International Journal of Engineering and Manufacturing @ijem
Статьи журнала - International Journal of Engineering and Manufacturing
Все статьи: 520
Guiding Aid for Visually Impaired
Статья научная
Visual impairment is where the person either can’t see or his vision has weakened to large extent. There is no alternative technique for visually impairment, but to some extent it can be trim down with devices, smart sticks and sensors. Although many techniques are there for helping out through electronic travelling aid, cost effective and minimum hardware solution was the expectation by impaired. The device which can identify and classify the object ahead of impaired person is needed so that person can be prevented from the accident. In this paper, a unified model of YOLO (You Only Look Once) is used for detection of object ahead of camera. The proposed model is based on phenomena of detecting small object and good detection speed of yolov3 makes system more robust. Once detected, labeled objects name is converted from text to speech, so that blind person can be alerted from colliding with obstacles. This paper is one step in the direction to help them by exactly classifying, detecting and localizing target object along with providing voice based guideline. The proposed model has proved accuracy in many real time scenes.
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HKCHB: Meta-heuristic Algorithm for Task Scheduling and Load Balancing in Cloud-fog Computing
Статья научная
Cloud-fog computing has emerged as the contemporary approach for processing and analyzing Internet of Things applications due to its ability to offer remote resources. Cloud fog computing technology provides shared resources, information, and software packages, supporting distributed parallel systems in an open environment. It constructs and manages virtual machines to enhance efficiency and attractiveness. We have consistently strived to tackle challenges affecting the efficiency of cloud fog computing, including ineffective resource utilization and response times. The improvement of these challenges can be achieved through effective task scheduling and load balancing between Virtual Machines, this problem considered as NP-hard problem. This paper proposes a Hybrid K-means Clustering Honey Bee algorithm (HKCHB) to cluster Virtual Machines into two or more clusters. Subsequently, the hybrid Honey Bee algorithm is employed for task scheduling, enhancing load balance performance. The proposed algorithm is compared with other task scheduling and load balancing algorithms, including Round Robin, Ant Colony, Honey Bee, and Particle Swarm Optimization Algorithm, utilizing the CloudSim Simulator. The results demonstrate the superiority of the proposed algorithm, yielding the lowest response time. Specifically, the response time is reduced by 22.1%, and processing time is reduced by 47.9%, while throughput is increased by 95.4%. These improvements are observed under the assumption of multiple tasks in a heterogeneous environment, utilizing one or two Data Centers with Virtual Machines. This contribution gives the impression that network systems based on the Internet of Things and cloud fog computing will be improved in the future to operate within the framework of real-time systems with high efficiency.
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Hand Arm Vibration Alleviation of Motorcycle Handlebar using Particle Damper
Статья научная
Vibration induced in the vehicle affects the performance of the driver or rider because of reduction in comfort & safety level. In case of motorcycle, poor suspensions system & uneven road condition make driving difficult, these also introduces vibration. The vibrations are directly transferred to the body through the seat & handlebar. It has been seen that handlebar vibrations are more serious & creates physical problem to the rider. Particle damping technology is a derivative of impact damping with several advantages. Particle damping is the use of particles moving freely in a cavity to produce a damping effect. In this paper a passive damper using particle damping technique is designed and developed to reduce the hand arm vibrations (HAV). The experiments are planned and conducted using DOE. Optimum configuration of particle damper has been derived through this research work. Experimental tests shows by employing a particle damper the vibration amplitude is minimized significantly.
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Статья научная
The World is moving toward Smart traffic management and monitoring technologies. Vehicle detection and classification are the two important features of intelligent transportation system. Several algorithms for detection of vehicles such as Sobel, Prewitt, and Robert etc. but due to their less accuracy and sensitivity to noise they could not detect vehicles clearly. In this paper, a simple and rapid prototyping approach for vehicle detection and classification using MATLAB Xilinx system generator and Zedboard is presented. The Simulink model of vehicle detection and classification is designed using a complex canny edge detection algorithm for vehicle detection. The canny edge detection algorithm offers 91% accuracy as compared to its counterpart Sobel and Perwitt algorithms that offer 79.4% and 76.1% accuracy. The feature vector approach is used for vehicle classification. The proposed model is simulated and validated in MATLAB. The Canny edge detection and feature vector algorithms for vehicle detection and classification are synthesized through the Xilinx system generator in Zedboard. The proposed design is validated with the existing works. The implementation results reveal that the proposed system for vehicle detection and classification takes only 8 ns of execution time with a 128MHz clock, which is the lowest and optimum calculation period for the smart city.
