Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable-Grasping-Mode Gripper With Different Finger Structures For Grasping Small-Sized Items

This letter presents a novel small gripper capable of grasping various types of small-sized items from flat surfaces for the assembly of small devices. Using a single actuator, the proposed gripper realizes two grasping modes: parallel-grip and turn-over modes. The gripper's mode can be switched via contact with a flat surface, such as a table. Handling thin thicknesses and light weights are the key challenges faced in attempts to grasp small-sized items. Although parallel grippers are effective in handling small items, there is a limit to the thinness of objects that can be grasped by parallel grippers. Accordingly, the turn-over mode was adopted to grasp items that exceeded this threshold. In the turn-over mode, one finger lifts the item, while another finger holds the item from above to keep it from flicking out. The proposed gripper is capable of picking up several types of items from a table, including thin (0.05 mm) and lightweight (0.007 g) items.
A multi-instance LSTM network for failure detection of hard disk drives

Hard disk (HDD) failure is the most important reliability issue in the data center. Therefore, the prediction of hard disk failure has become the focus of attention of major data centers. However, most current research work does not notice the fact that the data on the hard disk is mostly unlabeled data. Since the degradation period in HDD is very short, the mixture of health data and erroneous data can cause serious data imbalance. This makes fault prediction a difficult task. In response to the above problems, a multi-instance long-term sequence classification method based on long-short-term memory (LSTM) network is proposed. By dividing the longterm sequence data packet into multiple instances, the relationship between the instance and the sample label is studied to predict HDD failure. Through the analysis of the hard disk data of a communication company and the Backblaze data center, this method can obtain better results than other methods.
Formal Specification and Analysis of Spacecraft Collision Avoidance Run Time Assurance Requirements

One of the greatest challenges preventing the use of advanced controllers in aerospace is developing methods to verify, validate, and certify them with high assurance. One emerging approach is to push the burden of assurance from offline verification of an autonomous controller at design time, to online verification of safe behavior through a monitor and high assurance backup controller at run time. Run time assurance goes a step beyond alerting systems by detecting imminent unsafe behavior and intervening with a trusted control response. In the spacecraft domain, autonomous operations could be approved if run time assurance systems can provide collision avoidance capabilities. While several approaches to run time assurance have been developed and successfully demonstrated, the design and verification of these systems is ad hoc and specific to the application. This paper describes the elicitation, formal specification, and analysis of general collision avoidance system requirements for a conceptual spacecraft conducting autonomous close-proximity operations based on a run time assurance construct. This includes the first formally specified and analyzed generalized run time assurance architecture for spacecraft that includes a fault monitor, interlock monitor, and human-machine interface. Mathematically precise requirements are elicited through the process of formal specification based on common design elements, spacecraft guidance constraints in the literature, and a structured hazard assessment. Finally, the requirements are analyzed using compositional reasoning and formal model checking verification techniques.
Network Traffic Flow Based Machine Learning Technique for IoT Device Identification

Security and privacy issues are being raised as smart systems are integrated into our daily lives. New security issues have emerged with several new vendors that develop the Internet of Things (IoT) products. The contents and patterns of network traffic will expose vulnerable IoT devices to intruders. New methods of network assessment are needed to evaluate the type of network connected IoT devices. IoT device recognition would provide a comprehensive structure for the development of stable IoT networks. This paper chooses a machine learning technique to identify IoT devices linked to the network by analyzing network flow sent and received. To generate network traffic data, we have developed a dataset adapted from the IoT23 Pcap files to experiment with a smart home network. We have created a model to identify the IoT device based on network traffic analysis. We evaluate our proposed model via full features dataset, reduces features dataset, and flow-based features dataset. This paper focuses on using flow-based features to identify the IoT device connected to the network. Our proposed scheme results in 100% precision, precision, recall, and F score via a full features dataset, reduced features dataset, and flow-based features dataset. Through evaluations using our produced dataset, we demonstrate that the proposed model can accurately classify IoT devices.
Lunar Surface Mobility: Robotic and Crewed System Concepts

NASA is planning to land the first woman and the next man on the Moon by 2024 in order to enable human expansion across the solar system and bring back to Earth new knowledge and opportunities. The exploration and scientific discovery of the Moon can be enhanced using technology that aids capabilities of astronauts on the Lunar surface. In addition to the key objective of Lunar science, these supporting systems will demonstrate technology and operations that feed forward to the human exploration of Mars. Mobility systems are essential to the exploration of both the Moon and Mars and can transport scientific instruments, humans, or cargo extended distances, supporting both scientific discovery and exploration objectives. NASA has expressed interest in working with international partners, and surface infrastructure or mobility solutions are a good candidate for this collaboration. Lockheed Martin has conducted trade studies to determine the balance between robotic and human-driven Lunar mobility options, the benefits of each, the correlation between number of mobile systems and increased science return, requirements that drive the level of autonomy, and how mobility options may be operated to achieve the highest science return for the Artemis program objectives. The trades also examine the balance of purely robotic rovers, unpressurized rovers, pressurized rovers, and stationary and mobile habitats. Concepts have been developed for Lunar rovers based on the results of these trades. This paper will discuss the results of these trades and review the rover concepts developed.
Wideband Circularly Polarized Antenna With Reconfigurable 2-Dimensional Axial Ratio Beamwidth

