Miracle-dz/UniFAR-FTUs huggingface dataset:
Online 3-D Modeling of Complex Workpieces for the Robotic Spray Painting With Low-Cost RGB-D Cameras

The 3-D model of workpieces plays a significant role in robotic spray painting, which needs the geometry and shape information for painting-path planning or pose estimation. However, the models are rarely provided beforehand, even if they are available, random pose changes during workpiece conveying on the spray-painting pipeline also necessitates the usage of real-time 3-D reconstruction. In this case, we propose an online modeling approach for automatic spray-painting applications. Specifically, with two consumer RGB-D cameras mounted on the two sides of the pipeline, the data streams of the moving workpiece are continuously collected. Then, incorporating the 3-D contour feature and approximate linear motion characteristics of the workpiece, a modified iterative closest point (ICP) algorithm is proposed for fast registration of multiple workpiece point clouds with varying positions. Finally, based on the spatial distribution and point cloud quality evaluation, a joint weighted algorithm for fusing point clouds from different perspectives is proposed, which is effective for the issues of self-occlusion and has superior noise resistance. To estimate the system performance, an experimental platform was established to simulate the spray-painting production line. According to our experiments, the proposed method can completely model the complex workpieces with irregular polyhedral structure within 1500 ms, and the modeled geometry accuracy can reach less than 8 mm, which has a significant improvement compared with the conventional methods.
Transfer Learning on the Edge Networks

Transfer learning focuses on using extensive labeled data samples in the source domain to resolve a different yet related task for the target domain, even when there is no similarity among the training and testing problem’s datasets and distribution of features. This paper will discourse the implementation of the transfer learning model on edge networks to improve the performance factors and communication delay times within different servers. Any extensive system working with embedded systems is considered a high-performance system. An embedded system aims to perform some specific tasks based on the microprocessors, works on low resources and have less power consumption. An embedded system has a functional mapping, and various environment states to generate significant results. For the edge networks, the description of tasks and the dynamics of outer environment is crucial. For further clarification, we developed the transfer learning model. We experimented it on the embedded system using edge device (edge networks) and the local system to compare the time latency of the transfer learning model’s execution. As a result, we concluded that the transfer learning model works effectively and gives us decent accuracy. Implementing a transfer learning model on edge networks is better than implementing on a local system in terms of cost, performance and efficiency.
A Graph Convolutional Network With Multiple Dependency Representations for Relation Extraction

Dependency analysis can assist neural networks to capture semantic features within a sentence for entity relation extraction (RE). Both hard and soft strategies of encoding dependency tree structure have been developed to balance the beneficial extra information against the unfavorable interference in the task of RE. A wide application of graph convolutional network (GCN) in the field of natural language processing (NLP) has demonstrated its effectiveness in encoding the input sentence with the dependency tree structure, as well as its efficiency in parallel computation. This study proposes a novel GCN-based model using multiple representations to depict the dependency tree from various perspectives, and combines those dependency representations afterward to obtain a better sentence representation for relation classification. This model can maximally draw from the sentence the semantic features relevant to the relationship between entities. Results show that our model achieves state-of-the-art performance in terms of the F1 score (68.0) on the Text Analysis Conference relation extraction dataset (TACRED). In addition, we verify that the renormalization parameter in the GCN operation should be carefully chosen to help GCN-based models achieve its best performance.
Optimization of LLC Resonant Converter With Two Degrees of Freedom Based on Operation Stage Trajectory Analysis

LLC resonant DC-DC converter has been widely used in high power density and high efficiency applications. Pulse frequency modulation (PFM) is commonly applied due to its wide soft-switching range. In some special cases, phase shift modulation (PSM) is applied to widen voltage regulation range, improve light-load efficiency, or implement a soft start-up process. Because LLC is with 2 control degrees of freedom (2DOFs), switching frequency and duty ratio, a unified analysis and optimization for LLC with 2DOFs is desired. This paper makes an effort to present a unified time domain operation stage trajectories (OSTs) analysis of LLC. On this basis, a unified numerical optimization method is proposed with arbitrarily designed optimization objectives. With the proposed optimization method, the optimized LLC with 2DOFs is with better performance compared to conventional PFM and PSM, which is verified by the experimental results.
UAV and AI Application for Runway Foreign Object Debris (FOD) Detection

