Miracle-dz/UniFAR-FTUs huggingface dataset:
Robotic Learning for Increased Productivity: Autonomously Improving Speed of Robotic Visual Quality Inspection

Robotic learning has shown many impressive achievements in laboratory environments, but it is not yet very prominent in the industry, where hardware, engineering solutions and careful structuring of the environment are typically needed to successfully accomplish the desired task. In this paper we show one example of learning applied to industrially relevant problems, where a learning algorithm is applied for the optimization of the velocity of robotic motion for quality inspection. Through learning of optimal velocity of motion, which is done only at the start of the production, we show that we can achieve faster cycle times and thus greater productivity. The described approach is general and can be used with different types of learning and feedback signals.
Angular Separation of Channels in 5G System Multi-Beam Antennas

In fifth generation systems, the use of multi-beam antenna systems gives the possibility of spatial separation of transmission channels. In this paper, the influence of the frequency co-channel beam directions of the antenna system on the powers ratio of the useful and the interference signal is presented. The changes of this ratio are shown both versus the radiation angle of signal as well as the width of the antenna system characteristics and the distance between the transmitter and the receiver. To map propagation phenomena in an urbanized environment, we used a multi-elliptical propagation model, whose geometrical structure is defined based on the radio channel impulse response. The use of this model in simulation studies allows the determination of angular power distributions of received signals. The fields of these characteristics are the basis for the evaluation of the power ratio of the useful signal and the co-channel interference. The presented methodology makes it possible to determine the angular separation between the directions of the co-channel beams.
An Intelligent Secured Framework for Cyberattack Detection in Electric Vehicles’ CAN Bus Using Machine Learning

Electric Vehicles’ Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle network communication. Simplicity, robustness, and suitability for real-time systems are the salient features of CAN bus. Unfortunately, the CAN bus protocol is vulnerable to various cyberattacks due to the lack of a message authentication mechanism in the protocol itself, paving the way for attackers to penetrate the network. This paper proposes a new effective anomaly detection model based on a modified one-class support vector machine in the CAN traffic. The proposed model makes use of an improved algorithm, known as the modified bat algorithm, to find the most accurate structure in the offline training. To evaluate the effectiveness of the proposed method, CAN traffic is logged from an unmodified licensed electric vehicle in normal operation to generate a dataset for each message ID and a corresponding occurrence frequency without any attacks. In addition, to measure the performance and superiority of the proposed method compared to the other two famous CAN bus anomaly detection algorithms such as Isolation Forest and classical one-class support vector machine, we provided Receiver Operating Characteristic (ROC) for each method to quantify the correctly classified windows in the test sets containing attacks. Experimental results indicate that the proposed method achieved the highest rate of True Positive Rate (TPR) and lowest False Positive Rate (FPR) for anomaly detection compared to the other two algorithms. Moreover, in order to show that the proposed method can be applied to other datasets, we used two recent popular public datasets in the scope of CAN bus traffic anomaly detection. Benchmarking with more CAN bus traffic datasets proves the independency of the proposed method from the meaning of each message ID and data field that make the model adaptable with different CAN datasets.
Antenna Selection for Wiretap MIMO System with Wrapped Space Time Codes

Wiretap has drawn much attention since more and more mobile handsets and wireless communication systems are emerging. To step over the limitation of single link for both sides, multiple-input multiple-out (MIMO) has been introduced in wiretap channels (wiretap MIMO). In this paper, a wiretap MIMO system is proposed combined with wrapped space time codes (WSTC). To enlarge the mutual information between Bob and Alice, a new antenna selection is proposed in the system. It is declared that, the AS algorithm based on K-means clustering can approach the optimal capacity with low computational complexity. Finally, the simulated results are presented to validate the proposed system.
Bin-picking Robot using a Multi-gripper Switching Strategy based on Object Sparseness

Bin picking of various daily items is an important research problem in robotics. If the target items are diverse, multiple grippers are normally used. A design of gripper combinations depends not only on the item variations but also on the state of the bins, which changes while robots pick items from them. In this paper, we propose a gripper combination strategy to change the gripper combination during a bin-picking task based on the sparseness of objects inside bins. As an experiment, we build a robot system which has three different types of grippers. By using the proposed combination strategy, the system effectively changed the gripper combination during the task, and picked 18/20 items to obtain the 3rd place in the Stow task at the Amazon Robotics Challenge 2017. The successful picking rate and Mean Picks Per Hour (MPPH) were higher than the 1st place team. In this paper, we describe the problem, method to switch the combination, system including gripper design and recognition algorithm, and experimental results from the competition.
Iterative Adaptive Photon-Counting Compressive Imaging Based on Wavelet Entropy Automatic Threshold Acquisition

