Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Sensorless Control Strategy of Ship IPMSM at Full Speed Range

This paper proposed an improved sensorless control strategy for ship electric propulsion which bases on I/f control and back electromotive force integral (EMF), suppressing the speed fluctuation during algorithm switching, improving the flux estimation accuracy of internal permanent magnet synchronous motors (IPMSMs) and achieving the stable operation of the motor. Firstly, this strategy was adopted for controlling the stator current during the switching process to improve speed fluctuation. Then, a simple flux estimation method is presented by rewriting the mathematical model of IPMSM. To achieve the result which overcomes the problem of flux estimation and current measurement errors and improved LPF was developed which solved the phase delay and DC-offset problem of flux estimation. Finally, the propeller load platform is under construction to verify the effectiveness of the control strategy. Experimental results show that the system based on this method works smoothly and it has better speed tracking performance.
Performance Analysis of Stand-alone Axial Flux Permanent Magnet Generator Connected to Inductive-load

Electricity is one of the most important sources of energy in the world. Its use is increasing rapidly for home appliances and industrial apparatus to support every activity in both developed and developing countries. Small-scale, distributed and autonomous power system are all developed in concern about the energy conservation as well as environmental problems. The effect of the home appliances and industrial apparatus which are classified as inductive-loads causes the terminal voltage of the generator to decrease significantly. This paper describes the performance analysis of the generator by using the analytical method when the generator is connected to the inductive-loads. To maintain the voltage regulation not exceed 10% under the influence of the inductive-loads, the designer of the generator must calculate the performance of the generator first before it will be made. The analysis results show that the generator is in good condition when supply the inductive-load at its rated value.
The Performance Analysis of Second Order Band-pass Filter

The design of Band-pass filter is an important component in electronic circuits. The center frequency, bandwidth and phase-shift are most concerned parameters in design. We analyze the transfer function performance of second-order passive band-pass filter in this paper. The constraint conditions between center frequency, bandwidth and phase-shift are derived. In consideration of the fact that active filter is consisted by passive filters and active components. The derived constraint conditions are the same with active band-pass filters.
Blind DOA Estimation in a Reverberant Environment Based on Hybrid Initialized Multichannel Deep 2-D Convolutional NMF With Feedback Mechanism

The accuracy performance of traditional direction of arrival (DOA) estimation algorithms is seriously affected by the reverberation. Considering the advantage of the sparse characteristic of speech signal in time-frequency (T-F) domain, this paper presents a new blind DOA estimation method based on integrated deep learning and convolutional non-negative matrix factorization (NMF). Firstly, mathematic models of microphone array and room impulse response are built. In addition, we extracted blindly initialization parameters of 2-D convolutional NMF using k-means clustering algorithm and singular value decomposition algorithm, which can be used to accurately estimate the main components of desired sound source in the reverberation environment of multi-path propagation. Moreover, the feedback mechanism is introduced into deep 2-D convolutional NMF and correlation coefficient between the signal decomposed by NMF and the signal to be decomposed is used to select the best separated signal for DOA estimation, which make the separation algorithm simpler and more efficient. Finally, test of orthogonality of projected subspaces (TOPS) algorithm is used to validate the DOA estimation capability of this algorithm. Compared with the unprocessed reverberation speech, the estimation error is reduced, which shows that the proposed algorithm can effectively improve the estimation accuracy of DOA estimation when the received signals are in a reverberant environment.
MAC-Layer Packet Loss Models for Wi-Fi Networks: A Survey

Technical reports indicate that wireless and mobile devices will account for 71% of all IP traffic by 2022, an increase of 19% over four years. This increase is related to advances in wireless data communication technologies. Wireless networks have become one of the most important ways to connect devices to the Internet, therein improving productivity and encouraging information sharing. IEEE 802.11, known as Wi-Fi, has become the main standard for wireless local area networks. The most important metrics for measuring the quality of Wi-Fi are delay, jitter, and packet loss. Packet loss occurs when one or more packets fail to reach their destination and can occur for a variety of reasons. Packet loss influences the user’s perceived quality of applications over Wi-Fi networks, mainly multimedia and real-time applications. The availability of accurate models for packet loss in Wi-Fi networks enables the development of more efficient methods for performance analysis and network design, as well as better computational simulations. Modeling packet loss in such networks presents a major challenge because packets may be lost for many different reasons, including signal attenuation, noise, multipathing, signal refraction, thermal noise, competition for media access and buffer issues. In this paper, we provide an overview of the causes of packet loss and a comprehensive survey of the available models for packet loss in Wi-Fi networks. The potential benefits of the survey are: (i) the systematic presentation of available packet loss models for Wi-Fi networks, their parameters, and respective packet loss rate evaluation, (ii) comparison of models considering validation scenarios and input parameters, and (iii) description of open issues and future research directions. We hope that our analysis will help researchers understand the most important characteristics of the packet loss process in Wi-Fi networks and the strengths and weaknesses of the main packet loss models.
Analysis and Prediction of Breast Cancer using AzureML Platform

