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In the context of efficient generation expansion planning (GEP) and transmission expansion planning (TEP), value assessment method (VAM) is the critical topic to discuss. Presently, two well-known VAMs, min-cut max-flow (MCMF) and load curtailment strategy (LCS), are used for GEP and TEP. MCMF does not follow electrical laws and is unable to calculate congestion cost (CC) and re-dispatch cost (RDC). LCS calculates both, but in iterative way, thus takes a long time to provide solution. In the constrained network, multiple quantities like demand/energy not served (D/ENS) and generation not served (GNS), wheeling loss (WL), CC and RDC are existing together and thus have to be calculated together to encounter the loss in all aspects. Existing methods show limitations in this regard and do not calculate all above described quantities simultaneously. Thus, in this paper, a non-iterative VAM (NVAM) is presented based on electrical laws, which calculates value of the present and the planned systems by incorporating all system quantities of D/ENS, GNS, WL, CC and RDC together. Due to non-iterative batch approach, it is quite faster compared to the above-mentioned traditional VAMs, i.e., MCMF and LCS. Furthermore, comparative results on IEEE-5 bus and IEEE-24 bus power systems show its higher efficiency. The MATLAB code of the introduced NVAM is provided in “Appendix” for further development by the researchers.

The perceptual adaptation of the image (PAI) is introduced by inspiration from Chevreul–Mach Bands (CMB) visual phenomenon. By boosting the CMB assisting illusory effect on boundaries of the regions, PAI adapts the image to the perception of the human visual system and thereof increases the quality of the image. PAI is proposed for application to standard images or the output of any image processing technique. For the implementation of the PAI on the image, an algorithm of morphological filters (MFs) is presented, which geometrically adds the model of CMB effect. Numerical evaluation by improvement ratios of four no-reference image quality assessment (NR-IQA) indexes approves PAI performance where it can be noticeably observed in visual comparisons. Furthermore, PAI is applied as a postprocessing block for classical morphological filtering, weighted morphological filtering, and median morphological filtering in cancelation of salt and pepper, Gaussian, and speckle noise from MRI images, where the above specified NR-IQA indexes validate it. PAI effect on image enhancement is benchmarked upon morphological image sharpening and high-boost filtering.

An image quality index plays an increasingly vital role in image processing applications for dynamic monitoring and quality adjustment, optimization and parameter setting of the imaging systems, and finally benchmarking the image processing techniques. All the above goals highly require a sustainable quantitative measure of image quality. This manuscript analytically reviews the popular reference-based metrics of image quality which have been employed for the evaluation of image enhancement techniques. The efficiency and sustainability of eleven indexes are evaluated and compared in the assessment of image enhancement after the cancellation of speckle, salt and pepper, and Gaussian noises from MRI images separately by a linear filter and three varieties of morphological filters. The results indicate more clarity and sustainability of similarity-based indexes. The direction of designing a universal similarity-based index based on information content of the image is suggested as a future research direction.

Image representation through local descriptors is a research hotspot in computer vision. In this letter, we propose a novel local image descriptor based on intensity permutation and zone division. The oFAST detector is first employed to detect keypoints with orientations, and then steered patterns are applied to sample rotation-invariant points within the local keypoint patch. In the step of local patch description, intensity permutation and zone division are implemented to construct our descriptor, with the advantages of inherent robustness and invariance to monotonic brightness changes. Our proposed algorithm performed well in the experiments on benchmark dataset for descriptor evaluation.

All mobile carriers these days have provided 3G and 4G services to their customers. The evolution of cellular networks from 3G to 4G has improved several performance metrics of the data communications. The focus of this paper is to evaluate two of the most important performance metrics: throughput and delay. We consider the cellular network as an integrate infrastructure that includes mobile and fixed nodes. Based on this model we calculate and analyze the throughput and delay. Our results illustrate that the throughput is increased while the delay is decreased in 4G data network compared to the previous 3G architecture. In addition, we evaluate how the delay affects the security of the network.

