Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of Monorail Suspension System Model Under Different Driving Speeds

Designing a new monorail suspension system for an existing monorail bogie to accommodate larger cars, locomotives and more passengers is a complicated and challenging problem to solve. In this paper, a model of 15 degrees of freedom full-car Monorail suspension system model is proposed. In this paper, simulation is performed to predict some dynamic characteristics monorail suspension system. The model features the Monorail body, Front bogie, and rear bogie geometries, adopted equations of motion of the monorail body. Numerical Central Difference method was used to obtain the system responses subject to sinusoidal Track excitations. Different driving speeds of 36, 54 and 72 $km/h$ were simulated to investigate the train’s vertical, lateral, roll pitch and yaw displacements. Results, included monorail body displacements in terms of vertical, lateral, roll, pitch and yaw displacements. It was found that Monorail body displacemnets ere inversely poroprtional to driving sppeds within a range of 0.31mm nad 2.5mm. Also, roll, pitch and yaw displacements were small within a range of 0.75 to2mm.
Dual Band Rejected Low Profile Planar Monopole Antenna for UWB Application

In this paper, a new design of ultra-wideband band (UWB) antenna with dual band rejection characteristics and sizeminiaturization approach has been presented. The proposed antenna consists of unique patch of trapezoidal shape, 50 Ω impedance triple down step microstrip feed line (on top plane) and partial ground plane (on bottom plane). The patch tapering results to bandwidth (BW) improvement. It works efficiently over the impedance bandwidth of 3-13 GHz and gives Fractional Bandwidth (FBW) of 125% that covers the entire UWB band. It provides nearly omnidirectional radiation pattern, has good realized gain and impedance matching. A triple step feed increases electrical length of patch, which leads to antenna size miniaturization. Therefore, the proposed antenna has been designed with dimensions of 22 mm × 28 mm × 1.6 mm, dielectric constant (Ɛr) of 4.4 and tangent loss of 0.02. For wireless applications, some specific UWB systems suffer from strong narrowband interference of unlicensed (ISM band) wireless communication devices. To avoid this unwanted interference, band rejection characteristics should be inserted into the antenna patch structure. The proposed antenna has designed with dualband rejection capability that efficiently avoids Wi-MAX and WLAN interference. The V-slot is etched on patch radiator to reject Wi-Max (3.15–4 GHz) and single C-strip pair besides feed line is used to reject WLAN (5.18-5.95 GHz) band. The output performance has measured in terms of simulated reflection coefficient and VSWR.
Assisting gait with free moments or joint moments on the swing leg

Wearable actuators in lower-extremity active orthoses or prostheses have the potential to address a variety of gait disorders. However, whenever conventional joint actuators exert moments on specific limbs, they must simultaneously impose opposing reaction moments on other limbs, which may reduce the desired effects and perturb posture. Momentum exchange actuators exert free moments on individual limbs, potentially overcoming or mitigating these issues.We simulate unperturbed gait to compare conventional joint actuators placed on the knee or hip of the swing leg, and equivalent angular momentum exchange actuators placed on the shank or thigh. Our results indicate that, while conventional joint actuators excel at increasing toe clearance when assisting knee flexion, free moments can yield greater increases in stride length when assisting knee extension or hip flexion.
Approximate and Exact Selection on GPUs

We present a novel algorithm for parallel selection on GPUs. The algorithm requires no assumptions on the input data distribution, and has a much lower recursion depth compared to many state-of-the-art algorithms. We implement the algorithm for different GPU generations, always using the respectively-available low-level communication features, and assess the performance on server-line hardware. The computational complexity of our SampleSelect algorithm is comparable to specialized algorithms designed for - and exploiting the characteristics of - "pleasant" data distributions. At the same time, as the SampleSelect does not work on the actual values but the ranks of the elements only, it is robust to the input data and can complete significantly faster for adversarial data distributions. Additionally to the exact SampleSelect, we address the use case of approximate selection by designing a variant that radically reduces the computational cost while preserving high approximation accuracy.
Preliminary Evaluation of Disturbance Torque Estimation Approaches for Lower-limb Robotic Rehabilitation