We demonstrate an iterative adaptive photon-counting compressive imaging system. The low-resolution image obtained from the previous sampling are used to calculate the wavelet coefficients. A method based on multiple micro-mirrors combination is proposed to conduct iterative adaptive sampling on the imaging regions corresponding to important children wavelet coefficients at all levels. In order to reconstruct a better quality image directly with appropriate compression ratio and time and avoid speculative selection of thresholds, an automatic threshold acquisition method based on wavelet entropy is proposed, which can automatically follows the change of the object image to obtain the threshold. The iterative adaptive compressive sensing measurement method based on wavelet entropy automatic threshold acquisition is applied to photon-counting compressive imaging system. Experimental results show that a high resolution image can be reconstructed with low compression ratio and short time in extremely low light environment, the quality of reconstructed images of iterative adaptive compressive sensing measurement is better than that of adaptive basis scan.
To Study and Analyse Human Behaviours on Social Networks

Recently, the scope of the digital world in increased in a way that a number of smart objects are connecting in a huge quantity. It is due to the exploitation of low-cost internet extensions, air-interface, and revolution in miniaturization. Hence, it results in decreasing the cost of a smart device. The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyse such massive volume of data. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyse a stream of Big Data within their requirements. Hence, this paper presents a system architecture that integrates social network with the technical network. We derive a novel notion of 'Socio-Cyber Network', where a friendship is made based on the geo-location information of the user, where trust index is used based on graphs theory. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time.
Feature Engineering and Word Embedding Impacts for Automatic Personality Detection on Instant Message

Automatic personality recognition (APR) is a task to detect the personality of people using computerized method. Thanks to the advancement of technology these days, there are ample sources of data that could be collected to do this task especially ones with user generated data like instant messaging. We proposed the method for feature engineering impact on APR task done in the short messages type of data. The concatenation of the messages for each subject detected in the dataset has significant improvement result besides doing the preprocessing steps for each line of the text. This research enriches the current methodology and gives another option for data sources compared to previous study. It is also valuable for a company to profile its customer aiming in finding the best services for each person in his personality type.
Multi-source Knowledge Embedding Research of Knowledge Graph

In recent years, the knowledge graph has developed rapidly, but a single knowledge graph often fails to meet people's needs for actual use. Therefore, the multi-source knowledge embedding of knowledge graph has received extensive attention. Multi-source knowledge embedding was mainly divided into three steps: knowledge search, knowledge evaluation and knowledge fusion. The knowledge search was the basis of multi-source knowledge embedding. Knowledge evaluation was the guarantee of multi-source knowledge embedding. Knowledge fusion was the main means of multi-source knowledge embedding. Multi-source knowledge embedding can not only improve the performance of knowledge representation, but also improve the completeness of knowledge graph. At present, people mainly paid attention to the embedding of knowledge, ignoring the knowledge search and knowledge evaluation. This paper studied the whole process of knowledge embedding from the perspective of knowledge source, which was of great significance.
Going Deeper with CKELM

Convolutional Neural Networks (CNN) is known for its features extraction from images. Extreme Learning Machine (ELM), has been adopted in various researching fields for its fast learning. CNN-KELM has overcome the KELM'S shortage of extracting features as this model uses CNN to detect the input image before classifying with KELM. Our improved CKELM includes rank based pooling strategy and GPU training. We make experiments to evaluate our proposal on dataset CIFAR and result proves that our pooling methods perform better than traditional ones.
An Efficient Numerical Method for Forward Kinematics of Parallel Robots

Solving the forward kinematics of parallel robots efficiently is important for real-time applications. However, it remains a difficult problem due to its high nonlinearity. This paper combines artificial neural networks and the Global Newton–Raphson with Monotonic Descent (GNRMD) algorithm to decrease the training sets of neural networks while avoiding divergence problem. Furthermore, simplified Newton iteration is introduced to reduce the duration of solution time. The proposed method is demonstrated taking a Stewart platform as an example and the nonlinear equations are established with the geometrical method. Based on the continuous characteristic of real-time applications, the result of the previous solution cycle is used as the initial value of the current solution cycle. Moreover, a threshold adjusting the effective scope of GNRMD algorithm and simplified Newton iteration is set to balance the efficiency and number of iteration. The performance of the algorithm is verified in the environment of Microsoft Visual Studio 2013 based on the continuous feedback of the Stewart platform. Besides, it is compared with GNRMD algorithm and a higher-order numerical method. The results indicate that the proposed algorithm can improve the efficiency of solving the forward kinematics problem.
Adaptive Optical Transmission Systems employing Multidimensional Modulation

