Miracle-dz/UniFAR-FTUs huggingface dataset:
Image processing and Supervised Classification of LANDSAT data for Flood Impact Assessment on Land Use and Land Cover

Floods are one of the most frequent natural disasters in the world, causing economic damage as well as human losses. In the present research Pre and Post flood Landsat satellite data was processed through image processing techniques. Landsat satellite image was initially correct the geometry correction and radiometric correction carried out. Similarly follow the pre-processing techniques on satellite image. The GIS platform used to find out the pre and post flood impact on land use and land cover changes for supervised classification techniques in Chengalpattu taluk, Tamil Nadu. The study Area which are classified into five classes in supervised classification for water, urban, forest, barren land and agriculture using GIS platform. Using high resolution Landsat 8 images, study area were categorised into five types in Level 1 classifications. Supervised classifications provide better result in the representation of classes for pre and post flood impact on land use land cover. The overall accuracy of image classification obtained in 2015 and 2016 is 94.25 % and 90.8%. Hence the result proves that satellite data has capability for analysing the changes in LULC through image classification techniques due to flood.
Branch Line Coupler Based on Substrate Integrated Waveguide Technology

This paper presents a 3-dB Quadrature Branch Line Coupler (BLC) using substrate integrated waveguide (SIW) technology. The proposed design is a combination of equal width SIW lines. The structure is having fixed-width lines therefore, can be incorporated into the SIW based microwave circuits. The proposed coupler is designed using derived design equations and simulated at 15 GHz, simulation results show that the reflection coefficients of all the ports are better than 28 dB around the operating frequency, and a good isolation characteristic is achieved which is below 25 dB between the two input ports. At the operating band, two output signals have a phase difference value of $90^{\circ}\pm 1.5^{\circ}$. The insertion loss at centre frequency is 0.84 dB. The proposed structure can be used in butler matrix, mono-pulse comparator circuits, and antenna feeding networks.
Set Point of the Thermal Overload Protection of Large Induction Motors by Simulated Testing

This paper proposes and evaluates an electrical and thermal dynamic model to simulate the effect of voltage unbalance in the parameterization of digital protection of the large induction motors. Based on space vectors, the electrical model has variable parameters with temperature, skin effect and saturation. The thermal capacitances and conductance of the thermal model are identified from the MATLAB Simulink parameter estimation tool. The 1.8 MW motor of the feedwater pump of a power plant is evaluated. The errors concerning the stable state temperatures of the manufacturer’s heating test are 1.1 °C for the stator and 0.56 °C for the frame. The temperature increase of the winding during operating cycles with variable load and different unbalanced voltage were simulated and compared with the thermal image of the relay, demonstrating the importance of these tests in the protection adjustment.
The Speech Labeling and Modeling Toolkit (SLMTK) Version 1.0

This paper introduces the Speech Labeling and Modeling Toolkit version 1.0 (SLMTK 1.0), which facilitates automatic labeling of text and speech for constructing text-to-speech (TTS) systems and speech analysis. The SLMTK 1.0 supports mixed Mandarin-English speech and the associated texts. The following seven steps then process the inputs: 1) text analysis, 2) acoustic feature extraction, 3) linguistic-speech alignment, 4) integration of syllable-based linguistic and prosodic-acoustic features, 5) prosody labeling, 6) construction of prosody generation model, and 7) construction of acoustic models for speech synthesis. The outputs of the seven steps are, respectively, 1) linguistic labels, 2) acoustic features, 3) linguistic-speech alignment, 4) syllable-based linguistic and prosodic-acoustic features, 5) prosody tags, 6) prosody generation model, and 7) acoustic models for speech synthesis. The SLMTK 1.0 has been applied to constructing personalized TTS systems for augmentative and alternative communication. In addition, the toolkit has also been applied to phonetic and prosodic labeling of L2 Mandarin speech to facilitate prosody analysis studies. The SLMTK 1.0 is available at https://slmtk.ce. ntpu.edu. tw for non-commercial use and welcomes all parties to enrich the functions of the SLMTK.
Studying the Role of Self and External Touch in the Appropriation of Dysmorphic Hands

In Virtual Reality, self-touch (ST) stimulation is a promising method of sense of body ownership (SoBO) induction that does not require an external effector. However, its applicability to dysmorphic bodies has not been explored yet and remains uncertain due to the requirement to provide incongruent visuomotor sensations. In this, paper, we studied the effect of ST stimulation on dysmorphic hands via haptic retargeting, as compared to a classical external-touch (ET) stimulation, on the SoBO. Our results indicate that ST can induce similar levels of dysmorphic SoBO than ET stimulation, but that some types of dysmorphism might decrease the ST stimulation accuracy due to the nature of the re-targeting that they induce.
Biometric Individual Authentication System using High Performance ECG Fiducial Features

