Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Hand Gesture Recognition Based on RGB-D Data for Natural Human-Computer Interaction

To naturally interact with virtual environment by hand gesture, this paper presents a robust RGB-D data based recognition method of static and dynamic hand gesture. Firstly, for static hand gesture recognition, starting from the hand gesture contour extraction, the palm center is identified by Distance Transform (DT) algorithm. The fingertips are localized by employing the K-Curvature-Convex Defects Detection algorithm (K-CCD). On the basis, the distances of the pixels on hand gesture contour to palm center and the angle between fingertips are considered as the auxiliary features to construct a multimodal feature vector, and then recognition algorithm is presented to robustly recognize the static hand gestures. Secondly, combining Euclidean distance between hand joints and shoulder center joint with the modulus ratios of skeleton features, this paper generates a unifying feature descriptor for each dynamic hand gesture and proposes an improved dynamic time warping (IDTW) algorithm to obtain recognition results of dynamic hand gestures. Finally, we conduct extensive experiments to test and verify the static and dynamic hand gesture recognition algorithm and realize a low-cost real-time application of natural interaction with virtual environment by hand gestures.
Advancing Life Cycle Comparisons of Future Alternative Light-duty Vehicles

The overall objective of this thesis is to systematically compare the life cycle energy use, air emissions and costs of future alternative light-duty vehicles in a more robust manner than is done in the literature. Models are developed using GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation), Autonomie vehicle simulation software, Vehicle Attribute Model, Air Pollution Emission Experiments and Policy (APEEP) analysis model, and Crystal Ball (Monte Carlo analysis). Four questions are investigated:  Should the transportation sector use ethanol or bio-electricity? Life cycle assessment results indicate that neither has a clear advantage in terms of greenhouse gas (GHG) emissions or energy use. This finding is in contrast to those in the literature that favor the use of bioelectricity because this thesis develops pathways with comparable vehicle characteristics.  Do plug-in electric vehicles provide incremental life cycle air pollutant impact benefits over internal combustion engine vehicles using the same primary energy source? The results based on natural gas-derived fuels show that battery electric vehicles (BEV) may not provide benefits, in terms of climate change and health impacts, over hybrid electric vehicles
Determination of Soil Texture Using Image Processing Technique

Agriculture is the backbone of world’s economy and it is one of the largest employment sectors. Nowadays, the population is growing fast and simultaneously, the total cultivable land is lessening drastically. Soil texture has a significant impact on the agriculture affecting crop selection and crop growth. This paper presents the development of soil texture detection and pH value determination of the soil using the image processing technique. In this paper, two methods have been applied to identify the soil texture using two color-space methods in the MATLAB toolbox, which are the Hue Saturation Value (HSV) and Red Green Blue (RGB) color method. Furthermore, to determine the pH value of the soil, an image processing algorithm was applied to obtain the desired output. Moreover, these two proposed methods were applied in the Graphical User Interface (GUI) in MATLAB software. The proposed system is expected to contribute to the community by saving human effort, increases efficiency and generates more accurate results in shorter time.
Heallo: Conversational System for Communication Training in Healthcare Professional Education

Effective communication is critical in the healthcare setting to ensure the safety and quality of patient care. To increase the accuracy and efficiency of communication, specific communication protocols have been developed. However, the current teaching of communication protocols remains in the theoretical realm, lack of effective training methods and practice environment. The purpose of this research is to assist communication training in healthcare professional education. We begin by designing communication tasks and collecting conversations on real-world handover cases. Then we propose Heallo – a computer conversational system that provides simulated scenarios for healthcare communication tasks. Specifically, Heallo converses with the user in a simulated communication task, assuming the role of the receiver. After the conversation, it will analyze the entire communication history and generate a detailed evaluation report based on the communication protocol. Heallo has received positive responses from healthcare professionals and has been incorporated into a hospital training program.
Redundancy Concepts for Real-Time Cloud- and Edge-based Control of Autonomous Mobile Robots

