Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Mobile Image Recognition System for Nonwood Species Identification in the Field

Non-wood forest products (NWFPs) provide important source of income for millions of households world-wide especially for those in rural areas. Especially nonwood plants are main source of income for rural people in many parts of the world. These plants are also consumed by local people for health, nutrition and many other needs. In order to ensure benefits of NWFPs, it is important to identify nonwood plants and determine their population distribution. However, plant identification by using manual method is a complex task to fulfill especially in varying conditions in the field. The leaves are often used to identify plant species based on their visible characteristics. Image recognition systems have been developed to identify plant species by using leaf image. Comparing manual plant identification systems, image recognition systems are easier and, in most cases, more accurate alternatives. In recent years, some mobile applications have been developed to assist people to identify plants by taking pictures of their leaves using smart phones. In this study, it was aimed to investigate the capabilities of the most common mobile image recognition systems in identification of plant species based on leaf images.
Spectrum Analysis : Practical Operational Research in the Face of the Human Variable

This chapter is based on presentations given at the 20th International Symposium on Military Operational Research [1] and NEC – The Human Dimension , [2] combined with reflections on the presentations and discussions from Human Factors of Decision Making in Complex Systems [3]. It explores the need for Operational Research (OR) studies to embrace the br oad ange of variability arising from human participation in the operations and systems studied, and the consequent need for the human science communities t o integrate and present their knowledge in forms which are amenable to exploitati on by OR practitioners. Although the chapter is principally concerned with the exploitation of human sciences by OR there are clear implications for cro ss-disciplinary integration across the full spectrum of the human and system sciences as a pre-requisite for satisfying OR needs. The chapter is, therefore, a call to acti on for the various scientific and technical communities relevant to OR.
Interdisciplinary Approach to Emotion Detection from Text

Emotions not only influence most aspects of cognition and behavior, but also play a prominent role in interaction and communication between people. With current multidimensional research on emotions being vast and varied, all researchers of emotions, both psychologists and linguists alike, agree that emotions are at the core of understanding ourselves and others. As a primary vehicle of communication and interaction, language is the most convenient medium for approaching research on the topic of emotions. Not only is one of the main functions of language the emotive one, but the interplay of emotions and language occurs at all linguistic levels. Textual data, in particular, can be beneficial to emotion detection due to its syntactic and semantic information containing not only informative content, but emotional states as well. A general overview of the emotion models based on the research in psychology, as well as the major approaches to emotion detection from text found in linguistics, together with usage demonstration of emotion detection linguistic tools, will be given in this paper. Examples of useful applications – from psychologists analyzing session transcripts in search for any subtle emotions, over public opinion mining on social networks to the development of AI technology – will also be provided showing that emotion detection from text has an abundance of practical uses. As the methods for emotion detection from text become more accurate, uses and applications of emotion detection from text will become more numerous and diverse in the future.
Realization of pulse width modulation chip for high efficientcy high precision switching regulator

The realization of pulse width modulation chip for switching regulator is analyzed. An architecture of pulse width modulation chip is proposed, which is suitable for high current, high efficency, high precision and programmable switching regulator on PC mainboard. This chip integrates the control, output adjustment, monitoring and protection function of switching regulator. Its characteristics also include fast transient response, small turn on overshoot, small output voltage fluctuations with output current changes. Major circuits in this chip and the whole DC DC converter system are simulated by HSPICE. The chip is fabricated with 1. 7μm BiCMOS process, a switching regulator system based on this chip for Pentium serial PC mainboard is set up. At last, the performance of this system is tested, and the result conforms to Intel VRM8.1 standard.
On the Design of Truncated T-Type Power Dividers for X-Band with SIW Technology

In this paper, the compact design of a broadband 4-outputs power divider based on Substrate Integrated Waveguide technology operating in X-band frequency and that provides equal power distribution in all output ports is presented. The divider was designed, simulated, and optimized using CST MicrowaveStudio. An important factor that was considered in the SIW design was the transitions required to achieve an adequate impedance coupling between the truncated T-type power divider in SIW and the structure to be connected (Microstrip, SMA connectors). For verification, the proposed T-type power divider was fabricated using SIW technology and the results were compared with simulations obtained using CST Microwave Studio, confirming that the design works as expected.
Design of Tire Pressure Monitoring System Based on Embedded Operating System

Tire pressure monitor system(TPMS) of vehicles with high reliability and having good real-time is designed,considering the characteristics of vehicle tire pressure.Embedded real-time operating system μC/OS-II is adopted as the software platform,with software designed in multi-task,and 32 bit embedded ARM processor is used.Accident can be prevented effectively when a tire from running out of normal pressure for long hours,and automobile active safety is increased.This tire pressure monitoring system provides an advantageous and reliable solution for tire pressure monitoring system.
Fading channel suppression of TDCS system based on FRFT_LFM channel detection

