Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariable Support Vector Regression with Multi-sensor Network Data Fusion

Motivated by modeling a general behavioral function of a target system, a data-driven multivariable support vector regression (SVR) is developed. The multivariable SVR is sought to estimate a generalized relationship among multiple input variables which would be collected locally from a distributed multi-sensor network. Instead of an immediate estimation with a installed single sensor, the participation of distributed multi sensors gains the estimation of the system states more reliable and more generalized. The proposed SVR modeling method gives rise of the possibility to support multivariable input spectrum, and the flexibility to combine the input variables with respective decay weights. The paper herewith elaborates the development with experimental evidences.
SVM-Based Approach for Detecting Instantaneous Pain of Mice via Facial Expression

This research method is motivated by Rat Grimace Scale(RGS) method in [1]. Their approach indicates the utility of quantifying spontaneous pain in mice via blinded coding of facial expression, but there is a general concern about rating with subjective criteria of mice experts. Therefore, this research utilizes a supervised parameter linear SVM classifier for long-term monitor of pain face. The contributions are summarized in two points. First, a statistical analysis of pain face has been done for exploring facial patterns before training SVM classifier. Second, a blind test has been implemented to evaluate the rating performance of the mice experts versus linear SVM classifier. Surprisingly, SVM-based approach is much more reliable than the experts. In addition, the hypothesis does not rely on the subjective determination, this manuscript approach further automates whole process by implementing haar cascade for extracting the facial patterns. All in all, we believe that SVM-based pain face detection can be used as a comprehensive and no human interventions index for developing chronic pain scoring system in the future.
THz Wave Scattering by Graphene Strip Grating Embedded into a Dielectric Slab with PEC Plane

The scattering of the H-polarized plane electromagnetic wave by a planar graphene grating in the THz range is considered. The grating is embedded into a dielectric slab placed above a perfectly electric conducting plane. The method of singular integral equations is used. To describe the graphene the model of zero-thickness impedance sheet, characterized with surface conductivity via the Kubo formalism is used. Scattering and absorption characteristics are studied.
Early Experience of Depth Estimation on Intricate Objects using Generative Adversarial Networks

Object parts within a scene observed by the human eye exhibit their own unique depth. Producing a single image with an accurate depth of field has many implications, namely: virtual and augmented reality, mobile robotics, digital photography and medical imaging. In this work, we aim to exploit the effectiveness of conditional Generative Adversarial Networks (GAN) to improve depth estimation from a singular inexpensive monocular sensor camera sensor. The complexity of an object shape, texture and environmental conditions make depth estimations challenging. Our approach is evaluated on our novel depth map dataset we release publicly containing the challenging photo-depth image pairs. Standard evaluation metrics against other depth map estimation techniques demonstrates the effectiveness of our approach. A study of the effectiveness of GAN on different test data is demonstrated both qualitatively and quantitatively.
Field Oriented Control of Doubly Fed Induction Generator Integrated in Wind Energy Conversion System using Artificial Neural Networks

Nowadays, the Doubly Fed Induction Generator (DFIG) is increasingly used because of its advantages in better reliability and supply division. This paper presents the application of a simulation with a proposed model and control of a (DFIG) integrated into a wind energy conversion system with a variable speed wind turbine. Firstly, a mathematical model of the machine written in an appropriate d-q reference frame is proposed to investigate simulations. secondly, and in order to control the power flowing between the stator of the DFIG and the power network, a control law is synthesized using two types of controllers: Proportional-Integral (PI) controller and neural networks (NN) based controller. finally, their respective performances are compared in terms of power reference tracking and robustness against machine parameters variations.
Node Grouping Based on Pattern of Behavior in P2P Distributed System

For Peer to Peer (P2P) distributed systems the most critical feature is availability concerning behavior nature of nodes that comprise this type of distributed systems. In this paper we recognized several node patterns of presence that can be used in P2P storage distributed systems. We analyzed computers from real life environment trying to recognize certain groups of node pattern behavior, and tried to determine if we can use this grouping as basis for using resources in P2P storage systems. Using gathered information we tested nodes availability using different grouping approaches like: random approach, approach based on group of nodes that are recognized using tool for social group networks analysis, approach based on hierarchical cluster method and the use of coincidence matrix. Research filtered two approaches that showed best results and their main characteristics.
Investigating a Method for Automatic Construction and Population of Ontologies for Services: Performances and Limitations

