Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechatronic Exoskeleton Systems for Supporting the Biomechanics of Shoulder-Elbow-Wrist: An Innovative Review

Disability is defined as a condition of the human body that limits the execution of a task or activity. According to the World Health Organization (WHO), 15% of the world's population suffers from it, recent studies indicate that the growing prevalence are a significant problem and that, consequently, the demand for rehabilitation services is rising considerably. For this reason, different authors propose the use of exoskeletons in rehabilitation therapies as an alternative solution. Thanks to the progress of this kind of technology, it is possible to create robotic systems that help people with disabilities to recover, totally or partially, the original movement of their affected limbs. This Innovative Review Article presents an exhaustive review of the main features of upper-limb exoskeletons such as Degrees of Freedom (DoF), mechanism type, rehabilitation mode, movements allowed, applications and Technology Readiness Level (TRL). Firstly, the study provides a brief description of the biomechanics of the upper limbs of the human body. Next, the material, the rehabilitation modes and the Technology Readiness Level (TRL) of each of these devices are analyzed. As a result, it was observed that aluminum and PLA are the most used materials for exoskeletons' manufacturing. In addition, it was noticed that most of these exoskeletons perform passive rehabilitation. Besides, it was identified that the most common applications are the assistance and rehabilitation in the affected limb of patients who have suffered a stroke. Finally, using TLR scale, it was determined that these mechatronic systems are between TRL5 and TRL8.
A Weakly-Supervised Semantic Segmentation Approach Based on the Centroid Loss: Application to Quality Control and Inspection

It is generally accepted that one of the critical parts of current vision algorithms based on deep learning and convolutional neural networks is the annotation of a sufficient number of images to achieve competitive performance. This is particularly difficult for semantic segmentation tasks since the annotation must be ideally generated at the pixel level. Weakly-supervised semantic segmentation aims at reducing this cost by employing simpler annotations that, hence, are easier, cheaper and quicker to produce. In this paper, we propose and assess a new weakly-supervised semantic segmentation approach making use of a novel loss function whose goal is to counteract the effects of weak annotations. To this end, this loss function comprises several terms based on partial cross-entropy losses, being one of them the Centroid Loss. This term induces a clustering of the image pixels in the object classes under consideration, whose aim is to improve the training of the segmentation network by guiding the optimization. The performance of the approach is evaluated against datasets from two different industry-related case studies: while one involves the detection of instances of a number of different object classes in the context of a quality control application, the other stems from the visual inspection domain and deals with the localization of images areas whose pixels correspond to scene surface points affected by a specific sort of defect. The detection results that are reported for both cases show that, despite the differences among them and the particular challenges, the use of weak annotations do not prevent from achieving a competitive performance level for both.
Proactive Cooperative Communication Enabled Fifth Generation Wireless Sensor Network Using SCTP

The paramount aim of this paper is to develop a Hybrid Cooperative Communication Enabled 5G Wireless Sensor Network (HCC 5G-WSN) with an improved throughput. A reliable, message oriented, and infrastructure based transport protocol termed Stream Control Transmission Protocol (SCTP) can be preferred with the purpose to enrich the throughput through a hybrid approach instead of Proactive and Reactive approaches seperately in the 5G-WSN. SCTP combines an amalgamation of advanced and unique features of multistreaming and multihoming techniques that are not available in Transmission Control Protocol (TCP) which adds an attractive property in the form of enhancing throughput. A Quality of Service (QoS) parameter ‘throughput’ has been calculated for the proposed 5G-WSN scheme under the Hybrid environment, Proactive and Reactive scenarios under cooperative communication by selecting shortest cooperative relay nodes. The comparative analysis of the results specifies the throughput enrichment over the TCP is 22% and 17% in Proactive approach and Reactive approach respectively. But using SCTP, Proactive and Reactive methods obtained with 38% and 44% respectively as better throughput enhancement comparatively. From the experimental results, the proposed hybrid cooperative communication enabled 5G-WSN using SCTP has got an edge over other methods in improved throughput as 54% which shows a better performance of 5G-WSN.
Theoretical Optimization of the Si GSS-DMM Device in the BaSIC Topology for SiC Power MOSFET Short-Circuit Capability Improvement