In robotic rehabilitation, knowledge of human joint torques is very important to provide reliable data for clinical assessment and to provide feedback information about the user in order to design safe robotic control strategies. Nevertheless, their measurement can be complex and requires a high-cost implementation. Estimation approaches based on disturbance observers have been well studied for human joint torque estimation in robotic rehabilitation systems, most of them, for upper-limb devices. This paper represents our initial effort toward applying disturbance observer techniques for estimation of patient torque for lower-limb robotic rehabilitation. Three disturbance observer approaches for torque estimation are evaluated on a test scenario consisting of a robotic wearable device for ankle rehabilitation attached to a physical mock-up that replicates the ankle movement in the sagittal plane. The results obtained demonstrate the feasibility of the proposed methods and encourage us to test them with voluntary users.
A Soft+Rigid Hybrid Exoskeleton Concept in Scissors-Pendulum Mode: A Suit for Human State Sensing and an Exoskeleton for Assistance

In this paper, we present a novel concept that can enable the human aware control of exoskeletons through the integration of a soft suit and a robotic exoskeleton. Unlike the state-of-the-art exoskeleton controllers which mostly rely on lumped human-robot models, the proposed concept makes use of the independent state measurements concerning the human user and the robot. The ability to observe the human state independently is the key factor in this approach. In order to realize such a system from the hardware point of view, we propose a system integration frame that combines a soft suit for human state measurement and a rigid exoskeleton for human assistance. We identify the technological requirements that are necessary for the realization of such a system with a particular emphasis on soft suit integration. We also propose a template model, named scissor pendulum, that may encapsulate the dominant dynamics of the human-robot combined model to synthesize a controller for human state regulation. A series of simulation experiments were conducted to check the controller performance. As a result, satisfactory human state regulation was attained, adequately confirming that the proposed system could potentially improve exoskeleton-aided applications.
Polarization Reconfigurable Microstrip Slotted Antenna with Two Opposite Feeds

A microstrip patch antenna fed with two opposing microstrip lines is designed. The antenna has four cuts in its corners in order to introduce PIN diode to have a polarization agile antenna by changing the diodes states. Two vertical slots inside the patch are created for axial ratio and bandwidth enhancement. A separate DC biasing is inserted for proper operation of the diodes. Some antenna characteristics such as reflection coefficient, axial ratio, radiation pattern and gain are investigated and optimized through several parametric studies of the antenna geometry. This type of antenna is widely preferred in wireless communication systems.
Robot Learning of Assistive Manipulation Tasks by Demonstration via Head Gesture-based Interface

Assistive robotic manipulators have the potential to support the lives of people suffering from severe motor impairments. They can support individuals with disabilities to independently perform daily living activities, such as drinking, eating, manipulation tasks, and opening doors. An attractive solution is to enable motor impaired users to teach a robot by providing demonstrations of daily living tasks. The user controls the robot ‘manually’ with an intuitive human-robot interface to provide demonstration, which is followed by the robot learning of the performed task. However, the control of robotic manipulators by motor impaired individuals is a challenging topic. In this paper, a novel head gesture-based interface for hands-free robot control and a framework for robot learning from demonstration are presented. The head gesture-based interface consists of a camera mounted on the user’s hat, which records the changes in the viewed scene due to the head motion. The head gesture recognition is performed using the optical flow for feature extraction and support vector machine for gesture classification. The recognized head gestures are further mapped into robot control commands to perform object manipulation task. The robot learns the demonstrated task by generating the sequence of actions and Gaussian Mixture Model method is used to segment the demonstrated path of the robot’s end-effector. During the robotic reproduction of the task, the modified Gaussian Mixture Model and Gaussian Mixture Regression are used to adapt to environmental changes. The proposed framework was evaluated in a real-world assistive robotic scenario in a small study involving 13 participants; 12 able-bodied and one tetraplegic. The presented results demonstrate a potential of the proposed framework to enable severe motor impaired individuals to demonstrate daily living tasks to robotic manipulators.
A SURVEY ON INTERNET OF THINGS BASED SMART, DIGITAL GREEN AND INTELLIGENT CAMPUS

