Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Particle Swarm Optimization Algorithm Based on Local-World Evolving Network Model

In this paper, A new particle swarm optimization algorithm based on local-world evolving network model is proposed to solve the problem that particle swarm optimization algorithm is prone to local optimum. The topology of particle swarm is generally considered to be a very important factor and determine the performance of the algorithm. In this paper, the particle swarm optimization algorithm and the local-world evolving network model are briefly reviewed, and then we use the network generated by the local-world evolving network model to represent the topological structure of the particle swarm. The new algorithm and original particle swarm optimization algorithm are tested in 6 benchmark functions. Comparing the test results, the new algorithm performs better on multimodal functions, the ability of the new algorithm to optimize on complex problems is significantly improved.
Motion Control Algorithm for Path Following and Trajectory Tracking for Unmanned Surface Vehicle: A Review Paper

A capability of tracking and follows the target object on the water surface is an essential measurement in the control system for a Unmanned Surface Vehicle (USV). Path following algorithm usually used to describe the target, compute in the controller and minimize as well as to zero the distance between USV; and the angle between the vessel speed and the tangent to the path. USV needs to follow a time-parameterized reference curve are defined as a trajectory tracking. This paper gives an attention in reviewing a few common path following as well as path tracking techniques used in the design of USV where there is no predefined position will be declared in the control system. In particular, this paper is focused on a motion control approaches developed using USV by reviewing forty journals for last ten years. Based on the reviews, a line-of-sight (LOS) technique is a frequently implemented in the USV control system than another method such as constant-bearing guidance, Kalman filtering, PID guidance, Lyapunov-based guidance, pure-pursuit (PP) guidance, motion goal prediction, back stepping method and Jacobian task priority. As the conclusion, various types of mathematical computation are introduced in the USV control system is customizes with the requirement based on the situation needed will make this autonomous vehicle more advanced.
Vital Signs Monitoring with RFID: Opportunities and Challenges

Vital signs monitoring plays an important role in health assessing, especially for elderly healthcare. Traditional vital signs monitoring systems are intrusive. They require the users to wear or attach dedicated sensors and thus are inconvenient to use. Although recently developed contactless vital signs monitoring approaches (e.g., those based on WiFi sensing) become friendly, they cannot differentiate multiple target users. This article envisions the opportunity of utilizing the radio frequency identification (RFID) technology to perform accurate vital signs monitoring, which can combine the merit of contactless convenience as well as the advantage in distinguishing different users. We introduce the basic ideas of using RFID to perform vital signs monitoring and discuss the challenges to be addressed before the ideas can be put into practice. With commodity RFID devices, we implement a breath monitoring prototype that can accurately monitor the user's breath even when the user is moving. The prototype achieves over 97 percent accuracy in estimating breath rate and below 8 percent error in estimating individual breath length.
On the Use of LSTM Networks for Predictive Maintenance in Smart Industries

Aspects related to the maintenance scheduling have become a crucial problem especially in those sectors where the fault of a component can compromise the operation of the entire system, or the life of a human being. Current systems have the ability to warn only when the failure has occurred causing, in the worst case, an offline period that can cost a lot in terms of money, time, and security. Recently, new ways to address the problem have been proposed thanks to the support of machine learning techniques, with the aim to predict the Remaining Useful Life (RUL) of a system by correlating the data coming from a set of sensors attached to several components. In this paper, we present a machine learning approach by using LSTM networks in order to demonstrate that they can be considered a feasible technique to analyze the "history" of a system in order to predict the RUL. Moreover, we propose a technique for the tuning of LSTM networks hyperparameters. In order to train the models, we used a dataset provided by NASA containing a set of sensors measurements of jet engines. Finally, we show the results and make comparisons with other machine learning techniques and models we found in the literature.
Performance Analysis of Hybrid Protocol Based Two-Way Decode-and-Forward Relay Networks

In this paper, a wireless energy harvesting relay network based on decode-and-forward (DF) mode is considered, where the relay node is energy constrained and it harvests energy through radio-frequency (RF) signals from the source node. Then the relay node uses the harvested energy to decode and forward source information to the destination node. In the hybrid power-time switching based relaying (HPTSR), the block time T is divided into two parts. One is used for energy harvesting and information processing, and the other is used for information transmission from relay to destination nodes. During the part of energy harvesting and information processing, the portion of the received signal is used for energy harvesting (EH) and the other portion of the received signal. The rest of the received signal is sent to transmit source information to the relay node. We derive the analytical expressions of outage probability and ergodic capacity in the two-way DF network. The numerical results provide practical insight into the different system parameters. In particular, the numerical results show that the DF mode significantly outperforms the amplify-and-forward (AF) mode in terms of outage probability and ergodic capacity performance.
Accelerometer-based Gesture Recognition Using MFCC and HMM

