Miracle-dz/UniFAR-FTUs huggingface dataset:
Pedestrian Red-Light Violation Detection Based on Deep Learning Vision

With the increasing prevalence of cars and various modes of transportation in every household, pedestrians face growing potential dangers when crossing roads. Current traffic accident responsibilities often extend beyond drivers, as pedestrian traffic habits, such as jaywalking and distracted phone use, contribute to accidents. Against this backdrop, we have designed a real-time object detection system based on the YOLOv5 (You Only Look Once) algorithm, specifically tailored for detecting pedestrians violating red light signals. The system incorporates an alert mechanism to prompt pedestrians to return to a safe area promptly. YOLOv5 is renowned for its outstanding performance, flexible network architecture, and ease of deployment. This paper builds upon this foundation, aiming to reduce the occurrence of traffic accidents and ensure that pedestrians crossing roads receive timely safety alerts.
Predictive Energy Consumption Reduction for EV Adaptive Cruise Controllers with Uncertain Speed Information

Since the early days of research on autonomous vehicles, besides safety, the energy consumption of vehicles has played a crucial role in the study of their design. While many tried to optimize the vehicles' design, others researched path planning and estimating the energy consumed in a trajectory. As internal combustion engines use gas to power the vehicle, in electric vehicles, there is an electric motor connected to a battery that produces the power. Nowadays, with the increase in the utilization of electric vehicles capable of being more intelligent than their previous models, such as V2V communication, one can use this information to optimize the energy consumption of these vehicles. The focus of this paper is to study the effect of speed information forecasting on the energy consumption of an electric vehicle using an adaptive cruise controller to follow a leading vehicle moving on a longitudinal trajectory. We use an electric vehicle power loss model for our energy consumption calculation and a V2V communication system with uncertainty and packet loss to receive the speed information of the lead vehicle. Three different drive cycles are introduced to replicate scenarios such as urban areas, highways, or a mixture of them. Finally, we show our analysis of the energy consumption optimization on these drive cycles.
A Feature Enhanced Scale-adaptive Convolutional Network for Ship Detection in Maritime Surveillance*

Accurate ship detection is of great importance in intelligent maritime surveillance and numerous methods have been proposed over the years. However, due to the interference caused by water dynamics and the different sizes of various ships, existing methods are often prone to produce a large number of false alarms or missing detection. Therefore, in this paper, we propose a feature enhanced scale-adaptive convolutional network for effective ship detection in complex maritime scenarios. The proposed model is built on a one-stage deep convolutional network based detector and consists of feature enhancement modules, scale-adaptive modules, and an improved detection head. First, a feature enhancement module is proposed and incorporated into multiple convolutional layers. The statistical information calculated from both channel and spatial domains is fused to alleviate irregular noise and increase the weight of ship feature representation. In addition, the receptive field of deep layer is enlarged via a scale-adaptive module devised, so that the robustness of the proposed model over varying ship scales could be enhanced. Finally, an improved detection head is further applied on different feature maps. A centerness branch is introduced following the combination of classification and regression branches to suppress low-quality bounding boxes for more accurate ship detection. Experimental results on two real-world maritime surveillance datasets demonstrate that our proposed model is able to improve ship detection accuracy with less computational cost against a number of state-of-the-art ones.
Design of a Real-time Cloud-based Monitoring System for Network Security Events

A real-time cloud monitoring system supporting the processing of large-scale data has been designed and developed in this project through the analysis of security event logs generated by Intrusion detection system(IDS). The achievement of offline correlation analysis and real-time clustering of security logs is facilitated by the implementation of parallel Apriori and K-means algorithms.
Improved lightweight traffic sign detection method with YOLOv7-tiny