We propose to use multidimensional modulation as flexible mapping technique for software defined optics. The flexible mappings have an optimized constellation shape with regard to the number of amplitude levels and power efficiency. We demonstrate the applicability of various established algorithms for digital signal processing (DSP) and investigate the impact of impairments like quantization and IQ-mismatches in a simulation. An experimental verification is conducted at 42 GBd symbol rate over up to 568.2 km fiber.
Control Chart Recognition Method Based on Transfer Learning

Control chart is a useful method to identify process problems by detecting abnormal pattern, to keep process in control. Aiming at the problem that traditional abnormal patterns were fixed, and could not completely identify all process problems, a control chart recognition based on transfer learning was proposed. Firstly, six kinds of abnormal patterns were abstracted from the characteristics of abnormal processes. Apply Monte Carlo to create simulated data, and process data to reduce the effect of noise by standardizing and coding. Control chart images were used as target dataset instead of numerical data. According to the feature based transfer learning in the isomorphic space, apply VGG16 network model to extract feature to improve the generalization ability. Finally, the output of the feature extraction was the input of the classifier that has been well trained on the target dataset. Control chart recognition model was fine-tuned according to the recognition results during the process of training, to gain the optimal one. The experimental results show that compared with BP network model, the accuracy of control chart recognition network model based on transfer learning is more than 98% with the less sample data.
On the Multiplexing Capability of mmWave Doubly-Massive MIMO Systems in LOS Environments

This paper is concerned with the potential of spatial multiplexing in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) downlink system in which the base station (BS) is equipped with multiple distributed massive antenna subarrays to serve multiple users. The paper first introduces the concept of multiplexing gain when the number of antennas goes to infinity and the transmit power is finite. Assume that each of the user terminals is equipped with a large antenna array, the paper focuses on the simple and cost-effective analog beamforming scheme for the MIMO system operating in mmWave line-of-sight propagation environments. We obtain simple and approximate expressions for the sum rate of the system based on asymptotic analysis in the limit of the numbers of antennas at the BS and at the user terminals when the total transmit power is fixed or scaled down. The approximate expressions show that the employed analog beamforming scheme can achieve a multiplexing gain that is equal to the product of the number of subarrays at the BS and the number of users when the total transmit power is fixed, whereas the multiplexing gain decreases proportionally to such a product when the total transmit power is scaled down. Extensive numerical results are also provided to corroborate analytical results.
Optomechanical Oscillations in Microbubble Resonators: Enhancement, Suppression and Chaotic Behaviour

We report on the experimental and theoretical analysis of parametrical optomechanical oscillations in hollow spherical PhoXonic whispering gallery mode resonators due to radiation pressure. The optically excited acoustic eigenmodes of the PhoXonic cavity oscillate regeneratively leading to parametric oscillation instabilities.
Pj-AxMTJ: Process-in-memory with Joint Magnetization Switching for Approximate Computing in Magnetic Tunnel Junction

In order to realize high efficient magnetization switching in magnetic tunnel junction (MTJ), several potential alternative mechanisms have been realized to replace spin transfer torque (STT) method, such as the STT-assisted precessional voltage controlled magnetic anisotropy (VCMA), and the spin orbit torque (SOT) erasing plus STT programing. In this paper, we propose a method denoted as the Process-in-memory with Joint magnetization switching for Approximate computing in Magnetic Tunnel Junction (Pj-AxMTJ), by using the 1T-1M and 3T-1M bit-cell structures. The proposed method aims to implement a low-precision computational memory with dynamic approximate computing. Specifically, four nonvolatile approximate full adders (AxFAs) are proposed based on the writing operations of different types of magnetic random access memory. As no peripheral circuits but the memory bit-cells are used in the proposed design, the resultant area is significantly small. Moreover, the AxFAs can be easily reconfigured into memory units with simple wire connections. The simulation results for the proposed designs are then presented to show the precision-power-area-speed tradeoffs for addition operation. Finally, the accuracy of the AxFAs are further evaluated in an image processing application.
A Non-Cooperative Game Model for Overlapping Community Detection in Social Networks