In this paper, an accelerometer-based gesture recognition system for mobile devices interaction has been proposed. A new feature extraction method for gesture recognition based on Mel Frequency Cepstrum Coefficient (MFCC) has been put forward. Since the accelerometer signals are sensitive to device's rotation, firstly a resultant force of acceleration is obtained and then their MFCCs are extracted as features. The Classifier used is Hidden Markov Model(HMM). An important problem is how to choose initial estimates of the HMM parameters. In our work, the segmental k-means segmentation with clustering method is adopted to estimate initial model parameters. The average recognition result of twenty complex gestures using the proposed method is effective. The experimental results show that gesture-based interaction can be used as a novel human computer interaction for consumer electronics and mobile devices.
Lyric Emotion Estimation Using Word Embedding Learned from Lyric Corpus

In searching huge music databases, users typically use “title,” “artist” or “song writer's name” as their search query. When users have only vague search conditions, searching for a song using affective information - that is, emotional impressions of the piece such as “relief” or “sorrow” - can be an appealing possibility. For cases in which the song's audio information consists of vocal and instrumental performances, both words and music reflect the writer's thoughts and emotions and encourage the imagination of the listeners. While a number of studies have used audio and lyric features in the emotion estimation of songs, many of the studies that use only lyrics have been based on existing textbook emotion estimation methods. Because the way in which words used in song lyrics tends to differ from the way they are used in text sentences, an emotion estimation method specific to song lyrics would be more appropriate. In this study, we extract features from a lyric corpus to handle lyric-specific expressions and propose a method to estimate emotion from each lyric phrase. The results of our evaluation experiment confirmed the effectiveness of the proposed method (lyric corpus word vector + 3-NN), as it produced a higher F-measure than the other methods.
Document Concept Hierarchy Generation by Extracting Semantic Tree Using Knowledge Graph

Semantic Web, as an extension of traditional web, is concerned about the vast amount of unstructured data, and with its motive to make the entire knowledge content machine readable, as well as machine interpretable, all the processes of structuring the data is highly significant. Knowledge representation in trees has been a familiar mechanism for some time. However, such representations lack in existence when it comes to document content. In this paper, we present a general mechanism that can generate a representation of the concepts of any document in the form of knowledge trees. We further gather knowledge from knowledge graphs and analyze these data by mapping it with an existing ontology. Finally, we explain how this can be used to create hierarchical concept recommendations to make the documents search efficient.
A Low-Complexity Method of NB-IoT Identification

This paper presents a low-complexity method of identifying the Narrowband-Internet of Things (NB-IoT) signal. Generally, 128-point FFT is needed in the identification procedure and the FFT point cannot be reduced any more if the number of cyclic prefix (CP) sampling points is kept to be an integer. However, the NB-IoT signal does not need such high FFT point as the bandwidth is only 180 kHz. In the proposed method, the FFT point is further decreased and an approach of solving the non-integer number of CP sampling points is provided. Through the experiments on the practical NB-IoT signal, the proposed method shows excellent performance in identifying the NB-IoT signal with less running time and low computational complexity.
Broadband Miniaturized Bandpass Frequency Selective Surfaces Based on Fractal Transmissive Metasurface Elements

In this paper, two kinds of broadband miniaturized bandpass frequency selective surfaces (FSSs) based on fractal transmissive metasurface elements at X band are proposed. FSSs satisfy butterworth response type and stack structures are used to expand bandwidth. FSSs are designed based on the concept of antenna-filter-antenna (AFA) which is employing fractal geometries for patches combined back-to-back and a cross-shaped slot or a double cross-shaped slot etched on the common ground plane with the unit-cell dimension and periodicity of 4.5mm ($0.15\boldsymbol{\lambda}_{0}$). The AFA-based FSSs exhibit superior frequency selectivity and have lower profile than conventional FSSs with an overall thickness of 2mm ($0.067\boldsymbol{\lambda}_{0}$) and 3mm ($0.1\boldsymbol{\lambda}_{0}$) respectively. Prototypes of the proposed FSSs are designed and simulated. To facilitate the design of this structure, a simple equivalent circuit is also presented in this paper. Presented FSSs demonstrate fractional bandwidths of 40% from 8GHz to 12GHz and 64% from 6.8GHz to 13.2GHz, which are corresponding to 3dB bandwidth of 4GHz and 6.4GHz respectively for both reflection (S11) and transmission coefficients (S21) at an incident angle within 60°. Compared to traditional FSSs, the designed FSSs are extremely useful in microwave wireless communication applications with a higher oblique incidence angle across wide bandwidth.
Green Light Optimal Speed Advisory: a C-ITS to improve mobility and pollution