Ontological engineering is a complex process, involving multidisciplinary skills. The Semantic Web, and more specifically Semantic Web Services spreading suffer from the difficulty of producing an ontology sufficiently detailed to be able to correctly describe the data flows exchanged between services. These data are often described using sector-specific vocabulary. Linking these descriptions to external knowledge sources capable of unifying them is often a complex process, requiring adequate sources to be found and properly used. In this paper, we investigate a method combining existing string distance measurement, NLP-analysis and clustering algorithms for automatic construction and population of an ontology. This method takes services capacities descriptions as only input, without external sources of knowledge. It is tested on a set of more than 10,000 services for 106,000 different measures to classify in an ontology, performances and limitations are exposed.
Navigation Control Design of a Mobile Robot by Integrating Obstacle Avoidance and LiDAR SLAM

This paper presents a mobile robot navigation control system based on integration of laser SLAM localization and real-time obstacle avoidance control to provide personnel guidance for daily-life services. The LiDAR SLAM localization system is implemented in a ROS software architecture, in which Cartographer SLAM is adopted and the adaptive Monte Carlo localization is employed onboard the robot. An integrated guidance system is proposed in this paper to combine obstacle avoidance and SLAM so that the robot can move to the desired location without colliding with any unexpected obstacles. A safety-weight parameter is used to integrate goal seeking controller with the obstacle avoidance controller. The experimental results show that the robot can localize itself and navigate to the target location while avoiding obstacles on the path.
Study on the Education and Learning Path for Construction Engineering Cost Professionals in Science, Technology, Engineering and Mathematics

This study mainly explores the science, technology, engineering and mathematics (STEM) training courses for the engineering cost professionals. By means of literature review, in-depth interview, expert questionnaire and other research methods, the traditional education courses are summed up and classified; and the stem education courses are systematized by means of the Interpretative Structural Modeling Method, in order to construct the learning path. After gathering the opinions, by analysis of the learning path for stem training courses, the teaching system for the engineering cost professionals in theoretical and practical learning is developed to achieve the better educated effects. The research and analysis results can be used as a reference for the STEM training courses for the engineering cost professionals.
Radar Simultaneous Localization and Mapping (SLAM) for Stochastic Spread Targets

A Simultaneous Localization and Mapping (SLAM) algorithm for radar target detection is proposed to account for stochastic spread target as landmarks. A grid map construction method is developed for accurate reconstruction of the shape of spread targets. The simulation results indicate that the algorithm is able to deal with multiple stochastic landmarks within the proposed close-loop SLAM scheme.
Mapping of Rice Varieties with Sentinel-2 Data via Deep CNN Learning in Spectral and Time Domains

Generating rice variety distribution maps with remote sensing image time series provides meaningful information for intelligent management of rice farms and precise budgeting of irrigation water. However, as different rice varieties share highly similar spectral/temporal patterns, distinguishing one variety from another is highly challenging. In this study, a deep convolutional neural network (deep CNN) is constructed in both spectral and time domains. The purpose is to learn the fine features of each rice variety in terms of its spectral reflectance characteristics and growing phenology, which is a new attempt aiming for agriculture intelligence. An experiment was conducted at a major rice planting area in southwest New South Wales, Australia, during the 2016–17 rice growing season. Based on a ground reference map of rice variety distribution, more than one million labelled samples were collected. Five rice varieties currently grown in the study area are investigated and they are Reiziq, Sherpa, Topaz, YRM 70, and Langi. A time series of multitemporal remote sensing images recorded by the Multispectral Instrument (MSI) on-board the Sentinel-2A satellite was used as inputs. These images covered the entire rice growing season from November 2016 to May 2017. Experimental results showed that a good overall accuracy of 92.87% was achieved with the proposed approach, outperforming a standard support vector machine classifier that produced an accuracy of 57.49%. The Sherpa variety showed the highest producer's accuracy (98.46%), while the highest user's accuracy was observed for the Reiziq variety (97.93%). The results obtained with the proposed deep CNN learning provide the prospect of applying remote sensing image time series for rice variety mapping in an operational context in future.
MeSH-Based Semantic Query Expansion

In the medical field, medical terms are not necessarily used in the original query. However, they are the key allowing the information retrieval. Query Expansion approaches try to address the problem by providing a way to add words having a "similar meaning" in the original query. In this paper, we discuss various Query Expansion approaches with some of our case study. We focus on the "semantic query expansion" approach which exploits the semantic medical resource MeSH. We propose a new approach that provides the opportunity to improve the information retrieval efficiency, measured by the mean precision at the top 5, 10 returned documents using the TREC EVAL software. This approach offers different treatments (by synonymy, by specialization, and by generalization) according to the entity MeSH nature of the term for the expansion, with the intent to improve the query specification and to evaluate the effect of these treatments. The results show an improvement of retrieval performance on both OHSUMED and Cystic Fibrosis collections.
A 2.5D Wafer-Level CMOS-IPD Rectenna Using Wide-Range Efficiency and Self-Biasing Topology for a RF Wireless Power Harvesting System