Using the superiority of fractional Fourier transform(FRFT)to detect linear frequency modulated signal (LFM),the fading channel detection technology of transformdomain communication system(TDCS)is proposed based on FRFT_LFM. The technology makes use of FRFT algorithm to test pre-launched LFM signals so as to estimate the channel fading characteristics,and uses the pre-equalization approach to pre-amend the basis function amplitude spectrum of TDCS system in order to improve the ability of TDCS system about restraining fading channel characteristics. Theoretical analysis and simulation results show that FRFT_LFM technology can detect the channel fading characteristics effectively and the suppression performance of TDCS about restraining fading channel characteristics has improved evidently.
Building a Spanish/Catalan health records corpus with very sparse protected information labelled

Electronic Health Records (EHR) are an important resource for the research and study of diseases, treatments and symptoms. However, due to data protection laws, information that could potentially compromise privacy must be anonymized before making use of them. Thus, the identification of these pieces of information is mandatory. This identification is usually performed by linguistic models built from EHRs corpora in which Protected Health Information (PHI) has been previously annotated. Nevertheless, two main drawbacks can occur. First, the annotated corpora required to build the models for a particular language may not exist. Second, unannotated corpora might exist for that language, containing very few words related to PHI mentions (i.e., very sparse population). In this situation, the process of manually annotating EHRs results extremely hard and costly, as PHI occurs in very few EHRs. This paper proposes an iterative method for building corpus with labelled PHI from a large unlabelled corpus with a very sparse population of target PHI. The method makes use of manually defined rules specified in the form of Augmented Transition Networks, and tries to minimize the seek of EHRs containing PHI, thus minimizing the cost of manually annotating very sparse EHRs corpora. We use the method with primary care EHRs written in Spanish and Catalan, although it is language-independent and could be applied to EHRs written in other languages. Direct and indirect evaluations performed to the resulting labelled corpus show the appropriateness of our method.
Object Detection from a Range Image Using Sparse Keypoint Detector Technique

This proposal presents the object detection using Sparse Keypoint Detector technique, in which is computed the interesting points from directional surface. This surface is built with the normal vectors corresponding to the homogeneous surface, and this in turn, is obtained from range image. A probability dense function (pdf) analysis applied to the normal vectors contained in the directional surface, allows us to select the highlight points to obtain the contour of the scene. Furthermore, a probability mass function (pmf) analysis applied to the information contained in the surrounding of these points, aim to select a region of interest, in which is found the object 3D to be detected. Finally, it is applied a Chess distance to the interesting points contained in the region of interest to detect the object. The presented experimental tests involve a qualitative and quantitative analysis using the Middlebury and DSPLab dataset.
El fracaso escolar, ¿fracaso de quién? La modificación del concepto de fracaso School failure, whose failure? The transformation of the notion of school failure

The article contributes to the discussion on the conception of “school failure”, through the revision of the literature, and shows how it has evolved, since the introduction of the notion of inclusive education. It analyzes the current literature on how it has been faced in different contexts, and in the diverse levels of the educational systems (policy design, school and classroom levels). It concludes that numerous best practices exist, and proposes that the best results are attained when an inclusive, preventive, and systemic approach is put into action, complemented at the classroom level with a risk-taking and an experimentation approach.
Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching with Field Data

Asset optimization has recently become a crucial issue in Oil&Gas industry, considering oil price conjuncture and an increased awareness on environmental aspects. In this paper, an Artificial Intelligence (AI) technique is presented, which is able to manage big dataset to automatically match the entire production model against measured field data. The tool is based on a hybrid in-house developed AI technique, integrating deep neural networks, biogenetical algorithms, commercial simulators and real-time data. The workflow starts with the modeling of the production system through physics-based commercial simulators. A sensitivity analysis identifies the critical variables, which are then randomly varied with a Sobol distribution, exploring the entire solution domain. With these data, a proxy model to the commercial software is generated using an artificial neural network. Finally, the AI tool fed by real-time data is used to match the field behavior: uncertain parameters are modified through a differential evolution algorithm that minimizes the error between calculated and measured variables. The matching parameters are, then, passed to the simulators achieving a field representative model. The tool has been developed considering an operating field in offshore western Africa. The typical uncertain parameters in this kind of field are related to the fluid characteristics, in particular densities and compositions, but also to the physical characterization of the pipelines such as roughness and heat transfer characteristics. The matching process has been performed coupling the proxy model, which is a neural network able to replicate the field behavior, and a differential evolution algorithm as the optimization algorithm. The fitness function to be minimized is a Mean Absolute Percentage Error (MAPE) that represents the distance between the actual field production parameters and the modelled ones. The best configuration of both the neural network and the differential evolution algorithm required a computational time of 6 seconds with a MAPE equal to 2.6%. These results are compared to the one obtained coupling the same differential evolution algorithm with the commercial simulator to perform the matching. The required computational time is equal to about 20 hours (70400s) and a MAPE equal to 2.2%. The big gain with the novel approach is clearly the knocking down of computational time with a comparable error. In this paper, it has been shown how substituting the physical model with a proxy one can give substantial advantages in terms of computational time. In principle, with the velocity of the tool implemented, the matching procedure could be done on a daily basis. This is a breakthrough because it allows having the simulator model always tuned and ready to be utilized.
Fast Service Migration Method Based on Virtual Machine Technology for MEC

