Статьи журнала - International Journal of Engineering and Manufacturing
Все статьи: 508
A new approach for RFID tag data reading in FPGA by using UART and FIFO
Статья научная
Nowadays, Radio Frequency Identification (RFID) technology used for automatic object tracking and identifying purpose. RFID technology used in so many applications especially in automobiles, security and health care systems. RFID reader and RFID tags are main processing units in RFID system. RFID tags give the information on what an object is, where it is and even its condition, and what is happening, share related data and respond to the reader. RFID reader module used for reading data from lots of RFID tags. In convention methods [1][2] doesn’t contain any read controller methodology and doesn’t contain any storage element for RFID tags data storing. In this paper, we propose a new approach for RFID tag data reading in FPGA. This approach is more flexible in structure and easily updates the design for any applications. This design is easily adaptable for different chips and communication mechanism. This design has reading controlling methodology and along with storing capability of RFID tags by using FIFO. Here we are using Universal Asynchronous Receiver and Transmitter protocol for communicating RFID reader to PC. For simulation, we are using ModelSim software and for synthesis, purpose using XILINX ISE 14.7.
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A soft computing model of soft biometric traits for gender and ethnicity classification
Статья научная
There is paucity of information on the possibility of ethnicity identification through fingerprint biometric characteristics and so, this work is set to combine two soft biometric traits (Gender and Ethnicity) in order to ascertain if individual of different ethnicity and gender bias can be identified through their fingerprint. Live scan mechanism was used in order to minimize human errors and as well speed up the rate of fingerprint acquisition which unequivocally ensure good quality capturing of the fingerprint image. In this work, fingerprints of over a thousand people from three different ethnic groups of both male and female gender in Nigeria were captured and subjected to training, testing and classification using Gabor filter and K-NN respectively. Histogram equalization was used for image enhancement and the system performance was evaluated on the basis of some selected metrics such as Recognition Accuracy, Average Recognition Time, Specificity and Sensitivity. Result of this work indicated over 96% accuracy in predicting person’s ethnicity and gender with an average recognition time of less than 2secs.
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A study in Tabu Search Algorithm to Solve a Special Vehicle Routing Problem
Статья научная
In this paper, a kind of special vehicle routing problem based on reality-- vehicle routing problem with facultative demands is presented. The attributes of the problem and the optimization target are described. The mathematical model of the problem is set up. To solve the problem, A meta-heuristic approach called tobu search (TS) is put forward. The neighborhood structure and the parameters of TS algorithm are designed respectively. The proposed algorithm is successfully applied to a case and the result indicates the TS algorithm is practicable and valid.
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AI-Based Smart Prediction of Liquid Flow System Using Machine Learning Approach
Статья научная
Predicting the liquid flow rate in the process industry has proved to be a critical problem to solve. To develop a mathematical, in-depth of physics-based prognostics understanding is often required. However, in a complex process control system, sometimes proper knowledge of system behaviour is unavailable, in such cases, the complement model-based prognostics transform into a smart process control system with the help of Artificial Intelligence. In previous research a number of prognostic methods, based on classical intelligence techniques, such as artificial neural networks (ANNs), Fuzzy logic controller, Adaptive Fuzzy inference system (ANFIS) etc., utilized in a liquid flow process model to predict the effectiveness. Due to system complexity, Computational time &over fitting the performance of the AI has been limited. In this work we proposed three machine learning regression model: Random Forest (RF), decision Tree (DT) & linear Regression (LR) to predict the flow rate of a process control system. The effectiveness of the model is evaluated in terms of training time, RMSE, MAE & accuracy. Overall, this study suggested that the Decision Tree outperformed than other two models RF & LR by achieving the maximum accuracy, least RMSE & Computational time is 98.6%, 0.0859 & 0.115 Seconds respectively.
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Absorption, Diffraction and Free Space Path Losses Modeling for the Terahertz Band
Статья научная
With the explosive increase in the data traffic of wireless communication systems and the scarcity of spectrum, terahertz (THz) frequency band is predicted as a hopeful contender to shore up ultra- broadband for the forthcoming beyond fifth generation (5G) communication system. THz frequency band is a bridge between millimeter wave (mmWave) and optical frequency bands. The contribution of this paper is to carry out an in-depth study of the THz channel impairments using mathematical models to evaluate the requirements for designing indoor THz communication systems at 300GHz. Atmospheric absorption loss, diffraction loss and free space path loss were investigated and modeled. Finally, we discuss several potential application scenarios of THz and the essential technical challenges that will be encountered in the future THz communications. Finally, the article finds that propagating in the THz spectrum is strongly dependent on antenna gain.
