Miracle-dz/UniFAR-FTUs huggingface dataset:
Control Challenges of 3D Printed Switched Reluctance Motor

Additive manufacturing, also known as 3D printing, is opening up new ground for innovations in low-volume production due to faster and cheaper prototyping, reduced lead time and shorter supply chains. Using the 3D printing in design of electrical machines is also facing different challenges in control of machines. This paper discusses material and manufacture drawbacks caused by Selective Laser Melting technology, mainly mechanical defects. Special attention is paid to control algorithms of first prototypes and design of converter. Moreover, an overview of control strategies for small Switched Reluctance Motors is presented.
Face Expression Recognition Using Weber Local Descriptor and F-RBFNN

Face Expression Recognition (FER) is the challenging and interesting task in image processing and achieved by human or computer. This paper proposes the FER system is developed by using the combined approach of Weber Local Descriptor (WLD) and Firefly excited Radial Basis Function Neural Network (F-RBFNN). The WLD contains two phases such as image separation and feature extraction. In the first phase, the broad pools of sub-areas are formed by separating the entire face image into non-overlapping. In the second phase, from the large pool of sub-areas having one subarea as the center. The WLD histogram features are extracted by selecting the similar group of area. F-RBFNN classifier is used for recognition of facial expressions. In F-RBFNN, the natural associate groups of training face expression images due to differences in illumination are acquired by using the firefly algorithm and mainly for selection of centers for RBFNN. The WLD-F-RBFNN base FER system recognizes the six expressions such as disgust, sad, smile, surprise, fear and anger. The proposed WLD-F-RBFNN method is established with the facial expression database namely JAFFE and achieves improved performance than other existing methods.
Whale Optimization Algorithm Based Edge Detection for Noisy Image

Edge in image processing is considered as those pixels whose intensity value changes drastically and finding the object boundary is the main task of any edge detection technique. There have been various Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) based techniques that have been applied to solve edge detection problem, but most of them have not considered the noisy environment which in itself makes edge detection further more difficult task and the user-defined threshold approach doesn't always give desired results. The paper proposes a Whale Optimization Algorithm (WOA) based edge detection technique with weighted fitness function including homogeneity, uniformity and average gradient magnitude as main factors for detecting the edges of additive gaussian noise images. The experiment results have shown that the proposed technique has performed better under noisy environment for conventional edge detectors: Sobel, Canny and ACO based technique for both objective criteria i.e. restored edge images and subjective criteria i.e. PSNR, Precision, Recall and F -measure.
Unsupervised Extractive Summarization for Product Description Using Coverage Maximization with Attribute Concept

This paper presents a system that generates unsupervised extractive summarization of product descriptions. The system utilizes a coverage maximization model with concepts introduced as a practical solution for extractive summarization. Product descriptions present more challenges to this task, including but not limited to, shorter paragraphs and infrequent concepts distributed across the paragraph. To overcome these challenges, we propose domain-specific concepts, namely product attributes, in addition to general linguistic concepts such as named entities and syntactic dependency. The result demonstrates that product attributes can help generate better summarization for product descriptions,
Traffic-Aware and Memory-Aware Task Scheduling on Multi-Core Chips

With the development of semiconductor industry and integrated circuits, the performance of processors has been advanced steadily. More and more devices including cores, memories and peripherals are being integrated in chips to meet the requirements of high performance applications. The rapid increase in chip complexity makes it difficult for these devices to work efficiently. In order to facilitate efficient chips systems, we proposed a task scheduling algorithm for Chip Multi-Processors (CMP) which are called Homogeneous Earliest-Finish-Time (HoEFT) algorithm. We use this algorithm to finish two benchmarks on a chip system consisting of eight Processing Elements (PEs) and a 16MB shared memory. The results show that these PEs can reach reasonable utilization under HoEFT algorithm.
A Soft Robotic Gripper with Sensory Feedback Fabricated by Latex using Coagulant Dipping Process

This paper proposes a soft robotic gripper with sensory feedback where the gripper fingers are fabricated using latex by coagulant dipping process. Proposed gripper can be used to handle delicate food items. Gripper consists of a rigid base and four soft actuators. Each soft actuator has four separate chambers inflated using compressed air. The gripper allows two working modes: 1) inflating outwards to open the gripper to approach the objects, 2) inflating inwards to grasp the objects. Experiments to test the material, to determine bending angle variation of single finger with applied pneumatic pressure and comparison of sensor feedback to determine bending angles shown by gripper were done. Another experiment was conducted to grasp and lift different objects using the four fingered gripper to observe the lifting capabilities. The gripper is capable of picking up and hold wide variety of objects with different shapes such as eggs, tomatoes, sponge cakes and measuring beakers. Experiment results showed that finger design yielded different curvature along the finger when pressurized using different pneumatic pressures.
Mining Source Code Improvement Patterns from Similar Code Review Works

