Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Road Anomaly Detection Algorithm Based on SimAM Attention Mechanism

This paper introduces YOLO-Road, a lightweight object detection algorithm model, which integrates the SimAM attention mechanism on the basis of YOLOv7-Tiny, and improves the Loss function to SIoU Loss. In addition, we collected 3234 road disease images with professional equipment and verified the results on this dataset using YOLOv7-Tiny and YOLO-Road, respectively. The results show that the accuracy of our model mAP@0.5 reaches 0.879, which is 1% higher than that of YOLOv7-Tiny (0.855), successfully realizing the high-precision detection of multi-category road diseases.
Blockchain Model for Reliable Consensus Algorithm on the Autonomous Driving Data Management

This paper seeks to propose a reliable data management system for communication and agreement between nodes based on a blockchain system platform to safely and systematically manage the vast amount of situational data generated during autonomous driving. Autonomous driving vehicles are vehicles that can operate on their own without driver intervention. Artificial intelligence plays an essential role in recognizing surrounding situations through various sensors for autonomous driving and suggesting efficient and safe routes accordingly. In this process, as the autonomous vehicle(AV) learns situation data according to the route and shares the data with related organizations, it is important to select relevant organizations according to the driving route and manage and store the data. In the autonomous vehicle era, vast amounts of Internet of Things(IoT) devices must instantly interact and transmit data, and this data exchange must be transparent and shareable [1]. It is necessary to develop a new consensus algorithm that can complement the existing consensus algorithm to select organizations related to the driving path of autonomous vehicles and to manage and store data. Accordingly, in order to prevent forgery and falsification of surrounding data and solve the shortcomings of the consensus algorithm, we plan to design a blockchain model that utilizes a reliable consensus algorithm(RCA) with rapid data storage and appropriate data sharing strategies.
Complex Neural Networks for Indoor Positioning with Complex-Valued Channel State Information

In this paper, the use of channel state information (CSI) for indoor positioning is investigated. In the considered model, a base station (BS) equipped with several antennas sends pilot signals to a user that transmits the received pilot signals back to the BS. The BS will use the received CSI data to estimate the position of the user. To this end, we formulate this positioning problem as an optimization problem aiming to minimize the mean square error between the estimated position and the actual position of the user. To solve this problem, we design a complex-valued neural network (CVNN) based positioning algorithm. Compared to real-valued neural networks (RVNNs) that need to convert complex-valued CSI data into real-valued data, the proposed method uses original CSI data to train the CVNN model for user positioning. Since the output of our proposed algorithm is complex-valued and it consists of the real and imaginary parts, we can use it to implement two learning tasks. Based on this property, two use cases of the proposed algorithm are proposed: 1) the algorithm directly outputs the estimated position of the user. Here, the real and imaginary parts of an output neuron represent the 2D coordinates of the user, 2) the algorithm outputs two CSI features (i.e., line-of-sight/non-line-of-sight transmission link classification and time of arrival (TOA) prediction) which can be used in traditional positioning algorithms. Simulation results demonstrate that our designed CVNN based algorithm can reduce the mean positioning error between the estimated position and the actual position by up to 11.1%, compared to a RVNN based method which has to transform CSI data into real-valued data.
SIC-Free NOMA Designs Via Symbol-Level Precoding

The multi-antenna non-orthogonal multiple access (NOMA) technique is a promising method to enhance energy and spectrum efficiencies of wireless communication systems through advanced precoding algorithms. However, traditional NOMA schemes encounter high complexity issues due to the successive interference cancellation (SIC) process at the receiver end. Moreover, conventional precoding designs for multi-antenna NOMA systems only utilize user channel state information and overlook the modulation details of transmitted data symbols, which may result in suboptimal performance. To overcome these disadvantages, we propose a symbol-level precoding (SLP) scheme to maximize the energy efficiency of the system, which has been little studied in the literature. Furthermore, the proposed SLP scheme makes the “interference signals” to fall within the decoding region of the “desired signal”, eliminating the need for an SIC receiver, thereby reducing the complexity of the NOMA system in practical applications. To resolve the optimization problem associated with the SLP scheme, we develop a fractional programming and successive upper-bound maximization based algorithm. Our simulation results demonstrate the effectiveness of the proposed SLP scheme and algorithms in improving the energy efficiency of the system.
Unsupervised Network Traffic Classification Based on Multi-Source Synergistic Distribution Alignment