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Hash Function Construction Based on RBFNN and Chaotic Mapping
Статья научная
One-way Hash function is not only widely used in the aspects of the digital signature, identity authentication and integrity checking, etc. but also the research hotspot in the field of contemporary cryptography. In this paper, it firstly utilized neural network and practiced the chaotic sequences produced by one-dimensional nonlinear mapping. And then, it constructed Hash function with cipherkey by means of altering sequences. One of the advantages of this algorithm is that neural network hides the chaotic mapping relations and make it difficult to obtain mapping directly. Simulation experiment showed that the algorithm have good unidirectionality and weak collision, and stronger confidentiality than the tradition-based Hash function, as well as easy to achieve.
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Healthcare System Technology using Smart Phones and Web Apps (Case Study Iraqi Environment)
Статья научная
In the Past a Few Years, smart devices like smart phones and tablets have radically change in many aspect, started from Entertainment to Shopping services to transfer Money and Banking, the next is Health Services. With the development in information technology And the big development in cloud computing Here smart phones have entered heavily in all aspects of health care. Now with the revolution of the smart devices (smart phones or tablets) and it's applications, there is many applications and tools are available started from attachments that allow to diagnose an infections and Now remotely and continuously monitor each heartbeat , blood pressure readings, the rate and depth of breathing, body temperature, oxygen concentration in the blood, glucose, brain waves, activity, mood, so the end result will be can reduce using of doctor ,also reduce the cost , and give us speed up and give power to patients , so make it possible for Patient to use portable devices (smart phones or tablet) to access their medical information, and achieve the goal to put information technology to work in health care and make the integration of health information technology into primary care .So the using information technology give us the good solution that won’t replace physicians. Health Information technology give the providers of health care to give better manage patient care. By making the health information are available electronically anytime and anywhere is needed, Health Information technology can help us to improve the quality Of Health , so can decrease the cost. Now, after all these advantages should shed light on the side of personal privacy And Hacking side that must have been tested all application and tools, All of these tools must be accurate and needs to be tested. So that must provide the highest level of security and privacy for the patients. After that. The application does not arrive the goal with 100% percent, according to the limitation that mentioned later.
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Heterostructure Silicon and Germanium Alloy Based Thin Film Solar Cell Efficiency Analysis
Статья научная
Thin film solar cell along with enhanced absorption property will be the best, so combination of SiGe alloy is considered. The paper presented here consists of a numerical model of Si/Si1−xGex heterojunction solar cell. The addition of Ge content to Si layer will affect the property of material. The research has investigated characteristics such as short circuit current density (Jsc), generation rate G , absorption coefficient (α), and open circuit voltage (Voc), power, fill factor (FF) with optimal Ge concentration. The speculative determination of appropriate germanium mole fraction is done to get the maximized thin-film solar cell efficiency.
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High Accuracy Swin Transformers for Image-based Wafer Map Defect Detection
Статья научная
A wafer map depicts the location of each die on the wafer and indicates whether it is a Product, Secondary Silicon, or Reject. Detecting defects in Wafer Maps is crucial in order to ensure the integrity of the chips processed in the wafer, as any defect can cause anomalies thus decreasing the overall yield. With the current advances in anomaly detection using various Computer Vision Techniques, Transformer Architecture based Vision models are a prime candidate for identifying wafer defects. In this paper, the performance of Four such Transformer based models – BEiT (BERT Pre-Training of Image Transformers), FNet (Fourier Network), ViT (Vision Transformer) and Swin Transformer (Shifted Window based Transformer) in wafer map defect classification are discussed. Each of these models were individually trained, tested and evaluated with the “MixedWM38” dataset obtained from the online platform, Kaggle. During evaluation, it has been observed that the overall accuracy of the Swin Transformer Network algorithm is the highest, at 97.47%, followed closely by Vision Transformer at 96.77%. The average Recall of Swin Transformer is also 97.54%, which indicates an extremely low encounter of false negatives (24600 ppm) in contrast to true positives, making it less likely to expose defective products in the market.