Code review is key to ensuring the absence of potential issues in source code. Code reviewers spend a large amount of time to manually check submitted patches based on their knowledge. Since a number of patches sometimes have similar potential issues, code reviewers need to suggest similar source code changes to patch authors. If patch authors notice similar code improvement patterns by themselves before submitting to code review, reviewers’ cost would be reduced. In order to detect similar code changes patterns, this study employs a sequential pattern mining to detect source code improvement patterns that frequently appear in code review history. In a case study using a code review dataset of the OpenStack project, we found that the detected patterns by our proposed approach included effective examples to improve patches without reviewers’ manual check. We also found that the patterns have been changed in time series; our pattern mining approach timely achieves to track the effective code improvement patterns.
Closed-Loop Functional Electrical Stimulation for Gait Training for Patients with Paraplegia

This paper presents a novel functional electrical stimulation paradigm to generate locomotion in paraplegic patients. We propose a closed-loop surface functional electrical stimulation (sFES) of 16 lower-limb muscles to produce all subphases of the gait. The reproduction of cyclic and coordinated joint-movements is a challenge, especially if synchronic and synergic lower-limbs muscular contraction is considered. This paper presents the implementation and validation of a sFES closed-loop position control and the pilot clinical tests with a chronic, motor complete (ASIA B) SCI patient. We demonstrate that the patient can safely use our setup to produce a physiologically correct gait with minimal external help. The implemented sFES-gait training was beneficial to see an improvement in gait kinematics. The integration of the proposed setup with a neurorehabilitation protocol has the potential to become a valuable locomotion therapy for SCI patients.
PFC in Switch Mode AC-DC Converter with POESL Luo Converter for LED Applications

Power quality improvement at the input side is one of the major requirements that have to be considered while designing an AC to DC converter for a DC loads. In this paper average current mode control based power factor (PF) correction in switch mode AC-DC converter with positive output elementary super lift (POESL) Luo converter topology for light emitting diode (LED) applications is presented. POESL Luo converter is designed and the simulation is carried out using the MATLAB/Simulink environment. Total Harmonic Distortion (THD) of proposed PFC switch mode AC-DC converter is validated as per the international standard IEC61000-3-2 of class C equipments.
Rozpoznawanie i leczenie chorych na chłoniaka Hodgkina

Hodgkin lymphoma (HL) is a rare B-cell derived lymphoid malignancy. Its typical feature is the presence of small number of tumor cells surrounded by immune cell infiltrate of immunosuppressive character. Two entities of HL are distinguished: classical HL and nodular lymphocyte predominant HL. Assessment of clinical advancement stage and prognostic factors is important for tailoring appropriate therapy based on chemotherapy and radiotherapy. Prognosis in HL is good with cure rate reaching almost 80%. About 10% of patients (especially in advanced stages) does not achieve complete remission. In 20–30% patients, who initially achieved a response, relapses occur. For these patients’, besides chemotherapy, treatment with brentuximab vedotin, PD-1 inhibitors, autologous or allogeneic hematopoietic stem cell transplantation, or participation in clinical trial comprise poten¬tial therapy options. In this publication, we discuss the guidelines of diagnosis and treatment of HL.
Uji Tetrazolium Untuk Kriteria Vigor Benih Cabai (Capsicum Annum)

USI EVILIANI. Tetrazolium test for chili’s (Capsicum annum) vigour criteria. Supervised by ENY WIDAJATI. The continuous availability of superior quality chili seed (Capsicum annum) is very important to get high productions with excellent quality. Criteria for seed quality and proper evaluations are ones of the main keys. This research was conducted to obtain informations on tetrazolium test’s staining pattern for chili (Capsicum annum) seed’s vigor criteria quickly to support the availability of seed. The design used was a complete group randomized design with 3 replications. Tetrazolium concentration was used 1%. Tetrazolium test’s staining pattern on the chili (Capsicum annum) seed produced 16 staining pattern, which then was grouped into 4 groups. The four group was used as the standard coloring to distinguish the seeds that have the potential to grow into a strong normal seedling, less strong normal seedling, abnormal, and dead. Parameters germination potency and dry weight of normal seedling have a high correlation value with tetrazolium test. Staining patterns cannot be used yet to estimate chili seed’s vigor.
Applying the Theory of Planned Behaviour to understand nurse intention to follow recommendations related to a preventive clinical practice

