Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-L: A Novel Multi-Robot Cooperative Localization Method in Indoor Environment

Mobile robots often lose global localization in highly dynamic indoor environments. Enabling a mobile robot to determine where a catastrophic localization failure has occurred and how to recover from it is known as the kidnapped robot problem (KRP). However, due to the limitations of the view of sensors carried by individual robots, it is difficult for individual robots to recover from KRP. To address this issue, we can use the collaboration between multiple robots to increase the coverage of robot swarms and reduce the occurrence of localization errors. Here, we propose Multi-L, a novel multi-robot collaborative localization framework, Multi-L utilizes RSSI (Received Signal Strength Indicator) to determine the relative positions between robots when multiple robots communicate and combines the communication exchange of positional information to determine whether there is a significant error in the current localization and to help it recover. We have experimented and evaluated the Multi-L proposed in this paper in both simulated and real-world environments. The experimental results show that Multi-L greatly improves the probability of localization recovery when KRP occurs and can effectively help the robot reach its scheduled position.
Multi-Stream Deep Neural Network for Diabetic Retinopathy Severity Classification Under a Boosting Framework

Diabetic Retinopathy (DR) is an eye disorder in patients with diabetes. Detection of DR presence and its complications using fundus images at an early stage helps prevent its progression to the advanced levels. In the recent years, several well-designed Convolutional Neural Networks (CNN) have been proposed to detect the presence of DR with the help of publicly available datasets. However, these existing CNN-based classifiers focus on utilizing different architectural settings to improve the performance of detection task only i.e. presence or absence of DR. The further classification of the severity and type of the disease, however, remains a non-trivial task. To this end, we propose a multi-stream ensemble deep network to classify diabetic retinopathy severity. The proposed approach takes advantages of the deep networks and principal component analysis (PCA) to learn inter-class and intra-class variations from the raw image features. Ensemble machine learning classifiers are then applied to achieve high classification accuracy and robust performance on the obtained deep features. Specifically, a multi-stream network is made using pre-trained deep learning architectures i.e. ResNet-50 and DenseNet-121 to serve as the main feature extractors. Further application of PCA reduces the dimensionality of features and effectively separates the variation space of inter-class and intra-class images. Finally, an ensemble machine learning classifier using AdaBoost and random forest algorithms is built to further improve classification accuracy. The proposed approach has been compared with multiple conventional CNN-based approaches on Messidor-2 (two categories) and EyePACS (two, five categories) datasets. The experiment results show that our proposed approach achieves superior performance (upto 95.58% accuracy) and can be considered a promising method for automatic diabetic retinopathy detection.
Based on End-to-End Object Detection Algorithm with Transformers for Detecting Wafer Maps

With the gradual increasing of wafer fabrication yield, defect pattern recognition in wafer maps becomes more challenging, and thus it is especially important to identify problems in the process as early as possible to avoid waste, and new ideas are provided in deep learning for defect pattern recognition (DPR) of wafer maps. In this paper, an End-to-End Object Detection with Transformers is introduced to process wafer defect maps by migrating the learning weights and fine-tuning the parameters. The test results show that the DETR based algorithm for wafer detection improves 8.4% in accuracy compared to the improved Mask R-CNN detector.
Trade-off Between Safety and Traffic Flow for Connected Autonomous Vehicles in the Presence of Traffic Signals

Safety and compliance with traffic rules are the most important factors in the design of autonomous vehicles. Recently, provably safe automatic longitudinal controller (ALC) has been proposed to ensure safety, comfort, and compliance with the traffic rules. The macroscopic effects of the deployment of such fully verified systems on a large network are yet to be investigated. In this paper, we compare the performance of the provably safe ALC and human drivers in terms of traffic rule compliance, safety, and comfort. To do this, we use the traffic simulator VISSIM, which contains a human behavior model, and we incorporate the provably safe ALC in the simulations. We run scenarios with increasing penetration of autonomous vehicles and with different vehicle input flows. Our results confirm that the proposed provably correct automatic longitudinal controller never leads to any traffic light violations. Moreover, the controller decreases the risks considerably with at most 3% negative impact on traffic flow. The only noteworthy negative impact is on the comfort of human-driven vehicles, which can be attributed to the fact that VISSIM's human driver model is not adapted to mixed traffic conditions with the conservatively safe autonomous vehicles.
Systolic Array based FPGA accelerator for Yolov3-tiny