Network traffic classification is a key technology in network communication management, which is of great significance for building intelligent communication and so on. Due to the difficult and time-consuming process of network traffic labeling, it is difficult to obtain any labeled traffic data in some special networks. At the same time, in a real network environment, there are multiple network traffic domains, and the data distribution of each network traffic domain is different, making it extremely difficult to train a network traffic classification model that performs well on multiple traffic domains simultaneously. Therefore, this paper proposes a network traffic classification method in unsupervised scenarios, aiming to study how to learn traffic knowledge from multiple source traffic domains and achieve accurate classification of unlabeled network traffic without labeled traffic data in the target traffic domain. This paper divides three traffic domains from the data set, namely VPN, nonVPN and nonTor. And three traffic classification tasks of unsupervised multi-source domain are constructed. The accuracy of traffic classification tasks in the source traffic domain is nonTor and nonVPN, and the target traffic domain is VPN reaches 89.76%. The source traffic domain is VPN and nonTor, the accuracy of the classification task is 91.73% when the target traffic domain is nonVPN, and 90.35% when the source traffic domain is VPN and nonVPN, and the target traffic domain is nonTor. Experimental results show the effectiveness of the network traffic classification algorithm proposed in this paper.
Fine Grain Classification of Dog Breeds Based on HERBS Method

In an AI system, performing a fine-grained task like sorting dog breeds requires detailed observation. Therefore, the task of classifying images into highly specific and detailed categories, including birds [WBW+ 11], dogs [KJYL11], and medical images [ZWH+ 20] is known as fine-grained visual classification (FGVC). An effective FGVC model is high-temperature refinement and background suppression (HERBS) [CKL23]. The HERBS model can be integrated into various backbone networks, so in this article we apply the HERBS model to identify dog breeds in images and introduce the HERBS model from a technical point of view, outlining our model setup. The performance of HERBS model in the task of dog breed classification is also illustrated. This paper explores the application of deep learning methods in fine-grained classification of dog breeds, and experiments prove the effectiveness of the implemented HERBS method, demonstrating its ability to outperform other well-known classification methods on various datasets.
Advances In EEG-Based Multimodal Emotion Recognition: A Comprehensive Review

In the field of emotion recognition, computer-aided classification systems are typically designed to comprehend human emotional states by analyzing specific signal states. However, it is important to note that human emotional levels cannot be visualized as a single, monolithic process or event. Critical reviews in this field have consistently shown that multimodal accuracies outperform unimodal accuracies. The integration of multimodal environments adds complexity to the analysis process, which is the focus of this research. This paper presents the potential of EEG data and the integration of multimodal environments in emotion classification.
Breast Cancer Histopathological Image Classification Using an Ensemble of Deep Convolutional Neural Networks

Women’s mortality rate from breast cancer is currently greater than other types of cancer, and the rate is rising day by day. As a result, it is vital to analyze breast cancer at an early stage to avoid tragic deaths. Traditional approaches take a long time and require the experience of expert radiologists, who may or may not agree with their results. For all of these reasons to develop an automated breast cancer recognition system, in this study we have introduced an approach using breast cancer histopathological images and a variant of AlexNet, VGG-16, ResNet-50, and Inception-v1 models and we have proposed an ensemble classifier, that is based on a majority voting mechanism. The performance of all four variants of the CNN models was evaluated using the BreaKHis dataset and the 5-fold cross-validation process. This dataset consists of images with 40×, 100×, 200×, and 400× magnifying factors. We have performed binary classification to classify benign and malignant tumors on each magnification of the dataset separately. After evaluating the performance of all four CNN models, we have selected the three best-performed CNN models based on their accuracy, precision, recall, and f1-score. Then, these three best-performed CNN models, AlexNet, ResNet-50, and Inception-v1, were used for ensemble classification using a majority-based (hard voting) voting mechanism to classify benign and malignant tumors. Our proposed voting-based ensemble classifier shows better performance with 99.87%, 99.85%, 99.98%, and 99.86% accuracy respectively for 40×, 100×, 200×, and 400× magnification of the dataset in contrast to the latest up-to-date methods for classifying tumors, both benign and malignant.
Uncertainty-Aware Multitask Allocation for Parallelized Mobile Edge Learning

