Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Free Adaptive Control for Industrial Engraving Machine System

The engraving machine system is a typical multi-axis motion system, which has great application value in industrial production. The mechanism of input angular velocity and output position of a typical engraving machine system in speed control mode is analyzed. The engraving machine model is mainly composed of speed circuit, current circuit, motor and mechanical actuator. Some ideal assumptions and simplifications are made during the establishment of the mechanism model, which makes it difficult to estimate the parameters of the mechanism model accurately. The imprecise prediction model limits the application of model-based control algorithms and reduces the model control accuracy. Therefore, a model free adaptive control algorithm based on dynamic linearization of compact format is used to control the output position of the engraving machine. The stability of the proposed model-free adaptive control algorithm is analyzed and proved. The numerical simulation and practical experiment verify superiority and practicability of the proposed control algorithm for nonlinear time-varying system and engraving machine system.
Statistical Analysis of High Grade Samples from four grades Aquilaria Malaccensis Oil

Most fragrance, incense, and conventional medical medicines contain agarwood essential oil. Agarwood essential oil, also referred to as "Black Gold," is highly valued globally due to its various benefits. There is currently no established method for classifying various grades of agarwood essential oil. Since human sensory panel is used to evaluate the agarwood essential oil, the current grading method is ineffective. As different individual may have different perspectives on rating agarwood essential oil, the method is impractical to be applied universally. The research study used a boxplot to conduct a statistical analysis on high grade data samples from four grades agarwood essential oil. To determine which chemical elements are significant, the abundances for each chemical compound are summarised using a boxplot analysis in high grade data samples. From the boxplot analysis in this research study, the main chemical elements contained in the high grade agarwood essential oil are 10-epi-δ-eudesmol, β-agarofuran and α-agarofuran. The future research studies can categorize five grades and six agarwood essential oil using the chemical elements discovered in this research study as a guide.
BigMEC: Scalable Service Migration for Mobile Edge Computing

The proximity of Mobile Edge Computing offers the potential for offloading low latency closed-loop applications from mobile devices. However, to repair decreases in quality of service (QoS), e.g., resulting from user mobility, the placement of service instances must be continually updated - essential for mission critical applications that cannot tolerate decreased QoS, for example virtual reality or networked control systems. This paper presents BigMEC, a decentralized service placement algorithm that achieves scalable, fast, and high-quality placements by making local service migration decisions immediately when a drop in QoS is detected. The algorithm relies on reinforcement learning to adapt to unknown scenarios and to approximate long-term optimal placement updates by taking future transition costs into account. BigMEC limits each decentralized migration decision to nearby edge sites. Thus, decision computation times are independent of the number of nodes in the network and well below 10ms in our experimental setup. Our ablation study validates that, using its scalable approach to decentralized resource conflict resolution, BigMEC quickly approaches optimal placement with increasing local view size, and that it can reliably learn to approximate long-term optimal migration decisions, given only a black-box optimization objective.
Poster: Autonomous mobile robot for indoor enhanced living

The world population is aging at a rapid pace. It is projected that by 2050, the total number of older individuals (65 and older) will more than double their current number to rise to 1.57 billion [1]. Further, there is a growing shortage of caregivers and registered nurses to fill the emotional, and physical needs of the elderly population [2]. As a result, the world is projected to face the challenge of providing a good quality of life for its older population. Several nationwide projects are in exploratory stages to integrate technology into the long-term care regime [3]. Some of the notable ones are Smart-BEAR1, and PHArA-ON2, and SHINESeniors3. The underlying goal is to care for the elderly remotely so that elderly can age independently in their own homes while ensuring that they receive prompt help when needed.
Modeling Methodology and In-field Measurement Setup to Develop Empiric Weather Models for Solid-State LiDAR Sensors

