Chinese sausage is traditional air-dry sausage that loved by the Chinese community around the globe. This sausage can be served alone after steam cook or cook with other ingredients to create other tasty Chinese delic...
Chinese sausage is traditional air-dry sausage that loved by the Chinese community around the globe. This sausage can be served alone after steam cook or cook with other ingredients to create other tasty Chinese delicacies. However, a manual hand cutting process is required to cut out the sausage linking twist during the sausage's packaging phase. This hand cutting process is tedious, time consuming and danger to workers. This study has proposed two types of sausage casing twist detector using image processing technique based on HSL and RGB colour characteristic and a set of blob detection technique to detect sausage casing twist blob on the output image. Hue, green-blue and red-blue colour characteristics are found being significant to represent the sausage casing twist in the sample images and used in the proposed algorithms. The RGB based algorithm is capable to produce a low noise image with SNR of 3.73dB as compared to Hue-based detector at -8.89dB which unsuccessful to remove shadow and object outlining of the output image. The proposed blob detection algorithm is able to detect 73.02% of the blob in hue-based image while 94.18% in RGB based image. The false detection rate in hue-based image has reached 333.33% compared to 12.17 % in RGB based image.
Breakthroughs in the smart grid and promotion of 'green communication' have encouraged wireless communication network to harness the local environment's resources and to function economic and ecological be...
Breakthroughs in the smart grid and promotion of 'green communication' have encouraged wireless communication network to harness the local environment's resources and to function economic and ecological benefits in an energy-efficient manner. This paper presents a comtemporary review of recent advancement of energy trading including renewable energy sources, types of energy trading and the advantages of having energy trading.
RGB colour model is a basic colour model and complements together to produce full colour range but it is unable to produce sufficient information for digital image analysis. However, HSL is capable to provide other us...
RGB colour model is a basic colour model and complements together to produce full colour range but it is unable to produce sufficient information for digital image analysis. However, HSL is capable to provide other useful information such as colour in degree, saturation of the colour and brightness of colour. In this work, RGB to HSL mathematical conversion algorithm is implemented on FPGA chip. Parallelism and pipelining capabilities of FPGA helps to speed up conversion performance. The RGB to HSL equation is implemented by using two architectures which are parallel and 7-stages pipeline architectures. The designed parallel and pipeline converters have one clock and seven clock cycle of data latency respectively. The parallel and pipeline architectures for RGB to HSL converter have been achieved rate of accuracy by hardware verification up to 99% and 98% and possessed maximum operating frequency merit of 50 MHz and 120 MHz respectively.
作者：
F MokhtarR NgadiranEmbedded
Networks and Advanced Computing Research Cluster (ENAC) School of Computer and Communication Engineering Universiti Malaysia Perlis Pauh Putra Campus 02600 Arau Perlis MY.
The research on image quality assessment (IQA) has been become a hot topic in image processing. Many studies show that HVS edge information plays crucial role when human perceive the quality of an image. The proposed ...
The research on image quality assessment (IQA) has been become a hot topic in image processing. Many studies show that HVS edge information plays crucial role when human perceive the quality of an image. The proposed metric is called HEPSI. The method from HaarPSI metric is combined with edge structural similarity and a contrast map is added for pooling the structural similarity map, Validation is taken by comparing HEPSI with the well-known state-of-the-art IQA metrics: PSNR, SSIM, MSSIM, FSIM and HaarPSI over the LIVE database. Experiment shows that HEPSI achieved better performance than other 5 IQA metrics.
It is undeniable that advances in technology and the rapid spread of information are also accompanied by an increase of crime in the IT field, very valuable information is sought by criminals in the IT field in order ...
It is undeniable that advances in technology and the rapid spread of information are also accompanied by an increase of crime in the IT field, very valuable information is sought by criminals in the IT field in order to be misused so that they gain enormous profits. One of the information is student value data, so a system is needed that applies a cryptographic algorithm that can secure the information in this case the method used is securing student value data using the Data Encryption Standard (DES) algorithm. DES is asymmetrical cryptographic algorithm and is also classified as a cipher block with a 64-bit key size. DES converts plaintext into a ciphertext of the same size, 64 bits using a 56-bit internal key. By building a system to implement the DES algorithm in securing data values, it is hoped that it can help the Faculty of Medicine, University of North Sumatra in protecting the confidentiality of data values from irresponsible parties
The low-noise amplifier (LNA) is a vital part of the radio frequency (RF) transceiver system. It amplifies weak signals with minimal distortion. The LNA performance is mainly determined by its noise figure (NF), gain,...