Morphological filters (MFs) are composed of two basic operators: dilation and erosion, inspired by natural geometrical dilation and erosion. MFs locally modify geometrical features of the signal/image using a probe resembling a segment of a function/image that is called structuring element. This chapter analytically explains MFs and their inspirational features from natural geometry. The basic theory of MFs in the binary domain is illustrated, and at the sequence, it has been shown how it is extended to the domain of multivalued functions. Each morphological operator is clarified by intuitive geometrical interpretations. Creative natural inspired analogies are deployed to give a clear intuition to readers about the process of each of them. In this regard, binary and grayscale morphological operators and their properties are well defined and depicted via many examples.

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition.

The image perception by human brain through the eyes is not exactly what the eyes receive. In order to have an enhanced view of the received image and more clarity in detail, the brain naturally modifies the color tones in adjacent neighborhoods of colors. A very famous example of this human sight natural modification to the view is the famous Chevreul–Mach bands. In this phenomenon, every bar is filled with one solid level of gray, but human brain perceives narrow bands at the edges with increased contrast which does not reflect the physical reality of solid gray bars. This human visual system action in illusion, highlighting the edges, is inspired here in visual illusory image enhancement (VIIE). An algorithm for the newly introduced VIIE by deploying morphological filters is presented as morphological VIIE (MVIIE). It deploys morphological filters for boosting the same effect on the image edges and aiding human sight by increasing the contrast of the sight. MVIIE algorithm is explained in this chapter. Significant image enhancement, by MVIEE, is approved through the experiments in terms of image quality metrics and visual perception.

Modern data is increasingly complex. High dimensionality, heterogeneity and independent multiple representations are the basic properties of today's data. With increasing sources of data collection, a single object can have multiple representations, which we call views. In this paper we propose a multiview classification technique, which uses fuzzy mapping to obtain maximum similarity between an object and nearest multiview centroids. Our fuzzy mapping based approach obtains a unit L1 hyperplane as a common space for each view. To establish the efficacy of our proposed method we present experimental comparisons with number of baselines on two synthetic and two real-world data sets.

Blind Components Processing (BCP), a novel approach in processing of data (signal, image, etc.) components, is introduced as well some applications to information communications technology (ICT) are proposed. The newly introduced BCP is with capability of deployment orientation in a wider range of applications. The fundamental of BCP is based on Blind Source Separation (BSS), a methodology which searches for unknown sources of mixtures without a prior knowledge of either the sources or the mixing process. Most of the natural, biomedical as well as industrial observed signals are mixtures of different components while the components and the way they mixed are unknown. If we decompose the signal into its components by BSS, then we can process the components separately without interfering the other components signal/data. Such internal access to signal components leads to extraction of plenty of information as well more efficient processing compared to normal signal processing wherein all the structure of the signal is gone under processing and modification. This manuscript besides the introducing BCP, proposes a practical applications of BCP with technical merit for harmonic noise cancellation as well stock pricing model.

This paper proposes a new active learning approach, confidence-based active learning, for training a wide range of classifiers. This approach is based on identifying and annotating uncertain samples. The uncertainty value of each sample is measured by its conditional error. The approach takes advantage of current classifiers' probability preserving and ordering properties. It calibrates the output scores of classifiers to conditional error. Thus, it can estimate the uncertainty value for each input sample according to its output score from a classifier and select only samples with uncertainty value above a user-defined threshold. Even though we cannot guarantee the optimality of the proposed approach, we find it to provide good performance. Compared with existing methods, this approach is robust without additional computational effort. A new active learning method for support vector machines (SVMs) is implemented following this approach. A dynamic bin width allocation method is proposed to accurately estimate sample conditional error and this method adapts to the underlying probabilities. The effectiveness of the proposed approach is demonstrated using synthetic and real data sets and its performance is compared with the widely used least certain active learning method.

