Miracle-dz/UniFAR-FTUs huggingface dataset:
A Chi-Square Statistics Based Feature Selection Method in Text Classification

Text classification refers to the process of automatically determining text categories based on text content in a given classification system. Text classification mainly includes several steps such as word segmentation, feature selection, weight calculation and classification performance evaluation. Among them, feature selection is a key step in text classification, which affects the classification accuracy. Feature selection can help indicate the relevance of text contents and can better classify the text. Meanwhile feature selection has a great influence on the classification result. Text classification is a very important module in text processing, and it is widely applied in areas like spam filtering, news classification, sentiment classification, and part-of-speech tagging. This paper proposes a method for extracting feature words based on Chi-square Statistics. Because the feature words that appear together or separately may differ in different situations, we classify texts by using single word and double words as features at the same time. Based on our method, we performed experiments using classical Naive Bayes and Support Vector Machine classification algorithms. The efficiency of our method was demonstrated by the comparison and analysis of experimental results.
Real Time Probabilistic Mapping for Sonar Sensor by Optimization

Mapping with sonar sensor is highly interesting yet to be solved, due to the low-cost and high uncertainty of the sensor. A novel method for iterative probabilistic occupancy grid mapping using gradient ascent is introduced. The algorithm is based on the forward sensor model for sonar sensors. The experiments demonstrate that such method is capable of creating satisfying maps for a mobile robot in real time. It is rather robust to sensor noise and model errors and it can overcome the problem of sensor conflicts produced by some existing methods. We propose an efficient way of iterative mapping for mobile robots.
Design of an electric generator for an aircraft with a hybrid power system

The paper presents analysis of two types of electric generator with a power of 350-360 kW and rotational speeds of 18 000 and 30 000 rpm taking into account the requirements for fault tolerance at maximum power density and maximum efficiency. For considered electric generators Electromagnetic, hydraulic, mechanical and thermal calculations were performed. The obtained results can be used in the design of electric generators for aircraft with a hybrid power system.
Hyper/Text/Theory

From the Publisher: In his widely acclaimed book Hypertext George P. Landow described a radically new information technology and its relationship to the work of such literary theorists as Jacques Derrida and Roland Barthes. Now Landow has brought together a distinguished group of authorities to explore more fully the implications of hypertextual reading for contemporary literary theory.
Path Splitting Selecting Strategy-Aided Successive Cancellation List Algorithm for Polar Codes

Successive cancellation list (SCL) decoder provides a good trade-off between error-correcting performance and complexity. To simplify the decoding algorithm with negligible performance loss, a path splitting selecting (PSS) strategy is proposed based on analyzing the reason why the SCL decoder fails. On the basis of the PSS strategy, two schemes are proposed to find erroneous information bits more accurately. Simulation results show that the proposed strategy based on the search set and decision function is a good solution to reduce the complexity with almost no performance loss.
GaN VHF Converters With Integrated Air-Core Transformers

This paper proposes an air-core transformer integration method, which mounts the transformer straight into the multilayer printed circuit board (PCB), and maintains a proper distance between the inner transformer and other components on the top layer. The integration method reduces the PCB area significantly, ensuring higher power density. The design of the complete structure is accomplished with finite element analysis (FEA) to ensure that the unconstrained transformer magnetic field does not affect other components. It is applied to three resonant flyback converters operating at 20 MHz with Si mosfets, 30 MHz, and 50 MHz with enhancement-mode gallium nitride (eGaN) high-electron mobility transistors, respectively. The 30-MHz eGaN prototype achieves the full load efficiency of 80.1% (an increase of 1.1% compared with the 20-MHz Si prototype) and power density of 32 W/in3 at 5-V input and 5 V/ 2 W output. The 50-MHz eGaN prototype achieves the power density of 39.4 W/in3, which is 41% higher than the 20-MHz Si prototype. With the similar efficiency, the overall height of the converters in this paper is less than half of the commercial products, very suitable for low thickness and profile applications.
Real-Time Detection and Recognition of Live Panoramic Traffic Signs Based on Deep Learning

With the hotness of unmanned driving and the challenge of traffic safety, traffic sign detection and recognition is the core issue of unmanned driving. It is of great significance to study it. At present, due to the application of deep learning, the object recognition method has achieved good results, but there are few researches on real-time recognition of small object in live panoramic high resolution images or videos. This paper proposes a method for small object detection and recognition in high resolution images, and applies it to the detection and recognition of traffic signs in panoramic pictures. Firstly, the data preprocessing method for training dataset is proposed. Then a YOLOv3 algorithm based Traffic Signs Network(TSNet)is designed. After training and fine-tuning, the testing dataset with reasonable scaling is processed by the new sliding window algorithm proposed in this paper. The predictions for bounding boxes is processed by Delete Subsets - Nonmaximum Suppression(DS-NMS)to obtain the higher accuracy and realtime detection effect.
Chaos-Based Stream Cipher Using a Star Network of Chaotic Maps