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Achieving Performability and Reliability of Data Storage in the Internet of Things
Статья научная
Internet of things (IoT) includes a lot of key technologies; In this emerging field, wireless sensors have a key role to play in sensing and collecting measures on the surrounding environment. In the deployment of large-scale observation systems in remote areas, when there is not a permanent connection with the Internet, the network requires distributed storage techniques for increasing the amount of data storage which decreases the probability of data loss. Unlike conventional networked data storage, distributed storage is constrained by the limited resources of the sensors. In this research, we present a distributed data storage method with the combined K-means and PSO clustering mechanism organized with the binary decision tree C4.5 in the IoT area with considering efficiency and reliability approach. This scheme can provide reliability in responding to inquiries while minimizing the use of energy and computational resources. Simulation results and evaluations show that the proposed approach, due to the distributed data storage with minimal repeat publishing according to the decision tree structure, increases the reliability and availability, reduces the communication costs, and improves the Energy consumption, saving memory consumption without registering the same event and compared to other methods performed in this area have good results.
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Статья научная
The feasibility of utilizing an abundant agricultural waste (desert date seed shell) as an alternative low-cost adsorbent for the removal of hazardous basic dyes [crystal violet (CV) and malachite green (MG)] and hexavalent chromium [Cr(VI)] from synthetic industrial effluent was investigated. Five different adsorbents including the raw, carbonized and chemically activated carbons were prepared and screened with respect to adsorption efficiency of the chosen adsorbates. The prepared adsorbents were characterized using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and pH of zero point charge (pHzpc) analyses. The effects of operational variables such as solution pH, contact time and temperature on adsorption have been investigated. The removal of the adsorbates was found to be highly pH-dependent and the optimum pH was determined as 8.0 for the dyes and 2.0 for hexavalent chromium. The screening results revealed that the NaOH activated carbon (NAC) has the best adsorption characteristics with removal efficiencies of 91.10, 99.15 and 91.5 % for CV, MG and Cr(VI), respectively. The process dynamics was evaluated by pseudo-first-order and pseudo-second-order kinetic models. Experimental data have been found to be well in line with the pseudo-second-order model, suggesting therefore, a chemically-based sorption process. Negative Gibbs free energy change (∆G) values obtained from thermodynamic analysis indicated that the adsorption process was spontaneous and had a high feasibility. Positive values for enthalpy change (∆H) showed that the removal process was endothermic, implying that the amount of adsorbate adsorbed increased with increasing reaction temperatures. Additionally, positive values of entropy change (∆S) reflect the high affinity of the adsorbent material to the adsorbates. On the basis of results and their analyses, it has been established that adsorbent derived from desert date seed shell has a promising potential in environmental applications such as removing hazardous substances from industrial effluents. Through this work, it is believed that contributions are provided to the scientific investigations about the decontamination of precious water resources.
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Advancing Road Scene Semantic Segmentation with UNet-EfficientNetb7
Статья научная
Semantic segmentation is an essential tool for autonomous vehicles to comprehend their surroundings. Due to the need for both effectiveness and efficiency, semantic segmentation for autonomous driving is a difficult task. Present-day models’ appealing performances typically come at the cost of extensive computations, which are unacceptable for self-driving vehicles. Deep learning has recently demonstrated significant performance improvements in terms of accuracy. Hence, this work compares U-Net architectures such as UNet-VGG19, UNet-ResNet101, and UNet-EfficientNetb7, combining the effectiveness of compound-scaled VGG19, ResNet101, and EfficientNetb7 as the encoders for feature extraction. And, U-Net decoder is used for regenerating the fine-grained segmentation map. Combining both low-level spatial information and high-level feature information allows for precise segmentation. Our research involves extensive experimentation on diverse datasets, including the CamVid (Cambridge-driving Labeled Video Database) and Cityscapes (a comprehensive road scene understanding dataset). By implementing the UNet-EfficientNetb7 architecture, we achieved notable mean Intersection over Union (mIoU) values of 0.8128 and 0.8659 for the CamVid and Cityscapes datasets, respectively. These results outshine alternative contemporary techniques, underscoring the superior precision and effectiveness of the UNet-EfficientNetb7 model. This study contributes to the field by addressing the crucial challenge of efficient yet accurate semantic segmentation for autonomous driving, offering insights into a model that effectively balances performance and computational demands.