This paper proposes a wideband circularly polarized antenna with a reconfigurable 2-dimensional axial ratio. For mobile satellite communication, the low profile CP antenna with wide axial ratio scanning and a wide frequency bandwidth is essential. In this paper, we propose the wideband CP antenna with an axial ratio controller. The axial ratio deteriorates when the difference between transverse modes (TM)01 and TM10 gets larger. Therefore, in the proposed antenna, the axial ratio controller is used to compensate for the difference by controlling the amplitude and phase of the received signals. The proposed antenna has three mode states for the 2-dimensional axial ratio region. In a simulation of the wideband CP antenna, the axial ratio was below 3 dB in the 2-dimensional scanning region from −60° to 60° at every azimuth angle in the 20% factional frequency bandwidth. To verify the proposed antenna with the axial ratio controller, the wideband CP antenna is implemented at 5.8 GHz. A low noise amplifier (LNA) and digital phase shifters are employed for the axial ratio controller. The measured axial ratio was below 3 dB in the 2-dimensional scanning region from −60° to 60° at every azimuth angle in the 20% factional frequency bandwidth.
Research on braking&acceleration identification based on EEG

The driver needs to brake and accelerate on the right foot in the process of driving, and occasionally there will be misoperation, which will bring potential accidents, so the study of the driver's awareness of right foot movement is of great significance. In this paper, we study the car driving behavior identification of consciousness, as a brake or accelerate driving action consciousness as object recognition, EEG (Electroencephalogram, EEG) signals are collected in virtual driving. based on PDC (Partial Directed Coherence, PDC) build Functional Brain networks (Functional Brain Network, FBN), identify in two kinds of action under the consciousness of different Functional Brain networks. By using the graph theory method: Degree (D), Clustering Coefficient (C) and Global Efficiency (E) were input into Cubic SVM as features to classify and recognize different action consciousness. The results show that the method based on PDC can realize the classification and recognition of driving action consciousness, and its classification accuracy reaches 91.9%.
A Reconfigurable SRAM Based CMOS PUF With Challenge to Response Pairs

This paper presents a reconfigurable SRAM-based physically unclonable function (PUF) topology with multiple challenge-response pairs (CRPs) per cell. The proposed PUF structure enables a very large CRP space by connecting additional pull-up and pull-down paths to each SRAM cell. These alternate pathways to the supply rail and ground are activated by the challenge inputs, which effectively reconfigure the transfer characteristics of each cross-coupled inverter. A newly proposed response instability detector improves bit error rate (BER) performance by discarding unstable response. In addition, the proposed PUF adds indirect challenges by scrambling the responses using a Galois linear feedback shift register (LFSR). The proposed PUF can be applied to a wider range of applications as a CRP PUF because it has multiple CRPs in addition to the small area and fast operating speed, which are the advantages of the conventional SRAM structure. In order to verify the performance of the proposed architecture, a $32\times 32$ -bit reconfigurable SRAM PUF array with 32-bit challenge is implemented in a 65 nm CMOS process. Experimental results show a core area of $88.867~\mathrm {\mu } \mathrm {m^{2}/bit}$ , energy efficiency of 0.082 pJ/bit, and inter-chip Hamming distance (HD) of 48.93% across 40 chips. By applying an unstable bit discard (UBD) scheme, BER is improved from 13.7% to 0.9%. Compared to the state-of-the-art, the proposed PUF is shown to be highly competitive in area, throughput and energy efficiency.
Multiple Objects Localization Using Image Segmentation with U-Net

Precise object localization in an industrial environment is a significant task affecting follow-up processes for a pick and place application. One of the solutions to effectively ensure the success of this task is to use modern methods of machine vision. Machine vision is still a highly evolving topic, in which the use of approaches based on convolutional neural networks is rising. And so in this contribution, an innovative engineering approach based on convolutional neural networks is proposed for an object localization task. The approach is based on an atypical image segmentation, where the individual objects are represented by two colored gradient circles. These circles represent significant parts of the object like its center or ending. Each object type (class) is determined by a specific color. By use of a local maxima finder, all circles in an image are transformed to points. With knowledge of these points the coordinates and rotations are calculated. The proposed approach was tested on a legitimate localization problem with 100% precision, more than 99.52% recall on the positioning task and with an average of 6 minutes angle variance per object.
A GWO-AFSA-SVM Model-Based Fault Pattern Recognition for the Power Equipment of Autonomous vessels