There are several ways in which Foreign Object Debris (FOD) are detected on runways. Some of these methods include Radar, infrared technologies, and stationary cameras mounted on the runway and use image processing tools to find these FODs. Radar technology is highly accurate when finding FODs but is highly inaccurate with small FOD items causing a high false-positive rate. Stationary based RGB camera-based methods also have a high false-positive rate prompting the shutdown of runways, creating disruptions for both the airport and the airline carriers. The paper presents a new method of detection by using an Unmanned Aerial Vehicle (UAV) to fly above the runway at a low altitude (e.g. < 30 m) to find FOD in. We developed a system that combines a UAV, an RGB camera, an Artificial Intelligence (AI) detector trained using deep learning methods and locally collected images over a runway. The classes specifically looked at were paper, metal, bolts, plastic, and plastic bottles. Different lighting conditions of both full sunlight and cloudy weather were taken into consideration when the images were collected. The detector was trained with various data augmentation techniques including resize, rotate, and colour augmentation. Results have concluded that there is a potential use for UAV's as a method of FOD detection, with a high rate of accuracy in the detections. This could lead to shorter timeframes and fewer disruptions where runways are closed.
Incorporating Behavior in Attribute Based Access Control Model Using Machine Learning

Preventing unauthorized and illegitimate access to sensitive resources is the primary duty of access control models. However, the malicious activities by authorized users cause significant damages to their underlying systems. In many cases, existing access control models are incomplete in their ability to detect insider abuse, and rather than detecting and preventing insider attack, it seems to still operate by forensic analysis after an attack. Attribute-Based Access Control is a new access control model that can be used instead of other traditional types of access control models, and makes decisions according to the access requests by utilizing users’ as well as resources’ attributes. However, it still endures a quandary of how to permit the real eligible users to access the resources while blocking abnormal access by authorized users of a system. In this paper, an Attribute/Behavior-Based Access Control is proposed by understanding and deriving users’ behaviors from log files. Not only our model uses the user/resource attributes, but it also utilizes their behaviors to detect the abnormal users even with valid attributes. This model principally uses the behaviors of a given user to grant or deny access requests. The concept of a user’s behavior will be introduced, and we present a feature construction method to model users’ access behaviors. As the proof of concept, machine learning algorithms are trained and tested using a database from UCI Machine Learning Repository. Experimental results illustrate that our model is efficient, accurate, and promising in detecting authorized users with abnormal behaviors.
Adaptive Management Shell for Mapping the Process Capability of Manufacturing Components: A Systematic Mapping Study

Being successful and competitive on the market means that companies have to adapt to the demands of their customers. Personalised products are increasingly becoming a matter of course for consumers, which leads to a reduction in the number of similar orders for manufacturing companies. To satisfy these requirements, new information and communication technologies are needed in industrial manufacturing. Industry 4.0 aims to address these challenges. However, many approaches are not yet implemented or mature, so there is a need for further research in this field. For this reason, a comprehensive and systematic mapping study was conducted to structure and categorize the current state of research in the field of self-describing and self-organizing manufacturing. The literature considered was published between January 2014 and May 2019. The research carried out is based on the guidelines for conducting systematic mapping studies. With regard to the technical implementation of this technology, a number of research questions are carefully defined. Based on these questions, data from different levels of information are extracted and analyzed for the considered papers. The study results show in which areas more work is required and where there are future research perspectives. Furthermore, this study can help to better understand the field of research and the research gaps identified.
Use of iBeacon technology for safe movement of disabled people

The authors present the results of their practical research on the navigation of people with disabilities inside buildings using iBeacon technology. This technology's navigation potential lies in indoor use and everywhere, where GPS-like systems fail due to lack of coverage. Even though iBeacon technology is simple (it only broadcasts a signal), its potential is high if paired with suitable software. In such a case, it may not only serve as a navigation system, but also generate information. The authors used multi-criteria analysis to find out what type of iBeacon is the most suitable one for indoor navigation of people with disabilities. Their analysis also resulted into a set of recommendations for distribution and settings of iBeacon with respect of the topology of the building.
A Fast Self-Jamming Cancellation Architecture and Algorithm for Passive RFID Sensor System

In this letter, we propose a fast self-jamming cancellation (SJC) architecture for Ultra High Frequency (UHF) radio-frequency identification (RFID) interrogators. The core SJC architecture includes a Field Programmable Gate Array (FPGA) as the RFID baseband processor, a radio frequency (RF) agile transceiver which has two transmitting ports as the RF front-end, and a combiner. By detecting the amplitude and phase of the interference carrier, the SJC algorithm transmits a suppression carrier through the second transmitting port of the agile RF transceiver. The suppression carrier could effectively suppress the self-jamming carrier without designing complex circuits. Our test results showed the isolation between the transmitting and receiving port achieves 100dB at UHF band, and the cancellation processes can be finished within 0.4ms.
Permission-Based Approach for Android Malware Analysis Through Ensemble-Based Voting Model