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Статья научная
The increasing urbanization of many third-world cities has led to increased generation of solid wastes which are often ill-managed and indiscriminately dumped, posing grave challenges to local environmental engineers and designers. This has consequently reduced the sustainability of many built and natural African environments. Therefore, this study was aimed at practically applying aesthetics in solid waste management as a means of optimizing sustainability in urbanizing West African environments. Adopting a descriptive approach supported with direct observation, with a total sample size of 610, respondents were purposively sampled in selected research sites in Nigeria. Following one hypothesis testing, the study showed a significant association between improved environmental affordance (derived from aesthetics) and the alleviation of negative user responses to the environment (such as indiscriminate dumping of solid wastes). The study also showed that more aesthetically negative environments offers more negative environmental affordance than positive environmental affordance. The results confirm that the majority of users of the environments (humans) exhibit more positive environmental behaviours when positive affordance is perceived from the environment. The study therefore established the significance of the practical application of aesthetics in the management of solid wastes in urbanizing third world environments.
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Alleviating Unwanted Recommendations Issues in Collaborative Filtering Based Recommender Systems
Статья научная
The overabundance of information on the internet and ecommerce has resulted to the development of recommender system to discover interesting items or contents that are recommendable to the user. The recommended items might be of no interest or unwanted to the users and can make users to lose interest in the recommendations. In this work, a Collaborative Filtering (CF) based method which exploits the initial top-N recommendation lists of an item-based CF algorithm based on unwanted recommendations penalisation is presented. The method utilises a relevance feedback mechanism to solicit for users preferences on the recommendations while popularise similarity function minimises the chances of recommending unwanted items. The work explains the proposed algorithm in detail and demonstrates the improvements required on existing CF to provide some adjustments required to improve subsequent recommendations to users.
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Alternative device for non-ionizing radiation detection
Статья научная
Detection of non-ionizing electromagnetic radiation is central to managing health and environmental hazards resulting from its exposure. This research focused on the design and development of a non-ionizing electromagnetic radiation detector that is sensitive to the medium frequency of 50Hz to 30MHz and their corresponding power density. The device consists of the sensing, amplifying, filtering and microcontroller sections. The sensing section is made up of a coil wound on a ferrite rod, it detects radiations from the surroundings and converts it to a voltage signal. The voltage produced is then fed to the operational amplifiers in the amplifying section. Afterwards, the output signal is fed to the filtering section where unwanted signals are eliminated. The analogue signal output from the active filter is then fed to the microcontroller section where it is converted to a digital signal through the analogue to digital converter (ADC). The ADC then presents the converted signal in a readable form to be displayed on the liquid crystal display (LCD). The developed equipment was calibrated (in µW/cm2) using an existing detector EMF DT 1130. With an average calibration coefficient value of 2.32, the detector was found to perform excellently well at both medium and low-frequency ranges.
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An Adaptive User Authentication Architecture for Drunk Driving and Vehicle Theft Mitigation
Статья научная
The high rate of vehicle theft and the loss of lives occasioned by drunk driving has caused irreparable losses to people and businesses, from a personal, commercial and reputation perspective. Existing systems deployed to mitigate against vehicle theft have all been breached by the ever-adaptive criminals. Drunk driving has been estimated to be a leading cause of deaths on highways and motorways, through preventable accidents. Technology has provided the tools that can aid in mitigating the vices aforementioned with the aim of provisioning lasting solutions. This paper proposes a new architecture for adaptive user authentication in order to mitigate drunk driving and vehicle theft. It considered user authentication in three (3) phases and proposed an authentication architecture for each identified phase, with a step by step description of the implementation method and tools for each phase. The architecture proposed in this study can aid in real time prevention of vehicular theft, unauthorized vehicular access and usage, while also utilizing the benefits of the latest technologies in machine vision and alcohol breadth analyzers to detect and prevent drunk driving, and the associated accidents it causes.
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Статья научная
In this paper we propose a low-rank alternating direction implicit (ADI) method to solve projected generalized continuous-time Sylvester equations with low-rank right-hand sides. Such equations arise in control theory including the computation of inner products and norms, and the model reduction based on balanced truncation for descriptor systems. The requirements of this method are moderate with respect to both computational cost and memory. Numerical experiments presented in this paper show the effectiveness of the proposed method.
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An Analysis on Price Dispersion in Online Retail Market Based on the Different of the Product Levels
Статья научная
This paper cares about the online price dispersion and analyses the online retail commodity as a complex. This paper analyses the attributes of the product layer of the online retail commodity and the characters of the online customers, and divided the online payment into two parts: the payment of the core value and the willingness of the added-value payment. And then this paper explains the online price dispersion by the attributes of online retail commodity’s dimension which affect the online customers directly.