With the automotive industry's dedicated roadmap towards partly automated driving, the responsibility for reliable environmental perception moves from the driver to the vehicle's advanced driving assistance systems (ADAS). However, with steadily growing system complexity, the required test mileage to certify new driving functions increases to an unworkably high level. In order to validate ADAS functions like lane change assist (LCA), automated emergency breaking (AEB), or even path planning virtually, there is a strong demand for high fidelity sensor models which are capable of simulating automotive Radar, LiDAR as well as camera sensor perception in real time while providing realistic, artificial sensor raw data. Yet, especially LiDAR models mostly lack the capability of replicating the impact of specific weather characteristics, although optical sensors in particular are heavily influenced by precipitation, fog and sun irradiance. Furthermore, there is - in contrast to numerous publicly available LiDAR datasets in differing driving situations - a strong lack of datasets which are annotated with quantitative weather data such as particle size and velocity distribution in order to develop and validate such models. Hence, within this work, an automated infrastructure setup for targeted measurement of time-correlated LiDAR and weather data is presented with the aim to develop and calibrate weather models, which can eventually be used to augment virtual LiDAR data from raytracing capable driving simulation suits as well as real data, recorded under ideal weather conditions. In addition to that, the considerable effect of varying precipitation rates on an automotive Flash LiDAR system was demonstrated based on first measurements and quantified by calculating the pixel-wise temporal coefficient of variation for measured depth and intensity, reaching up to approximately 50% and 350%, respectively.
Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-based Classifier

Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers.
Longitudinal Evaluation of Diadochokinesia Characteristics for Hemiplegic Ankle Rehabilitation by Wearable Systems with Machine Learning

The ability to objectively evaluate the efficacy of a rehabilitation regimen from a longitudinal perspective is significant and enabled through the confluence of wearable and wireless inertial sensor systems and machine learning. These capabilities are demonstrated in the context of a 10 month longitudinal study involving a rehabilitation regimen for improving a hemiplegic ankle, such as with respect to the hemiplegic ankle's diadochokinesia characteristics. Diadochokinesia represents the ability to alternate between agonist and antagonist muscles, such as the dorsiflexion and plantar flexion musculature. The ability to smoothly transition between dorsiflexion and plantar flexion musculature is inherent for the rhythmic process of gait. Using a smartphone as a functional wearable gyroscope platform secured to the dorsum of the foot by an armband, the kinematic properties of the hemiplegic ankle are quantified and recorded in a longitudinal context. A support vector machine implemented through the Waikato Environment for Knowledge Analysis (WEKA) machine learning platform successfully distinguished between the initial phase and final phase of a 10 month longitudinal study involving a rehabilitation regimen for a hemiplegic ankle with considerable classification accuracy. The implications of the research findings establish a pathway for the ascertaining the efficacy of a rehabilitation regimen based on the signal data acquired by wearable systems in conjunction with machine learning.
MICROSTRIP ANTENNA WITH MODIFIED RADIATION PATTERN ( SLOPING )

In this work the transmission-line model was used, considering the characteristics of the dielectric and that’s of the patch to determine the self and mutual conductance, as well as the input resistance for matching. The cavity model was used to calculate and plot the radiation pattern for E and H planes, both for the radiating slots and for the non radiating ones, obtaining also the directivity of the antenna. The analysis, design and simulation were realized exciting the antenna with the dominant mode TM for different dimensions and the response for different modes of excitation are investigated. The obtained results allow the antenna to be used as a single antenna or as an element for designing antenna arrays.
An EEG-Based Diagnostic Framework for Strokes Using Spectral Analysis and Deep Learning

Strokes affect over 15 million people annually, and treatment must be given within one hour to prevent brain damage. CT and MRI scans are used for stroke diagnosis but are time-consuming and expensive. Electroencephalograms (EEG) solve both problems but are not used due to their complexity. This paper presents a novel application for the automated detection of ischemic and hemorrhagic strokes using EEGs. We propose using the averaged power spectral density to extract important features. We use deep neural networks to respectively detect and classify the stroke type, location, and severity with accuracies of 97.5%, 94.4%, and 99%. Additionally, to allow for easier EEG interpretation and detection of abnormalities, we use azimuthal projection and spline interpolation to reshape 3D electrodes onto 2D contour maps showing the power of frequency bands around the brain. This research could represent a step towards increasing the speed and decreasing the cost of comprehensive stroke diagnosis.
DEAL: Construction of a Disease-aware Human Cell Knowledge Graph from Biomedicine Literature

Exploring disease-associated human cells can help researchers identify pathogenesis and develop treatments. However, mining the relationships between diseases and human cells is difficult due to several challenges, including information extraction from unstructured knowledge sources. Biomedicine literature on diseases and cells contains a wealth of potentially useful information but is underutilized due to the complexity of unstructured information. A knowledge graph provides organized and structured relationships among entities and can be served as a solution. Nevertheless, few existing tools can be found for constructing a disease-aware human cell knowledge graph from biomedicine literature. To fill this gap, we propose a novel approach, called DEAL, to construct a disease-aware human cell knowledge graph from biomedicine literature. Technically, DEAL builds the knowledge graph through a series of steps including the processing of distant supervision data, the training of a distant supervision relation extraction model, the extraction of triples, and the knowledge graph construction. Experiments demonstrate that DEAL can effectively express the extracted entities related to diseases and cells and their relationships.
920 MHz Band Radio Wave Propagation Characteristics from Underground for Detecting Victims by Unmanned Aerial Vehicles