FPGAs are increasingly significant for deploying convolutional neural network (CNN) inference models because of performance demands and power constraints in embedded and data centre applications. Object detection and classification are essential tasks in computer vision. You Only Look Once (YOLO) is a very efficient algorithm for object detection and classification with its variant Yolov3-tiny specially designed for embedded applications. This paper presents the FPGA accelerator for multiple precisions (FIXED-8, FIXED-16, FLOAT32) of YoloV3-tiny. We use a homogenous systolic array architecture with a synchronized pipeline adder tree for convolution, allowing it to be scalable for multiple variants of Yolo with a change in host driver. We evaluated the design on Terasic DE5a-Net-DDR4. The Fixed point (FP-8, FP-16) implementations attain a throughput of 57 GOPs/s (> 23%) and 46.16 GOPs/s (> 340 %). We synthesized the first FLOAT32 imnlementation attaining 11.22 GFLOPs/s.
DDRICFuse:An Infrared and Visible Image Fusion Network Based on Dual-branch Dense Residual And Infrared Compensation

Benefitting from the strong feature extraction capability of deep learning, infrared and visible image fusion has made a great progress in recent years. In this paper, we implement a dual-branch dense residual infrared and visible image fusion network based on auto-encoder. Specifically, the encoder has two branches that extract the shallow features and deep features of the image, respectively. The fusion layer adopts the residual block to fuse the two sets of features from the same branch of infrared and visible image to get fused features. The decoder is utilized to generate a fused image. To improve the overall performance of the fusion image, an infrared feature compensation network is added that can compensate salient radiation features of the infrared image. Experimental results show that our proposed method achieves reasonable performance compared with other state-of-the-art image fusion methods on structural similarity.
Lane change decision-making with active interactions in dense highway traffic: A Bayesian game approach

It is foreseeable that automated and human-driven vehicles will coexist in a mixed traffic environment in the predictable future. One of the crucial tasks of automated vehicles is to make human-like driving decisions when interacting with human drivers to ensure safety and efficiency. To this end, this paper proposes a lane change decision-making method based on the Bayesian game approach, focusing on dealing with the strong interactions in dense highway traffic. Firstly, the driver aggressiveness is estimated using Gaussian Mixture Model (GMM) based on the naturalistic driving data. Then the driver aggressiveness is integrated into the Bayesian game model as a critical factor to affect the decisions of the autonomous vehicle. Finally, the tentative behavior is generated to actively gather surrounding vehicles' responses when the recognized driver aggressiveness does not help the ego vehicle to make safe and efficient decisions in the uncertain driving environment. The results show that the proposed model can generate more human-like decisions after being validated and compared in the multiple interactive lane change scenarios.
Integrated Sensing and Communication Waveform Design: A Survey

Integrated sensing and communication (ISAC) has been widely recognized as a key technology in future sixth-generation (6G) wireless networks, especially for emerging applications and scenarios demanding both high-performance sensing and communication functionalities. The ISAC technique has the potential of enabling sensing and communication functionalities in a single hardware platform, even sharing the same waveform, thus, achieving the reduced cost of hardware implementation and the efficient use of spectrum resources. In this paper, we commence with the discussion on sensing and communication at the current stage from the view of motivations, applications, and challenges. Then, we provide a comprehensive survey of the state-of-the-art approaches to the ISAC technique from the waveform design perspective. To be specific, we classify the waveform design methods into three categories, namely, communication-centric waveform design, sensing-centric waveform design, and joint waveform optimization and design. In addition, potential research directions and challenges for future ISAC waveform design are outlined.
Cardinality Estimation in Inner Product Space

This article addresses the problem of cardinality estimation in inner product spaces. Given a set of high-dimensional vectors, a query, and a threshold, this problem estimates the number of vectors such that their inner products with the query are not less than the threshold. This is an important problem for recent machine-learning applications that maintain objects, such as users and items, by using matrices. The important requirements for solutions of this problem are high efficiency and accuracy. To satisfy these requirements, we propose a sampling-based algorithm. We build trees of vectors via transformation to a Euclidean space and dimensionality reduction in a pre-processing phase. Then our algorithm samples vectors existing in the nodes that intersect with a search range on one of the trees. Our algorithm is surprisingly simple, but it is theoretically and practically fast and effective. We conduct extensive experiments on real datasets, and the results demonstrate that our algorithm shows superior performance compared with existing techniques.
A 10-W6-12GHz GaN MMIC Supply Modulated Power Amplifier