Deploying navigation algorithms on an edge or cloud server according to the Software-as-a-Service paradigm has many advantages for autonomous mobile robots in indus-trial environments, e.g. cooperative planning and less onboard energy consumption. However, outsourcing corresponding real-time critical control functions requires a high level of reliability, which cannot be guaranteed either by modern wireless networks nor by the outsourced computing infrastructure. This work introduces redundancy concepts, which enable real-time capability within these uncertain infrastructures by providing redundant computation nodes, as well as robot-controlled switching between them. Redundancies can vary regarding their physical location, robot behavior during the switchover process and degree of activeness while quality of service concerning the primary controller is sufficient. In the case that fallback redun-dancies are not continuously active, when a disturbance occurs an initial state estimation of the robot pose has to be provided and an activation time has to be anticipated. To gain some insights on expected behavior, redundant computation nodes are deployed locally on the robot and on an outsourced computation node and consequently evaluated empirically. Quantitative and qualitative results in simulation and a real environment show that redun-dancies help to significantly improve the robot-trajectory within an unreliable network. Moreover, resource-saving redundancies, which are not continuously active, can robustly take over control by using an estimated state.
A Model-Based Approach to Automatic Generation of TSN Network Simulations

The IEEE 802.1 TSN working group published a set of standards which adds new functionalities to switched Ethernet networks. These new functionalities notably aim at making the design of deterministic Ethernet networks possible, enabling their use for real-time applications. This determinism comes however at the cost of a greatly increased complexity in configuration effort, especially for largescale networks. In addition, this new complexity also affects the network simulation tools that are commonly used when designing such networks. As most of the existing ones only partially support new TSN functionalities, one has often to combine several simulators for achieving a reliable TSN network design. In this paper, we propose a model-based approach which aims at assisting the design of TSN networks. Modeling allows the creation of a formal representation of the network which can then be used to automatically generate configurations. This approach has been successfully implemented as a TSN configuration software called MoBACT, that enables the use of multiple simulation/emulation tools during the design phase by generating configurations for different targets for easing crosscheck of simulation results. Since each generated configuration is derived from the same representation of the network, our approach guarantees the consistency of the different configurations generated for each tool.
An Optimized Soil Moisture Prediction Model for Smart Agriculture Using Gaussian Process Regression

An accurate soil moisture model is critical in the design and implementation of a smart agriculture system. Accurate soil moisture prediction allows an efficient water resources allocation. This paper presented a soil moisture model using different environmental parameters such as humidity, temperature, light intensity, and rain occurrence as inputs or predictor variables. Gaussian process regression algorithm, a non-parametric machine learning algorithm, was used to develop the model. The most effective kernel function was also determined by developing four different GPR models using a different kernel function. In terms of RMSE, the rational quadratic function obtained the lowest value. To further improve the accuracy of the GPR model, an automated hyperparameter tuning was done using a Bayesian optimization algorithm. Three hyperparameters were tuned using the Bayesian optimization algorithm, which improved the GPR model's performance. The optimized GPR model achieved the lowest RMSE and MAE of 3.596 and 1.176, respectively.
Real-Time Multirate Multiband Amplification for Hearing Aids

Hearing loss is a common problem affecting the quality of life for thousands of people. However, many individuals with hearing loss are dissatisfied with the quality of modern hearing aids. Amplification is the main method of compensating for hearing loss in modern hearing aids. One common amplification technique is dynamic range compression, which maps audio signals onto a person’s hearing range using an amplification curve. However, due to the frequency dependent nature of the human cochlea, compression is often performed independently in different frequency bands. This paper presents a real-time multirate multiband amplification system for hearing aids, which includes a multirate channelizer for separating an audio signal into eleven standard audiometric frequency bands, and an automatic gain control system for accurate control of the steady state and dynamic behavior of audio compression as specified by ANSI standards. The spectral channelizer offers high frequency resolution with low latency of 5.4 ms and about $14\times $ improvement in complexity over a baseline design. Our automatic gain control includes a closed-form solution for satisfying any designated attack and release times for any desired compression parameters. The increased frequency resolution and precise gain adjustment allow our system to more accurately fulfill audiometric hearing aid prescriptions.
Individual Identification of Black Pig through Ear Images using Support Vector Machine

Individual pig can be identified by analyzing ear vein patterns but is very hard to observe in natural condition due to thickness of ear leaf. The extraction of venation tree is also difficult. In this paper, an effort has been made to propose a prediction model for identification of individual black pig based on their ear images. An artificial ambience has been created to capture ear images of pigs. A green light has been projected on the front side of ear in such a way that vein webs are visible from the opposite side of ear. The images from ear of each individual pig have been captured through mobile phone. The features from the captured images have been extracted using the histogram of oriented gradient method after pre-processing. The selected pictures have been divided into training and test image set. The extracted features have been classified using support vector machine (SVM) for prediction of individual pig. It is observed that the trained SVM classified pig ear images with 98.5% accuracy. This technology has been compared with decision tree based prediction model and other published technologies in the same domain where it outperformed all others technologies in terms of accuracy and precision
Building a Logistic Optimization Model for Mekong Delta in Vietnam