Smart devices have become an integral part of our lives as mobile and wireless technology has advanced. Although Android has the largest market share, it is also the platform most frequently attacked by hackers. More Android malware is being created as the use of Android cell phones grows. Malware detection on Android has become a critical mission. We present a permission-ensemble-based malware detection mechanism for Android in this paper. Permission combinations declared in the device manifest file is used to detect malware on Android. We collected the permission combinations that malware and benign apps often request. We developed an ensemble model that uses permission combinations to distinguish between malicious and benign apps. We showed that the combination of classifiers into an ensemble model provided better accuracy than an individual classifier. We experimentally proved that our ensemble model is robust to the changing nature of data. The malware detection rate is up to 99.3% in our experimental evaluation. According to our experiments with real malware, our proposed Android malware detection scheme is compelling and reliable.
Accelerating Spike-by-Spike Neural Networks on FPGA With Hybrid Custom Floating-Point and Logarithmic Dot-Product Approximation

Spiking neural networks (SNNs) represent a promising alternative to conventional neural networks. In particular, the so-called Spike-by-Spike (SbS) neural networks provide exceptional noise robustness and reduced complexity. However, deep SbS networks require a memory footprint and a computational cost unsuitable for embedded applications. To address this problem, this work exploits the intrinsic error resilience of neural networks to improve performance and to reduce hardware complexity. More precisely, we design a vector dot-product hardware unit based on approximate computing with configurable quality using hybrid custom floating-point and logarithmic number representation. This approach reduces computational latency, memory footprint, and power dissipation while preserving inference accuracy. To demonstrate our approach, we address a design exploration flow using high-level synthesis and a Xilinx SoC-FPGA. The proposed design reduces <inline-formula> <tex-math notation="LaTeX">$20.5\times $ </tex-math></inline-formula> computational latency and <inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> weight memory footprint, with less than 0.5% of accuracy degradation on a handwritten digit recognition task.
Deploying Two-Tiered Wireless Sensor/Actuator Networks Supporting In-Network Computation

The centralized computing model in traditional Wireless Sensor/Actuator Networks (WSANs) can lead to large delays and unbalances, which severely restricts the adoption of WSANs in applications requiring high network performance. To address this limitation, the in-network computation model has been proposed, in which the computation capability is distributed among wireless nodes in WSANs, i.e., wireless nodes perform not only data communication but also data processing. Node placement is a primary step to build the underlaying topologies of WSANs. Nevertheless, the problem of node placement to design underlaying network topologies supporting in-network computation is still unexplored. To this end, we propose an In-network-oriented Node Placement Algorithm (INPA) to build WSANs supporting in-network computation. Moreover, we investigate the time complexity of INPA and verify the efficiency of INPA through extensive simulations.
Regolith Cohesion Measurement via Induced Electrostatic Lofting

Electrostatic dust lofting has been hypothesized to occur on airless bodies such as the Moon and asteroids, but in-situ evidence of this phenomenon has yet to be observed. Nonetheless, experiments and numerical models have provided insight into the fundamental physics of electrostatic dust mobilization. Prior to lofting, grains are bound tightly to the surface by cohesion, the dominant force for sub-mm particles. However, the magnitude of cohesion in regolith remains poorly constrained. We introduce the design of the Electrostatic Sample Collection and Cohesion Quantification (E-SACCQ) system, a technology that induces electrostatic dust lofting to measure regolith cohesion. E-SACCQ induces electrostatic lofting of charged regolith grains via a biased attractor plate and simultaneously images their size and trajectory. Since the local gravity is known and the electrostatic force on the regolith grains is controlled by the attractor plate potential, it is possible to solve for the cohesive force on the grains. Furthermore, the ability to induce electrostatic lofting may also provide a new method of dust removal from spacecraft surfaces and for sample collection on rubble pile asteroids, which may not have suitable, powdery surfaces for conventional scoop style collection methods. In this work, we discuss the preliminary design of the instrument. The feedback between the E-SACCQ electrode and the near-surface plasma environment is numerically modeled. Our models predict that solar wind bombardment will be a significant perturbation to the electric field between the surface and the electrode. The system's sensitivity to key design parameters such as attractor plate potential, size, and operating distance above the surface are also assessed. With respect to grain characterization and position tracking, stereo vision is selected as the preferred solution. Additional plasma simulation modeling and experimental demonstration is required to mature this technology.
Design and Implementation of a Semi-supervised Anomaly Log Detection Model DDA

