Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully-printed stretchable pressure sensor arrays

In this paper we present elastomeric large-area arrays of pressure sensors based on printed electronics. Two applications are presented to demonstrate the versatility and scalability of the presented technology. The first consists of a shoe inlay with over 700 sensors (5 mm pitch) and the second consists of large-area sensor mat (30 cm × 40 cm) for posture detection. Both designs have been manufactured using sheet-to-sheet screen printing on stretchable polyurethane substrates yielding sensors thinner than 350 μm.
History-Guided Configuration Diversification for Compiler Test-Program Generation

Compilers, like other software systems, contain bugs, and compiler testing is the most widely-used way to assure compiler quality. A critical task of compiler testing is to generate test programs that could effectively and efficiently discover bugs. Though we can configure test generators such as Csmith to control the features of the generated programs, it is not clear what test configuration is effective. In particular, an effective test configuration needs to generate test programs that are bug-revealing, i.e., likely to trigger bugs, and diverse, i.e., able to discover different types of bugs. It is not easy to satisfy both properties. In this paper, we propose a novel test-program generation approach, called HiCOND, which utilizes historical data for configuration diversification to solve this challenge. HiCOND first infers the range for each option in a test configuration where bug-revealing test programs are more likely to be generated based on historical data. Then, it identifies a set of test configurations that can lead to diverse test programs through a search method (particle swarm optimization). Finally, based on the set of test configurations for compiler testing, HiCOND generates test programs, which are likely to be bug-revealing and diverse. We have conducted experiments on two popular compilers GCC and LLVM, and the results confirm the effectiveness of our approach. For example, HiCOND detects 75.00%, 133.33%, and 145.00% more bugs than the three existing approaches, respectively. Moreover, HiCOND has been successfully applied to actual compiler testing in a global IT company and detected 11 bugs during the practical evaluation.
Research on Hierarchical Division of Topology in Communication Network

The routing of large-scale communication network is a NP problem. There is a consensus in the industry that hierarchical division of network can solve this problem and ensure the scalability of the network system. Vector Network(VN), as a new network architecture, requires the network to meet the requirements of QoS, reliability and security under scalable condition. This paper studies the division method of multi-level communication network, which is the basic technology for the excellent performance of vector network. Based on the Louvain community division algorithm, this paper proposes an improved Louvain algorithm. This algorithm can effectively divide the network into hierarchical structure. And the experimental results show that the new algorithm improves the execution efficiency and achieves good results.
Deep Learning-Based System for Automatic Melanoma Detection

Melanoma is the deadliest form of skin cancer. Distinguishing melanoma lesions from non-melanoma lesions has however been a challenging task. Many Computer Aided Diagnosis and Detection Systems have been developed in the past for this task. They have been limited in performance due to the complex visual characteristics of the skin lesion images which consists of inhomogeneous features and fuzzy boundaries. In this paper, we propose a deep learning-based method that overcomes these limitations for automatic melanoma lesion detection and segmentation. An enhanced encoder-decoder network with encoder and decoder sub-networks connected through a series of skip pathways which brings the semantic level of the encoder feature maps closer to that of the decoder feature maps is proposed for efficient learning and feature extraction. The system employs multi-stage and multi-scale approach and utilizes softmax classifier for pixel-wise classification of melanoma lesions. We devise a new method called Lesion-classifier that performs the classification of skin lesions into melanoma and non-melanoma based on results derived from pixel-wise classification. Our experiments on two well-established public benchmark skin lesion datasets, International Symposium on Biomedical Imaging(ISBI)2017 and Hospital Pedro Hispano (PH2), demonstrate that our method is more effective than some state-of-the-art methods. We achieved accuracy and dice coefficient of 95% and 92% on ISIC 2017 dataset and accuracy and dice coefficient of 95% and 93% on PH2 datasets.
Combining Interactive Spatial Augmented Reality with Head-Mounted Display for End-User Collaborative Robot Programming