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Home Occupancy Classification Using Machine Learning Techniques along with Feature Selection
Статья научная
Monitoring systems for electrical appliances have gained massive popularity nowadays. These frameworks can provide consumers with helpful information for energy consumption. Non-intrusive load monitoring (NILM) is the most common method for monitoring a household’s energy profile. This research presents an optimized approach for identifying load needs and improving the identification of NILM occupancy surveillance. Our study suggested implementing a dimensionality reduction algorithm, popularly known as genetic algorithm (GA) along with XGBoost, for optimized occupancy monitoring. This exclusive model can masterly anticipate the usage of appliances with a significantly reduced number of voltage-current characteristics. The proposed NILM approach pre-processed the collected data and validated the anticipation performance by comparing the outcomes with the raw dataset’s performance metrics. While reducing dimensionality from 480 to 238 features, our GA-based NILM approach accomplished the same performance score in terms of accuracy (73%), recall (81%), ROC-AUC Score (0.81), and PR-AUC Score (0.81) like the original dataset. This study demonstrates that introducing GA in NILM techniques can contribute remarkably to reduce computational complexity without compromising performance.
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Hotspot sequence patterns with an improvement in spatial feature
Статья научная
Forest fires in Sumatra and Kalimantan resulted in degradation of peatlands significantly. The strong indicator of forest and land fires including in peatland can be identified using hotspots which occurred consecutively in 2 to 5 days. The previous studies have been conducted in mining sequence patterns on hotspot datasets in Sumatra and Kalimantan. However, those studies applied the sequential pattern algorithms on the datasets containing temporal and rough spatial features. This study aims to generate sequence pattern of hotspot datasets using the SPADE algorithm with the improvement of the spatial feature. The study results in 892 1-frequent sequences and 28 2-frequent sequence patterns at the minimum support of 0.02%. A total of 484 hotspots were found from the 28 2-frequents sequence patterns, most of which were occurred in September to November 2014 and 2015. Central Kalimantan, Riau, and South Sumatra are the area where hotspots mostly occurred in 2014 and 2015. The visualization module for hotspot sequences was successfully developed in two iterations using the JavaScript.
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Human Identification Using Foot Features
Статья научная
The goal of this paper is to investigate a new technique for human identification using foot features. This work can be mainly decomposed into image preprocessing, feature extraction and pattern recognition by Artificial Neural Network (ANN). Foot images are rarely of perfect quality. To obtain good minutiae extraction in foot with varying quality, we conducted preprocessing in form of image enhancement and binarization .To extract features from human foot based on shape geometry of foot boundaries by extracting 16 geometric features from a human foot image. The foot center has been determined, and then the distances between the center point and outer points are measured with different angles .The angles are from 30˚ to 360˚ by increment with 30˚ gradual. The 13th feature that can be extracted is the length of a foot which is defined as the distance between the top point of the foot and the bottom point. The 14th, 15th and 16th are three major features the width of the foot. The first width is passing through center point, therefore, the second widths of foot is measured from the upper part above the center point and third width from the region the center point under the center point at the bottom of the foot. Euclidean distance is used in the proposed system. Artificial Neural network used for recognition. MATLAB version 8.1(R2013a) and windows 7 with 32 bit is used to build the application and performed on pc of core i3 processor, and our test system on 40 persons, results were satisfactory up to more 92.5%.
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Hybrid Solar Power Plant in Saint Martin's Island can Enlarge Tourist Attraction in Bangladesh
Статья научная
Saint Martin's Island is the best tourist spot in Bangladesh and one of the most beautiful tourist places in the world. But the accommodation facilities are not suitable for tourists. The supply of electricity in the hotels is only 4-5 hours from the generator. For the geographical position, the electricity cannot supply from the mainland grid and the cost of electricity is so high and is not favourable to the environment. In this paper, Hybrid system of photovoltaic (PV), diesel generator, battery for generating electricity in the Saint Martin's Island is analyzed for 18 hotels. The main objective of the present study is to determine the optimum size of Hybrid system which can fulfil the requirements of 528 kWh/day primary load with 125 kW peak for 18 hotels in this island. By using HOMER (Hybrid Optimization Model for Electric Renewables) software an optimum model is established for the renewable system. The aim is to configure a renewable system with low interest and low energy cost. The diagrams and tables which show prices and performances of the types of equipment on the optimum model are also presented. The result shows that PV (185 kW), diesel generator (105 kW), converter (96 kW) and 615 piece batteries of the Hybrid system is most commercially reliable and least cost of energy is about 19.48Tk per kWh or $ 0.253 per kWh ($1=77Tk) with total net present cost $ 624,391 or 48,078,107TK. The emission of CO2 is very low in this Hybrid system.