This work achieves a two-and-a-half-dimensional (2.5D) wafer-level radio frequency (RF) energy harvesting rectenna module with a compact size and high power conversion efficiency (PCE) that integrates a 2.45 GHz antenna in an integrated passive device (IPD) and a rectifier in a tsmc™ $\pmb{0.18},\mu \mathbf{m}$ CMOS process. The proposed rectifier provides a cross-coupled voltage doubling full-wave topology which can reach relatively high PCE by means of a relatively simple circuitry. The rectenna (including an antenna and rectifier) achieves an output voltage of 1.2 V and PCE of 36 % when the operation frequency is 2.45 GHz, with -10 dBm input power. The maximum efficiency of 81 % and maintain 75 % with 11 dB extension input power. The die size of the RF harvesting module is less than 1 cm2, The performance of this module makes it possible to energy mobile device and it is also very suitable for implantable medical devices (IMDs).
SAT-Based BMC Approach to Verifying Real-Time Properties of Multi-Agent Systems

We present a SAT-based bounded model checking (BMC) method for timed interpreted systems (TIS) and for properties expressible in the existential fragment of a Real-Time Computation Tree Logic with epistemic components (RTECTLK). We implemented the standard BMC algorithm and evaluated it for two multi-agent systems: a timed train controller system and a timed generic pipeline paradigm. We also compared our new SAT-based technique with an SMT-based technique. For experiments we used the SAT solvers YicesSAT and Cryptominisat5, and the SMT solvers Z3 and YicesSMT.
Sector-Beam Antennas for Wide Detection Area in 79 GHz Automotive Short Range Radar(SRR) Sensor

This paper proposes sector-Beam transmitting antennas for 79GHz automotive short range radar (SRR) application. The antennas with three channels have different beam directions, respectively, to cover a wide detection area. A microstrip patch array technique is used to implement the sector-beam antennas and antenna prototypes are fabricated and measured. The measurement results of the prototypes verify the possibility for this application. A 79 GHz SRR sensor is manufactured using the proposed antennas and will be tested in the near future.
Deep Reinforcement Learning with Fully Convolutional Neural Network to Solve an Earthwork Scheduling Problem

This paper proposes a deep reinforcement learning approach in order to optimize a sequence of tasks efficiently with the aid of image processing techniques used in computer vision. The proposed algorithm can be employed to solve the traveling salesman problem (TSP), a combinatorial optimization problem that determines the optimum trajectory of city visits so that the total traveling distance is minimized. The proposed algorithm accepts a set of images as an input, and outputs the priority over alternative tasks (or sites to visit) that should be conducted at the next time step. The proposed method applies a stacked convolutional network layer to effectively process and extract the meaningful features and uses a fully convolutional network to map the processed features to the output tasks without losing the local connectivity in the input images. The proposed algorithm has been employed to optimize the excavation schedule of a single digger for completing a 20 by 20 grid world, which is equivalent to the TSP problem with a node size of 400. The simulation results showed that the proposed method can achieve an effective schedule with optimality comparable to state of the art algorithms.
Automated Generation of E-R Diagram from a Given Text in Natural Language

This paper proposes an Automated E-R Diagram Generation (AGER) System that can generate E-R Diagram from a given text in Natural Language given as input. Natural language texts are used to perform the information extraction by parsing the syntax of the sentences and semantically analyzing their content. The AGER System parses an input text in natural language, semantically analyzes it and internally uses some domain specific database and POS tagging to detect Entity and Relations from the given passage and builds a graph that represents the E-R Diagram. The E-R Diagram can be traversed to generate Data Definition Language to create the actual relations in any RDBMS system. The abstract nature of entity relationship diagram makes it difficult for database designers to directly create E-R Diagram from the natural language text input statement which is used to create the physical model of the database. In the requirement analysis phase of any software design, often Databases designers need to have elaborate discussions with the client on the use cases of the databases and at the end of the work they come up with a neat E-R Diagram which shall be used in subsequent phases to physically realize the relations and implement them. The AGER system proposed here is aimed to assist database designers to create E-R Diagram directly from Client’s requirements in natural language.
Hybrid bonding for 3D stacked image sensors: impact of pitch shrinkage on interconnect robustness

Hybrid bonding is a high-density technology for 3D integration but further interconnect scaling down could jeopardize electrical and reliability performance. A study of the influence of hybrid bonding pitch shrinkage on a 3D stacked backside illuminated CMOS image sensor was performed from a process, device performance and robustness perspectives, from $8.8\ \mu\mathrm{m}$ down to $1.44\ \mu \mathrm{m}$ bonding pitches. As a result no defect related to smaller bonding pads was evidenced neither by thermal cycling nor by electromigration, thus validating fine-pitch hybrid bonding robustness and introduction for next generation image sensors.
Novel Compact Dual-Band BPF Using Stub-Loaded Shorted Stepped-Impedance Resonators