Intersections, and more traffic signals, introduce stops along trip increasing travel time, stress, fuel consumption and emissions. To confine the stop-and-go phenomenon due to traffic signals several researchers focused on the use of actuated traffic lights. In last years the digital transformation of cities give us lot of information could be used for several purposes, one of them is the implementation of cooperativeintelligent transportation systems (C-ITS). The paper introduces a specific C-ITS named Green Light Optimal Speed Advisory (GLOSA) that use V2I communication to calculate an optimal speed for the vehicle to pass the intersection avoiding idle time. Two simulations scenarios have been built to study the benefit of the service. The results demonstrate that even using a simple control algorithm with the aim to avoid the stop&go we observe a reduction of fuel consumption and emissions in the region of 5 to 12%
Hardware-Based Adversary-Controlled States Tracking

Return Oriented Programming is one of the most important software security challenges nowadays. It exploits memory vulnerabilities to control the state of the program and hijacks its control flow. Existing defenses usually focus on how to protect the control flow or face the challenge of how to maintain the taint markings for memory data. In this paper, we directly focus on the adversary-controlled states, simplify the classic dynamic taint analysis method to only track registers and propose Hardware-based Adversary-controlled States Tracking (HAST). HAST dynamically tracks registers that may be controlled by the adversary to detect ROP attack. It is transparent to user application and makes few modifications to existing hardware. Our evaluation demonstrates that HAST will introduce almost no performance overhead and can effectively detect ROP attacks without false positives on the tested common Linux applications.
Towards Heterogeneous Computing Platforms for Autonomous Driving

This paper introduces a new project: Research and Development of System on a Chip and Software Platforms for Autonomous Driving, started in April 2019. State-of-the-art high-end embedded platforms have trade-offs between general-purpose processing and power consumption. In order to solve the trade-offs, this project develops heterogeneous computing platforms for autonomous driving. In particular, the heterogeneous computing platforms have a heterogeneous System on a Chip (SoC), which consists of Accelerated Processing Units for AI and robotic algorithms, multi-core CPUs for general-purpose processing, and many-core CPUs for real-time processing. This project also develops software platforms including compilers, operating systems, middleware, and applications for the heterogeneous SoC. The authors believe that this project contributes to early realize autonomous driving.
Optimal Tilt Angle of Reader Antennas for Truck Tolling Based on UHF RFID

Free flow truck tolling systems based on Ultra High Frequency (UHF) Radio Frequency Identification (RFID) need a highly reliable communication between the readers and transponders. To this end, a large number of reads of the transponders is desirable, which can be affected by physical effects (e.g. multipath propagation, Doppler effect) as well as the chosen protocol parameters (e.g. Miller coding). There has already been a great interest in investigating these effects. However, to the best of our knowledge, the effect of the tilt angle of the antenna on the number of reads has not been covered yet. This paper describes an implemented simulation framework in MATLAB which allows to find the optimum tilt angle for a single patch antenna and a patch-based antenna array for truck tolling at one lane. It turns out, that in both cases the optimal angle is about 71°.
Kernel-Correlated Filtering Target Tracking Algorithm Based on Multi-Features Fusion

When the color of the target is similar to the color of the background or illumination of a scene, scale, and shape of the target change significantly, the traditional kernel-correlated filtering (KCF) algorithm often fails to track the target. Therefore, a multi-features fusion target tracking algorithm based on KCF is proposed. The algorithm makes the most of the illumination and rotation invariance of local binary patterns (LBP) feature, the effective capability of extracting color and contour information from the target by color Name (CN) feature and histogram of oriented gradient (HOG) feature, respectively. The multi-features linear weighted fusion rule is established by calculating the HOG, LBP, and CN histograms of the current frame, and calculating the weighted fusion coefficients of the normalized histograms according to the variance of each histogram adaptively. On this basis, multi-scale sampling is used in the target areas and bilinear interpolation is used to adjust the sample size, which can improve the tracking robustness of the KCF algorithm to adapt to the target scale. The comparison experimental results of 50 video data sets in OTB-2015 show that the average success rate and precision of the proposed algorithm are 24.74% and 8% higher than that of the KCF tracking algorithm, respectively, and also significantly better than the other tracking algorithms ranked top ten in OTB-2015, UAV123, and LaSOT data sets, respectively.
Secure Fusion Filtering and Clustering for Distributed Wireless Sensor Networks