We have developed a star network of chaotic maps, referred to as the augmented Lorenz map, which can be used as a pseudorandom number generator for a stream cipher. The augmented Lorenz map is capable of faster generation of chaotic time series in comparison with the augmented Lorenz equations, which were used as the pseudorandom number generator in the previous chaos-based stream cipher. In this paper, we assess the security of the binary pseudorandom numbers generated by the augmented Lorenz map and discuss the effect of the stream cipher using the map.
Robust Scalable Part-Based Visual Tracking for UAV with Background-Aware Correlation Filter

Robust visual tracking for the unmanned aerial vehicle (UAV) is a challenging task in different types of civilian UAV applications. Although the classical correlation filter (CF) has been widely applied for UAV object tracking, the background of the object is not learned in the classical CF. In addition, the classical CF cannot estimate the object scale changes, and it is not able to cope with object occlusion effectively. Part-based tracking approach is often used for the visual tracker to solve the occlusion issue. However, its real-time performance for the UAV cannot be achieved due to the high cost of object appearance updating. In this paper, a novel robust visual tracker is presented for the UAV. The object is initially divided into multiple parts, and different background-aware correlation filters are applied for these divided object parts, respectively. An efficient coarse-to-fine strategy with structure comparison and Bayesian inference approach is proposed to locate object and estimate the object scale changes. In addition, an adaptive threshold is presented to update each local appearance model with a Gaussian process regression method. Qualitative and quantitative tests show that the presented visual tracking algorithm reaches real-time performance (i.e., more than twenty frames per second) on an i7 processor with 640×360 image resolution, and performs favorably against the most popular state-of-the-art visual trackers in terms of robustness and accuracy. To the best of our knowledge, it is the first time that this novel scalable part-based visual tracker is presented, and applied for the UAV tracking applications.
An Experimental Study of Ion Motion Optimization for Constraint Economic Load Dispatch Problem

This article implements the Ion-Motion Optimization (IMO) optimizer to solve non-convex economic load dispatch (EcLD) problem in power system. The concept of Ion motion optimization is modeled from the attraction and repulsion forces among anions and cations in the real world. The mathematical model of IMO is quiet simple and very easy to implement. The liquid phase of IMO performs exploration and crystal phase simulates the exploitation. To handle the power balance equality constraint, exterior penalty method is used. Finally, EcLD problem having 13-generators is solved using ion motion optimization.
Impact of Optimal Hop Distance on the Network Lifetime for Wireless Sensor Networks With QoS Requirements

In this letter, the impact of the optimal hop distance (OHD) on the network lifetime (NL) is analyzed in wireless sensor networks with the consideration of link quality of service (QoS) requirements. OHD is defined as the distance between the node and its next-hop’s optimal relay position (ORP) to the destination node, where the ORP determines the performance of the energy-efficient relay. If OHD equals the characteristic distance (CD) of the path, the minimum-energy path for a given node pair can be achieved. Moreover, the link rate also influences the NL. Hence, considering an expected link outage probability, the minimum link rate, which is related to OHD, and its corresponding expected estimation of the average link transmit power of the node are calculated. Finally, an algorithm for joint energy-efficient relay selection and link-rate allocation based on OHD selection is proposed to extend the NL. Simulation results show that the NL can be extended when OHD is less than CD.
Knowledge-Driven Deep Deterministic Policy Gradient for Robotic Multiple Peg-in-Hole Assembly Tasks

It remains a formidable challenge for traditional control strategies to perform automatic multiple peg-in-hole assembly tasks due to the complicated and dynamic contact states. Inspired by that human could generalize the learned skills to perform the different assembly tasks well, a general learning-based algorithm based on deep deterministic policy gradient (DDPG) is proposed. To make robots learn the multiple peg-in-hole assembly skills from experience efficiently and stably, the learning process is driven by the basic knowledge like PD force control strategy. To achieve a fast learning process in the real-world assembly tasks, a hybrid exploration strategy is applied to drive a efficient exploration during policy search phase. A dual peg-in-hole assembly simulation and real-world experiments are implemented to verify the effectiveness of the proposed algorithm. The performance measured by the assembly time and the maximum contact forces demonstrates that the multiple peg-in-hole assembly skills could be improved only after 150 training episodes in dual peg-in-hole assembly task.
Decoding CMOS Rolling-Shutter Pattern in Translational or Rotational Motions for VLC