Harvesting the profuse yet underutilized computational resources of IoT devices, also referred to as Extreme Edge Devices (EEDs), can significantly curtail the delay in parallelized Mobile Edge Learning (MEL). However, EEDs are user-owned devices, which causes them to experience a highly dynamic user access behavior. Such dynamicity can lead to uncertainty in the available computation and communication capabilities of learners. In this paper, we propose the Minimum Expected Delay (MED) scheme. MED is the first data allocation scheme in MEL that accounts for uncertainty in learners' capabilities and enables multi task allocation. Given the state probabilities of learners, MED strives to minimize the sum of the maximum expected delay of all tasks, while abiding by certain training time and budget constraints. Towards that end, MED formulates the data allocation problem as an Integer Linear Program (ILP) and makes uncertainty-aware decisions. We conduct rigorous experiments on a real testbed of Jetson Nano devices. Extensive performance evaluations show that MED outperforms a representative of state-of-the-art uncertainty-naive schemes by up to 11 %, 11 %, 42 %, and 5 % in terms of training time, satisfaction ratio, data drop rate, and occupancy time, respectively. In addition, MED approaches a baseline scheme that assumes a perfect knowledge of the learners' states, yielding a gap of up to 10%, 5%, and 14% in terms of satisfaction ratio, data drop rate, and occupancy time, respectively.
Enhanced Lightweight Hazardous Waste Detection Algorithm of YOLOv5s

Aiming to address the issues of the existing harmful waste classification detection algorithm, such as excessive parameters, complex network structure, extensive computational requirements, and limited identification types that hinder the deployment on embedded devices, this paper proposes a lightweight target detection algorithm called MCFG-YOLO. Firstly, we introduce the MobileNetV3_Small network into the architecture of YOLOv5s to reduce complexity. The lightweight Ghost convolution is employed for efficient target feature extraction, while the C3Ghost module is constructed based on CSP structure to significantly decrease parameter count and computational cost. Moreover, we adopt the Focal-Loss EIoU loss function instead of the CIOU loss function, and replace the Hard-swish activation function in MobileNetV3_Small with the GELU activation function to improve the model performance and accuracy. Finally, experimental results on our self-made harmful garbage dataset demonstrate that our proposed algorithm achieves an average accuracy of 94.9%. Furthermore, it reduces model weight by 65.3%, computation amount by 71.7%, and number of parameters by 67.6%. These improvements indicate great potential for practical applications in hazardous waste management and environmental protection.
Design and Performance Assessment of a Si/GaSb-PC-TFET for Label-Free Bio-Sensing Applications