Due to its broad real-life application, overlapping community detection (in the realm of a social network) has attracted considerable interests from many researchers. However, current methods fail to reveal the full community structure and its formation process. Thus, here we present an effective and scalable overlapping community detection algorithm, which formulates the target problem as a non-cooperative game played by multiple social actors (players). Specifically, we allow each player to join multiple communities, and the strategy of each player is denoted by a community membership vector. The adopted utility function in our game integrates both the high-order proximity and the similarity between membership vectors of social actors. By properly weighting and rewiring the original social network, our approach can nicely enhance the global community structure by incorporating higher-order node proximities. Moreover, we formally prove that the proposed game resembles and matches how a potential game works (in the classical sense in game theory), indicating that the Nash equilibrium point must exist. To find such Nash equilibrium point, we use a stochastic gradientascent method to update the community membership vectors of players. Extensive experiments are conducted on both synthetic and real-world social networks. After comparing our method with six baseline methods, we obtain convincing results in terms of how well the methods reveal communities, as well as its scalability.
Experimental Research to Determine the Conditions for Successful Reception of Messages in the LoRaWAN

The paper describes the technique of experimental research to determine the conditions for successful reception of messages from end-devices in LoRaWAN with the consideration of interference by other frames. We present results of experiments made by this method. Results compare with results from other articles. We provide clarify the existing model of successful reception of messages in LoRaWAN. Our model refines the theoretical formula for the successful reception of a message with the interference of two frames.
Fault Diagnosis Using Unsupervised Transfer Learning Based on Adversarial Network

The fault diagnosis is very important for the modern industry. Due to machine working conditions changing frequently, most of current fault diagnosis models built on the training (source) domain can’t perform well in test (target) domain. In addition, in test domain, there are few labeled data to adjust model to be adaptive to test working conditions. Domain adaptation, as one type of transfer learning, can be used to solve this problem. This paper proposes a novel fault diagnosis method using unsupervised transfer learning based on adversarial network. In this method, deep neural network is used to extract feature of fault signal while the adversarial network is used to accomplish the transfer learning process. Firstly, the fault signal is converted into RGB images as inputs of networks. Then, the adversarial training methods are used, which includes three training processes: the regular training process using source data, the maximum discrepancy training process and the minimum discrepancy training process. These three steps are adversarial to each other to adjust the model to be more adaptive. The method is tested on motor bearing dataset provided by Case Western Reserve University (CWRU). The prediction accuracies are better than other four comparison methods.
Longitudinal and Lateral Trajectory Planning for the Typical Duty Cycle of Autonomous Load Haul Dump

With the development of intelligent mining, autonomous driving will be the basic function of intelligent Load Haul Dump (LHD). Trajectory planning is the key part of autonomous driving. Due to the articulated structure, the motion state variables of the front and rear bodies are strongly nonlinear, leading to complex nonlinear collision avoidance constraints. In addition, the articulated angle and angular velocity of the LHD have physically constraints. At the same time, the terminal attitude constraint should be considered since LHD is a mining equipment. All these factors bring great difficulties to LHD trajectory planning and none of the existing methods can deal with these factors directly. In order to solve these problems, according to the most common working scenario, a novel trajectory planning method is proposed for autonomous LHD based on numerical optimization, leading to a safe and feasible time-space trajectory for efficient production. The novelty of this work is the introduction of a longitudinal and lateral trajectory planning for the typical duty cycle of LHD. More importantly, by the ingenious concept for modeling, there are two salient features of the proposed method. Firstly, no angles are used as the decision variable, and secondly, the collision constraints of the rear car are not directly considered. Through this way, the number of nonlinear constraints and the complexity of the model can keep in a reasonable level, which makes the model easy to solve. At the same time, by giving the collision-free condition and limiting the heading angular velocity, the generated trajectory is collision-free and satisfies the physical constraints of articulated angle. Case studies confirm the effectiveness of the proposed method. Adopting the proposed method to generate a spatiotemporal trajectory is beneficial to ensure safety and improve production efficiency.
Building Human Mobile Robot Audio Communication Interface with Artificial Intelligence and Deep Learning

Voice conversation is the natural way of communication between persons and mobile robots. First of all, the person as a speaker sends usually the appropriate voice commands to navigate and control mobile robot movements. Second, having speaking ability, the mobile robot can interact with human, i.e. can speak to answer or report the person for accurate or wrong understanding and executing of human voice commands. The common way to realize the voice communication between persons and mobile robots is to build an appropriate human mobile robot audio communication interface. This is the main goal of this article and the chosen means to build the proposed human mobile robot audio communication interface are artificial intelligence (AI) and deep learning (DL). The results achieved are presented as the suitable experiments of sending voice commands to the mobile robot and the corresponding answers or reports as speech of mobile robot to inform the person whether or not the voice commands are accurately or wrong understand and executed.