This paper proposes an intuitive approach for collaborative robot end-user programming using a combination of interactive spatial augmented reality (ISAR) and headmounted display (HMD). It aims to reduce user’s workload and to let the user program the robot faster than in classical approaches (e.g. kinesthetic teaching). The proposed approach, where user is using a mixed-reality HMD – Microsoft HoloLens – and touch-enabled table with SAR projected interface as input devices, is compared to a baseline approach, where robot’s arms and a touch-enabled table are used as input devices. Main advantages of the proposed approach are the possibility to program the collaborative workspace without the presence of the robot, its speed in comparison to the kinesthetic teaching and an ability to quickly visualize learned program instructions, in form of virtual objects, to enhance the users’ orientation within those programs. The approach was evaluated on a set of 20 users using the within-subject experiment design. Evaluation consisted of two pick and place tasks, where users had to start from the scratch as well as to update the existing program. Based on the experiment results, the proposed approach is better in qualitative measures by 33.84% and by 28.46% in quantitative measures over the baseline approach for both tasks.
Statistical Log Differencing

Recent works have considered the problem of log differencing: given two or more system's execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which accounts for frequencies of behaviors found in the logs. We present two algorithms, s2KDiff for differencing two logs, and snKDiff, for differencing of many logs at once, both presenting their results over a single inferred model. A unique aspect of our algorithms is their use of statistical hypothesis testing: we let the engineer control the sensitivity of the analysis by setting the target distance between probabilities and the statistical significance value, and report only (and all) the statistically significant differences. Our evaluation shows the effectiveness of our work in terms of soundness, completeness, and performance. It also demonstrates its effectiveness compared to previous work via a user-study and its potential applications via a case study using real-world logs.
Development and Validation of an Automatic Steering Control System for Rubber-Tire Transit Revenue Service

Manually driving a 32 meters long rubber-tire transit with highly intensive concentration could easily get driver fatigue. Alongside the vision navigation method, this paper describes an automatic path tracking control method. Firstly, introduce the automatic steering control principle which is based on image recognition with optical guidance. Then, specifically describes the path tracking controller design. Finally, the control effect is verified by both joint simulation of Trucksim-MATLAB/Simulink and the real vehicle tests. The results show that the precise docking clearance between the platform and the transit is kept at 10-20cm. When driving at high speed, the stable speed is up to 72km/h, and the lateral deviation is controlled within 0.15m.
A Deep Learning Approach to Driver Distraction Detection of Using Mobile Phone

Using mobile phone while driving is a big threat to traffic safety. In the rail transit, in order to prevent the driver from being distracted by the mobile phone, the real-time monitoring of driversâ€™ behavior through video analysis technology is especially important. At present, the driver's cell phone usage detection methods are prone to object occlusion, image rotation, illumination change and are difficult to extract deep features of the image, etc., which results in a low accuracy. Therefore, this paper proposes a driver's cell phone usage detection algorithm based on deep learning. The proposed algorithm comprises 2 steps, Firstly, face detection and face tracking using PCN (Progressive Calibration Networks), determining the calling detection area. Secondly, the convolution neural network-based driver's cell phone detection method is used to detect the cell phone in the candidate area. Our experiments show that the accuracy of the proposed algorithm reaches 96.56%, the false positive rate reaches 1.52%, and the processing speed reaches 25 frames per second. It can effectively detect the driver's behavior of using cell phone.
Antlion optimization and Whale optimization Algorithm for multilevel thresholding segmentation

Multi-level image segmentation is a critical task in image processing that involves multiple threshold values. As the high computational cost of an exhaustive search is inefficient and cumbersome, the optimal thresholds algorithms make for a better path to venture; hence a comparison of optimization algorithms to set the optimal thresholds is highly essential and beneficial. In this paper, a practical comparison is made to deduce the best optimization technique amongst the whale optimization and antlion optimization algorithm, to solve the multilevel threshold problem, to find the optimal multilevel thresholds. Otsu’s function is maximized to perform optimized thresholding-based image segmentation. The experimental results showed that the Antlion optimization algorithm gave better performance in solving the problem for higher level multi-thresholding.
Evaluation Of Physical Therapy Through Analysis Of Depth Images

The support through robots in orthopaedic rehabilitation is an opportunity to relieve physiotherapists. However, to be able to provide a control in the robot-patient cooperation in the therapy process a certain standard in interpreting the exercise has to be established. In this paper we present an evaluation approach of the health subject performance in a tibiofemoral rehabilitation on the example of squat exercises. The proposed method utilizes only depth images for the performance evaluation and any human-robot interaction system for the performance correction. Thus, this method can be easily applied to a mobile service robot in the robot-aided physical therapy. The patient is observed while performing the exercise and the motion is evaluated and segmented using Motion History Images. Concrete, depth images are used to monitor local points of interest on the performer during the exercise. The proposed approach was evaluated on custom image sequences with a multitude of varying subjects and shows the suitable performance for assisting in the correctness of the exercise execution.
Using novel noise filtering techniques to ensemble classifiers for credit scoring * Using noise filtering techniques to ensemble classifiers for credit scoring