In the era of the Internet of Things (IoT), mobile edge computing (MEC) has become an effective solution to meet the energy efficiency and delay requirements of IoT applications. In MEC systems, tasks can be offloaded from lightweight mobile devices to edge nodes that are nearer to the users. To improve user experience, we combine remote loading and redirection to accelerate the service migration. By tracing historic access patterns, the proposed method first generates a loading request list that locates the core codes in the image file of service applications for booting. The core codes are then be prefetched and cached automatically. Furthermore, to avoid the potential UI lagging caused by incomplete service migration, edge nodes can continuously load the remaining codes in the image file. Once the image file is completely migrated, the file will be reconstructed. The running virtual machine (VM) then switches data access to the merged image file. Experiments show that this method can observably reduce the loading time of a VM-based application.
A New Type of Water Blocking Yarn for Electrical Wire & Cable and Optical Fiber Cable

A new type of water blocking yarn for electrical wire cable and optical fiber cable is presented.The new yarn features simple process,easy operation,low costs,steady structural properties,absence of powder loss and agglutination amongst fibers as well as excellent water-blocking performance and intensity of gelatin,which ensures good filling effect.
PRINCIPLE AND REALIZATION OF A DYNAMIC VOLTAGE REGULATOR (DVR) BASED ON LINE VOLTAGE COMPENSATING

Dynamic Voltage Regulator (DVR) based on the line voltage compensating (LVC) is proposed in the present paper. Following the introduction of the LVC DVR topology, the authors describe the compensation strategy and the control methodology. After moving some key obstacles such as phase-locking failure, unbalance, voltage level loss, DC voltage pumping-up, the authors have realized a prototype LVC DVR. The effectiveness of DVR is illustrated by the dynamic compensation .
Analysis of robustness control characteristics of servo loading rotational speed system with secondary regulation

The mathematical model is built of servo loading rotational speed system with secondary regulation and PID control as well as dynamic robustness compensation control simulated analysis are made through MATLAB software.Comparing the results of the two simulated analysis,the robustness of dynamic robustness compensation is stronger than that of PID control system and therefore its control performance is higher.
Emotional Resonance: An Interactive Installation Controlled by Biometric Data

Emotional Resonance is an interactive installation that transforms humans’ pulse into motion and light reflections. As our pulse is influenced by emotion, and as a machinery reflection on our body signals creates a fascinating feedback loop, this work aims to explore emotions mediated by an interactive art installation. The project aims to foster the discourse on how biometric technology can cause emotions and consequently, how it connects us with a responsive machine or interactive art installation. The core questions, this work is provoking are (1) Can a machine, which is synchronized with our pulse create an emotional response? (2) How can an interactive machine or an art installation generate emotional resonance?
A REVIEW OF STUDIES ON CASING TREATMENT OF AXIAL-FLOW COMPRESSOR

Compressor stability in the form of surge and stall is a major concern for designers of compressor. Under many conditions, unstable flow conditions are initiated in the endwall flow region of a compressor. If aerodynamic stall in the endwall region can be delayed, the stability of the compressor may be improved. Over the last 30 years, various forms of casing treatment have been employed for improving the endwall flow and hence enhancing the compressor stability. This paper reviews the experimental investigations, theoretical analyses and numerical investigations on the casing treatment of the axial-flow compressor. The fundamental mechanisms of casing treatments and their relative effects on the compressor performance and stability are summarized. Some recommendations are made for future researches.
Incremental Banerjee test conditions committing for robust parallelization framework