The Electrocardiographic (ECG) recording is a reliable human heart vital status measurement. Automatic processing of these signals through several computational approaches such as machine learning tools has recently emerged in modern biometric systems. Evaluating ECG potential for biometrical applications has been the purpose of several research papers. In this paper, we developed a new model for individual authentication by detecting high-performance fiducial features of ECG signals. We used SVM and Naive Bayes classifiers to study the impact of high-order statistical features of QRS complexes and R-R intervals within ECG-ID and MIT-BIH Arrhythmia Databases. We integrated these features into a biometric model that we developed. The system reaches an accuracy of 96% up to 99% for the ECG-ID and MIT-BIH databases, respectively. The obtained results approve the reliability of the developed model for robust biometric recognition.
A Multi-Layer Hybrid Intrusion Detection Method Based on Nb And SVM

Intrusion detection aims to identify malicious behaviors in computers and networks, but common firewalls are difficult to detect unknown intrusion behaviors. Intrusion detection system is mainly developed based on machine learning. Due to the imbalance of intrusion detection related data set, the detection of low-frequency attacks and high-frequency attacks can't be considered at the same time. In order to solve this problem, a multi-layer hybrid intrusion detection model (MLH-IDM) is proposed. The redundancy of the features can be reduced by filtering the features of data set with Pearson correlation coefficient, and the data set can be balanced with the random down sampling technique. The whole detection adopts the method of layered detection. The first layer is to filter the Probe separately by the Naive Bayes (NB) classifier; the second layer uses Naive Bayes classifier to filter DoS and Probe, so as to prevent the first layer from filtering impure; in the third layer, Support Vector Machine (SVM) is used to detect low-frequency attacks U2R and R2L. The simulation experiment based on NSL-KDD data set shows that compared with the benchmark paper, MLH-IDM improves F1 score and recall rate by 1.05% and 3.22%; The detection rates of R2L, Probe and DoS attacks are increased by 2.02%, 9.13% and 1.77%.
Research on Facial Expression Recognition Based on Deep Learning Method

Facial expression recognition has been a heated investigate topic during recent years which can be applied in various aspects of people’s daily life, such as entertainment pictures’ generation, check-in machine of a company, etc. In this work we investigate the effect of adding visual attention mechanism to a facial expression recognition deep learning method. The main contribution is to form a new architecture to apply SE Block to normal VGGNet which shows that a significant improvement on the accuracy of facial expression recognition. These findings are properly tested on 66.8% accuracy, which validates the effectiveness of the proposed SE-VGGNet.
Design of a PRS-Based Wideband Circularly Polarized 2 X 2 MIMO Antenna for 5G / Millimeter Wave Communication

In this research work, a multiple input multiple output (MIMO), circular polarized (CP) antenna with Partially Reflective Surface (PRS) is presented for fifth generation (5G) communication systems having low profile and wideband operation. The antenna is made by square patch and a partially reflecting surface (PRS) of high dielectric above the substrate layer. The wideband circular polarized (CP) radiations are achieved by pruning the orthogonally opposite patch corners and cutting a rectangular slot diagonally. As per simulation results, the designed PRS-based antenna has a broad 10 dB impedance bandwidth and 3-dB axial ratio bandwidth. Moreover, the proposed antenna design is used to create a 4-port MIMO antenna by putting each of the element orthogonal to each other. The designed PRS based MIMO antenna has excellent multeity performance in terms of envelope correlation coefficient (ECC), separation between antenna elements and diversity gain even without any devoted decoupling structure. Most notably, the bandwidth in which antenna is operating covers the millimeter wave frequency band 24.5-28.5 GHz which is designated for 5G applications. Above mentioned characteristics make suggested antenna system appropriate fit for 5G sensors, smart devices and other 5G applications.
Parallel Adaptation: Switching between Two Virtual Bodies with Different Perspectives Enables Dual Motor Adaptation

Virtual Reality (VR) lets us experiment embodiment. Here we investigate dual embodiment under the prism of dual motor adaptation. We asked participants (N=21) to perform reaching motions in VR with opposite gradual visuomotor perturbations. The participants sequentially switched between 2 VR bodies and had to adapt to the VR body’s perturbation (up to +15° for the VR bodyA, and-15° for the VR bodyB). We then designed a 2$\times$2 within-subject study: 1 factor being the perspective (1st person or 3rd person), and 1 factor being the head rotation (without head rotation before the reaching motion or with head rotation before the reaching motion). We found that by providing strong visual cues between bodies (alternating symmetric perspective and/or symmetric head rotation), participants had little awareness of the perturbations, good adaptation, and large aftereffects in both VR bodies. Those elements are consistent with implicit dual adaptation. In contrast, a naive 1st person perspective resulted in little to no adaptation with a high cognitive load and no aftereffects.
Fixed camera drone based photogrammetry for indoor mapping