Credit scoring using ensemble classifications has been applied widely. Ensemble classifications benefit from each single models, however biased models and miss-classification happens because of noisy data. To improve the performance of ensemble classifications, the proposed model extends the noise filtering technique that increases the accuracy of the deployed classifier. In addition, a new hybrid model combines novel noise filtering techniques and ensemble classification methods for scoring based on voting. In the hybrid model, the new label noise of data is assigned by support vector machine, then ensemble classifications to score noise. Using the novel noise filtering techniques and ensemble classifications for credit scoring obtains the high robustness and the superior performance. To be fair, some data sets for credit scoring problem are executed to compare results. The performance of the proposed model implemented by the experiment is better than the other models from 1% to 2%.
The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

The morphological modeling methods are efficient in quantifying the change of arterial blood pressure (ABP) waves. The related works focus on minimizing the modeling error but ignore the classification related modeling expression in practical applications. In this study, we explored the optimal modeling method for ABP wave related classifications. Two types of conventional models, Gaussian or Lognormal kernel function mixtures, were employed to quantitively describe the change of ABP signals, and the parameters of different models were engaged to train the different classifiers by probabilistic neural network (PNN) and random forest (RF) for identifying the ABP waves by age, gender, and whether belonging to extreme bradycardia (EB) or extreme tachycardia (ET). Then, we defined some indexes about the performance of modeling and classifications as the references to compare the different models. The ABP signals of Fantasia and 2015 PhysioNet/CinC Challenge databases were exploited as the experimental data to select the optimal model. The modeling results show that the Lognormal kernel function mixtures have a lower error in ABP wave modeling. The two-sample Kolmogorov-Smirnov test (ks-test) results indicate that the parameters of all models are markedly different at a highly significant level ( $h = 1$ , $p < 0.05$ ) between different groups. The classification results show that the classifiers based on the four-Gaussian function model have the best performance with the average Kappa coefficients (KC) of 99.160 ± 0.123%, while the average KC for the classifiers of two-Lognormal function models is 97.585 ± 0.172%, which means there is excessive information redundancy in the classifications by the three and four kernel functions models. Considering some other indexes such as time consumption and RAM space, the 2 Lognormal function model has more potential in practical applications.
Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity

In this paper, we examine electro-chemically accurate, multi-compartment, neurosynaptic computational elements, and analyze their complexity, accuracy, and flexibility in signal processing of a time-varying task. We evaluate distributed patterns of simultaneously firing neurons in space and time, and we establish a transient synchrony and homeostatic regulation mechanism upon the underlying synaptic connectivity. With synchronic spiking, we form synchronous groups of neuronal subpopulations, which represent content forming a coherent entity. The neurosynaptic computational elements implemented on Xilinx Virtex 7 XC7VX550 FPGA board illustrate feasibility of the methodology.
The Comparison of deep learning recognition methods based on SAR image

Aiming at the problem of Synthetic Aperture Radar (SAR) target recognition, a new deep learning method is proposed. The Stacked Auto Encoder (SAE) network and the convolutional Neural Network (CNN) have remarkable performance. Through the implementation and comparison of these methods, it is shown that the proposed deep learning recognition method has strong adaptability to different situations and has robustness to attitude angle, background and noise.
Multi-rate DAG Scheduling Considering Communication Contention for NoC-based Embedded Many-core Processor

Computing platforms for embedded systems are increasingly being transformed into multi/many-core platforms because embedded systems have become extensive, complex, and automated. In the case of an autonomous driving system, various applications are simultaneously running, and low power consumption and large-scale calculation are required. Many-core processors with a multiple instruction, multiple data (MIMD) architecture can meet these requirements. This paper proposes a scheduling algorithm for an automotive driving system expressed in a directed acyclic graph (DAG) and we use Kalray MPPA-256 as the target many-core processor. On the basis of the architecture of Kalray MPPA-256, task processing that requires large-scale calculation and intercore communication is performed while avoiding communication contention by using a proposed grouping computational resource. In addition, we propose a scheduling method for a multi-rate DAG which is a DAG with multiple periods. This method generates a DAG task in a hyperperiod and schedules the DAG with dependency on tasks that have been released closely. The formulas for prioritization and processor selection are proposed for various generated tasks in a hyperperiod. Evaluation results show that the proposed algorithm is superior to existing DAG scheduling algorithms with regard to schedulability and deadline miss ratio.
Overview of High Frequency Electronics Integration Concepts for Gap waveguide based High Gain Slot Antenna Array