The use of filter needles reduces the number of particles found in parenteral solutions after the opening of glass ampoules and has been recommended by many authors. Even so, nurses do not use filter needles unilaterally in their practice. In order to understand the psychosocial determinants of nurse intention to follow recommendations related to the use of filter needles in the preparation of parenteral medication, we conducted a cross-sectional study in a large university medical centre in the province of Quebec (Canada). We developed a questionnaire based on Ajzen’s Theory of Planned Behaviour and distributed it to all nurses (n = 364) from eight care units. A total of 242 questionnaires were completed and returned (response rate of 66.5%). Attitude towards the behaviour and perceived behavioural control predicted nurse intention to use filter needles according to recommendations. Three specific beliefs related to these variables – ease of use, enjoyment and reason – explained 50.3% of the variance in nurse intention to use filter needles. The results of this study support the use of the Theory of Planned Behaviour as a theoretical basis that can help identify important avenues to inform behaviour change strategies regarding healthcare professional adoption of guidelines to improve patient safety.
Review of Small-Signal Modeling Methods Including Frequency-Coupling Dynamics of Power Converters

Over the past years, the linearized modeling techniques for power converters have been continuously developed to capture the small-signal dynamics beyond half the switching frequency. This paper reviews and compares the small-signal modeling approaches based on a buck converter with voltage-mode control. The study includes the small-signal averaged modeling approach, the describing function method, and the harmonic state-space modeling approach, in order to be able to better select the correct method when modeling and analyzing a power electronic circuit as well as a power-electronic-based power system. The model comparison points out that the describing-function-based models do improve the modeling accuracy beyond the half-switching frequency of the converter, yet they fail to predict the frequency-coupling interactions (e.g., beat frequency oscillations) among multiple converters, and instead, harmonic state-space models in the multiple-input multiple-output form are required.
Image-Based Flame Detection and Combustion Analysis for Blast Furnace Raceway

Blast furnace tuyere is an important “window” between the inside and outside of blast furnace. Stable and accurate detection of the combustion condition of tuyere raceway plays a significant role in maintaining longevity, safety, and smooth of blast furnace. The adiabatic combustion temperature as a traditional measurable indicator of the raceway combustion condition considers the raceway temperature as a constant. It cannot explain the productivity and quality fluctuations of iron in practical production while the operation parameters are fixed and unchanged. Thus, this paper presents the image-based flame detection system specific to the blast furnace raceway to research the actual combustion condition in the raceway. The system mainly consists of an optical detector to capture raceway images and a digital image processing unit to extract flame features. In this paper, the nonlinear partial least-squares colorimetry and Monte Carlo method with iterative optimization are developed to obtain the temperature distribution of the raceway. Power spectral analysis is used to extract the flame flicker frequency. Massive raceway images captured from 15 raceways of a 2500-m3 blast furnace were used to analyze raceway flames. The results demonstrate that temperature distribution of the raceway can fluctuate in a wide range and considerable temperature nonuniformity exists among the 15 raceways. This case can explain why productivity and quality of iron are unstable under the same operation parameters. And, the detection system performs well in revealing the nature of the raceway combustion condition.
Development of Community Grid: Review of Technical Issues and Challenges

The concept of a community grid is presented here. It involves the distribution grid and an increased use of renewable energy coming from distributed resources along with consumer/prosumer engagement in the energy trading mechanism. The possible operation and management with energy trading flexibility are briefly outlined. Under such scenario, the classical operation of the distribution grid is challenged by the issues brought about by the large penetration level of the new energy resources. This paper presents a status review of the technical issues that may appear under the community grid scenario. Building up on these surveyed issues, this paper also reviews and discusses approaches to solutions, which are required in order to make the community grid highly renewable and sustainable.
Tree-based Secure Comparison of Secret Shared Data

A secure integer comparison protocol is one of the most fundamental building blocks to construct protocols of rich functionality in multi-party computation. It allows parties to compute the less-than functionality on shared values in privacy preserving manner. In this paper, we present a tree-based secure two-party comparison protocol in the client-aided client-server model, which outperforms existing approaches in terms of round complexity when it is used for 64-bit data. Our proposed protocol requires only 9 communication rounds to compare 64-bit data, which is at least 3 times fewer rounds than existing protocols. This suggests that our protocol is adequate to be used in low-latency networks such as WAN.
Trajectory Tracking of Unicycle-type Robots with Constraints