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An Application of Rule-Based Classification with Fuzzy Logic to Image Subtraction
Статья научная
Surveillance camera is used as a new technology for security. In this research, the combination of OpenCV with image processing will be discussed. Saving the space in the hard drive by recording only video when here would be an image formed in the subtraction of the original image to the next image captured. With the use of Image Processing and Fuzzy logic, the research was enhanced by eliminating the recording of same image captured. After analyzing the background images, it can now determine when to start recording the video or when to stop recording a video by subtracting the images in the backdrop image and comparing the image if there was an object in motion using template matching. With the application of the project, memory storage saved up to forty-six percentage points.
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Статья научная
Researches on the relation of a loaded mechanical energy to the damaged banana maturation and its quality evolution has significance to its storage, but there are few researches at present. This paper studied on the effect of the loaded energy on banana to its internal maturation substance evolution and stored quality with time, including the change of maximum impact stress, re-enduring impact energy. The results showed there existed a significant correlation between energy transformation and increment of maturity substance in the damaged banana. Also, there existed a significant association between energy transformation and increment of maturity substance in the damaged banana in its storage time, and appeared a trends that the mechanical energy being transferred into fruit internal energy increased its maturity substance and decreased its stored quality, but individual maturity difference had less effect. Thus, decreasing the energy and the substance transformation in damage area are the important means of extending the physiology life of fruits, which is an urgent problem to be solved in their field of processing and storage at present.
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An Automated Model for Sentimental Analysis Using Long Short-Term Memory-based Deep Learning Model
Статья научная
A post, review, or news article's emotional tone can be automatically ascertained using sentiment analysis, a natural language processing approach. Sorting the text into positive, negative, or neutral categories is the aim of sentiment analysis. Many methods, including rule-based systems and machine learning algorithms, can be used to analyse sentiment, or deep learning models. These techniques typically involve analyzing various features of the text, such as word choice, sentence structure, and context, to identify the overall sentiment. Here long short-term memory-based deep learning is applied in this research for the model development purpose. Deeply interconnected neural networks are used in this method. Sentiment analysis can be used in many different applications, such as market research, brand reputation management, customer feedback analysis, and social media monitoring. It shows the use of sentiment analysis in a variety of fields and increases the need of technology to perform it on the existing machines.
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Статья научная
This study explores the conversion of English to Hindi, first to text, and subsequently to speech. The first part of the implementation is the text recognition from images, in which two approaches are used for text character recognition: a maximally stable extensible region (MSER) and grayscale conversion the second part of the paper deals with the geometric filtering in combination with stroke width transform (SWT). Subsequently, letter/alphabets are grouped to detect text sequences, which are then fragmented into words. Finally, a 96 percent accurate spell check is performed using naive Bayes and decision tree algorithms, followed by the use of optical character recognition (OCR) to digitize. The word Give our text-to-speech synthesizer (TTS) the recognized text to convert it to Hindi language using the text-to-speech model. Based on aspects such speech speed, sound quality, pronunciation, and clarity.
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An Efficient Genetic Algorithm Orienting to the Protein Fold Prediction
Статья научная
Proteins are amino acid chains that acquire their biological and biochemical properties by folding into unique 3-dimensional structures. The biological function of a protein is dependent on the protein folding into the correct, or "native", state. At present, there are so many ideas to predict the structure of the protein folding. This paper first present the concept of protein folding and how is significant to study protein fold prediction. In this paper we join the simulated annealing factor into Parallel Genetic Algorithm and use this hybrid Parallel GA to predict the structure of protein fold. The revised algorithm is more efficient than traditional Genetic Algorithm and simulated annealing algorithm.
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An Empirical Study on Consumers’ Continuance Intention Model of Online Group-buying
Статья научная
This paper examines the factors influencing consumers’ intention to continue using a new pattern of online group-buying, which benefits consumers from high discounts and is prevalent in many countries of the world since 2009. A theoretical model is proposed based on Expectation-Confirmation Model of IS Continuance (ECM-ISC) from the previous IS continuance literature, integrating with e-recovery service quality, perceived risk and two external variables. Data collected from a questionnaire survey of OGB consumers provides empirical support for the proposed model. The results indicate that consumers’ satisfaction with prior use and perceived usefulness significantly influence consumers’ continuance intention. Consumers’ satisfaction is determined primarily by consumers' confirmation of expectation from prior use and secondarily by perceived e-recovery service quality. Further, confirmation also has a significant influence on post-acceptance perceived usefulness. Confirmation, in turn, is significant influenced by both product quality and information quality. Some suggestions for practitioners are also offered.
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