In this paper, a novel compact dual-band bandpass filter (BPF) is proposed. It consists of a pair of stub-loaded shorted stepped-impedance resonators (SIRs) and a pair of dual-feedline structures (DFSs). It is shown that the dual-band BPF can be designed with individually controllable mid-band frequencies and passband bandwidths by appropriately choosing the geometrical parameters of the proposed filter structure. Moreover, the filter has three adjustable transmission zeros which improve significantly the performance of the filter. The dual-band BPF, operating at 1.96 GHz and 3.5 GHz, is designed and fabricated, and its measured frequency responses agree well with the simulated results.
A Characterization of Parameter-Dependent LMIs on Bernstein Polynomial Basis

A solution method of polynomially parameter-dependent LMIs (Linear Matrix Inequalities) is proposed by employing Bernstein polynomial basis. Based on the fundamental properties of the Bernstein basis, it is shown that the original problem falls into standard LMI conditions, and the finite division of the parameter range enables to treat the necessary and sufficient condition with arbitrary resolution and precision.
2-Dimensional Homogeneous Distributed Ensemble Feature Selection

Big data can be seen from the number of its samples and features. The selection of the most representative feature is an important task in Uig data analysis to reduce the dimension. The feature selection method is used to handle this problem. In this research, a homogeneous distributed ensemble feature selection method with 2-dimensional partition is used as new approach of feature selection. The results showed that the proposed method can improve the accuracy from the other feature selection method with an increase of 2% for several datasets. In addition, it also speeds up the computation to almost two times faster.
Quaternion-based attitude estimation reducing magnetic sensor effects on pitch and roll estimation

Inertial and magnetic sensors are increasingly used in wider area of application, especially in attitude estimation problems. When magnetic sensor output is used with accelerometer output as measurements to compensate the quaternion error, the disturbance of the magnetic sensor affects the estimation errors in pitch and roll angles in addition to yaw angle. In this paper, a new quaternion-based indirect Kalman filter with the separation property is proposed, where the attitude error is represented in a reference coordinate system. A constrained structure filter gain is given in the form of scalar equations, which is computationally efficient in real-time applications. The separation property of the proposed filter is verified through simulation.
A Review of Nonlinear Image-Denoising Techniques

This paper discusses different nonlinear techniques for removing noise from images; i.e., image denoising. These techniques differ in terms of algorithm design, purpose, effectiveness, and efficiency. In this paper, different denoising techniques are described briefly where their advantages and disadvantages are discussed. The performances of these techniques are analyzed and compared according to different implementations using statistical methods. Finally, the paper infers when each denoising method is most effective what type of noise each method can handle effectively.
Investigation of Antennas Integrated Into Disposable Unmanned Aerial Vehicles

The integration of antennas into disposable paper drones using inkjet printing technology is presented. These drones or unmanned aerial vehicles (UAVs) are developed using origami folding structures. Two vertical monopole antennas based on the same design concept are proposed and their performance assessed for two different conditions. The conditions relate to the placement of the other electronic components and circuits on the origami drones as reported in the literature. The first is when the electromechanical components and corresponding metallic layers are located in the wings. In this case, the effect of the possible location of the antenna as well as the deformation of the wings on S11 is discussed. The second is a more general case scenario which includes when the components and motors are placed at the tail and lower part of the body of the drone. The antenna elements are directly printed onto a photo paper substrate using silver nanoparticle conductive ink. Subsequently, the substrate is folded to create a paper drone. Low-cost desktop inkjet printing equipment is used to deposit the metallic tracks of the antenna. The designs target the current frequency bands employed in the control and wireless communication of commercial drones (2.4 GHz and 5 GHz bands). The purpose of this work is to investigate potential antenna scenarios for disposable drones which may one day be fully fabricated using inkjet printing technology. All antenna designs and studies have been simulated using CST Microwave Studio and compared well with experimental results.
Two-Way Current Combining Power Amplifier with Multi-Stage Adaptive Bias Control

A two-way transformer-based current combining power amplifier (P A) with the proposed multi-stage adaptive bias control scheme has been designed and fabricated in a 90-nm CMOS process. The prototype achieves a linear gain of 13.6 dB with a 3-dB bandwidth of 13 GHz from 40 GHz to 53 GHz. The output 1-dB compression point $(\mathbf{O}\mathbf{P}_{\pmb{1}\mathbf{d}\mathbf{B}})$ is over 11 dBm from 42 GHz to 46 GHz. The peak power-added-efficiency (PAE) is 10.5% and PAE at OP1dBis 8.3% while the saturation output power is 13.7 dBm and the OP1dBis 11.8 dBm.