Landslides caused by torrential rains and typhoons have caused extensive damage. We are studying methods for detecting disaster victims by radio wave using UAV (Unmanned Aerial Vehicles) equipped with a receiving terminal. In this study, focusing on the influence of the ground surface, we evaluated the propagation characteristics of 920 MHz band through actual measurements and simulations using the FDTD (Finite Difference Time Domain) method.
Internet of Things Technology and LSTM-based Data Imputation and Prediction for Transportation Industry

The Corona Virus Disease 2019 (COVID-19) has touched individuals from all walks of life in recent years, and the movement of people and commodities has become a crucial route for the disease's spread. Reasonable control of the transportation industry has become a vital to the epidemic's prevention and control. Simultaneously, a succession of tight blockade inspection measures in the transportation business have significantly restricted the movement of people and commodities, putting a strain on the industry. As a result, it is crucial to study the transportation industry's comprehensive measurement in this environment for future regulation and prevention. In this paper, the transportation industry's freight and passenger transport are used as indicators to assess the economy, and the data source is multi-source time series data from government statistics. To anticipate future economic trends in the transportation business, we chose freight and passenger transportation in the air, land, and marine industries, respectively. Due to the significant amount of missing data, this paper develops a time series data imputation approach for its specific missing situations in order to fill in the gaps and make future prediction tasks easier. In addition, this paper builds a long and short-term memory network to train the data in order to predict future transportation industry economic trends.
Voice Controlled Personal Assistant

Today technological advances are growing by the day. Previously there was a computer program where we could do only a few tasks. But now machine learning, artificial intelligence, in-depth learning, and a few more technologies have made computer systems so advanced that we can do any kind of work in a very short time. Because of rapid technological advancements, we can now do things we never thought we could before, but in order to do so, we need a platform that can smoothly perform all of our responsibilities. As a result, A Personal Assistants must be designed with more output power as well as the ability to engage with the surroundings through one among the most wonderful characteristics of human contact, the HUMAN WORD. Through a microphone, Audio is collected by a hardware device. applications and they're processed in order for the device to reply to each person with the use of a built-in microphone module. For instance, when you ask a device, “How is the weather? ” ‘What's the status of the traffic? ’ It checks the weather and other information using its built-in capabilities.
Online programming learning platform: The influence of gamification elements

Numerous studies have been conducted to discover educational technologies that could be utilised to enhance students, understanding of fundamental programming concepts. The advantage that various online programming learning platforms provide, is immediate feedback and automated guidance with the completion of assignments, enhancing student comprehension. Despite the various advantages of online learning environments, the lack of online persistence is viewed as one of the most pervasive disadvantages. Gamification of online learning environments have been proposed as a solution to enhance student engagement, and persistence, but little research has focused on how specific gamification elements influence the persistent use of online programming learning environments. The aim of this study was therefore to fill this gap and to determine what influence various gamification elements have on the persistent use of an online programming learning platform. The population of the study consisted of first year students enrolled at the IT department of the Central University of Technology (CUT) in the Free State Province of South Africa. Data was collected by making use of a survey that was posted on the learner management system of CUT. The results of the study showed that the points and badges that students received as rewards, as well as the badges and avatars that assisted them to express themselves in a unique way, had a very important influence on their engagement and immersion in a gamified programming learning environment. Moreover, the use of a leaderboard to create a competitive environment had no influence on the engagement and immersion of students. The study also showed that higher levels of meaningful engagement and flow experience have a significant influence on keeping students motivated to persistently use a gamified programming learning platform. The practical implication of the findings of the study are that designers and developers of gamified programming learning environments should prioritise designing a comprehensive reward system over the creation of a competitive environment.
Prevent Over-fitting and Redundancy in Physiological Signal Analyses for Stress Detection

Stress detection is an emerging field. WESAD is a commonly used public dataset for automated stress detection. It contains physiological signals including ECG, EDA, EMG, ACC, BVP, EDA, and skin temperature. The time window approach is used to extract features from time-series physiological signals. We find in previous studies that a 60-second time window with a 0.25-second window shift is widely used, but such window settings may cause redundancy and over-fitting. Thus, we propose to use (1) new window settings and (2) normalization per subject to tackle this problem. The experiment results show that our proposed methods significantly increase the classification performance.
In vitro Testbed Platform for Evaluating Small Volume Contrast Agents via Magnetic Resonance Imaging