Logistics is a part of the supply chain with many different stages, including planning, implementing, and controlling the movement of goods. Based on a study of factors affecting logistics in the Mekong Delta (MD), Vietnam. In this study, the authors used the Logistics Performance Index (LPI) with integrated approaches, including Improved Descriptive Statistics (IDS) and Descriptive Statistics (DS). An interpretive structural model (FISM) to recognize the importance of factors related to the competitiveness of the logistics industry in the region. In this paper, the authors based on theoretical research and a primary survey to apply information technology to the development of a management system based on an Agile software model, with development steps being carried out. This includes development steps through the development and application of the Model - View - Control (MVC) model to build a system with optimized functions and a built-in logistic optimization equation.
Inference of Student Needs in an Online Learning Environment Based on Facial Expression

Due to the limited interaction between students and teachers in online environments, teachers often struggle to keep track of their students' needs in terms of teaching response. The purpose of this research is to construct a model that infers student needs based on their facial expressions while they are learning online. An experiment collected video recordings of students' faces while learning and a survey collected their reported needs. A neural network model was created to infer the reported needs from facial expression data extracted from the videos as action units in Facial Action Coding System (FACS). A neural network was trained to infer the student need responses from the factors obtained through Factor Analysis. Inference model testing demonstrated that the model correctly identifies reported needs 87% of the time on video samples not used for training. The results suggest this approach may contribute to the development of improved online learning systems that will allow teachers to understand in real-time how students want them to respond.
Space Vector Modulation Technique to Reduce Leakage Current of a Transformerless Three-Phase Four-Leg Photovoltaic System

Photovoltaic systems integrated to the grid have received considerable attention around the world. They can be connected to the electrical grid via galvanic isolation (transformer) or without it (transformerless). Despite making galvanic isolation, low frequency transformer increases size, cost and losses. On the other hand, transformerless PV systems increase the leakage current (common-mode current, (CMC)) through the parasitic capacitors of the PV array. Inverter topology and switching technique are the most important parameters the leakage current depends on. As there is no need to extra hardware for switching scheme modification, it's an economical method for reducing leakage current. This paper evaluates the effect of different space vector modulation techniques on leakage current for a two-level three-phase four-leg inverter used in PV system. It proposes an efficient space vector modulation method which decreases the leakage current to below the quantity specified in VDE-0126-1-1 standard. furthermore, some other characteristics of the space vector modulation schemes that have not been significantly discussed for four-leg inverter, are considered, such as, modulation index, switching actions per period, commonmode voltage (CMV), and total harmonic distortion (THD). An extend software simulation using MATLAB/Simulink is performed to verify the effectiveness of the modulation technique.
Lyapunov Stability Analysis of Covid-19 SIRV Model

Until now, it is not known when the COVID-19 pandemic in Indonesia will end. As COVID-19 cases continue to increase, predicting the number of cases infected with COVID-19 is very important to design a control strategy to reduce the disease spread. Towards the end of 2020, several manufacturers announced high efficacy rates of COVID-19 vaccine candidates. Vaccines have been believed to be an important tool for improving the health of the population so that the disease spread can be controlled without hindering economic growth. A Mathematical model of infectious diseases is an important tool that has been focused on predicting the dynamics of the disease spread. It can be used to predict the future situation of a potential outbreak and evaluate the best strategy to reduce the spread of the outbreak. There are many types of mathematical models to predict the behavior of an infectious disease that is transmitted from human to human. One of the commonly used is called the compartment model. In this paper, we use a modified SIR model with vaccination to predict the behavior of the disease spread after vaccination. Theoretically, a successful vaccination program should slow down the rate of the virus spread. The modified SIR model with vaccination is adopted to predict the spread of coronavirus. Here, we proof that the model has a unique equilibrium point that is globally asymptotically stable by using Lyapunov function if the vaccination rate is greater than zero. Otherwise, if there is no vaccination is done, the equilibrium points only stable if reproduction number of infection is less than one. Further, the model will be implemented to Indonesia data to predict the behavior of the spread of the disease after the vaccination program.
Development Of A Novel Direct Motor Driven Seal-less Centrifugal Blood Pump (Baylor Gyro Pump)

