1,720,995 research outputs found
Optimal kernel design of smooth-windowed wigner-ville distribution for digital communication signal
No full textBilinear time-frequency analysis has been widely used to analyze time-varying signals such as in speech, music and other acoustical signals, sonar, radar, geophysics and biological signals. However, a major drawback of this method is the presence of cross-terms in the time-frequency representations (TFR’s) [1]. These terms, if not removed, will reduce the auto-terms resolution and make interpretation of the true signal characteristics difficult [2]. To overcome this, most of the TFD’s employ some kind of smoothing kernel, window, or filter [3]. Smoothing however, causes the autoterms to be smeared and as a result, the TFR losses its concentration [4]. For signal analysis and classification, an optimal distribution should have reasonable cross-terms suppression and minimal smearing of the auto-terms. Previous works have shown that the optimal kernel is signal-dependant [2,3,5]. Generally, there is no known practical fixed kernel TFD which would perform well for all signals. A kernel might perform very well for a certain class of signal but is not optimal for other type of signals. Most of the researches in optimal kernel design focus mainly on linear FM [2,3,5,6] and biological signals [7,8]. Not much attention has been given to digital communication signals
Feature based vessels classifications from partial view image
No full textAutomatic object recognition has diverse applications in various fields of science and technology ranging from military to civilian industries. It can provide better tracking and automatic monitoring to control from potential enemy ships. Classification of objects based on their silhouettes is particularly useful in autonomous ship recognition. However, problem arises when part of object becomes invisible (e.g. due to partially shifting out of view) or partially occluded by with another silhouette; see Figure 1 for an example. For clipping conditions, the shift level indicates the fraction of columns by which the object has been shifted to the right. While for occlusion, the overlap level indicates the fraction of columns which have been corrupted by the secondary silhouette, which may be stationary. In this case, when moving detection algorithm is involved , only parts of the moving vessel will appear
Design and analysis of adaptive coding schemes for multipath fading channels
No full textWireless communication is characterized by multipath fading, path loss and interference due to white noise. Multipath fading results in time variation of the signal amplitude and phase with time delay that minimize the reliability of communications by increasing the bit-error rate (BER) in data transmission [1]. In order to minimize the BER, there are many types of modulation and error control techniques were introduce and with appropriate combination, optimize wireless system can be achieved. Several features have been identified and recommended in [2] to maximize system capabilities and adaptive data communication system is one of them to ensure the system deliver high throughput and robust in good and poor channels respectively. This is accomplished by varying the modulation and coding scheme according to the channel condition [3]. Usually, an adaptive data communication includes additional features such as sounding or control channel and link quality analysis (LQA) used to estimate channel condition and decide which modulation and channel coding suitable for appropriate time. The parameters estimated are bit-error rate (BER), packet-error rate (PER) and instantaneous signal-to-noise ratio (SNR) [4,5]
Features construction for starfruit quality inspection
Get PDFUp to present, the starfruit quality inspection process is performed manually. Manual inspection will cause inconsistency in quality due to human subjective nature, slow processing and labor intensive. Hence, this thesis presents automation process development for the starfruit quality inspection in terms of techniques and algorithms design based on image processing. Basically, there are three main processes of the starfruit quality inspection discussed in this thesis, which are the maturity index classification, skin defect estimation and shape defect estimation. Throughout these processes, new features constructed based on colors and shape are proposed. In maturity index classification, a two-color feature, M, is proposed to differentiate six maturity indices of the starfruit. With the two-color feature, one third of computational data is reduced compared to the typical 3-color features. For skin defect estimation process, a new gray level co-occurrence matrix (GLCM) statistical feature is introduced. This feature has the ability to segment skin defect areas on non-homogenous in illumination and color of the starfruit image. As the GLCM consumes high computation, this thesis proposed a new algorithm based on Haar wavelet that reduces computational burden. Lastly, a shape-based feature is constructed for the shape defect estimation process where a modification of Melkmen convex hull algorithm is designed in order to construct the feature. Experimental results prove that these features are able to convey the three main processes of the starfruit quality inspection process where high accuracies were achieved; 93.33% for the maturity index classification feature, 82% for the skin defect estimation feature and 96% for the shape defect estimation feature
Linear one-dimensional color feature for starfruit classification
No full textMalaysia has been the largest exporter of starfruits in the world since 1989 [1]. The biggest starfruit farm has also been setup in Selangor in 2002 [2]. It becomes a serious production because the fruit is not only to be fond among Malaysian but also to the other communities over the world. Over the years, significant export growth has been recorded. Malaysia’s export figures for 2000 and 2001 are 8,745 metric ton and 9,182 metric ton respectively. This is more than 60% increase from Malaysia’s export in 1991, which was 2,723 metric ton [3]. Some of the major starfruit importers include the Netherlands, Germany, Singapore and Hong Kong. These four major importer countries contributed 82.28% to Malaysia’s starfruit export in 2003 [3]. As an export commodity, the production of good quality starfruit is vital because most of the importer countries are a quality conscious customer. These countries are generally less price conscious and they are willing to pay more for good quality exotic tropical fruits such as the starfruit. Thus, an effort towards the best quality production of the starfruit should be discovered. Besides, it will complement Malaysia’s ambition in expanding the agriculture products to support growth in the economy