This paper presents a gallium nitride (GaN) Monolithic Microwave Integrated Circuit (MMIC) PA with octave bandwidth, designed specifically for amplifying high peak-to-average power ratio (PAPR) signals with high efficiency from 6 to 12 GHz. Across the octave bandwidth, greater than 23.0% PAE is achievable over 10-dB of back off power using a dynamic supply voltage range from 6 to 20 V. We present the design and CW characterization of the PA as well as measurements using a 10-MHz multi-carrier noise-like signal and a 4-level discrete supply modulator comparing four different tracking functions. At 6.5 GHz a flat-gain, uniform voltage spacing function yields a 2.5 dB improvement in noise power ratio (NPR) over the static 20 V results with 3.8% points of PAE improvement, reaching 35.0 dBm of average output power. Furthermore, for a target NPR of 20 dB supply modulation yields a 9.7,% point improvement in PAE over the static case with a 1.5 dB increased output power.
Cascading Failure of Railway Hazard Causation Network

The high-speed rail signal system needs to effectively control lots of personnel, equipment and complex environment. This adds to the complexity of securing safety for rail transport. Aiming at the high-speed rail signal system, this paper uses systematic analysis method to evaluate the safety of hazard causes. According to the characteristics of the high-speed rail signal system and considering the causal relationship, a new cascading failure model is proposed. The model redefines capacity. When the node fails, the overloaded load is redistributed by the proportion of the downstream node capacity. Through case analysis, the validity of the model is verified, which helps to reveal the possibility of railway hazards and their propagation process. It can assist railway transportation safety decision-making and promote the safe operation of the system.
A Testing Method for Detecting Data Inconsistencies in Model View Controller Architecture

Testing user acceptance is a complex and labor-intensive task in software applications. Many methods and tools have been proposed to detect user interface layer errors and ensure user satisfaction. In addition, the layered architecture of software complicates the testing process. However, manual tests of the user interface are required because of the extensive number of input-output-database state combinations and difficulties in measuring user acceptance in many projects. Preparing a test scenario can increase the opportunity to detect errors and improve user satisfaction. This paper describes a generic test scenario for the model-view-controller pattern that is one of the most widely used web development architectures. This scenario provides predefined and generic test cases for detecting data errors in layers with extension points to develop project-specific tests. We evaluate the effectiveness of our approach through a web application using a controlled experiment with a predefined testing procedure. Our evaluation results show that the scenario-based approach gives promising results to improve test outcomes.
Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

Unmanned aerial vehicles (UAVs) have been instrumental in enabling many new applications and services, including military and rescue operations, aerial surveillance, civilian applications, precision farming, as well as providing extensive wireless network access in remote areas. They have also been used in other areas such as transmission line and oil rig monitoring, disaster recovery, etc. With their increasing payload ability and flight duration, using UAVs is a preferred choice for a multitude of upcoming wireless communication systems. However, because of their open operational nature, UAVs are highly vulnerable to severe security breaches via cyber attacks, eavesdropping on navigational and communication links, etc. Given their widespread applications, the requirement of secured UAV communications has become more and more critical since security failures can lead to detrimental consequences. Several works have examined the extent of privacy and security issues in UAV-assisted networks and introduced various mitigation techniques to address different security challenges. In this paper, a comprehensive survey is conducted that centers on the security issues related to UAV-aided networks. A descriptive taxonomy of various security intrusions on the UAV networks and commonly-used secrecy performance metrics are thoroughly reviewed. An in-depth discussion is provided on alleviating threats with proactive security techniques blended with key wireless communication technologies such as mmWave, non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and cognitive radio. Moreover, several emerging topics like machine learning, software-defined networks, fog and edge computing, blockchain, are discussed in the context of UAV-aided secure communications.
Maximum Total Quaternion Correntropy for Adaptive Filtering

Quaternion adaptive filters have been widely used in processing three-dimensional ($\mathbf {3}$-D) and $\mathbf {4}$-D signals. However, the performance of existing quaternion adaptive filtering algorithms will be deteriorated when both the input and output signals are disturbed by noises. To solve this problem, this paper first proposes a quaternion error-in-variables (QEIV) model in which the input and output signals are disturbed by noises, and develops a maximum total quaternion correntropy (MTQC) criterion to resist non-Gaussian noises. Then, combined with the total least squares (TLS) method, an MTQC algorithm is proposed based on the stochastic gradient method and quaternion generalized Hamilton-real (GHR) calculus for the proposed QEIV model. Furthermore, a variable kernel width strategy is given to avoid the problem of kernel width selection and a variable kernel width MTQC (VKWMTQC) algorithm is therefore proposed, simultaneously. More importantly, the local stability, the convergence condition, and steady-state performance of the proposed MTQC algorithm are theoretically analyzed by the quaternion calculus and quaternion matrix theories. Finally, simulation results verify the correctness of the theoretical analysis and demonstrate the superiority of the proposed MTQC algorithm for the QEIV model.
An Adaptive Ant Colony Clustering Algorithm and Application in the TSP