Nowadays, healthcare sector starts relying on the datasets that are collected by clinics or some organizations to help doctors in predicting and analyzing the patient’s status in early stage. There are many dangerous diseases around the world that people suffer from them, but one of the most dangerous diseases is cancer. Recent research shows that about 12% US women over the course of their life, develop invasive breast cancer. Thus, in this case, the breast cancer (BC) is categorized as a dangerous type among all cancer types. This study focuses on BC by using a well-known dataset titled Breast Cancer Wisconsin (Diagnostic) Data Set. It has 32 attributes and 569 instances. Some of those attributes have missing values and others are not necessary for our work. So, we removed the ID column and any instance that has a missing value. Our aims in this research is analyzing BC dataset and understand its features. Then, we upload it to Microsoft Azure machine learning (AzureML) platform for building our model. We use two classes Decision Jungle and two Classes Decision machine learning algorithms to predicate whether the patient diagnose is Benign or Malignant. We assess the performance of each algorithms in terms of different measures like Accuracy, Precision, Recall, F1 and AUC. The results of our study in this paper show that the accuracy of Decision Jungle is approximately 97%. On the other hand, the accuracy of Decision tree is approximately 95%.
Study of NLOS effect on Indoor Visible Light Positioning in Different Room Sizes

In indoor visible light positioning systems, indoor reflections in different room size will cause different impact on positioning accuracy. In this work, we investigate the RSS-based positioning errors with different room size. Results show that when the room size increases, positioning accuracy is becoming higher. When room size reaches 15m*15m*3m, the maximum positioning error due to non-line-of-sight (NLOS) is only 1 mm worse than that for line-of-sight (LOS) case. Thus, if room size exceeds this value, we can ignore NLOS effect. We also considered the effect of LED half power angle and receiver field-of-view changes on maximum positioning error. Results show that the former has greater impact on positioning.
Impact of Engineer Personality on Sustainable Environment

Engineers are the ones who design the anthropic space and apply the principles of sustainable development. The way they feel and see reality is projected on their work and finished products. The purpose of this research is to identify sensory stimuli that produce pleasure and comfort in the socio-professional category of engineers. The questionnaire survey revealed that engineers prefer materials that promote a sustainable economy, as well as the organization of the anthropological environment by the model of nature, practicing in the unconscious psyche a mimicry of the natural environment.
Deployment of Fog Computing Platform for Cyber Physical Production System Based on Docker Technology

Industry 4.0 is the most recent industrial revolution that is based on data collection, exchange, and analysis, in order to improve the productivity of manufacturing systems. Thus, Fog computing is an emerging technology, which enables to process data close to its source and will be responsible for decentralized decision-making, interoperability, data analytics, information security, real time treatment, etc. Hence, deploying the Fog computing for Industry 4.0 can be very useful to improve the production systems efficiency and reliability. In this paper, we deploy a real Fog Computing enabled platform to preproccess data about cyber-physical production system. Our platform is also based on Docker technology that enables containerizing data processing. Experimental results show the efficiency of our platform in terms of communication latency, scalability, and interoperability.
Intrusion Detector for Blockchain based IoT Networks

Internet of Things (IoT) is a fast growing technology in the digitalised world where each device in the network interconnect and interact with other device to collect the information and automate tasks.A lot of vulnerabilities are exposed in IoT devices, which can be exploited to compromise the devices and an infected device can disrupt all other nodes in the network. The severeness of the vulnerabilities can vary from launching a Distributed Denial of Service (DDOS) attack to entirely draining the batteries of a heart pacemaker. Although IoT providers constantly working on reducing the vulnerabilities, an Intrusion Detection (ID) system can be built to find, whether the nodes are behaving abnormally or not. ID system can be built with a centralized architecture, but compromising the centralized entity can result in compromising the entire network. For storing data and running ID system in a decentralized manner, blockchain technology is executed to increase trust between the peers. With this decentralized blockchain architecture each node will be able to find out whether the peers in the group are malicious or not. So, we propose a architecture, IDBIoT (Intrusion Detector for Blockchain based IoT networks) for Intrusion detection in an IoT environment, that mainly focuses on the securing the network by detecting intruders, using statistical significance while preventing Goldfinger attack.
Inference of Biomedical Relations Among Chemicals, Genes, Diseases, and Symptoms Using Knowledge Representation Learning