Internet Of Things known for Global network in the cloud which connects various things through sensors. The IoT is a system that connects various devices which are intelligently communicates with other objects, machines, infrastructures and also environments [1]. This research paper highlights the important components of a model “Smart and Digital Green Educational Campus”. A smart campus is enabled through Internet-of-Things (IoT) technology. A Smart campus links server, peripherals, campus infrastructure and people connected to the Internet of Things. And also the smart campus uses inter-connected with networks and Internet of Things (IoT) to well connect campus infrastructure on the IP network. , such as outdoor lighting, building, lighting, heating, cooling power systems, security surveillance and physical access systems, parking facilities, and more. In this way in a smart campus everything share infrastructure of campus through Internet of Things. The campus becomes a platform for innovation [2]. Smart campus brings in new experiences like location based and context - aware services, smart parking, smart lighting, smart transits and smart building climate control. It also increases safety and security within the campus with connected surveillance IP cameras, smart locks, asset tracking and smart emergency services. It enables tracking of the facilities / resources and use them more efficiently. This helps in reducing the cost by the intelligent use of water, electricity and building controls. Innovative learning environment in a smart campus enhances the learning outcomes and the campus experience. It also improves the higher education facilities and operational efficiencies. Moreover, conserving electricity and reducing carbon footprint results in a greener smart campus [2] In this paper describes the importance of configuring the Green campus. The Green campus provides the connected computer and servers that is very useful for well established technology in a campus. This includes cloud computing, big data maintenance, Internet of Things enabled and wireless sensors network campus. By establishing the sensor network and Internet of Things in a green campus, the data and information will be collected in a cloud database then after the store of the data, process the data and analysis the data will be carried out time to time [4]. To make campus as Intelligent i.e. i-campus is very important. The idea of making intelligent campus is ability to manage or surveillance by un-expected situations. And capable to run the campus intelligent way. The intelligent campus not only establish to build infrastructure in intelligent way but also to manage and survive in any circumstances [5].
Convolutional Autoencoder for Feature Extraction in Tactile Sensing

A common approach in the field of tactile robotics is the development of a new perception algorithm for each new application of existing hardware solutions. In this letter, we present a method of dimensionality reduction of an optical-based tactile sensor image output using a convolutional neural network encoder structure. Instead of using various complex perception algorithms, and/or manually choosing task-specific data features, this unsupervised feature extraction method allows simultaneous online deployment of multiple simple perception algorithms on a common set of black-box features. The method is validated on a set of benchmarking use cases. Contact object shape, edge position, orientation, and indentation depth are estimated using shallow neural networks and machine learning models. Furthermore, a contact force estimator is trained, affirming that the extracted features contain sufficient information on both spatial and mechanical characteristics of the manipulated object.
Learning Analytics in Universiti Teknologi Brunei: Predicting Graduates Performance

Research in Educational Data Mining has enabled many applications that positively impacted teaching, learning, and their management process. This study uses data mining techniques to study the performance of full-time undergraduate students in School of Computing and Informatics, in Universiti Teknologi Brunei (UTB). Two aspects of students’ performance have been focused on. First, predicting undergraduates’ performance at an early stage of their study program. Second, identifying modules that can serve as strong indicators of performance at the end of the degree program. We have collected data on students’ academic performance throughout the four years of their study, starting from 2009, as well as related demographic and background information. We present the approach we have taken to answer two research questions identified for this study. Several classification techniques and sampling methods, to overcome data imbalance challenges, have been experimented with. Despite the small data size available, we achieved reasonable accuracy in predicting the three graduation classifications adopted in UTB (average true positive rate of 0.754). This was using Naïve Bayes method with Feature Selection technique based on Gain Ratio attribute evaluator. In overall, modules in semesters 2 to 4 are more prominent than modules of first semester in serving as strong predictors. We also draw some conclusions from insights we observed from the best Decision Tree model.
Managing Time and Resources of Construction Projects Using Colored Petri Nets and a Genetic Algorithm

Time and resource management are two crucial activities within the project management framework. This paper demonstrates how resource assignment Petri net (RAPN) a class of colored Petri nets, is applied effectively for modeling and scheduling of construction projects, known as a resource- constrained project scheduling (RCPSP). It captures the dynamic behavior of the project at runtime and enables the decision maker to investigate interactions between resources and project requirements. To determine the most suitable scheduling of project resources, the dynamic population and steady-state ge- netic algorithm (DP–SS GA) is integrated into the modeling framework. It is used to identify the best scheduling plan for different working schemes. In order to illustrate different resource assignment strategies and capture the dynamic inter- actions between resources and activities, a running case study is used. It is found that the modeling framework is capable of capturing the model requirements of a construction project. Compared to the exact solution generated by classic project management tools, the proposed model produced a near optimal solution.
A Novel DC-DC Single-inductor Double-Input Bidirectional Converter for Hybrid Energy Storage System in DC-Micro Grid

DC micro-grids are the key technology for integrating distributed renewable energy resources (DERs) in the decentralized generation system since most of them are DC inherent. Energy storage systems play important role in stabilizing DC micro grids. In recent years hybrid energy storage systems (HESSs) draw a tremendous attention because of their excellent dynamics. In this paper, novel DC-DC single-inductor double-input bidirectional converter is presented for integrating the HESS to the DC micro-grid. The steady-state analysis of proposed converter is presented. Small-signal model of the converter is derived out and control strategy is demonstrated. Finally, detailed simulations are carried out in MATLAB SIMULINK. The capability of proposed converter for proper power-sharing between the battery and the super capacitor is validated through simulations.
Detection and Correction of Head Motion and Physiological Artifacts in BOLD fMRI: A Study