A lightweight traffic sign detection method MCBAM-F-YOLOv7-tiny, which improves YOLOv7-tiny, is proposed to address the issues of low computational power, difficulty in improving real-time performance, low detection accuracy in low light environments, and detection omission errors in vehicle terminals and other devices. Firstly, the CBAM-driven MCBAM network is used as the backbone feature extraction network in the algorithm, which effectively saves computational resources and reduces the size of the network model; Then, a spatial pyramid pooling SPPFCSPCSIM module based on cascaded pooling layers was designed to reduce computational time and obtain an efficient and concise detection algorithm model. Finally, combining CIoU Loss with Focal Loss to construct an optimized Focal CIOU Loss function for bounding boxes, speed up the model training convergence speed and improve the learning ability of the network. The experimental results show that the improved algorithm achieves 89.4% precision, 76.7% recall, and 85.0% mAP on the CCTSDB traffic sign detection dataset, which are comparable to the original model, and the model size is reduced to 69.3% of the original model, and the detection speed reaches 101.1f·s-1, which is a 19.4% improvement and meets the real-time requirement. The visualization experiment further verifies that the improved algorithm can improve the detection accuracy in low light and avoid wrong detection and leakage.
Energy-Efficient High-Speed Architecture for Vehicle Speed Prediction Using Microcontrollers

Deep neural network (DNN) models are widespread across various applications. However, designing a computationally intensive DNN model on a resource-constrained platform is challenging and limits its applicability for hardware-based applications. Developing optimized architectural designs of DNNs is one way of addressing the hardware constraints and expanding the application space. The current study proposes an architectural design optimization for a gated recurrent unit (GRU) based regression model using microcontroller units (MCUs), and we refer to the optimized network as embedded GRU (eGRU). In addition, we also present hardware architectures with different precision formats for application-specific energy and speed benefits. The performance of the proposed methodology is investigated and tested on two different MCUs: ARM Cortex LPC1768 and AURIX TC277. We validate the proposed architecture design methodology for vehicle speed prediction (VSP) based intelligent vehicle braking system applications using time-series four-wheel speed data alone. The hardware experimental results for regression-based eGRU inference per test sample for the VSP task show a mean reduction of 3.5X in energy usage, 3X in latency, and 33% model parameter memory reduction for single-precision format architecture. A further decrease of 50% model parameter memory, 9% latency, and 9.1% in energy usage yielded for half-precision against single-precision architecture.
Research on Automated Operation and Maintenance Methods for Power Management Networks Based on Ansible

Currently, power management network server operation and maintenance tasks are primarily manual, resulting in a substantial workload and low efficiency. This paper conducts an in-depth investigation into server automation and maintenance technology using Ansible. To begin, it analyzes the existing challenges in server cluster operation and maintenance and highlights the significance of automated server maintenance. The paper delves into the Ansible framework and array extraction method, with a specific focus on exploring the automation of server account password generation and modification techniques. To enhance the response speed of the Ansible architecture, this paper incorporates the configuration management module with Intel SGX functionality. Storing remote host configuration information permanently, as utilized by Ansible, significantly reduces server operation time and enhances the security of server cluster login information. Experimental test results demonstrate that the proposed approach can generate highly complex and controllable-length passwords effectively. Moreover, the server system's operation time is noticeably reduced, confirming the efficacy of the proposed solution.
A Multipopulation Ant Colony System Algorithm for Multiobjective Trip Planning

Trip planning service can save the time and energy of tourists for preparing a trip and provide a more comfortable and satisfying travel experience. This paper particularly considers the planning of transportation mode between point of interests (POI) and formulates a multiobjective trip planning model to simultaneously maximize the visit time in POIs, minimize the travel time between POIs, and minimize the travel fare needed for the trip. To simulate the real-world environment, the formulated model incorporates the real-world POI and transportation data crawled from Tripadvisor and Baidu Map API, respectively. To obtain efficient trip planning schemes, a multipopulation ant colony system algorithm for trip planning, abbreviated as MACS-TP, is proposed. First, MACS-TP uses two colonies to optimize the time-related objective and fare-related objective respectively, which enhances the search efficiency. Second, an archive is employed to store the nondominated solutions found by both colonies and a new pheromone global update rule is designed based on the archive to help colonies optimize their corresponding objective sufficiently. Third, an elite learning strategy is proposed to further enhance the quality of solutions in the archive. Experimental results on a real-world dataset of Guangzhou, China illustrate the effectiveness of MACS-TP.
Emotion Classification Through Speech Data Analysis