Point cloud data is a way of implementing and recreating the surrounding with the help of 3D imaging and rendering. These point clouds can be further converted into mesh and then 3d models that can be used for localisation and mapping. LIDAR (Light Detection and Ranging) is a common device that is used when we want to reconstruct a space(room or building) in a software 3D Mesh however LIDAR based systems are expensive and can be tricky to stitch and reconstruct, the proposed system uses a single camera, this single camera is going to capture the frame in a continuous manner. This frame capturing will be followed by feature extraction which is essentially going to help in comparing the motion of the camera by comparing the transformation of the frames relative to the previous frame. This process when implemented over a continuous frame path is going to help us build a 3D Space with the help of a process called photogrammetry. This can then be used in spatial planning or areas where the possibility of using lidar is not possible or the cost is too high for implementation. The estimated size from the sensor usually only differs a few centimeters from manually measured sizes. From 24 images the drone can generate more than 12000 feature points for photogrammetry and a point cloud with almost 10000 points for a room with a lot of features.
Balanced models in isentropic coordinates and the shallow water equations

It is shown that there is an appropriate family of three balanced models in isentropic coordinates and the shallow water equations. “Appropriate” means the equations include exact hydrostatic, continuity and heat equations and form a complete system; some family members are second-order accurate in a midlatitude Rossby number expansion; the models conserve at least one integral invariant analogous to the primitive equation energy and enstrophy invariants; and the terms retained in the vorticity and divergence equations are the minimum required for the accuracy of the model in an asymptotic scale analysis. We compare vorticity and divergence in physical and isentropic coordinates to illustrate an aspect of the difference in physical content between balanced models defined in the different coordinates. We show that the early balanced models of Bolin and Charney are similar to members of our family in isentropic coordinates, but fail some of our criteria for appropriateness. Finally, we discuss why we think the above criteria are the right ones for balanced model families, and compare and contrast similar criteria for other intermediate models . DOI: 10.1111/j.1600-0870.1984. tb00236.x
Applications on Deep Reinforcement Learning in Traffic Signal Control

Under the transportation resources and infrastructures that exist today, artificial intelligence is used as an efficient and intelligent solution to traffic congestion in strategic decision-making and trajectory planning control. This paper will introduce one of the methods - Deep Reinforcement Learning (DRL), and the application of DRL in intelligent traffic signal control. This paper would introduce the basic knowledge of DRL, explain a traffic signal control model application based on Markov Decision Process and present the relating knowledge of MDP, introduce the signal decision-making model based on Q reinforcement learning and compare the pros and cons of both models. This paper compares different solutions of traffic signal controls of varying learning modes with the standard of efficiency, implementation difficulty, advantages, and disadvantages.
Information Leakage Techniques in Cloud Computing: A Review

Cloud computing is referred as a model that facilitates universal, suitable and on-demand network access for commonly used configurable computing resources having the ability of quick provisioning and releasing with minimum management effort from the client side and least service provider communication. Cloud computing is also regraded as the development of various technological advancements that work cooperatively for changing the approach an organization to construct their IT set-up. Cloud computing does not make use of any new technology. The cloud computing has various issues which include security, load balancing, information leakage etc. In this paper, various schemes for the information leakage are reviewed in terms of methodologies and results.
Estimating the Region of Attraction on Controlled Complex Networks With Time-Varying Delay

The importance of estimating the region of attraction (ROA) on complex networks has drawn attention very recently. However, challenging problems arise when applying the existing theory to networks with delay. In this article, we estimate the ROA on controlled complex networks with time-varying delay. A delay-independent ROA estimation method is derived at first. This method can deal with general delay and is convenient to use due to its independence of the delay. Accordingly, it may cause conservativeness. To further reduce the conservativeness, delay-dependent ROA estimation is established for networks with small delay by developing a new technique of dealing with the delay. Numerical simulations are provided to verify the theoretical results.
A Hybrid Approach for Discovering Concept Prerequisite Relations in Wikipedia

Nowadays, the Internet is flooded with a large number of different types of learning resources. Due to the unclear relations between these learning resources, it is difficult for teachers and students to use these learning resources for curriculum planning and learning path design. In this paper, we propose a hybrid approach that aims to identify concept prerequisite relations in Wikipedia using handcrafted and embedded features of concept pairs. Experimental results show that the proposed hybrid approach achieves the best performance on both the Chinese and English datasets compared to the state-of-the-art methods.
Transliteration of Foreign Words in Burmese: Descriptions by a Mortise-and-Tenon Notation

This paper provides general descriptions on transliteration of foreign words in the Burmese language. Phenomena of phonetic and orthographic issues are discussed. A notation scheme to eliminate the inherent vowel and to address the dependent vowels is designed, which can be generally used to describe transliteration issues in an abugida writing system. Based on this work, we expect that prescriptive guidelines can be gradually established to normalize the transliteration of modern foreign words in Burmese.
A Novel Approach for Alzheimer Detection Using SMLT