Quantitative magnetic resonance imaging (MRI) is a non-invasive imaging method with high resolution and unlimited penetration depth. Contrast agents (CAs) can assist in disease diagnosis and tissue screening via MRI. In vitro characterization of CAs in development is often carried out using sample sizes in the milliliter range or higher. Particularly when reagent costs are high, MRI would benefit from a standard platform for precise quantification of small volume CAs (microliter scale), ultimately enabling translation from in vitro to in vivo applications. In this initial study, we developed and evaluated a microliter-scale concentric “MiSCo” testbed as a platform to optimize MRI quantification of small volume samples in vitro. The platform facilitated accurate, repeatable, and reproducible MRI quantitative measurements with a 5-fold and 30-fold increase in precision and signal-to-noise-ratio, respectively, when compared to more traditional configurations. We believe this approach could serve as a path for future improvements in the field of quantitative MRI, ensuring high sensitivity measurements of small volume CAs.
Multivariable fractional order PID controller design via LMI approach

Abstract In this paper, we study the design problem of multivariable fractional-order (FO) PID controllers which guarantee the stability of the FO closed-loop systems. The idea is to transform the problem of FO PID controller design to that of static output feedback (SOF) controller design for an FO system in descriptor form. In terms of linear matrix inequalities, the PID gains can be found once the SOF matrix is obtained. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.
Bandwidth Enhancement by Interconnecting Double Hexagonal Loops FSS

The paper proposed a mosaic frequency selective surface (MFSS) which comprises of the integration between Koch fractal and a basic double hexagonal loop FSS. Through this integration, the bandwidth (BW) within the first bandstop and bandpass frequency responses achieved a wideband frequency response (fractional bandwidth (FBW) >50%), while the BW of the second bandstop frequency response improved (FBW $\sim 20\%$). The simulation process is conducted by using the CST software, and the FR4 substrate is used as the dielectric substrate for all of the proposed unit cells. With narrow trace width of the MFSS, the structural element can be further applied for optical transparency application with wideband filtering characteristics.
OMINACS: Online ML-Based IoT Network Attack Detection and Classification System

Severa1 Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents. However, detecting and classifying attacks with high accuracy and precision is still a major challenge. This paper proposes an online attack detection and network traffic classification system, which combines stream Machine Learning, Deep Learning, and Ensemble Learning technique. Using multiple stages of data analysis, the system can detect the presence of malicious traffic flows and classify them according to the type of attack they represent. Furthermore, we show how to implement this system both in an IoT network and from an ML point of view. The system was evaluated in three IoT network security datasets, in which it obtained accuracy and precision above 90% with a reduced false alarm rate.
Accessible interactive systems for deaf users in museums: systematic mapping review

Multiple interactive systems have been developed to enable deaf users to understand the content displayed in cultural venues. Furthermore, some of these solutions have been developed in different ways. For example, either through a mobile context, using a user-centered design, universal design, augmented reality, or virtual reality. All this is to provide accessibility to deaf people. For this reason, a systematic mapping was carried out to analyze a set of articles on interactive systems accessible to people with hearing impairment in museums. For this, 13 articles were evaluated as a result of a systematic mapping review with the conclusion that the existing solutions are focused on a single disability, the technologies used are repeated, there are areas of opportunity in tools such as VR360 and there is a limited number of scientific articles on deaf people and museums.
A Study on Yield Analysis Method Using Classifiers for Semiconductor Manufacturing

In semiconductor manufacturing, semiconductor wafers have spatial signature of electric failure due to various causes. It is called “wafer map In general, the wafer map depends on manufacturing process and can be classified into several types. However, a small number of unknown maps that differ from known maps may occur at the manufacturing site. Currently, many approaches using machine learning techniques for wafer map classification have been reported. These methods generally aim to classify known maps, and they cannot detect unknown maps. We proposed a method to create and concatenate 2-class classifiers specialized for each map pattern. In this study, four algorithms were used to create each classifier. The proposed method has the advantage of using the most effective algorithm for classifying each wafer map class. Experimental results showed that it was possible to classify known classes at approximately 77%, and that the actual number of unknown classes was almost 48% of the data predicted to be unknown.