Good quality healthcare services require effective communication between the patient and the healthcare provider. This work will help improve the areas of healthcare systems automation and optimization by applying Speech Emotion Recognition (SER) in health consultations to prevent miscommunication between patients and healthcare providers. Crowd-Sourced Emotional Multimodal Actors Dataset (CREMA-D) was used to compare the performances of different machine learning models in classifying emotions. Before feeding the raw dataset to the models, exploratory data analysis was done to determine features that should be considered for future analysis. Our results showed that depending on the emotion, there are some syllables in the text that were emphasized or took time to be pronounced by the speaker. After data analysis, the dataset was fed into different models and determined that the Support Vector Machine (SVM) is a machine-learning model for SER.
Evaluating the effectiveness of countermeasures to control the novel coronavirus disease 2019 in Jilin Province, China

Objective: Based on differences in populations and prevention and control measures, the spread of new coronary pneumonia in different countries and regions also differs. This study aimed to calculate the transmissibility of coronavirus disease 2019 (COVID-19), and to evaluate the effectiveness of countermeasures to control the disease in Jilin Province, China. Methods: The data of reported COVID-19 cases were collected, including imported and local cases from Jilin Province as of March 14, 2019. A Susceptible–Exposed–Infectious–Asymptomatic– Recovered (SEIAR) model was developed to fit the data, and the effective reproduction number ( R eff ) was calculated at different stages in the province. Finally, the effectiveness of the countermeasures was assessed. Results: A total of 97 COVID-19 infections were reported in Jilin Province, among which 45 were imported infections (including one asymptomatic infection) and 52 were local infections (including three asymptomatic infections). The model fit well with the reported data ( R 2 = 0.593, P < 0.001). The R eff of COVID-19 before and after February 1, 2020 was 1.64 and 0.05, respectively. Without the intervention taken on February 1, 2020, the predicted cases would reach a peak of 177,011 on October 22, 2020 (284 days from the first case). The projected number of cases until the end of the outbreak (on October 9, 2021) would be 17,129,367, with a total attack rate of 63.66%. Based on the comparison between the predicted incidence of the model and the actual incidence, the comprehensive intervention measures implemented in Jilin Province on February 1 reduced the incidence of cases by 99.99%. Therefore, according to the current measures and implementation efforts, Jilin Province can achieve good control of the virus’s spread. Conclusions: COVID-19 has a moderate transmissibility in Jilin Province, China. The interventions implemented in the province had proved effective, increasing social distancing and a rapid response by the prevention and control system will help control the spread of the disease.
Optimization of Electrode Configuration for the Removal of Eye Artifacts with Adaptive Noise Cancellation

Scalp electroencephalography (EEG) is a neural source signal that is extensively used in neuroengineering due to its non-invasive nature and ease of collection. However, a drawback to the use of EEG is the prevalence of physiological artifacts generated by eye movements and eye blinks that contaminate the brain signals. Previously, we have proposed and validated an H∞-based Adaptive Noise Cancellation (ANC) technique for the real-time identification, learning and removal of eye blinks, eye motions, amplitude drifts and recording biases from EEG simultaneously. However, the standard electroocu-lography (EOG) electrode configuration requires four electrodes for EOG measurement, which limits its applicability for reduced-channel mobile applications, such as brain-computer interfaces (BCI). Here, we assess multiple configurations with varying number of EOG electrodes and compare the ANC effectiveness of these configurations to the ideal four-electrode configuration. From an analysis of the root mean squared error (RMSE) and differences in signal to noise ratios (SNR) between the ideal four-electrode case and the alternative configurations, it is reported that several three-electrode alternative configurations were effective in essentially replicating the ability to remove EOG artifacts in an experimental cohort of ten healthy subjects. For nine subjects, it was shown that only two to three EOG electrodes were needed to achieve similar performance as compared to the four-electrode case. This study demonstrates that the typical four-electrode configuration for EOG recordings for adaptive noise cancellation of ocular artifacts may not be necessary; by using the proposed new EOG configurations it is possible to improve electrode allocation efficiency for EOG measurements in mobile EEG applications.
Virtual Model Interaction Based on Single RGB Camera