In this paper, a Si/GaSb-polarity control tunnel field effect transistor (Si/GaSb-PC-TFET) is proposed as a label-free biosensor. To enhance the sensing performance of the proposed biosensor, a narrow band-gap heteromaterial named GaSb is employed in the source region. The dielectric constant $(k)$ and charge density ($\mathrm{N}_{\text {bio }}$) of various biomolecules are utilized for assessing their presence via the proposed Si/GaSb-PC-TFET-based biosensor. In the proposed work, we considered the biomolecules with dielectric constants $(k)$ from 1 to 12 and charge densities ranging from $\pm 1 \times 10^{10} \mathrm{C.cm}^{-2}$ to $\pm 1 \times 10^{12} \mathrm{C.cm}^{-2}$ with a fixed k-value. The sensing efficiency of the proposed biosensor has been determined in terms of drain current ($\mathrm{I}_{\mathrm{DS}}$), and the maximum $\mathrm {I_{D S}}$ sensitivity is obtained as $1.11 \times 10^{13}$ for neutra $(k=12)$ and $1.28 \times 10^{13}$ for negatively charged $(N_{\text {bio }}=-1 \times 10^{12}$ C. $\mathrm{cm}^{-2}, k=12$) biomolecules. Furthermore, the sensitivity of the proposed Si/GASb-PC-TFET biosensor has also been investigated by employing different partially filled nano-cavities.
Multiclass Classification Approaches for Intrusion Detection in IoT-Driven Aerial Computing Environment

Aerial Computing is one of the applications of Internet of Things (IoT) which makes use of autonomous aerial devices, such as drones and unmanned aerial vehicles (UAVs). The ubiquitous nature of IoT and aerial computing is poised to revolutionize our daily lives by enabling seamless real-time information sharing among interconnected objects. However, ensuring the safety and security of such network is crucial in preventing potential threats and attacks. The purpose of this study is to develop a sophisticated intrusion detection system that is effective, efficient, and intelligent using complex machine learning models trained on relevant intrusion detection datasets. In this article, the multiclass classification approaches for intrusion detection in IoT-driven aerial computing environment are presented (in short, MCA-IDAC). In the comparative study, it has been observed that proposed MCA-IDAC performs significantly better than the other existing competing schemes, in terms of important performance parameters.
Enhancing Residential Real Estate Search with Classification Strategies Using Diffusion and CLIP

The significance of the real estate search engine in the economy necessitates the development of a reliable room image luxury level annotation method that addresses current limitations, including the inability to assess room quality, underutilization of deep network capacities, and the need for more annotated house images. This paper proposes a novel real estate image annotation model, leveraging the diffusion model and contrastive language-image pre-training (CLIP) network, through a multi-stage algorithm. First, the diffusion network is employed as a data augmentation technique to generate additional real estate images for network training. Then, a CLIP model is utilized to categorize images into the kitchen, bathroom, dining room, living room, and foyer. Finally, five CLIP models assess the condition of each room, categorizing it as contemporary and standard. Experimental results on a newly collected real estate image dataset demonstrate that the proposed approach surpasses existing house image classification algorithms.
Opportunistic Transmission for Heterogeneous Cognitive Cellular Networks

Heterogeneous cellular structure is a potential scheme in the sixth generation (6G) network, where macro and micro base stations (BSs) are collaborated to improve quality of service (QoS) for users. For the conventional heterogeneous cellular networks, data of users is transmitted at one frequency band and perfect channel state information (CSI) is required to beamforming. However, adjacent frequency interference and imperfect CSI decrease the spectrum efficiency (SE) of cellular network and QoS of users. In order to deal with performance loss of SE and QoS, this paper proposes a cognitive radio (CR)-based heterogeneous cellular networks, where opportunistic scheme is applied to improve QoS of users with imperfect CSI. Then, an optimization problem is established, considering both resource allocation and user selection under the constraints of users' QoS requirements. For the convenience of solving, we divide the optimization problem into two suboptimal issues and use an iterative method to approximate the global optimal solution. Numerical results reveal that the proposed scheme can achieve higher SE than the conventional scheme in heterogeneous cellular networks.
Investigating and Implementing the Efficiency of Image Restoration Techniques in Digital Image Processing

