation of transformer temperature field based on multi-layer Autoencoder (MAE) and decomposition linear layers (DLinear), and evaluates its effectiveness in terms of computational accuracy and efficiency. The results demonstrate that the proposed reduced-order model for transient simulation achieves high computational accuracy, with a maximum relative error of only 1.82% compared to simulation calculations. At the same time, the model significantly improves computational efficiency, with the ability to make 200-step predictions in only 3s. The research findings of this paper provide strong technical support for monitoring the thermal performance of transformers and the operation and maintenance of substations.
Analysis and Design of a Metal Object Detection Method for Wireless Power Transfer Systems

There will be eddy current effect while foreign bodies inevitably enter the gap between the transmitting coil and the receiving coil in the process of magnetic field induction radio energy transmission, which could lead to the high temperature and inefficiency of the system or even significant security risks. In this paper, a detection method of metal foreign bodies in the auxiliary coil perpendicular to the energy coil is designed. The magnetic coupling mechanism model based on MAXWELL software was built to obtain parameters of the magnetic coupling mechanism by simulation. On this basis, the Simulink circuit simulation model of radio energy transmission system is established, and its energy transmission characteristics are analyzed. Based on the designed radio energy transmission system, a detection coil perpendicular to the energy coil was designed, and its MAXWELL 3D simulation model was established to obtain its coupling characteristics. The experimental prototype is built and the metal foreign body detection experiment is conducted. The results of experiment are consistent with that of the simulation, which verifies that the vertical detection coil proposed in this paper is a feasible method for the detection of metal foreign bodies in the radio energy transmission system.
Traffic Verification Based on API Function Control in IIoT Service Cluster

Typically, traffic within a cluster of IIoT (Industrial Internet of Things) services is secure by default. However, accidental access by privileged users inside the cluster, compromised services, and malicious requests carefully constructed by attackers can cause sensitive data leakage and threaten the data security of IIoT services and even the security of other applications themselves. In order to defend against possible threats from inside the IIoT service cluster, we propose an approach to implement traffic verification inside the IIoT service cluster based on API function control. The API function of the service, when handling requests from IIoT devices or other services, will asynchronously request the internal verification service to verify whether the request comes from a reliable intracluster service or IIoT device, and decide whether to return a response to the requester based on the result of the verification.