Trajectory tracking is a decisive factor that affects autonomous driving performance of mobile robots in real-world applications, such as industry, service and other areas. Nowadays, there has been many works about path planning and trajectory tracking. Whereas there are problems to be improved to make mobile robot well applied in practical world. For example, generated trajectory can't be tracked smoothly, and the robot has to stop and rotate itself to adjust pose and pass segments in trajectory because of constrains of path curvature and robot's kinematics model. In this paper, a new trajectory planning algorithm with cubic spline interpolation method was proposed, which fully considered constrains of robot model including velocity and acceleration bounds. And corresponding trajectory was generated. Then a robust controller was built to conduct tracking tests. Experimental results demonstrate that our planning and tracking algorithm could help robot achieve a smooth, accurate and efficient trajectory tracking performance without any stop.
An Automatic Scoliosis Diagnosis and Measurement System Based on Deep Learning

Adolescent idiopathic scoliosis (AIS) is a three-dimensional structural deformity of the spine which affects 1–4% of adolescents and causes not only deformed appearance but also compromised mental status, pulmonary function, motor function and life quality. Currently, the diagnosis of AIS depends on the measurement of Cobb angle on spine radiographs, which is performed manually by doctors. Intra-observer and inter-observer variation exist in such method and causes errors in diagnosis. The purpose of this study is to design an automatic scoliosis diagnosis and measurement system based on deep learning to improve measurement accuracy and assist doctors in diagnosis. U-net segmentation network was used to segment the spine radiographs. Based on the segmented images, specific positions of upper and lower end vertebrae (UEV and LEV) and slopes of their endplates were identified by minimum outer envelope rectangle and least square method. Subsequently, Cobb angles were measured as angles between superior endplates of UEVs and inferior endplates of LEVs. After comparing manually measured Cobb angles with computer- measured Cobb angles, the result showed that the computer- measured Cobb angles were similar to the manually measured ones. To sum up, this system for automatic measurement of Cobb angle can assist doctors in clinical diagnosis.
SOA Patterns Selection and Application Based on Software Quality Requirements

Applying software architecture patterns in SOA architecture design is an important way to construct high quality design of software architecture. Meanwhile, the selection of architecture patterns has a close relationship to quality requirements of software systems to be developed. Applying a specific architecture pattern can usually address specific quality requirements, and further optimize architecture design. In this paper, we first made a comprehensive survey and analysis on the type of quality requirements, and refined twenty-one quality requirements that are frequently used in software architecture design. We then classified the SOA patterns in the perspective of quality requirements, which can help architects select appropriate SOA patterns according to the quality requirements of their system. Finally, we illustrated the proposed method by selecting a specific SOA pattern to design an online book distribution management system.
Towards MAV Navigation in Underground Mine Using Deep Learning

The usage of Micro Aerial Vehicles (MAVs) is rapidly emerging in the mining industry to increase overall safety and productivity. However, the mine environment is especially challenging for the MAV's operation due to the lack of illumination, narrow passages, wind gusts, dust, and other factors that can affect the MAV's overall flying capability. This article presents a method to assist the navigation of MAVs by using a method from the field of Deep Learning (DL), while considering a low-cost platform without high-end sensor suits. The presented DL scheme can be further utilized as a supervised image classifier that has the ability to process the image frames from a single on-board camera and to provide mine tunnel wall collision prevention. The efficiency of the proposed scheme has been experimentally evaluated in two underground tunnel environments that were used for data collection, training, and corresponding testing under multiple flying scenarios with different cameras configurations and illuminations.
Visual Manipulation Relationship Detection with Fully Connected CRFs for Autonomous Robotic Grasp

In multi-object scenes, objects may be stacked and interact with each other, which brings an enormous difficulty for robotic grasping tasks. Therefore, exploring the manipulation relationships between objects is necessary. In robotic fields, there have been some works focusing on this task. However, most of them only detect the relationship between each pair of objects, regardless of dependency among them. In this paper, we construct a fully connected Conditional Random Fields (CRFs) on the output of front-end network, which models the dependency among all relationships in a scene. Besides, an exact inference algorithm and a variational inference algorithm are deployed for the CRFs, which immensely improves the performance of our framework while meeting the real-time requirements. Furthermore, we expand the types of visual manipulation relationship, which make the description pattern more powerful and stable. Our experiments show that the proposed approach gets a state-of-the-art result on Visual Manipulation Relationship Dataset (VMRD). Finally, based on this work, we build a robotic system for multi-object grasping, which demonstrates the practicality of our algorithm.
Super-Resolution of Color Images Using CNN

Convolutional neural networks (CNN) offer superior performance for Single Image Super Resolution (SISR) tasks. Super-resolution is a technique that improves low-resolution image quality and converts it into high-resolution images to provide better viewing. As the network grows, the features of the previous levels are prevented or not used in subsequent levels. In SISR, the previous layers are mainly composed of local characteristics that are essential for the activity. The proposed super resolution CNN approach is developed for x3 and x4 scaling factor. The proposed approach achieves the good results.