This paper presents an overview of different low-loss microstrip to waveguide transition designs suitable for integrating millimeter wave electronics to a gap waveguide based slot array. Typically, E-plane probe type of transitions are widely used at mmWave frequency range to couple RF signal from a TX/RX MMIC to the waveguide section. H-plane split-blocks are avoided due to leakage problem from tiny slits formed by imperfect metal connections. On the other hand, the traditional slot arrays are built using H-plane split blocks. This makes it very challenging to integrate electronics and other passive components such as diplexer filter directly to a high gain planar antenna array. To overcome this above mentioned problem, we propose to use low-loss H-plane transitions to integrate RF electronics with the multi-layer gap waveguide based slot array. We demonstrate a completely packaged frontend at E-band, showing the potential of the gap waveguide technology to build a very compact full-duplex wireless system.
Event-Triggered H-Infinity Control for Continuous-Time Singular Networked Cascade Control Systems With State Delay

This paper is concerned with the problems of modeling, stabilization, and <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control for continuous-time singular networked cascade control systems (SNCCSs) with state delay and event-triggered control. An event-triggered scheme is firstly introduced to this system for utilizing the network bandwidth resources efficiently. Considering the effects of both time-varying network-induced delay and event-triggered control, a singular networked cascade control system (SNCCS) model is established. By constructing a suitable Lyapunov-Krasovskii functional, sufficient condition of admissibility for this system is proposed, and the co-design method of event-triggered parameter, primary state feedback controller and secondary state feedback controller are also derived. Furthermore, <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control is concerned for SNCCS via linear matrix inequality (LMI) technique. Finally, a simulation example considering a heating furnace with the structure of SNCCS and event-triggered control is given to illustrate the effectiveness of the proposed method, where it can be seen this method is superior to the existing one with periodic control.
Deep-Pack: A Vision-Based 2D Online Bin Packing Algorithm with Deep Reinforcement Learning

This paper looks into the problem of online 2D bin packing where the objective is to place an incoming object in a way so as to maximize the overall packing density inside the bin. Unlike off-line methods, the online methods do not make use of information about the sequence of future objects that are going to arrive and hence, are comparatively difficult to solve. A deep reinforcement learning framework based on Double DQN is proposed to solve this problem that takes an image showing the current state of the bin as input and gives out the pixel location where the incoming object needs to be placed as the output. The reward function is defined in such a way so that the system learns to place an incoming object adjacent to the already placed items so that the maximum grouped empty area is retained for future placement. The resulting approach is shown to outperform existing state-of-the-art-method for 2D online packing and can easily be extended to 3D online bin packing problems.
Bidirectional Boost Converter via Adaptive Sliding-Mode Control Used for Battery Active Equalization

In this study, a new bidirectional boost converter- based battery state-of-charge (SOC) active equalization system via an adaptive backstepping sliding-mode control-based balancing strategy is proposed. The system configuration and modeling are developed by analyzing the equivalent circuits of the battery and bidirectional boost converter. An adaptive backstepping sliding-mode control with the balancing selection strategy is subsequently applied to implement the regulated charging and discharging of battery cells, which possesses the capability in addition to against the external disturbance as well as parameters variation. Finally, a comparative simulation for battery cells' equalization according to the same balancing strategy between the proposed bidirectional boost converter-based system and the typical top-down equalization scheme is carried out, and the results indicate that the proposed system can shorten the equalization time about 9.8 %.
Proportionate Minimum-Symbol-Error-Rate Based Sparse Equalization for Underwater Acoustic Channels

The minimum-symbol-error-rate (MSER) decision feedback equalizer (DFE) has been developed for underwater channels. Comparing with conventional normalized least-mean-square (NLMS) with DFE, MSER-DFE shows significant performance improvement in terms of bit-error-rate (BER). However, it suffers from slow convergence. In this paper, to improve the convergence speed of MSER-DFE, we modify the objective function to exploit the sparsity of the channel equalizer and use the sparseness measure, which is referred to as sparse control proportionate minimum-symbol-error-rate (SC-PMSER) DFE. By assigning larger weights to equalizer taps with larger values, we can speed up the convergence of the proposed equalizer at the cost of higher computational complexity. Simulation results show the effective of the proposed detector.