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IOT Based Burglar Detection and Alarming System Using Raspberry Pi
Статья научная
In today’s world, security has become the most difficult task. With increasing urbanization and the growth of big cities, the crime graph is also on the rise. In order to ensure the security and safety of our home while we are away, we propose the use of Raspberry Pi to implement an IOT-based burglar detection and alert system. IoT involves the improvement of networks to efficiently acquire and inspect statistics from different sensors and actuators, then send the statistics via Wi-Fi connection to a personal smartphone or laptop. The concept of antitheft devices has been around for decades, but most are only CCTVs, IP cameras, or magnetic doorbells. There is a limited amount of work devoted to face recognition and weapon detection. The design of anti-theft protection devices relies primarily on face recognition and remote tracking. Here, our objective is to improve this system by incorporating weapon detection feature by image processing. The system uses Raspberry Pi, in which a person is only permitted access to the house if his/her face is recognized by the proposed system, and if he/she does not carry any weapons. From the standpoint of security, this system is more reliable and efficient. The proposed system is intended to develop a secure access control application based on face recognition along with weapon detection. By using the Telegram app, the proprietor can monitor the digital camera mounted on the door frame. As a means of improving the accuracy and efficiency of our system, we use the Python language and the Open CV library.
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Identifying Cross-Site Scripting Attacks Based on URL Analysis
Статья научная
Cross-site scripting (XSS) is one of the major threats to the security of web applications. Many techniques have been taken to prevent XSS. This paper presents an approach to identify Cross-Site Scripting attacks based on URL analysis. The fundamental assumption of our method is that the URL contains a part that can produce a valid JavaScript syntax tree. First, we extract the parameters of the URL to produce a valid JavaScript syntax tree and weight its parsing depth. If its depth exceeds a user-defined threshold, the URL is considered suspicious. Second, to the exception URLs, a second level of defense is formed by analyzing its structure. The experimental results demonstrate that our approach can effectively distinguish most of the malicious URLs from the benign ones.
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Image Denoising by Nonlinear Diffusing on Mixed Curvature
Статья научная
A basic problem in the image denoising is noise pressing and edge preserving, while it is difficult to do well in the two aspects at the same time. The Partial Differential Equation (PDE) based methods, such as nonlinear diffusing method, energy minimal method and active contour method, provide a new choice. Here, focus is put on the classic Total Variation and hypersurface minimal problems, which consider regularizing term of isolevel smoothing and mean curvature. In fact, Total Variation smoothing term works well for preserving clear edges and inefficiently in plain areas, while hypersurface minimal smoothing term does well on denoising in plain areas and excessively on edges causing blurring. A projected isolevel curvature is proposed here just as the Beltrami-Laplace operator to mean curvature, considering the gradient while smoothing and keeping edge sharp effectively. And a mixed curvature of mean curvature and projected isolevel curvature forms by a weighting variable. The new denoising method based on the mixed curvature, smoothing in plain areas of image like hypersurface minimal and on edges like a projected isolevel curvature diffusing. Results of relative experiments indicate the proposed mixed curvature denoising method possesses the merits of the two original.
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Image Mosaics Technology for Video Sequence of Inspecting Security of Pit Shaft
Статья научная
In the vision technique and the wide visual seeing surveillance system; it always happened to actualize multiple images, non-slot merging. Phase Correlation Algorithm (PCA) is widely used as image matching method. Based on lots of simulation of phase correlation algorithm, it makes the image mosaics improve in the computing speed and accuracy.
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Image Recognition Based Autonomous Driving: A Deep Learning Approach
Статья научная
Autonomous vehicle (AV) is a broad field in artificial intelligence which has seen monumental growth in the past decade and this had a significant impact in bridging the gap between the capability the intelligence of human and the efficiency of machines. With millions of people losing their lives, or have being a victim of road traffic accidents. There is a need to find a suitable algorithm for a navigation system in an autonomous vehicle with the purpose of help mitigate the traffic rule violation that most human drivers make that lead leads to traffic accidents. With both researchers and enthusiasts developing several algorithms for AVs, this field has been split into several modules which continually broaden the scope of AV’s technology. In this paper, we focus on the lane navigation which has an important part of the AV movement on the road. Here lane decision making is optimized by using deep learning techniques in creating a Neural Network model that focuses on generating steering commands by taking an image the road mapped out with lane markings. The navigation aid is a front-facing camera mounted and images from the camera are used to compute steering commands. The end to end learning scheme was developed by Nvidia cooperation to train a model to compute steering command from a front-facing camera. The model does not focus on detecting the lane but only generating the appropriate command for steering AVs’ on the road. This focus on one objective of the model helps in maximizing the potential of better accuracy in lane navigation of our AVs. The modeled car navigates through the designed lanes accurately with the level of intelligence the car shows in maneuvering through the lanes shows this method is more suitable in lane navigation.