Nowadays, gesture recognition has been sufficiently developed and used in many areas, for example, you can control the model by your gesture, which can provide the audience with a more intuitive understanding of the displayed model and make it easier for the operator to present. However, most methods are based on specific device such as depth sensor, which contributes to gesture recognition cannot be extensively implemented, using a normal RGB camera to track the hand and recognize the gesture becomes a significant issue. Lightweight is also a problem, real-time hand detection and tracking is feasible due to the low level of lightweight. Under the circumstance, cost saving of gesture recognition is a significant issue to exploit. We present a new method using a real-time on-device hand tracking solution to control virtual models in Unreal Engine. We have introduced OneEuro Filter to handle the jitter that occurs when the hand is in motion, affecting the control of the model.
Simultaneous Perturbation-Based Stochastic Approximation For Quantile Optimization

We study a gradient-based algorithm for solving differentiable quantile optimization problems under a black-box scenario. The algorithm finds improved solutions along the descent direction of the quantile objective function, which is approximated at each step using a simultaneous perturbation technique that involves the difference quotient of the output random variables. Compared to existing quantile optimization methods, our algorithm has a two-timescale stochastic approximation structure and uses only three observations of the output random variable per iteration without requiring knowledge of the underlying system model. We show the local convergence of the algorithm and establish a finite-time bound on the convergence rate of the algorithm. Numerical results are also presented to illustrate the algorithm.
Simple 2 DoFs Hybrid Dynamic Modeling of Biped Gait Considering Foot Rotation

The ankle joint plays a vital role in enabling normal locomotion in healthy individuals. However, diseases and conditions can result in weakening or dysfunction of the ankle joint. In many cases, the use of active ankle orthoses or exoskeletons can prove beneficial in assisting ankle joint function. The necessity for simplified models makes it essential to design an appropriate controller for the ankle joint to recover normal gait in patients. In this paper, we present a two degree of freedom hybrid dynamic model, considering foot rotation and various phases of gait in both single support and double support phases. This model contains only one active degree of freedom under control at the ankle joint. Ultimately, the simulation results of the passive walking of this model are investigated.
Electric Vehicle Energy Consumption Measurement Using GPS Data Logger

In this paper a GPS data logger was used for logging the measured data of electric vehicle energy consumption. The data logger records the received GPS data for speed and distance traveled by the electric vehicle. Current and voltage sensors are also used, and their signals were recorded by the data logger. The results are presented in graphical form and analyzed.
Enhancing the Performance of Dog Breed Classification and Food Recommendation System using Visual Geometry Group 16 in comparison with Long Short-Term Memory