The low-noise amplifier (LNA) is a vital part of the radio frequency (RF) transceiver system. It amplifies weak signals with minimal distortion. The LNA performance is mainly determined by its noise figure (NF), gain, and power consumption. In this paper, the design of a 6 GHz low-noise amplifier (LNA) using enhancement-mode pseudomorphic high-electron-mobility transistor (E-pHEMT) technology is presented. In order to attain high gain with low S-parameters losses, a two-stage LNA configuration with single-stub matching is devised. The same bias conditions are applied to both of the LNA stages, VDS = 2.7 V and IDS = 10 mA. The LNA design is simulated and optimised by using electromagnetic (EM) software. To further improve the overall LNA performances, high impedance inductors and series resonators are implemented into the circuit. Simulated results of the designed LNA indicate a power gain, S21 of 25.2 dB and NF of 2.4 dB at 6 GHz with 27 mW dissipation per stage. The circuit layout is fulfilled with an E-pHEMT technology (ATF-55143) on the FR4 substrate. The LNA is powered by a 3 V DC power supply.
作者：
F KamsainiM S RazalliS Z IbrahimM Z IlyasEmbedded
Networks and Advanced Computing Research Cluster (ENAC) School of Computer and Communication Engineering (SCCE) Universiti Malaysia Perlis (UniMAP) Pauh Putra Campus 02600 Arau Perlis MY.
A low-noise amplifier (LNA) plays an indispensable role in a communication system for amplification purposes at the transceiver. LNA design in radio frequency (RF) circuits involve various key attributes, namely noise...
A low-noise amplifier (LNA) plays an indispensable role in a communication system for amplification purposes at the transceiver. LNA design in radio frequency (RF) circuits involve various key attributes, namely noise figure (NF), gain, and power consumption. This paper focuses on the design of a C-band LNA for satellite communication system with a centre frequency of 6 GHz using ATF-55143 enhancement-mode pseudomorphic high-electron-mobility transistor (E-pHEMT) technology. The LNA performance is augmented by adding inductors to the drain and gate of the ATF-55143 transistor. Smith chart impedance matching technique is implemented to foster a more precise matching for input and output of the LNA. In this work, the C-band LNA is biased at VDS of 2.7 V and IDS of 10 mA. Electromagnetic (EM) software is used to design and simulate the performance of the LNA circuit layout. Simulation results indicate NF of 2.66 dB and power gain (S21) of 12.29 dB. The LNA consumes 27 mW from a 3 V DC supply.
Despite the description of schedule type which can be easily found at any OpenMP reference material, little is known about the effect of different schedule type and chunk size on the parallel performance of shared mem...
Despite the description of schedule type which can be easily found at any OpenMP reference material, little is known about the effect of different schedule type and chunk size on the parallel performance of shared memory multicore processor. Literature shows that performance analysis on different multicore platform overlooked the effect of different schedule type and chunk size, where often it was not explicitly specified. Hence, default assignment of the loop iterations among threads is assumed. By default, static schedule is used and size of chunk which is the ratio of total number of iteration to the number of threads is implemented. This research analyses the effect of different schedule type and chunk size on speedup achieved of different shared memory multicore platform under regular workload. Apart from that, the performance gain obtained after turning on/off certain multicore technologies and after turning on/off selected number of active cores per processor is also analysed. Results shows that different multicore technology exhibit different speedup value under different combination of schedule type and chunk size. Apart from that, it also observe that different multicore platform is better than the other in terms of speedup as the number of cores are increased.
Recent standard ripeness classification for mango is via manual inspection by human naked eyes. However, the manual mango ripeness classification in agricultural setting has several drawbacks which need labor intensiv...
Recent standard ripeness classification for mango is via manual inspection by human naked eyes. However, the manual mango ripeness classification in agricultural setting has several drawbacks which need labor intensive, inconsistent, prone to error and it is also a time consuming process. Based on an extensive literature search, study to extract data patterns from mango images has never been conducted. Data pattern extraction or generally known as discretization, is one of data pre-processing method that stimulates classification. This paper presents the work on discretization that promotes classification process of mango (Mangifera Indica L.) dataset. Comparison between existing swarm-based discretization algorithms on mango dataset is studied throughout this paper in order to avoid inefficient manual effort and provide an improvement for future research in agricultural industry. The swarm-based discretization algorithm implemented on extracted features from mango images has reduced both discretization time and error rate concurrently. Hence, it generates good generalization of the data pattern to the extracted mango features. As a consequence, determining discretized data patterns from the extracted mango images may improve the entire classification process in terms of accuracy and learning time.
Network monitoring system consists of large data streams, distributed architecture, and multiple computers that are geographically located all over the world caused a difficulty to detect abnormalities in the system. ...
Network monitoring system consists of large data streams, distributed architecture, and multiple computers that are geographically located all over the world caused a difficulty to detect abnormalities in the system. In addition, when handling network traffic, the data in network is fast incoming and requires an online learning where immediately response and predict the pattern of network traffic for classification once there is an event or request occur. Therefore, this paper aims to develop an effective and efficient network anomaly detection system by using distributed online averaged one dependence estimator (DOAODE) classification algorithm for multi-class network data to overcome these issues. The finding of DOAODE algorithm for multi-class classification is high in accuracy with average 83% and fast to train the network traffic recorded less than ten seconds and takes shorter time when the number of nodes increases.
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