The secure distributed fusion filtering problem for the networked multi-sensor system with possible deception attack is concerned in this paper. A statistical test for the received measurements and a distance-based clustering for the local estimates are sequentially utilized to detect the cyber-attack. The estimates detected as normal ones are fused in the sense of minimum mean square error. The final simulation is provided to illustrate the feasibility and the effectiveness of the proposed method.
Assessment of mental load of an employee who operate a computer program supporting the didactic process

The paper attempts to determine the relationship between length and type of activity related to mental work and the degree of mental fatigue and the heart rate reserve. The research was carried out on a group of 25 people who, by implementing various stages of the training process in the field of using modern farm tractors through special logic tests and heart rate measurements, generated information on mental fatigue and stress during the experiment. It was found that along with the increase in the length of mental work, the time needed to complete the logic task and the number of mistakes made were increased. It was also observed that at each subsequent test with a similar logical construction, the stress factor decreased and a learning process took place. The result is a relatively better (compared to previously achieved) results obtained by the subjects during the last measurement
Robust Model Predictive Control of the Cutterhead System in Tunnel Boring Machines

In this paper, a systematic robust model predictive control structure is designed for the cutterhead system in tunnel boring machines. The main concerns are the robustness of the control system with respect to the external load uncertainty and the handling of the constraints on the driving torques. First, a state-space linear dynamical model of the cutterhead system is developed based on first principles. Then, a robust model predictive control structure is designed to handle the constraints and disturbance under the complex working conditions. Finally, extensive simulations are performed to illustrate the effectiveness of the proposed control system.
Image-Based Label Placement for Augmented Reality Browsers

In this paper, we introduce a label placement technique for placing labels in Augmented Reality systems. One of the common challenges in Augmented Reality applications is lacking in knowledge of the real environment, limiting efficient representation and optimal layout of the digital information augment onto user's view. In order to overcome this problem, we propose an image-based label placement method, which combines identifying potentially important image regions and determining manual placement tendencies while labeling. Then we add geometric constraints for placing labels in the optimization problem to obtain the final label layout. Our method will provide special benefits to Augmented Reality browsers which is usually in the absence of scene knowledge. Also it can be used to many similar applications in the domain of Augmented Reality.
Novel PWM Technique for Quasi Switched Boost Converter for the Nano-grid Applications

Scaled down version of the Micro grid is nanogrid, where each residential home, commercial building etc. are optimized and power is shared between the Battery Energy Storage (BES) and the utility grid. The recently proposed impedance source converters (ISC) are attractive solution for the nanogrid applications. Among them Conventional Simple Boost Converter (SBC) is a good choice. Here, a novel quasi Switched Boost converter (qSBC) is proposed. With the proposed structure, high gain from the DC-AC for multiple input (Photovoltaic (PV) and BES) and multiple output (BES and AC gird) is obtained. This paper proposes new Pulse Width Modulation (PWM) control strategy to increase the gain and the modulation index. The benefits of the proposed circuit include: 1) High modulation index during DC – AC conversion, 2) Significantly reduced voltage stresses across the components 3) Continuous input current from PV panel 4) Reduced inductor current ripple 5) Low shoot-through current and 6) high efficiency. All the operating modes and circuit analysis for the proposed converter are investigated in detail. Mathematical modeling of the proposed circuit is carried out in MATLAB/Simulink® and obtained results are discussed. Experimental prototype for 500W, 240V/50Hz output voltage is developed to validate the proposed converter with the developed PWM control algorithm. Hardware results obtained validates the performance and advantages of the proposed circuit and associated modulation method.
Reinforcement Learning for Distantly Supervised Relation Extraction

Relation extraction is a task of identifying semantic relations between entity pairs from plain text, which can benefit a lot of AI applications such as knowledge base construction and answer questioning. The distant supervision strategy is introduced to automatically create large-scale training data, which inevitably suffers from noisy label problem. Recent works handle the sentence-level denoising by reinforcement learning, which regards the labels from distant supervision as the ground-truth. However, few works focus on the label-level denoising that corrects noisy labels directly. In this paper, we propose a reinforcement learning-based label denoising method for distantly supervised relation extraction. The model consists of two modules: extraction network (ENet) and policy network (PNet). The core of our label denoising is designing a policy in the PNet to obtain latent labels, where we can select the actions of using the distantly supervised labels or the predicted labels from the ENet. More concretely, the task can be modeled as an iterative process. First, the ENet predicts the relation probability, through which the model generates state representation. Second, the PNet learns the latent labels with taken actions and uses them to update the ENet. Then the optimized ENet gives the rewards to the PNet. The joint learning of two modules can obtain a reliable latent label and effectively improve the classification performance. The experimental results show that reinforcement learning is effective for noisy label correction and the proposed method can outperform the state-of-the-art relation extraction systems.