This paper describes the design of an automatic parallelization framework. The kernel supplied at its front end was suggested as an instrument for parallel potential assessment. It was used to measure the maximum achievable speedups in the major set of the CHStone benchmark suite programs. In such framework, we suggested the liberation of parallelism incrementally. We proposed a data dependency heuristic-based transformation method to make true dependences dissociation. We generated an internal representation ($ IR^{2} $), where the Banerjee test conditions are met. Two among three of Banerjee test conditions came to be committed. In shared memory many/multicore platforms, the third condition could be satisfied by privatization. We would be able to choose the safe and the opportune pairwise (mapping-privatization) scheme among a number of threads mapping scenarios that become available in the $ IR^{2} $ structure. Instrumentation on a subset of CHStone benchmark was carried out as a validity proof of our proposal, and the results confirmed that our framework kernel is robust.
Predicting vehicle motion and driver intent using deep learning

This thesis investigates the application of Deep Learning and Mixture Models on the prediction of human drivers in traffic. The chosen approach is a Mixture Density Network where the Neural Network is composed of Long-Short Term Memory units and the Mixture Model consists of univariate Gaussian distributions. The model outputs accelerations for the next four seconds in longitudinal and lateral directions as well as indicating whether the driver intends to change lane during this time or not. The results of the motion prediction is promising with a Mean Absolute Error of around 1 meter after a 4 second prediction. The intention prediction results in around 75% accuracy in identifying lane change trajectories 1.7 seconds prior to the lane change and around 95% accuracy 1 second prior to the lane-change. The conclusion of the study is that in order to accurately predict humans, the model has to make use of as many variables as possible. However it will not be possible to fully predict humans by just observing the environment since there are many unobservable variables affecting the decision-making. One way to take this into account is to make use of signals humans use to display their intentions, such as indicator lights.
Some methods of RSSI filtering in wireless sensor networks

Wireless sensor network is an emerging technology,which has a wide range of applications,but the wireless sensor networks based on the received signal strength indicator(RSSI)application need to filter the multi-RSSI values of the same node received by network. In order to have a comprehensive understanding of RSSI filtering,several filtering ways are introduced. The principles and characteristics of each filtering method are summarized in detail. Finally,it is pointed out that the fil tering effect,algorithm complexity,node energy consumption and other factors should be weighed to select a filtering method or a hybrid filtering method mixed with more algorithms.
Monte Carlo Techniques for Drug Design: The Success Case of PELE

This chapter summarizes the most representative software packages that readily allow running Monte Carlo (MC) simulations in relevant scenarios for drug design. It explores in detail the Protein Energy Landscape Exploration (PELE) program, providing first the main characteristics of the technique, followed by an analysis of the different application studies in mapping protein‐ligand interactions. The ligand, formed by a rigid core and a set of rotatable side chains, is perturbed by translating and rotating it. PELE creates a list of perturbation poses, and then chooses the one with the lowest system energy. PELE was originally designed to map ligand migration pathways: its first applications aimed at finding exit pathways starting from ligand‐bound crystallographic structures. Additional applied studies have centered on modeling enzymatic mechanisms and engineering; the same techniques applied in mapping protein‐drug interactions can be used in the study of substrate recognition by enzymes.
Visual-UWB Navigation System for Unknown Environments

Navigation applications relying on the Global Navigation Satellite System (GNSS) are limited in indoor environments and GNSS-denied outdoor terrains such as dense urban or forests. In this paper, we present a novel accurate, robust and low-cost GNSS-independent navigation system, which is composed of a monocular camera and Ultra-wideband (UWB) transceivers. Visual techniques have gained excellent results when computing the incremental motion of the sensor, and UWB methods have proved to provide promising localization accuracy due to the high time resolution of the UWB ranging signals. However, the monocular visual techniques with scale ambiguity are not suitable for applications requiring metric results, and UWB methods assume that the positions of the UWB transceiver anchor are pre-calibrated and known, thus precluding their application in unknown and challenging environments. To this end, we advocate leveraging the monocular camera and UWB to create a map of visual features and UWB anchors. We propose a visual-UWB Simultaneous Localization and Mapping (SLAM) algorithm which tightly combines visual and UWB measurements to form a joint non-linear optimization problem on Lie-Manifold. The 6 Degrees of Freedom (DoF) state of the vehicles and the map are estimated by minimizing the UWB ranging errors and landmark reprojection errors. Our navigation system starts with an exploratory task which performs the real-time visual-UWB SLAM to obtain the global map, then the navigation task by reusing this global map. The tasks can be performed by different vehicles in terms of equipped sensors and payload capability in a heterogeneous team. We validate our system on the public datasets, achieving typical centimeter accuracy and 0.1% scale error.