In this paper, a semi-supervised log anomaly detection model DDA was proposed. By modeling and deeply optimizing the log extraction function of Drain and the log sequence anomaly detection function of DeepLog and by combining the first-order outlier detection algorithm of parameters, an end-to-end log anomaly detection model - DDA was constructed. The DDA model was divided into front-end and back-end, where the front-end is responsible for preprocessing the logs such as specification, extraction of parameters and templates, and clustering, and the back-end implements the core functions of log anomaly detection. Experiments demonstrated the superior performance of DDA in terms of anomaly detection rate.
Securing Implantable Medical Devices Using Ultrasound Waves

Modern Implantable Medical Devices (IMDs) are vulnerable to security attacks because of their wireless connectivity to the outside world. One of the main security challenges is establishing trust between the IMD and an external reader/programmer in order to facilitate secure communication. Numerous device-pairing schemes have been proposed to address this specific challenge. However, they alone cannot protect against a battery-depletion attack in which the adversary is able to keep the IMD occupied with continuous authentication requests until the battery empties. As a result, energy harvesting has been employed as an ancillary mechanism for implementing Zero-Power Defense (ZPD) functionality in order to protect against such a low-cost attack. In this paper, we propose SecureEcho, a device-pairing scheme based on MHz-range ultrasound that establishes trust between the IMD and an external reader. In addition, SecureEcho achieves ZPD without requiring any energy harvesting, which significantly reduces the design complexity. We also provide a proof-of-concept implementation and a first ever security evaluation of the ultrasound channel, which proves that it is infeasible for the attacker to eavesdrop or insert messages even from a range of a few millimeters.
A systematic mapping of feature extraction and feature selection methods of electroencephalogram signals for neurological diseases diagnostic assistance

Electroencephalogram (EEG) is a non-invasive tool used to monitor the electrical activities of the brain. EEG signal analysis has several applications in the medical field. It is widely used for clinical diagnostics and for advances in the Brain-Computer Interface (BCI) area. In recent years, several studies about the automatic execution of this analysis have been proposed, motivated by the fact that the visual inspection demands a long time from an expert, besides being subject to a misdiagnosis. In order to extract and select relevant information from recordings of brain electrical activity, many computerized analysis methods have been developed based on different approaches. Although some proposed methods have achieved good performance, many of them are not suitable for real-world application due to their high computational cost. Thus, there is opportunity in the area to identify and categorize techniques in order to support studies about the influence of these techniques on diagnostic performance and to propose the optimization of this task. In this context, this Systematic Mapping evaluates the 144 primary studies identified according to the criteria defined in the protocol. The purpose is to highlight which neurological disorders have been studied in recent years and which techniques for feature extraction and feature selection have been applied during signal analysis, individually or jointly, to provide specifically an automatic diagnosis of a neurological disorder using a classifier.
High Single-Event Burnout Resistance 4H-SiC Junction Barrier Schottky Diode

This paper presents a single-event burnout (SEB) resistance method for 4H-SiC Junction Barrier Schottky Diode (JBS) under high bias voltage and linear energy transfer (LET) conditions. The method is validated via two-dimensional numerical simulations. The analysis and comparison of conventional 4H-SiC JBS and 4H-SiC Multi-Buffer Layer JBS (MBL-JBS) diodes verify the resistance tolerance of the latter device to SEB. Silvaco TCAD simulation results prove that the 4H-SiC MBL-JBS modulates the drift region’s electric field distribution and disperses the high-peak electric field at the N−/N+ junction. Moreover, it reduces the impact generation rate at the Schottky, PN and N−/N+ junctions to achieve SEB tolerance. The device’s SEB performance is optimized by adjusting the 4H-SiC MBL-JBS structure parameters, and the SEB threshold voltage is significantly improved compared with the traditional structure.
A Satisfactory Detection Framework on Online-shopping Comments

Online shopping is becoming one of the main channels of daily shopping. After buying, shoppers usually express their experience in the shopping process through ratings and written comments, which are also important bases for other shoppers to make choices between similar goods and similar sellers. The purpose of this paper is to design a structure for analyzing the text to quantify the consumer satisfaction hidden behind the review text, so as to guide sellers and consumers to a more refined understanding of the potential consumption behavior and make the comparison easier and more direct.
Imaging Through Turbulent Media Using Deep Learning Method

We present deep learning method that can be used to reconstruct high-quality objects through turbulent media mixed with water and milk. The objects are placed behind turbulent media, and a series of speckle patterns are correspondingly recorded. By using many pairs of the recorded speckle patterns and input object images, a designed convolutional neural network (CNN) is fully trained, and then enables the recorded speckle patterns to be processed in real time. The proposed method is promising for imaging through turbulent media, and it is also believed that the proposed method can be applicable in many areas, e.g., imaging and information optics (such as optical encoding).