Shape defect estimation based on closed curve convex hull
No full textShape representation is a well-researched domain, which plays an important role in many applications ranging from image analysis and pattern recognition to computer graphics and computer animation. Therefore, many methods for shape representation exist in the literature. One of the techniques for shape representation is based on convex hull. The convex hull of a shape is the smallest polygon that positions the entire points of the input shape within the polygon [1]. In this work, a starfruit shape defect estimation technique based on convex hull is presented. The aim is to transform the input shape into convex hull with better efficiency compared to the previous technique. Based on the resulting convex hull, the shape defect of the starfruit will be quantified. The convex hull algorithm has been introduced as early as 1972 by Graham [10]. Then, few other algorithms have been introduced [3, 4]. The problem with these early-introduced algorithms is their low computational efficiency because of the involvement of sorting process of the input points. Sorting the input points consumes O(n log n) running time and it is less memory efficient, as it requires an extra temporary array. After the sorting process, the algorithm employs a stack-based method to form the convex hull, which runs in just O(n) time
Design & development of street light monitoring and management system
Get PDFThe management of streetlights by the power utility company and local authorities are typically faced with the problem of high operational expenditure, low efficiency and increase customer complaint. They are also faced with increase customer complaint due to unattended faulty streets lights and frequent power outages. There is a significant pressure to reduce these operational expenses, improve efficiency and image. By operational efficiency we meant how faulty street lights are managed effectively through the use of a low cost automated system, thus improve efficiency and enhancing customer services. Operational cost reduction is achieved through accurately identification of faulty lights and timely action taken to rectify such fault. Currently these maintenance routines [i.e. random patrols around the street light zones] are conducted daily in parallel to records of faulty lights reported by customers. These incurred substantially high operational expenditure year to year. The objective of this project is to develop a low cost SLM system with features suffice enough for the utility companies to effectively manage and maintain street lights and also monitor power quality to ensure continuous and uninterrupted supply to customers both residential and industries. SLMS consist of interface modules (FC-Feeder Controller, FIU-Feeder Interface Units) installed at the substation or streetlights panel to collect the status of power over each feeder pillar. Information of faulty lights collected by the FIU (which will measure the current/power on the feeder and record any changes e.g. a drop in power indicating a faulty light in that feeder line) is passed back to FC using the PLC (Power line carrier) technique. The FC manages several feeder pillars and relay back the information received from FIU to a management system server located at the office (RCC-Regional Control Center & NCCNational Control Center) using GSM network. RCC then sends an alert SMS to operational personnel to inform them of the faulty record. The management system keeps records for management reporting and analysis
Image segmentation based on cooccurrence matrix edge information
No full textThresholding techniques are segmentation technique used to segment images consisting of dark objects against bright backgrounds, or vice versa. It also offers data compression and fast data processing [1]. The simplest way is through a technique called global thresholding, where one threshold value is selected for the entire image, which is obtained from the global information. However, when the background has non-uniform illumination, a fixed (or global) threshold value will poorly segment the image. Thus, a local threshold value that changes dynamically over the image is needed. This technique is called adaptive thresholding. Many works have been done to formulate the best technique for the adaptive thresholding to accommodate image conditions such as non-uniform illumination, noisy image and complex background [1-15]. Basically these techniques can be divided into region-based and edge-based thresholding. Regionbased technique uses the whole image to extract the information for the threshold value computation, while edge-based technique is based on the attriibutes along the contour between the object and the background
Low-resolution image classification of cracked concrete surface using decision tree technique
No full textCracks are essential for assessing the quality of concrete structures since they influence the structure’s safety, application, and durability. Cracks on the concrete surface are one of the earliest signs of structural damage, and detecting the crack is essential for maintenance. The first step in a manual examination is to sketch the crack and note the conditions. A lack of impartiality in quantitative analysis from the manual approach is utterly reliant on the specialist’s knowledge and experience. As an alternative, automated image-based crack detection is suggested. There are many features extraction and classification techniques available for crack detection, including the k-nearest neighbors (KNN), Artificial neural network (ANN), and Decision Tree (DT). This paper aims to detect the building cracks using low-resolution images where KNN, ANN, and DT were trained and evaluated with different images sizes of 50 × 50, 35 × 35, 25 × 25, 10 × 10, and 5 × 5. On the sample images 50 × 50 and 5 × 5, the DT classification approach produced the highest precision values of around 90% to 95%, compared to the other two techniques, KNN and ANN, which provided 76% to 86% and 93 to 88%, respectively. The new findings revealed that KNN, ANN, and DT algorithms give high accuracy with the low-resolution image of 5 × 5 as with the higher resolution image of 50 × 50
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