We propose and demonstrate a CMOS rolling-shutter pattern decoding scheme based on two newly introduced schemes, named row grayscale redistribution and differential grayscale packet selection to improve the performance in decoding the CMOS rolling-shutter pattern in translational or rotational motions. Results show that even the CMOS image sensor is in translational or rotational motions, no significant signal degradation is observed at wireless transmission distance up to 150 cm. The maximum visible light communication data rate at different wireless transmission distances is also analyzed.
How to Computationally Determine the Maximum Stable Operation Current of an HTS Magnet

The short-sample critical current is only an indicative property for the maximum current a magnet can be continuously operated with. This was especially visible in the experiments of one of the world's first Roebel-cable-based high temperature superconductors dipole magnet prototype built and tested at CERN in 2017 where the thermal runaway developed very slowly in many cases. Consequently, the maximum stable operation current could be overstepped and stable operation could be recovered by lowering the current below the maximum of the stable range again. It is non-trivial to quantitatively predict this behavior from the critical current measurements which are observed under specific cooling conditions and based on an arbitrarily selected electric field criterion for the critical current. To make more rigorous predictions on the maximum stable operation current, one needs to consider in detail the interplay of cooling over the magnet surface and heat generation in the winding. This paper presents a methodology to determine the maximum stable operation current for a given magnet, as well as studies its mathematical background. Insight to this problem comes from the Roebel-cable-based dipole magnet studied at CERN during 2017.
Implementation of A* Algorithm for Optimal Transmission Expansion Planning Portfolios Comprised of Competitively Bid Projects

The Federal Energy Regulatory Commission Order 1000 removed incumbent utilities’ federal right of first refusal, resulting in a competitive bid process for the awarding of transmission expansion projects. The computation time required when optimizing an expansion project portfolio based on factors such as cost and quality quickly becomes infeasible as the number of projects and the transmission issues they address increases. Implementing a project selection methodology with the A* algorithm can provide a cost and time effective method for choosing an optimal reliability transmission expansion portfolio comprised of a subset of the project pool.
Routing in Internet of Things: Review

The unification of the sensor devices, Internet and Radio Frequency Identification frame-works has instigated the idea of Internet of Things (IOT), which helps to connect our daily things, making them smart through perception of signals, thinking, and collaborating with other devices. The IOT frame work tries to connect anybody with anything at anyplace, in spite of the fact that the internet of things becoming a key framework for the improvement of smart IOT network. IOT network has the great potential for the extensive variety of applications like smart City, smart homes, atmospheric condition, human health monitoring, logistics, and so forth. To transfer this IOT visualization into actuality, routing protocols are essential to create bridge between these devices in a decentralized, self-composed and changing infrastructure. Large numbers of nodes in the IOT may subject to constant mobility that may leads to asymmetrical interconnection between the devices which may results in the dynamicity of the topology. Because of this topological variations and constraint resources in IOT devices become great challenge of routing in IOT. In this paper we made survey on several papers to know the cluster based IOT network, IOT routing, routing techniques, issues and challenges in IOT.
Data Privacy and Prediction Using Neural Network and Homomorphic Encryption

To enhance the correctness of learning result multiple parties can associate through conducting Back-Propagation neural network learning jointly on the combination of their individual data sets. In this process, none of the parties reveal his/her private data to the world. Traditional schemes support a combined learning but it is limited to two parties only. So there to perform a combined learning if a data is partitioned arbitrarily for more than two parties. This paper resolves this open hassle via using a strength of cloud computing. In this scheme, every single party creates encoded text and transfers the encoded text to the cloud. Cloud then performs ample no of operations on encoded text with the help of learning schemes without knowing the original private data. While securely transferring operations towards the cloud, the cost incurred during transfer of operations should be minimal and independent to parties involved. To perform diverse operations on encoded text, “homomorphic asymmetric encryption” scheme is used for multiple parties.
A Study on Computational Methods of the Logistic Map over Integers for a Pseudorandom Number Generator

In this paper, we will discuss properties of different computational methods of the logistic map over integers. We will give four computational methods of the map in order to implement the map on a computer with limited memories, and then will evaluate average processing times and lengths of sequences generated by these methods. By these results, we will provide a constructive use of these four computational methods of the logistic map over integers for a design of the pseudorandom number generator.
Combining Convolutional Neural Networks and Word Topic Features for Chinese Short Text Sentiment Analysis

The Chinese short text sentiment classification method based on word embedding and convolutional neural network has already achieved effective results. The recent classification method mainly adopts single channel CNN. In order to enhance classification performance, this study proposes a classification method based on Dual-channel CNN. The word2vec semantic feature and topic model (LDA)feature of words were used as input of Dual-channel CNN to extract sequence sentimental features which, were subsequently applied on short text sentiment classification. The experimental results show that the classification performance of Dual-channel CNN is better than those of single channel CNN and Naive Bayes on hotel review data.
A Sliding Mode Pitch Control for Multi-Megawatt Offshore Wind Turbines