The BaSIC(DMM) topology has been experimentally demonstrated to improve the short-circuit time for a 1.2 kV SiC power MOSFET product from <inline-formula> <tex-math notation="LaTeX">$4.8~\mu \text{s}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$7.9~\mu \text{s}$ </tex-math></inline-formula> with a 17% increase in on-state resistance by utilizing a commercially available 100 V rated Gate-Source-Shorted (GSS) Si Depletion-Mode power MOSFET (DMM). The optimization of the Si GSS-DMM is discussed in this paper to achieve even superior performance, namely larger short-circuit time with less increase in on-resistance. It is theoretically demonstrated for the first time that a highly desirable short-circuit time of <inline-formula> <tex-math notation="LaTeX">$10~\mu \text{s}$ </tex-math></inline-formula>, similar to Si IGBTs, can be achieved for two SiC power MOSFET products with less than 3% increase in on-resistance. This was accomplished by reducing the breakdown voltage rating of the Si GSS-DMM from 100 V to 30 V, altering the cell design parameters, and utilizing the trench-gate design. The theoretical analysis provided in this paper provides valuable design guidelines for manufacturers of Si GSS-DMM devices to achieve optimum performance for use in the BaSIC(DMM) topology.
Fog Layer-based DDoS attack Detection Approach for Internet-of-Things (IoTs) devices

The Internet of Things (IoT) represents the network of smart devices that are connected through the internet. The devices in the IoT network collect data from the physical world and transmit it to a cloud server. The decentralized nature of IoT made it vulnerable to many cyber attacks. The DDoS attack is one of the attacks among all the cyberattacks and it attacks the availability of the network. Due to DDoS attacks legitimate devices in the IoT network not able to transmit the information to the cloud server. In this paper, we propose a Fog layer-based DDoS attack detection method for IoT devices. The proposed approach uses clustering and entropy-based method to identify malicious nodes in the IoT network. We implement our proposed approach on ommnet++ simulator and efficiency is evaluated by statistical parameters like packet delivery ratio, throughput, precision.
A Framework for Automatic Field Evaluation of DTV Receivers

Field testing is crucial for the deployment of digital TV receivers, as it assures that the latter can operate under real conditions provided by digital TV networks. Usually, field-testing procedures are manually performed, which often leads to long execution times and wrong measures and conclusions. The present work addresses this problem and proposes a methodology for automatic field-testing procedures, which does not require trained professionals and provides reliable results. Experiments with an implementation based on it showed its efficacy and efficiency, with testing procedures that are, in average, 13.7 times faster than manual ones.
Learning Optical Scattering Through Symmetrical Orthogonality Enforced Independent Components for Unmixing Deep Tissue Photoacoustic Signals

Noninvasive mapping of chromophore distribution in deep tissue is highly desired in numerous biomedical applications. Multispectral photoacoustic (PA) imaging fused with linear spectral unmixing is employed to generate 2-D and 3-D maps of tissue chromophores. However, wavelength and depth dependent attenuation of optical fluence leads to nonuniform variations in the spectral behavior of biomolecules affecting the accuracy of conventional linear unmixing methods. To address this, a modified independent component analysis (ICA) model is proposed to construct adaptive and statistically independent components for each chromophore from the given spectral PA data. The model was trained with custom designed multispectral PA imaging simulations consisting of both endogenous (oxy- and deoxy-hemoglobin) and exogenous (indocyanine green - ICG) chromophores inside 30 mm deep tissue. End-to-end unsupervised nature of the proposed approach made the process independent of human labeling and outperformed the standard linear spectral unmixing method when tested on a set of simulated phantoms, experimental phantoms, and in vivo mouse imaging data.
Food Image Recognition and Calorie Prediction

With the increasing number of health issues reported due to obesity and overeating, people have become cautious about their diet intake to prevent themselves from the diseases such as hypertension, diabetes, and other heart related problem which are caused due to obesity. As per the data shared by WHO, at least 2.8 million people are dying each year because of being overweight or obese. The important part of any healthy diet plan is its calories intake. Hence, we propose a deep learning-based technique to calculate the calories of the food items present in the image captured by the user. We used a layer-based approach to predict calorie in the food item which include Image Acquisition, Food item classification, Surface area detection and calorie prediction.
Model-Based Ideal Testing of GUI Programs–Approach and Case Studies