The power equipment of autonomous vessels usually works in a particular environment, which provides few samples of fault monitoring signal. The method of the support vector machine (SVM) is adopted to deal with the problem of fault pattern identification under small sample conditions. An improved GWO-AFSA algorithm is proposed to avoid the poor convergence accuracy caused by the random selection of kernel parameters and penalty factors for SVM. The Grey Wolf algorithm (GWO) is adopted to optimize the erratic behavior of fish swarm, which improves the problem that the traditional Artificial Fish Swarm Algorithm (AFSA) is easy to fall into local extremum. A benchmark example demonstrates that the GWO-AFSA-SVM model can accurately and effectively identify the fault pattern types of ship power equipment for autonomous vessels.
A Novel Access Control Method Via Smart Contracts for Internet-Based Service Provisioning

The dramatic rise in internet-based service provisioning has highlighted the importance of deploying scalable access control methods, facilitating service authorization for eligible users. Existing centralized methods suffer from single-point-of-failure, low scalability, and high computational overhead. In addition, in these methods, users pay for the service provider as well as the network provider independently for a specific service, imposing extra cost for the user. New business models are needed to resolve such shortcomings. The realization of these models calls for sophisticated access control methods which consider the requirements of all parties who want to: 1) access a service; 2) provide that service; and 3) provide the network connection. Blockchain is an enabling technology that provides unprecedented opportunities to novel distributed access control methods for new business models. We propose an Attribute-based access control solution by leveraging Blockchain to share network providers’ and service providers’ resources. Our solution offers access flexibility based on the requirements of the parties while fulfilling reliability, accountability, and immutability. Besides, it decreases the overall service cost which is beneficial for each party. Our solution makes it possible for service providers to outsource their access control procedures without requiring a trusted third party. The experiments confirm that our solution can provide a fast, comprehensive, and scalable access control mechanism.
Multi-View Attention-Guided Multiple Instance Detection Network for Interpretable Breast Cancer Histopathological Image Diagnosis

Deep learning approaches have demonstrated significant progress in breast cancer histopathological image diagnosis. Training an interpretable diagnosis model using high-resolution histopathological image is still challenging. To alleviate this problem, a novel multi-view attention-guided multiple instance detection network (MA-MIDN) is proposed. The traditional image classification problem is framed as a weakly supervised multiple instance learning (MIL) problem. We first divide each histopathology image into instances and form a corresponding bag to fully utilize high-resolution information through MIL. Then a new multiple-view attention (MVA) algorithm is proposed to learn attention on the instances from the image to localize the lesion regions in this image. A MVA-guided MIL pooling strategy is designed for aggregating instance-level features to obtain bag-level features for the final classification. The proposed MA-MIDN model performs lesion localization and image classification, simultaneously. Particularly, we train the MA-MIDN model under the deep mutual learning (DML) schema. This transfers DML to a weakly supervised learning problem. Three public breast cancer histopathological image datasets are chosen to evaluate classification and localization results. The experimental results demonstrate that the MA-MIDN model is superior to the latest baselines in terms of diagnosis accuracy, AUC, Precision, Recall, and F1. Notably, it achieves better localization results without compromising classification performance, thereby proving its higher practicality. The code for the MA-MIDN model is available at https://github.com/lcxlcx/MA-MIDN.
A Novel Multi-Sensor Fusion Based Object Detection and Recognition Algorithm for Intelligent Assisted Driving

The object detection and recognition algorithm based on the fusion of millimeter-wave radar and high-definition video data can improve the safety of intelligent-driving vehicles effectively. However, due to the different data modalities of millimeter-wave radar and video, how to fuse the two effectively is the key point. The difficulty lies in the data fusion methods such as insufficient adaptability of image distortion in data alignment and coordinate transformation and also the mismatching of information levels of the data to be fused. To solve the problem of data fusion of millimeter wave radar and video, this paper proposes a decision-level fusion method of millimeter-wave radar and high-definition video data based on angular alignment. Specifically, through the joint calibration and approximate interpolation, projected to polar coordinate system, the radar and the camera are angularly aligned in the horizontal direction. Then objects are detected by a deep neural network model from video data, and combined with those detected by radar to make the joint decision. Finally, object detection and recognition task based on the fusion of the two kinds of data is completed. Theoretical analysis and experimental results indicate that the accuracy of the algorithm based on the two data fusion is superior to that of the single detection and recognition algorithm on the basis of millimeter-wave radar or video data.
Four-Port Wide-Band Cavity-Backed Antenna With Isolating X-Shaped Block for Sub-6 GHz 5G Indoor Base Stations