A sliding mode controller with conditional integrator is proposed for pitch angle regulation in multi-megawatt wind turbines working under high speed wind conditions. The control objective is to improve the transient and steady-state performance of conventional continuous sliding mode pitch controllers by adding a conditional integrator, that is only active within the boundary layer. The control design is validated using the 5-MW offshore wind turbine model from NREL’s FAST comprehensive aeroelastic simulator and considering pitch actuator dynamics. Furthermore, the mechanical response of different elements of the wind turbine is evaluated using MLife.
Improvement of Cochlear Implant Coding Strategy Based on Chinese Speech Boundary Information

The cochlear implant coding strategy is generally designed for Western languages and has certain limitations in Chinese speech recognition. The Chinese speech boundary information and the advanced combination encoding (ACE) strategy are combined to improve the ACE strategy by using the information of the silence segment, the special noise segment and the syllable interval extracted from the Chinese speech boundary information, which can enhance the Chinese speech recognition ability and suppress the noise components of the noisy speech. Through objective evaluating the effect of speech enhancement by modified bark spectral distortion (MBSD), the results show that the improved ACE strategy based on Chinese speech boundary information, compared with the traditional CIS strategy and the ACE strategy, speech enhancement and the average recognition rate are improved. It can effectively improved the recognition effect of the cochlea implant on Chinese speech.
GA-Based Optimization of Normal Weight Concrete Compressive Strength with Maranta Arundinacea

One of the notable innovations in the field of concrete technology is the development of chemical admixtures as an effective enhancer of concrete properties. However, it is acknowledged recently the harmful environmental and health impacts of these chemical admixtures, thus, paving the way to utilize sustainable green alternatives such as starch-based organic admixtures. In the case of the Philippines, arrowroot (Maranta arundinacea) is an agricultural crop considered as one of the common sources of starch. Hence, making arrowroot a potential commercializes concrete admixture. The present study explored through optimization the applicability of arrowroot starch as organic admixture intended to improve the compressive strength of normal weight concrete. The study generally aimed to apply the Genetic Algorithm (GA) to optimize the amount of arrowroot starch along with the other variables such as curing time and water-cement ratio. Furthermore, GA was able to generalize the compressive strength optimization of normal weight concrete at f’c=67 MPa. Moreover, the GA optimization validated the sensitivity of predictor variables particularly the percentage content of arrowroot starch ($\lt 1$%) in the improvement of compressive strength.
Design on Airborne Positioning System Based on Improved Kalman Filtering

For improving the efficiency of positioning during radio signal source locating, it designs airborne positioning system based on the modified Kalman filtering. Using software radio technology, FPGA is adopted to improve algorithm efficiency. The sequential fusion algorithm with Kalman filtering is introduced to TDOA-AOA joint locating. Different data observing values are uploaded to the fusion center. Finally, according to the different observing data, the optimal estimation results are obtained. Field tests show the positioning system proposed here can improve the positioning accuracy efficiency.
Non-invasive Multimodality Cardiovascular Imaging of the Right Heart and Pulmonary Circulation in Pulmonary Hypertension

Pulmonary hypertension (PH) is defined as resting mean pulmonary arterial pressure (mPAP) ≥25 millimeters of mercury (mmHg) via right heart (RH) catheterization (RHC), where increased afterload in the pulmonary arterial vasculature leads to alterations in RH structure and function. Mortality rates have remained high despite therapy, however non-invasive imaging holds the potential to expedite diagnosis and lead to earlier initiation of treatment, with the hope of improving prognosis. While historically the right ventricle (RV) had been considered a passive chamber with minimal role in the overall function of the heart, in recent years in the evaluation of PH and RH failure the anatomical and functional assessment of the RV has received increased attention regarding its performance and its relationship to other structures in the RH-pulmonary circulation. Today, the RV is the key determinant of patient survival. This review provides an overview and summary of non-invasive imaging methods to assess RV structure, function, flow, and tissue characterization in the setting of imaging's contribution to the diagnostic, severity stratification, prognostic risk, response of treatment management, and disease surveillance implications of PH's impact on RH dysfunction and clinical RH failure.
Automatic Waste Segregation

Dynamic increase in the amount of waste and despicable dumping of waste has become a matter of concern because of the threat it causes to the environment. There comes the pivotal role of an automated waste segregate which avoid this plight and also reduces the difficulty of recycling. The importance and the economic value of waste is realized only when it is segregated. Currently there is no such system for segregation of metal, dry and wet wastes. This project proposes a spot automatic waste segregation unit that effectively gives a solution to this problem. In order to segregate the metallic waste a parallel resonance impedance system is used, and for the separation of wet and dry waste capacitive sensors are used. The benefits of this work are, the waste has a higher potential for recovery and the occupational hazards of waste separating workers is also reduced.