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Implementation of Edge Detection at Multiple Scales
Статья научная
Edge detection provides a great platform for feature detection which is very useful for applications related to Digital Image Processing and Medical Imaging. Edge detection went through different steps during its life time. There are various operators proposed for edge detection. Some of them are Sobel operator, Prewitt operator, Robert operator, Kirsch operator, Robinson operator, Laplace of Gaussian Operator (LOG) and Canny Operator. Sobel operator, Prewitt operator, Robert operator, Kirsch operator and Robinson operator produces well results in front of quality images but produces bad result in front of noisy images because they have no noise removal filter. For noise removal gaussian filter is mostly used. However Laplace of Gaussian operator and Canny operator use a Gaussian filter for noise removal. The factors which are considered to be most challenging for edge detection are noisy images, direction in which the maximum edges are produced and edge localization. Another factors which are most suitable for finding of appropriate edge detections are Multiscaling and Thresholding. Multiscaling can be done from fine to coarse scale and coarse to fine scale. As far as this paper is concerned this paper provides implementation of edge detection by various edge detection techniques from fine to coarse scale by using Gaussian filter. Different parameter values for Multiscaling and Thresholding were considered and implemented in this paper which is useful for appropriate edge detection. But prior to that we have described various techniques for edge detection. All implementation is performed in MATLAB R2008b using the database of Minear and Parker [7]. The significance of this research is to observe the edges by employing numerous edge detection techniques from fine to coarse scale.
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Implementation of Gas Scathe Admonisher and Control System Prototype
Статья научная
With the advent of and rapidly increasing use of organic, chemical, hazardous, non-hazardous, natural and man-made gases into various industrial processes like food processing industries, oil and gas sector, domestic usage and etc., the detection of sophisticated gas leakage system has become a concern and need of the hour. Chernobyl and Bhopal gas tragedies are some of the horrible examples that lay the foundations for the significance of this project. This paper is based on working model of an automated, low-cost, simple design, computer-based embedded instrumentation system, which can detect leakage of Liquefied Petroleum Gas and Carbon Dioxide Gas in surrounding environment using fast and accurate gas sensing technologies and then provide control action over the surrounding using final control elements to maintain the gas concentration within levels that human body can bear. To accomplish this objective a Graphical User Interface has been developed on LabVIEWTM that is interfaced with the gas sensor modules through Arduino (controller and data acquisition device) to track the real-time gas concentration and energize actuator mechanism to lower down the concentration by turning ON exhaust fans and hence perform function of a 'Control System'. The controlling action is effective and the results are promising. The energizing action is further used to generate alarms of gas leakage and hence this paper is aptly entitled as 'Gas Scathe Admonisher'. Data Acquisition System used in this process overcomes the human intervention and increases the overall efficiency and safety of the system. The Graphical User Interface with detailed information of prototype makes it useful for teaching purposes in laboratories for experimentation.
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Implementation of Support Vector Machine for Identification of Skin Cancer
Статья научная
Skin cancer is one of the most death causing cancer with the increase of infections on skin due to various parameters of the nature, atmosphere and geographical area .The abnormal growth of skin cells has become common in today’s world this abnormal growth is termed as skin cancer. Skin cancer mostly develops on the part of the skin which is exposed to sun light continuously or for long duration develops on body exposed to sun light, but it can occur anywhere on the body. Skin cancer in beginning stage is curable. Patient’s life can be saving from skin cancer by early & fast detection. Early detection of skin cancer in achievable at beginning stage with the new technology. Biopsy method was used to detect the cancer in the earlier days. During biopsy, a small part of the skin tissue is extracted from the carcinoma patient; this part of the tissue will be processed in various laboratories for the identification of the presence of infected cells and the stage at which the cancer is in. Biopsy was a very time consuming and painful for the patients, and the result of biopsy process was not accurate and correct. To overcome the loner procedure and to increase the accuracy Support Vector Machine Algorithm was used in identifying the infection/ Carcinoma at the early stage and cure the infection before it leads to death.
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