Functional Magnetic Resonance Imaging (fMRI) can be defined as a technique to produce images of activated brain regions by certain task and stimuli. Blood Oxidation Level Dependent (BOLD) signals in fMRI measures oxygen level in the blood of active neurons to determine the activated brain regions. Numerous artifacts including different kind of motions such as head movement, rigid body motion, and physiological (cardiac and respiratory motions) are introduced while capturing fMRIs. These artifacts may contaminate the signal of interest and are generally more as compared to the signal. Many researchers have tried to remove these artifacts using various statistical, wavelet based and feature based techniques. In this review, detailed discussion on various artifacts and techniques of their removal is presented.
Fire Detection System using Raspberry Pi

Fire presents significant threat to life due to its severe hazards and ability to spread rapidly. Fire detection systems, specially vision-based system offers flame detection prior to any loss or destruction. In this present model, new vision-based device is designed which works on Raspberry Pi and detects flame (visible part of fire) over remote regions. An immediate alert is generated on android application. Here in, HSV color combination is used for proposed model and changes in flame color and texture is studied.
Private Virtual Cloud Infrastructure Modelling using “VCPHCF-RTT” Security Agent

Cloud Security issue is one of the biggest challenges that hampers the growth of Cloud for its various service provisioning. Major Service Provisioning of cloud is On-Demand access to shared pool of resources involving delivery of hosted services over the inter-network connections. Security of Private Virtual Cloud Infrastructure will be proposed against IP-Spoofing based DDoS Attacks using Private Virtual Cloud Infrastructure Model. Virtualization Enhancement will be done in Cloud using proposed Security Agent VCPHCF-RTT. Performance Parameters will be analysed after introspection to cloud security techniques to resolve focussed Research Problem Issues and Challenges. VCPHCF-RTT technique which will be implemented on virtual machine monitor would help in catching malicious packets while packets transmissions between the virtual machines server and virtual machine clients through virtual intermediate routers with the unique patch of probability and round trip time in hop count filter method.
Simulation of Undersea Optical Communication System using DCF and SSF

Optical fiber is the link and the backbone of the communication system. This is especially where fiber optic undersea is found low loss, high efficiency and large effective area major for enabling system to transmit big data up to thousand kilometers with less distortion. In this paper, we designed 220 Km fiber optic system with data rate 40 Gb/s in the term of two scenario: the first scenario used fiber optic cable with 80 $\mu M$ core diameter and the second scenario used optic fiber cable with 17 $\mu M$ core diameter which made the transmission signal for long distance very difficult. Observing the first scenario, the signal transmitted with less dispersion and less bit error rate $2.956^{\star}10^{-44}$ over 220 Km. the signal transmitted over 220 Km with high chromatic dispersion in the second scenario. To avoid chromatic dispersion using, an ideal dispersion compensation FBG, was used to reflect the undesired signal. The received signal on the other side with bit error rate of 4.8625 *$10^{-13}$.
Development of Simultaneous Sensor Location-Detection and Powering with Harmonic Radar Technology for Biomedical Sensing

This paper concerns the development of a biomedical sensing sensor which can be detected and powered by a harmonic radar. We propose some sensor component for the considering of sensing and charging simultaneously by detecting the second-harmonic signal strength. The sensor components are operated at 2.4-GHz band, including a compact dual-band antenna, a second-harmonic enhancement coupler, and a rectifier with a switched second-harmonic detection circuit. The maximum rectification efficiency is 57% when the RF switch off, and the difference of second-harmonic signal strength is 3.9 dB between switch on/off without the second-harmonic enhancement coupler when input power is 10 dBm. After adding the second-harmonic enhancement coupler, the maximum rectification efficiency is 45% with the RF switch off, and the difference of second-harmonic signal strength is 4.4 dB between switch on/off when input power is 10 dBm.
A Nyström Scheme with Lagrange’s Interpolation for Solving Surface Integral Equations

In this paper, we take the electromagnetic scattering of PEC cylinder as an example to study the calculation method of Surface Integral Equation. The Moment of Method and Nyström, two methods commonly used at present, are compared. We innovatively proposed a combination of Nyström and Lagrange’s Interpolation to make the source and matching points evenly valued, in preparation for the combination of Nyström and Sparse Matrix Canonical Grid.