Traditionally, software testing is aimed at showing the presence of faults. This paper proposes a novel approach to testing graphical user interfaces (GUI) for showing both the presence and absence of faults in the sense of ideal testing. The approach uses a positive testing concept to show that the GUI under consideration (GUC) does what the user expects; to the contrary, the negative testing concept shows that the GUC does not do anything that the user does not expect, building a holistic view. The first step of the approach models the GUC by a finite state machine (FSM) that enables the model-based generation of test cases. This is always possible as the GUIs are considered as strictly sequential processes. The next step converts the FSM to an equivalent regular expression (RE) that will be analyzed first to construct test selection criteria for excluding redundant test cases and construct test coverage criteria for terminating the positive test process. Both criteria enable us to assess the adequacy and efficiency of the positive tests performed. The negative tests will be realized by systematically mutating the FSM to model faults, the absence of which are to be shown. Those mutant FSMs will be handled and assessed in the same way as in positive testing. Two case studies illustrate and validate the approach; the experiments’ results will be analyzed to discuss the pros and cons of the techniques introduced.
A Model-Driven Framework for Android Supporting Cross-Platform GUI Development

The rapid growth of users makes android application development one of the most focused areas in the software industry. Among others, the screen size is a considerable problem in developing mobile graphical user interfaces. Quite a work has been done in the field to solve this issue programmatically. However, truncated reusability and insufficient portability along with other challenges are posed, which results in multiple designs of an application that need to be maintained equally as major applications. This article introduces a novel model-driven framework that serves the purpose of a prompt development of responsive User Interfaces (UI) for various screen sizes. Particularly a Meta Model is proposed. As tool support, a Sirius graphical modeling tool has also been developed. Furthermore, Model to Text Transformation code has been written using Acceleo language to transform the modeled UI scenario into an executable Java and XML code. The framework allows modeling of UI scenarios using our graphical modeling tool and generates executable platform-specific code (Java & XML) resulting in prompt development of a complex application with simplicity. The applicability of the proposed framework has been demonstrated by developing a mobile application as a case study.
Grasping Action Recognition in VR Environment using Object Shape and Position Information

In this paper, we propose a method for recognizing the user’s grasping action in VR environment. The proposed method utilizes the fact that the positions and shapes of virtual objects are known in VR environment. The position and shape information of the virtual object in addition to the hand posture is used for classification to improve the recognition accuracy. The result of evaluation showed that the recognition rate was improved by adding the position and shape information of the virtual object.
Phosphatidic acid-functionalized monolithic stationary phase for reversed-phase/cation-exchange mixed mode chromatography

A novel phosphatidic acid functionalized polymeric monolithic column was prepared through the thermally initiated co-polymerization of 12-methacryloyl dodecylphosphatidic acid (MDPA) and ethylene glycol dimethacrylate (EDMA) in the presence of 1,4-butanediol and isopropanol as porogens within 100 μm I.D. capillaries. The polymerization conditions of monolithic columns were systematically optimized in order to obtain good permeability, stability and column efficiency. The reproducibility of the optimized monolithic column was also satisfactory. The physicochemical properties of the monolithic column were evaluated by use of instrumental techniques including scanning electron microscopy, Fourier transform infrared spectra, ζ-potential analysis and micro-HPLC. A series of test compounds such as small peptides, alkylphenones, etc., were employed to investigate the retention mechanism on the poly(MDPA-co-EDMA) monolithic column. The results demonstrate that both hydrophobic and cation-exchange interactions could contribute to the overall retention of analytes. Furthermore, the novel reversed-phase/cation-exchange mixed mode monolithic column was applied to the separations of small peptides, phenols, water-soluble vitamins B, and pharmaceutical compounds. The successful applications indicate the potential of the poly(MDPA-co-EDMA) monolithic column in complex sample analysis.
FoodieCal: A Convolutional Neural Network Based Food Detection and Calorie Estimation System

According to recent studies across the world, we can see that a healthy diet is the key to having a sound health and body. People nowadays are more concerned with their diets than ever before. With the advancement of science, it is now viable to construct a unique food identification system for keeping track of day to day calorie intake. However, building this kind of system creates several complications on constructing and implementing the model. In our paper, we have developed a new neural network based model which will predict the food items from a given image and show us the estimated calorie of the detected food items. In order to achieve our goal, we have prepared a dataset of around 23000 images for 23 different food categories. For this, we have built a system which can detect multiple foods by training CNN with features extracted by Inception V3. We have achieved 89.48% accuracy for this model and we deployed our system on a webpage. The user has to upload an image of food item in the webpage and our system will predict the food item along with the estimated calories in real time.
Automated Machine Learning Approaches for Emergency Response and Coordination via Social Media in the Aftermath of a Disaster: A Review