This look at explores the efficiency of diverse photo recovery strategies, inclusive of noise reduction, histogram change, facet enhancement, polishing, and smoothing, in digital picture processing. An extensive evaluation of the techniques becomes conducted to assess their effectiveness in restoring degraded virtual pictures. Numerous objective metrics, together with Structural Similarity (SSIM) Index, peak sign-to-Noise Ratio (PSNR), and mean Squared blunders (MSE), have been used to quantitatively examine the first-rate of the restored photographs. Effects indicated that noise discount and area enhancement furnished the finest improvements within the snap shots. Histogram changes and sprucing also stepped forward the high-quality of the pictures, at the same time as the smoothing technique ended in a moderate lower in pleasant. This examines offers a detailed insight into the efficiency of various photo healing techniques and can serve as a reference for in addition investigations.
Pathological Lung Segmentation in Chest X-Ray Images using Modified U-Net-Based Architecture

Lung segmentation is often considered to be a critical task in radiography and medical-imaging analysis as it enables the measurement of lung volumes and the detection of abnormalities such as fluid buildup, and inflammation in the lung tissues, tumours and nodules. This paper proposes a method for lung segmentation based on a type of convolutional neural network (CNN) called U-Net which is designed for biomedical image segmentation. The proposed method combines the U-Net architecture with Dilated Convolutions and Bi-linear Interpolation to improve the performance and robustness of the model. The proposed methodology has been trained and tested on publicly available datasets, outperforming existing lung segmentation methods with respect to both Dice and Intersection Over Union Scores. The obtained results substantiate the effectiveness of the proposed U-Net-based approach for lung segmentation.
A Novel Ultra-Massive MIMO Beam Domain Channel Model for 6G Maritime Communications

In this paper, a novel ultra-massive multiple-input multiple-output (MIMO) ship-to-ship beam domain channel model (BDCM) for maritime communications is proposed, which is transformed from a geometry-based stochastic model (GBSM). The location-dependent property and sparse user and scattering distribution property for maritime communication channel are considered. In addition, the scattering effect of the sea surface and the limited range of angle for the evaporation duct propagation are modeled by the scattering masking function and angle filtering function, respectively. Important statistical properties are studied, including the spatial cross-correlation function (SCCF) and temporal autocorrelation function (TACF), etc. The channel capacity is studied. Simulation results show that the ship speed, wind speed, and antenna configuration have great influence on channel characteristics and system performance. In addition, the simulation result of the root mean square (RMS) delay spread agrees with the measurement data, which proves the correctness of the model.
Generative Strategy for Low and Normal Light Image Pairs with Enhanced Statistical Fidelity

Low light image enhancement remains challenging due to limited availability of real low/normal light image pairs for training. Simple image simulation techniques used for data augmentation fail to accurately model noise and distortions present in real low light photos. In this work, we propose N2LDiff, a novel generative model leveraging diffusion processes to synthesize realistic low light images from normal light counterparts. Our model leverages the noise modeling capabilities of diffusion processes to generate low light images with accurate noise, blurring, and color distortions. We make the following key contributions: (1) We develop a novel N2LDiff model that can generate varied low light images from the same normal light input via diffusion processes. (2) We introduce a new benchmark for low light image synthesis using existing datasets. (3) Leveraging N2LDiff, we construct a large-scale low light dataset. Our generated data will facilitate training and evaluation of deep learning models for low light enhancement tasks.
Detection of Alzheimer's Disease Through Convolutional Neural Networks: An Analysis of Magnetic Resonance Imaging Images

Alzheimer's disease is a progressive ailment characterized by progressive deterioration of cognitive function, typically observed in the senior citizen population. This condition presents a substantial worldwide health concern. The timely identification and intervention have a key role in enhancing the overall well-being of persons who are impacted. This research paper introduces a Convolutional Neural Network (CNN) architecture designed for the purpose of classifying Alzheimer's disease based on brain MRI scans. The CNN model that has been presented utilizes deep learning capabilities to autonomously extract pertinent information from brain MRI scans, hence allowing the precise categorization of Alzheimer's disease. The model has a notable level of classification accuracy and displays promising prospects for timely identification and action. By presenting the possibility of enhancing patient outcomes and quality of life, this research adds to the continuing attempts to develop automated and non-invasive diagnostic techniques for Alzheimer's disease.