The ultimate aims to improve the accuracy in dog breed classification and food recommendation systems by using Novel Visual Geometry Group 16 (NVGG16) instead of Long Short-Term Memory (LSTM). The researchers used NVGG16 and LSTM to analyze 20,981 images of 120 dog breeds from the Dog Breed Identification dataset on Kaggle. ClinCalc is used to calculate a sample size of 95% confidence and an alpha value of 0.05. The performance of the dog breed classification and food recommendation system was evaluated based on its accuracy. In comparison to Long Short-Term Memory, which scored 67.72%, the mean accuracy in classifying Dog Breeds and recommending food using Novel Visual Geometry Group 16 is significantly higher at 92.12%, according to the Results section. With an accuracy p-value of 0.036 (where p < 0.05), a statistical analysis, more precisely A substantial difference between the two approaches was shown using an Independent Sample T-Test. Conversely, with regard to loss, a similar statistical difference was seen. As a result, this study highlights the superiority of the Novel Visual Geometry Group 16 method over the Long Short-Term Memory algorithm in terms of accuracy for both Dog Breed Classification and Food Recommendation System.
Axisymmetric Vibration of Piezoelectric Semiconductor Disc Based on Double Legendre Polynomial Method

Piezoelectric semiconductors (PSCs) have attracted much attention in recent years for combining piezoelectric with semiconducting properties. As far as we know, there is very limited theoretical research on the vibration problem of three-dimensional PSC disc structure. In this paper, a double Legendre polynomial method was developed to analyze axisymmetric free vibration for axially polarized PSC disc. The present method directly incorporated the boundary conditions into the control equations of PSC. Simultaneously, the mechanical displacements, the electrical potential and the carrier density were defined as the form of double Legendre polynomials series. Then, the natural frequencies are obtained through solving eigenvalue. The numerical example shows that the steady-state carrier density has important influence on the natural frequency only in a certain range, and the effect on the higher modes is more obvious than the lower modes. Finally, the effects of radius-to-thickness ratio on the frequencies were also studied.
Adjusting Twitter Data as a Source for Blood Donation Analysis: BDT-UC Dataset and BERT Implementations

Collecting data related to Blood Donation (BD) from Twitter often faces challenges related to noise. This study addresses this issue through automated classification, implementing Bidirectional Encoder Representation from Transformers (BERT) models. We begin by creating an unsupervised Twitter dataset focused on blood donation. Subsequently, we introduce the Blood Donor Tweet-User Classification Dataset (BDT-UC), a multi-class labeled dataset that considers user roles in blood donation. BDT-UC was used to train and validate BERT models with two approaches: fine-tuning the models on BDT-UC and customizing the models with additional layers. The results demonstrate consistent user classification in our proposed dataset. This dataset paves the way for more efficient BD-related data analysis by categorizing data and reducing noise from Twitter data.Medical relevance: This work showcases the possibility of adapting Twitter as a reliable source for blood donation-related data, with a categorization implementation of 78% in accuracy.
Medical Image Classification Using Transfer Learning and Network Pruning Algorithms

Deep neural networks show great advancement in recent decades in classifying medical images (such as CT-scans) with high precision to aid disease diagnosis. However, the training of deep neural networks requires significant sample sizes for learning enriched discriminative spatial features. Building a high quality dataset large enough to satisfy model training requirement is a challenging task due to limited disease sample cases, and various data privacy constraints. Therefore in this research, we perform medical image classification using transfer learning based on several well-known deep networks, i.e. GoogLeNet, Resnet and EfficientNet. To tackle data sparsity issues, a Wasserstein Generative Adversarial Network (WGAN) is used to generate new medical image samples to increase the numbers of training instances of the minority classes. The transfer learning process itself also allows the building of strong classifiers by transferring knowledge from the pre-trained image domain to a new medical domain using a small sample size. Moreover, the lottery ticket hypothesis is also used to prune each transfer learning network trained using the new target image data sets. Specifically, the L1 norm unstructured pruning technique is used for network reduction. Hyper-parameter fine-tuning is also performed to identify optimal settings of key network hyper-parameters such as learning rate, batch size and weight decay. A total of 20 trials are used for optimal hyper-parameter selection. Evaluated using multi-class lung X-ray images for pneumonia conditions and brain tumor CT-scans, the fine-tuned EfficientNet model obtains the best brain tumor classification accuracy rate of 96% and a fine-tuned GoogLeNet model with pruning has the highest pneumonia classification accuracy rate of 81.5%.