Social media communication serves as an integral part of the crisis response following a mass emergency (disaster) event. Regardless of the kind of disaster event, whether it is a hurricane, a flood, an earthquake or a man-made disaster event like a riot or a terrorist attack, social media platforms like Facebook, Twitter etc. have proven to be a powerful facilitator of communication and coordination between disaster victims and other communities. Consequently, several research articles have been published on social media utilization for disaster response. Many of those recent research articles discuss automated machine learning approaches to extract disaster indicating posts, useful for coordination from various social media posts. Despite this, there is a scarcity of comprehensive review of all the major research works pertaining to the utilization of machine learning approaches for disaster response using social media posts. Thus, this study reviews academic research articles in the domain and classifies them across three disaster phase dimensions – early warning and event detection, post-disaster coordination and response, damage assessment. This review would help researchers in choosing further research topics pertaining to automated approaches for actionable information classification and disaster coordination and would help the emergency teams to make well-informed decisions in disaster situations.
Abnormal Detection of Track Fastener Based on Improved YOLOV3 Algorithm

In order to improve the low efficiency and insufficient accuracy of manual inspections in traditional railway track maintenance, an improved YoloV3 (you only look once V3)algorithm is proposed to detect abnormalities of track fasteners. First, the K-means++ algorithm was used to make some clustering analysis of the data set from the rail fastener, and determine the height-width ratio and the number of prediction boxes. Then, according to the characteristics of the track fastener size, the network multi-scale recognition was improved to solve the problem that YoloV3 can not recognize when the target box size is too small. For the last, the loss function, using GIOU(Generalized Intersection over Union)loss instead of the MSE(the Mean Square Error)loss, prevents abnormal change of the scale. Meanwhile, the imbalance between positive and negative samples is also settled by using Focal Loss instead of the cross entropy loss. The results show that the high rate of data detection with Yolo V3 algorithm can reach a speed of 37 frame/s and the average precision is as high as 93.51 %, which is 10.64% higher than that of original network. The result indicates the accuracy and efficiency of our method.
iBuffet: A Deep Learning-Based Intelligent Calories Management System for Eating Buffet Meals

In this paper, we propose a deep learning-based intelligent calorie management system, named iBuffet, in which deep learning and IoT technologies are integrated for calorie management. The proposed iBuffet system comprises intelligent buffet table modules, a mobile device application, and a cloud-based platform. The purpose of the proposed iBuffet system is to improve the eating health of institutional personnel. By recording daily meal records and management to improve the health of institutional personnel. Moreover, the related information will be sent to the cloud-based platform to calculate a calorie value, allowing users to manage their health.
A Multi-Objective Scheduling Technique for Home Energy Management System

Scheduling the home appliances is not an easy task. In general, the householder is forced to choose between reducing his electricity bills while scarifying his discomfort or minimize his discomfort while paying more. In this paper, the user can adjust the tolerated discomfort and know in advance its correspondent of electricity daily payment. The power scheduling approach is elaborated as an optimization problem containing integer variables in case of time flexible appliances and continuous variables in case of power flexible appliances. Optimal solvers are used to generate exact solutions according to the householder predefined objectives. The simulation results demonstrate that the suggested approach can optimally schedule the home appliances according to the householder objectives: reducing the electricity payments, minimizing the discomfort, or finding a compromise between the two objectives.
A Study on Agent-Based Box-Manipulation Animation Using Deep Reinforcement Learning

This paper focuses on push-manipulation in an agent-based animation. A policy is learned in a learning session in which an agent perceives its own internal state and the surrounding environment and determines its actions. In each time step, the agent performs an action. Then it receives a reward that is a combination of different types of reward terms, including forward progress , orientation progress , collision avoidance , and ﬁnish time . Based on the received reward, the policy is improved gradually. We develop a system that controls an agent to transport a box. We investigate the effects of each reward term and study the impacts of various inputs on the performance of the agent in environments with obstacles. The inputs include the number of rays for perceiving the environment, obstacle settings, and box sizes. We performed some experiments and analyzed our ﬁndings in details. The exper-iment results show that the behaviors of agents are affected by the reward terms and various inputs in certain aspects, such as the movement smoothness of the agents, wandering about the box, loss of orientation, sensitivity about collision avoidance, and pushing styles.
Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation

In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for artifact denoising on a constructed semi-simulated database with varied ten-fold frequency stimulation. By implementing the GS-APEF in the field-programmable gate array (FPGA) and using the edge noise mitigating technique, a stimulation artifact denoising system is designed to realize real-time stimulation artifact removal under varied ten-fold frequency functional electrical stimulation. Good performance of the artifact denoising is demonstrated in proof-of-concept experiments on able-bodied subjects with a mean correlation coefficient between the root mean square profile of denoised surface electromyography and volitional force of 0.94, verifying the validity of the proposed prototype.