In this paper, a new design concept of a direct motor driven seal-less Centrifugal blood pump is presented. A rotor of a brushless DC motor is placed in the inlet side of the centrifugal pump and is directly connected with the impeller. The rotor-impeller is contained in a seal-less pump case. A thin pipe separates the rotor and stator of a brushless DC motor. The blood passes through the gap around the rotor and enters the impeller eye. A small pivot bearing and a small journal bearing are used to support the rotor-impeller. The rotorimpeller rotates as a "Gyroscope" in the blood pump case. The model pump study showed that this design concept is promising for development of a small direct motor driven seal-less implantable centrifugal blood pump.
Driving Behavior Recognition using Multiple Deep Learning Models

Malaysia has one of the highest traffic fatality rates in the world. The main cause towards the increment of annual rate on traffic accident in Malaysia is due to distracted driver on wheel. Due to the advancement and integration of technologies within the society, drivers tend to get distracted either by their devices or infotainment that is build-in with the vehicles. This paper presents the application of deep learning to classify driver’s distracted behavior behind the wheel. This paper implements deep convolution neural network to classify driver’s distracted behavior behind the wheel. The experiment was conducted to classify drowsiness dataset of 10 classes from State Farm and 2 classes from National Tsing Hua University (NTHU). Fast and accurate models for driving behavior classification are desired for real-world deployment and application in vehicle system. The results of this investigation show that MobileNetV2 outperforms other models, presenting a good balance between accuracy and processing runtime for real-world deployment.
Prediction of Terrorist Activities by Using Unsupervised Learning Techniques

Terrorism now considered as a major threat to the world population. Terrorism is increasing day by day by different means. From the last decade terrorism rate is increasing exponentially. But there is no efficient way for prediction of terrorism activities. Our paper focuses on prediction of different terrorist attacks by using data mining techniques. In this paper we proposed prediction of attacks by using unsupervised clustering technique. We proposed a framework in which we do sentiments analysis of our data and then by using a combination of density based clustering and distance based clustering we assign classes to our data. Class labels help us to predict terrorism attacks. By research we come to know that combination of these two clustering techniques give accurate results. This proposed framework gives high level of accuracy and it is useful in prediction of attacks types. It gives us a way to deal with terrorism attacks in advance and makes our society peaceful.
Segmentation and Classification for Breast Cancer Ultrasound Images Using Deep Learning Techniques: A Review

Deep Learning (DL) has rapidly become a methodology of choice for analyzing medical images and increasingly attracts researchers’ attention in the medical research community. Breast cancer is a common disease among women throughout the world. The medical images and especially Breast Ultrasound (BUS) images are of poor quality, low contrast, and ambiguous. To avoid misdiagnosis, a Computer-Aided Diagnosis (CAD) system has been created for the diagnosis of breast cancer. This study discusses a variety of ultrasonic image segmentation approaches, with an emphasis on several methods developed in the recent four years. As a result, breast ultrasound image segmentation remains a difficult and demanding problem because of several ultrasound aberrations, including strong speckle noise, preprocessing, classification, feature extraction, and segmentation technique to find the accuracy. Lastly, this study outlines the current trends and issues in breast ultrasound images diagnosis, segmentation, and classifications. This review may be useful for both clinicians and researchers who utilize CAD systems for early breast cancer detection.
Implementation of Spurious Power Suppression based Radix-4 Booth Multiplier using Parallel Prefix Adders

In VLSI, for performing multiplication, we use a*b. We use the booth multiplication mechanism rather than the standard conventional method to improve the speed since it generally requires lower power, area and reduces delay. Some bits overlap with each other during the multiplication process in the convectional multiplication method (whereas booth multipliers consist of shifting process), making the system more complex and decreasing output efficiency. To overcome this, SPST is implemented such that unwanted transactions are neglected and also eliminate the overlapping bits. This affects the carry changes and data generation parts and even reduces the unwanted power flow in adders. It uses Radix 4 multiplier, which requires addition processors. In the previous multipliers usage of Ripple carry adder, Carry save adder, carry ahead adder, which are the basic types of serial adders where they utilize serial adding mechanism where the data is processed bit by bit that make the system more complicated. To reduce these problems, we are using parallel prefix adders that work in a higher speed manner, which uses carry propagation and carry generation internally to perform additional operations and improve speed. So, this project is implemented in VLSI prototype with respect to Xilinx ISE software and Verilog programming language.