A four-port wideband cavity-backed antenna is presented for indoor base stations applications. The antenna is composed of a square open cavity with an X-shaped isolating block and 4 feeding monopoles symmetrically and orthogonally arranged in the aperture of the cavity. A novel methodology based on Characteristic Modes Analysis (CMA) is used for identifying the modes which are contributing to the coupling. As a result of this analysis, an X-shaped isolating block placed at the center of the cavity is proposed for increasing the isolation between ports. A wide-band four-port antenna with unidirectional radiation patterns is obtained with a measured impedance bandwidth (<inline-formula> <tex-math notation="LaTeX">$\text{S}_{11} < -10$ </tex-math></inline-formula> dB) ranging from 1.55 to 6 GHz (118%), covering most of the sub-6 GHz 5G bands. The proposed antenna provides four independent radiation patterns, with 16 dB of measured minimum isolation between ports and an efficiency higher than 84%. Multiple-input multiple-output (MIMO) compatibility is confirmed with a <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> MIMO simulated system with an envelope correlation coefficient (ECC)< 0.5 in different propagation conditions. The antenna is easy to fabricate and presents a compact size of <inline-formula> <tex-math notation="LaTeX">$129.5\times 129.5 \times 28.2$ </tex-math></inline-formula> mm<sup>3</sup> (<inline-formula> <tex-math notation="LaTeX">$0.68\lambda \times 0.68\lambda \times 0.15\lambda $ </tex-math></inline-formula>, at a frequency f <inline-formula> <tex-math notation="LaTeX">$=\,\,\text{f}_{min} =1.55$ </tex-math></inline-formula> GHz). Moreover, the antenna has the advantage of avoiding complex feeding structures with baluns or directional couplers.
Using Recurrence Quantification Analysis to Quantify the Physiological Synchrony in Dyadic ECG Data

Socio-physiological compliance (SPC) is highly regarded in team settings and has attracted significant interest from science of teams researchers. Linear measures of heart rate variability (HRV) has been widely adopted to observe and analyse SPC by evaluating physiological interconnection between members of collaborating teams. In the recent past, nonlinear measurements such as Recurrent Quantification Analysis (RQA) has gained a significant momentum in the measurement of physical stress experienced by individuals using the electrocardiogram (ECG) recordings. In a team context, however, literature shows variants of RQA such as Cross-RQA and Multivariate-RQA have been commonly employed to analyse behavioural data from accelerometer or eye-tracking recordings, respectively. The current study presents an analysis employing RQA in a team setting using the HRV data derived from ECG recordings. From individuals, RQA is employed to extract nonlinear features of HRV, and the physiological synchrony between cognitively engaging participants is quantified via Pearson’s correlation coefficient. Most importantly, the study explores the effectiveness of these nonlinear HRV features as alternate options to assess SPC. In terms of capabilities to distinguish collaborating pairs’, the RQA measures; recurrence rate, laminarity, determinism and entropy, were observed to be robust in short-term window lengths and equally sensitive compared to linear features of HRV.
Content Delivery Analysis in Cellular Networks With Aerial Caching and mmWAVE Backhaul

In this paper, we investigate the successful content delivery (SCD) performance in the unmanned aerial vehicle (UAV) integrated terrestrial cellular networks, where the caching-enabled UAVs are dispatched to offload the burst traffic from the cellular networks. Specifically, the UAV and terrestrial cellular network share the same spectrum resources for user downlink communications and each UAV uses millimeter wave (mmWave) communications for self-backhaul. We derive a closed-form expression of the achievable rate of the mmWave wireless backhaul link and then analyze the minimum cache hit probability to achieve a certain backhaul rate requirement. By approximating the general probabilistic line-of-sight (LoS) propagation model as a LoS ball model, we analyze the conditional SCD probabilities by leveraging stochastic geometry tools. Simulation results demonstrate that the UAV integrated cellular network can not only provide more access opportunities but also achieve higher SCD performance than the conventional terrestrial network for ground users. Moreover, there exists an optimal UAV density and height to maximize the SCD performance. Our results provide useful guidelines for the design and deployment of future UAV-assisted networks.
Inter-patient ECG Classification Based on Residual Networks and Active Learning

In this paper, an inter-patient electrocardiogram (ECG) classification algorithm based on residual networks and active learning is proposed. Our model is an end-to-end model. We use Convolutional Neural Networks (CNN) to extract morphological features of each ECG beat and use Long Short-Term Memory (LSTM) to extract temporal features. we apply active learning to select the most informative beats and incorporate them into the training set to improve algorithm performance. When evaluated on the MIT-BIH Arrhythmia Database, the method our proposed gives 97.7% sensitivity and 61.6% positive predictive for ventricular ectopic beat (VEB). For supraventricular ectopic beat (SVEB), our method gives 89.4% sensitivity and 41.5% positive predictive. Considering the high sensitivity in detecting these two pathological classes, this method has potential clinical application.