The absence of the brain chemical acetylcholine is the characteristic of Alzheimer's disease. In the early stages, this condition is thought to cause minimal memory loss, but as the disease progresses, it causes some loss of ability during discussion. It is a brain illness in which memory is progressively lost along with the capacity to think and carry out some daily duties. Some areas of the brain are also involved, including those that regulate the ability to think, remember, and use language. Alzheimer's treatment choices vary depending on whether or not the Alzheimer's is present. An Alzheimer's disease biopsy is required prior to final brain surgery in order to confirm the diagnosis. There are an estimated 6 million persons in the United States who suffer from Alzheimer's disease, and the majority of them are over the age of 65. Memory and other essential internal processes are lost as brain cell connections and cells themselves degrade and die. The most common signs and symptoms are disorientation and loss of memory. There is no cure, however medications and surgical procedures can alleviate symptoms for a while. For example, there are various hazard features to an Alzheimer's complaint, as well as a requirement for time to gather correct and accepted procedures in order to form an early assessment and resolve the complaint as quickly as possible. Data mining is a common practice in the healthcare industry for reusing large amounts of data. To better understand Alzheimer's disease, researchers use data mining and machine learning approaches to evaluate large volumes of medical information. Machine learning is being used to develop a model for diagnosing Alzheimer's disease at this time. In order to estimate the rise, various algorithms are examined and a current model is employed.
A Reflection Self-Canceling Design Technique for Multidrop Memory Interfaces

We propose a reflection self-canceling design technique for multidrop memory interfaces. In this technique, lengths of branch lines are designed, so that dominant multiple reflections become self-canceling. As a result, reflective intersymbol interferences (ISIs) can be greatly reduced to increase the bandwidth without utilizing serial resistor insertion, reflection compensation lines (RCLs), or advanced circuits, such as equalization circuits. Using the proposed and the conventional techniques, two eight-drop channels were designed with 50- $\Omega $ microstrip lines on FR-4 printed circuit board (PCB) and tested with the pseudo-random binary sequence (PRBS) 31 pattern for comparison. At 10.5 Gb/s, the worst eye height was measured 28.0 mV in the proposed design, while the worst eye diagram of the conventional design was closed. The proposed design achieved the maximum data rate of 12.5 Gb/s and achieved the worst eye height of 10.0 mV. The achieved data rate of 12.5 Gb/s is 95.3% faster than the prior art measured with an eight-drop channel without equalization circuits.
A practical approach based on IoT, Video, Audio, and ITS for Micro-Sleep detection in public transportation systems

Microsleep detection in a safety scenario has been a big issue that the research community is trying to solve due to its impact on vehicle safety. In this paper, we present a practical undergoing work to analyze, design, and implement a drowsiness classifier that allows through several different research phases to forecast this behavior. Normally, these systems submit the alarm after the eyes have been closed for a given number of seconds. We are considering a system that can detect and send alarms before the microsleep took place. So, our approach uses audio and video combined to pre-identify the drowsiness condition.
PSO based Optimal Placement of Distributed Generation for Loss Minimization and Voltage Profile Improvement under Contingencies

Power systems are desired to operate within defined limits of voltages and line power flow. But many times, they get severely constrained and breach these limits and therefore the security of power system gets compromised. Such challenging situations generally occur in case of occurrence of some form of contingency in the system. Contingency can be a simple one such as a line outage or a complex one like cascade line outages leading to blackout etc. To suggest some ways to save the system from disastrous consequences of these contingencies is the motivation of the present work. This work is synthesized basically in two parts. First part deals with the study of contingency in detail, such as, in what form it occurs, where in a power system it could be most severe etc. For this part some power flow indices have been calculated via simulations and then these indices are used to determine severity in each case. The second part makes an attempt to propose a way out to mitigate the negative impacts of these contingencies which usually are driving the system beyond its operational limits in terms of bus voltages, line power flows etc. The technique proposed in this part is the use of Distributed Generations optimally in the power system to increase its reliability. All simulations in this paper are carried out in MATLAB environment and test system deployed for this work is IEEE 6 bus system.
A multi-modal feature fusion method for pilot expression recognition

Facial expression recognition technology possesses important utility worth in pilot emotion and flight safety monitoring. In order to resolve the issue of insufficient information and inferior recognition accuracy of single modal features in facial expression recognition, a multi-modal feature fusion method for pilot expression recognition is proposed. Laboratory findings show that the recognition precision of the presented method on CK+ and our newly-proposed pilot expression dataset reaches 99.68% and 98.38% respectively, which can not only effectively identify pilot expression, but also outperform other competing methods.