Knowledge representation learning represents entities and relations of knowledge graph in a continuous low-dimensional semantic space. Recently, various representation learning models have successfully been developed to infer novel relations in general-purpose knowledge bases such as FreeBase and WordNet. However, few studies have used such models for biomedical data for inferring useful relations among biomedical entities such as genes, chemicals, diseases, and symptoms. This study aimed to compare the potential of representation learning models in extracting biomedical relations by using four different types of representation learning models, viz., TransE, PTransE, TransR, and TransH. For training and evaluating the models, we collected and utilized manually curated data from public databases, including relations among chemicals, genes, diseases, and symptoms. Overall, TransE, the most efficient translation-based monolingual knowledge graph embedding model, displayed the best performance with a higher learning speed for large-scale biomedical data. Using TransE, we inferred new relations among chemicals, genes, diseases, and symptoms, and evaluated the reliability of these inferred relations. Furthermore, TransE outperformed an existing statistical method used in the Comparative Toxicogenomics Database for inferring new chemical-disease relations. Together, the present results show that the representation learning model is useful for inferring new biological data from numerous existing biomedical data.
Development of Tank-Based Military Robot and Object Tracker

Indonesia is a country whose economy is developing very fast. According to World Bank data, Indonesia is the country with the largest economic development in Southeast Asia. Unfortunately, the threat of terrorism in Indonesia is very large, therefore it requires a smart system such as military robots. Today’s vision-based robot technology has also been used in the development of autonomous military robots. A sophisticated military robot is a robot that is needed by the military/police because it can be deployed to the battlefield or the eradication of terrorism in a remote or autonomous manner. This system is needed to reduce the remaining casualties from the army, and this combat robot system can also be operated at any time with more numbers than regular soldiers and with minimal operator needs. In this paper, we propose a prototype of a tank-based military robot with object detection and tracking and turrets for simulation of shooting the enemy target based on the computer vision. The object tracker will detect an upper body of the target using a library in OpenCV and the tank will track until it reaches the best position to shoot the target. The methods explained, and experimental results were presented.
$Q$-learning Based Network Selection Mechanism for CRNs with Secrecy Provisioning

In near future, the spectrum resources shared by primary networks to spectrum pools are likely to use unlicensed band because of its increasing popularity. Due to the open access nature, in unlicensed band communication, eavesdroppers are capable of overhearing the traffic channels. This may lead to significant throughput degradation. Existing network selection mechanisms for cognitive radio networks (CRNs) do not consider this security threat adequately. In this work, we first propose a $Q$-learning based throughput estimation mechanism considering the possibility of throughput degradation caused by eavesdroppers. Then, based on estimated throughput values, we formulate the network selection problem in orthogonal frequency division multiple access (OFDMA) based CRNs as an integer linear program (ILP). Next, based on the ILP formulation, we propose a greedy algorithm for network selections in OFDMA based CRNs. Finally, performance of our proposed network selection mechanism has been compared with existing approaches through system level simulations.
Enhancement of Underwater Images using Improved CLAHE

Basically, the contrast and sharpness of the images captured in underwater will be significantly deteriorated and diminished caused by the less perceptibility of the image which is due to the water medium’s physical properties. In this work, Improved version of CLAHE mechanism is used. In this technique, First of all input color image gets enhanced by using dynamic histogram equalization technique and then apply CLAHE technique to improve contrast of an input image. DHE technique enhances an input image without loss of imput image details. Proposed experimental results show that the accuracy of proposed technique is much more better than the previously proposed technique(Tri-threshold based fuzzy intensification operator) for under water images in terms of PSNR and entropy value.
The Application of the Internet of Things Technology in Apple Production

To improve the yield and quality of apples, reduce the management cost in apple orchard, the introduction of the Internet of Things (IOT) technology in fruit tree management can perceive every segment in fruit tree management and provide the basic data support. The IOT technology can help to monitor the environment information of apple orchards and the detailed data of apple growth, which is useful for management suggestion and planting decision. Therefore, the application of IOT technology in apple orchard can make the apple tree management more scientific and intelligent.
An Enhanced Radio Tomographic Imaging Localization Method Based on Low-cost Wireless Sensor Networks