Clustering is the process of partitioning or grouping a given set of patterns into disjoint clusters. Clustering has been a widely studied problem in a variety of application domains including data mining, knowledge discovery, artificial intelligence and etc. As a swarm-intelligent method, the ant colony clustering algorithm (ACCA) is inspired by the behavior of ant colonies. In the ACCA, ants are allowed to move randomly, pick up and drop objects in clusters at certain probability, which depends on similarity with surrounding objects. Ant Colony optimization (ACO) is being used by many applications for past few years. In this paper, an effective clustering algorithm based Ant Colony optimization (ACO), in which adaptive strategy is integrated, is proposed. ACCA is applied to solving TSP. The experiment results indicate that the proposed algorithm is effective.
An Extension of Shapiro and Wilk's W Test for Normality to Large Samples

Shapiro and Wilk's (1965) W statistic arguably provides the best omnibus test of normality, but is currently limited to sample sizes between 3 and 50. W is extended up to n = 2000 and an approximate normalizing transformation suitable for computer implementation is given. A novel application of W in transforming data to normality is suggested, using the three‐parameter lognormal as an example.
Chronic Kidney Disease Prediction using Deep Neural Network

Chronic Kidney Disease (CKD) is a global health issue and symptoms are not always visible at the early stage. Deep learning techniques can be developed to determine the factors that potentially cause CKD at an early stage to enable patients to receive timely treatment. This paper attempts to forecast Chronic Kidney Disease (CKD) by analysing a set of attributes. A publicly available dataset with information collected in India was used for carrying out the research. Data was first preprocessed using different techniques to deal with missing values and outliers in the dataset. Next, classification between CKD and notCKD was performed using both Random Forest and Deep Neural network. The results of both methods were compared, and it was found that the proposed DNN model yielded a superior accuracy of 98.8% for the binary classification.
Responsibility-Sensitive Collision Risk Assessment and Maneuvering Safety Evaluation

Safety is one important basis for autonomous vehicles being accepted by the human society. Ideally, autonomous vehicles are expected to never cause accidents. However, the safety is somehow antagonistic to efficiency. Considering the real-world complex traffic conditions, e.g., traffic jams, drivers ignoring traffic rules, etc., a zero-accident transportation system is barely possible. To balance between traffic safety and efficiency, autonomous vehicles should be able to estimate the scenario risk and adjust its maneuvering strategy accordingly. This paper proposes a method to quantitively assess the collision risk of two vehicles during interactions. In other words, it measures the chance of a vehicle responsible for an accident. The risk is decided by the complexity of the interaction scenario, and thus is termed as responsibility-sensitive collision risk of the scenario. Based on the scenario risk assessment, a safety evaluation method is proposed to measure to what degree the maneuvering strategy is safe. It not only assesses what risks the target vehicle can afford, but also measures the relative safety capability compared to other vehicles in a statistical sense. The risk and safety evaluations also help to understand the maneuvering styles, i.e., aggressive or conservative.
Safe and Rule-Aware Deep Reinforcement Learning for Autonomous Driving at Intersections

Driving through complex urban environments is a challenging task for autonomous vehicles (AVs), as they must safely reach their mission goal, and react properly to traffic participants while obeying traffic rules. Deep reinforcement learning (DRL) is a promising method to generate driving policies for AVs because it can explore complex environments and learn suitable reactions. In this work, we present a DRL algorithm for AVs to handle intersection scenarios while considering traffic rules. Furthermore, we enhance the safety of our DRL algorithm's decisions by introducing a safety checker based on a responsibility-sensitive safety (RSS) model. Evaluations show that our DRL algorithm outperforms the baseline method by driving safely to reach the mission goal while obeying the traffic rules at an intersection.
Analysis of Emotions through the Use of Physiological Signals

Emotion is a psychosomatic process that is caused either by a conscious or non-conscious perception of an object or a situation whereas, the result of that process is depicted either by the expression of the face, or physically, or with a combination of the above. An important mechanism playing a significant role in the recognition of emotions is the encephalogram, which enables us to detect significant signals of the brain. The methodology developed aims to the identification of basic emotions by using an electroencephalogram through an experimental process where participants were asked to evaluate the experiment according to the valence, arousal and dominance of each emotion inflicted on them by videos they were subjected to watching. Through the experimental process, a vector of characteristics is analyzed for each of the electrodes of the electroencephalogram and for a series of videos. The training of a model and classification of algorithms were the key to finding the best methods in terms of sensitivity, accuracy, and specification of our data.