As one of the main methods of device free localization (DFL) that can locate a target without attaching any devices, the radio tomographic imaging (RTI) method based on the low-cost wireless sensor network (WSN) has wide application prospects. The current RTI positioning methods mainly determine a target position by searching the maximum brightness pixel in a RTI map. However, due to the existence of environmental noises and interference, the brightness of the pseudo-target is often greater than that of the real target, resulting in a misjudgment of the target and even a great positioning error. In this paper, an enhanced RTI (E-RTI) positioning method that combines target shape features with the target brightness feature is proposed for overcoming this problem. This method can effectively reduce the negative influence of pseudo-targets and environmental noises by using the target shape prior information. The experimental results show that the positioning accuracy of this method is better than the existing RTI method.
Dynamically Tunable Dual-Frequency Terahertz Absorber Based on Graphene Rings

A dual-frequency absorber with periodic double-layer graphene ring arrays separated by dielectric spacer is proposed. Coupled-mode theory and finite difference time domain method are used to analyze the perfect absorption and the absorption mechanism. Moreover, the influences of structural parameters and chemical potential of graphene on the peak positions have been investigated. Benefiting from chemical potential can be controlled by external bias voltage, thus the proposed structure can be used as a dynamically tunable absorber. Such simple absorber has potential applications in optical storage devices and frequency-selective detectors for terahertz regime.
The Rebirth of the Current Source Inverter: Advantages for Aerospace Motor Design

It is well known and widely accepted that the voltage source inverter (VSI) now dominates the world of electrical drives. Its success is probably due to its simplicity, high efficiency, and the widespread availability of VSs. This popularity has, in turn, influenced the evolution of the semiconductor industry, which has focused in recent years on devices tailored for VSIs. Thus, products such as depletion devices (normally off) and those without reverse voltage blocking have been widely marketed and used.
A Novel Nuclei Segmentation Algorithm Based on Convolutional Neural Network

With the continuous improvement of medical diagnostic techniques, the segmentation of the nucleus has important diagnostic significance in the examination of clinical pathology. However, as the clinical proportion increases, the traditional segmentation technique (manual segmentation) becomes a cumbersome and unstable method because of its time consuming and the technical level of the pathologists. On the other hand, the detection of nuclear equipment required for manual segmentation is extremely expensive and the requirements of the operating environment are badly demanding. Compared with traditional image processing technology, deep learning based segmentation technology can better summarize the appearance of nuclei. At present, the most prominent deep learning semantic segmentation network architecture is U-Net. Here, in this paper we propose a novel convolutional neural network combined with the existing segmentation technologies to independently learn the pixel-level prior information in the nuclei. The special connection method adopted by its internal structure can extract the scale information of different network layers. The proposed architecture is compared with the classical U-Net and its transforms on nuclei dataset from kaggle data science bowl 2018 dataset fixes. Experiments show that the proposed method shows higher performance than other networks in accuracy, Jaccard index and F1 score. Our network has more prominent advantages for the treatment of irregular nuclei.
Prediction of Potato Disease from Leaves using Deep Convolution Neural Network towards a Digital Agricultural System

Potato is one of the most used crops in the world and 2nd most important crop in Bangladesh. Our economy is largely affected by the production of potato. But its production is hampered due to different diseases of potato leaves. These diseases decrease production and increase the price of potatoes. Our objective is to develop an automated system which will predict the potato disease and helps farmers to take necessary steps. In this work, we implemented a model based on Convolutional Neural Network (CNN) which provides 98.33% accurate result in predicting different diseases of potatoes. This is the maximum accuracy gained for only potato disease prediction to the best of our understanding. The system is cost effective, less time consuming and provides an efficient way of predicting potato diseases from leaves. This will help the farmers and lead our country towards a digital agricultural system.
Moviefication: Teaching Activities Based on Theory of Multiple Intelligences

Knowledge is gained in stages according to Bloom’s Taxonomy. Teaching and learning activities expedite knowledge gaining. The aim of this research is to implement Multiple Intelligence (MI) theory based teaching activities for learning. There are eight categories of smartness possessed by human beings viz., verbal, logical, naturalistic, visual, kinesthetic, musical, inter-personal and intra-personal. Good movies make use of verbal smartness in dialogues, logical smartness in storytelling, nature smartness in cinematography, visual smartness in screenplay, kinesthetic smartness in dance, fight scenes & body languages, musical smartness in songs and interpersonal & intrapersonal smartness in characters that portray main roles. It adapts the concept of multiple intelligence theory to attract the audience. Moviefication is defined as the method of using the movie concept for teaching/learning process. This research paper proposes teaching activities based on multiple intelligence theory matching the smartness of the learners. The moviefication resulted in increased engagement, learning and it made learning more interesting. Performance of the learners has been improved by more than 15% in attendance, average marks, internship and placement salary package.