The goal of this paper is to describe an exploration system for large image databases in order to help the user understand the database as a whole, discover hidden relationships, and formulate insights with minimum effort. While the proposed system works with any type of low-level feature representation of images, we describe our system using color information. The system is built in three stages: the feature extraction stage in which images are represented in a way that allows efficient storage and retrieval results closer to the human perception; the second stage consists of building a hierarchy of clusters in which the cluster prototype, as the electronic identification ( e ID) of that cluster, is designed to summarize the cluster in a manner that is suited for quick human comprehension of its components. In a formal definition, an electronic IDentification ( e ID) is the most similar image to the other images from a corresponding cluster; that is, the image in the cluster that maximizes the sum of the squares of the similarity values to the other images of that cluster. Besides summarizing the image database to a certain level of detail, an e ID image will be a way to provide access either to another set of e ID images on a lower level of the hierarchy or to a group of perceptually similar images with itself. As a third stage, the multi-dimensional scaling technique is used to provide us with a tool for the visualization of the database at different levels of details. Moreover, it gives the capability for semi-automatic annotation in the sense that the image database is organized in such a way that perceptual similar images are grouped together to form perceptual contexts. As a result, instead of trying to give all possible meanings to an image, the user will interpret and annotate an image in the context in which that image appears, thus dramatically reducing the time taken to annotate large collection of images.

Multi-label classification has received more attention recently in the fields of data mining and machine learning. Though many approaches have been proposed, the critical issue of how to combine single labels to form a multi-label remains challenging. In this work, we propose a novel multi-label classification approach that each label is represented by two exclusive events: the label is selected or not selected. Then a weighted graph is used to represent all the events and their correlations. The multi-label learning is transformed into finding a constrained minimum cut of the weighted graph. In the experiments, we compare the proposed approach with the state-of-the-art multi-label classifier ML-KNN, and the results show that the new approach is efficient in terms of all the popular metrics used to evaluate multi-label classification performance.

Traffic sign recognition (TSR) is an essential research issue in the design of driving support system and smart vehicles. In this paper, we propose a permutation-based image feature to describe traffic signs, which has an inherent advantage of illumination invariance and fast implementation. Our proposed feature LIPID (local image permutation interval descriptor) employs interval division and zone number assignment on order permutation of pixel intensities, and takes the zone numbers as the descriptor. A comprehensive performance evaluation on German Traffic Sign Recognition Benchmark (GTSRB) dataset is carried out, which reveals the great performance of our proposed method. Experiment results exhibit that our feature outperforms some state-of-the-art descriptors, showing a potential prospect in TSR applications.

The problem of face detection in images – locating image areas corresponding to human faces – has received a considerable amount of attention in recent years due to numerous possible applications. In this paper, we describe a face detection system for an image annotation task that requires detection of faces at multiple scales against a complex background. The proposed system is a top-down system; it uses the presence of skin tone pixels coupled with shape and face-specific features to locate faces in images. The main distinguishing feature of the proposed system is its use of an iterative region partitioning procedure to generate candidate face regions. To date, the performance of the system has been tested with over 200 test images of varying complexity with promising results.

This paper presents a scheme for image retrieval that lets a user retrieve images either by exploring summary views of the image collection at diAerent levels or by similarity retrieval using query images. The proposed scheme is based on image clustering through a hierarchy of self-organizing feature maps. While the suggested scheme can work with any kind of low-level feature representation of images, our implementation and description of the system is centered on the use of image color information. Experimental results using a database of 2100 images are presented to show the eAcacy of the suggested scheme. ” 1999 Published by Elsevier Science B.V. All rights reserved.

The organization of video information for video databases requires segmentation of a video into its constituent shots and their subsequent characterization in terms of content and camera work. In this paper, we look at these two steps using compressed video data directly. For shot detection, we suggest a scheme consisting of comparing intensity, row, and column histograms of successive I frames of MPEG video using the chi-square test. For characterization of segmented shots, we address the problem of classifying shot motion into different categories using a set of features derived from motion vectors of P and B frames of MPEG video. The central component of the proposed shot motion characterization scheme is a decision tree classifier built through a process of supervised learning. Experimental results using a variety of videos are presented to demonstrate the effectiveness of performing shot detection and characterization directly on compressed video.

Image representation through local descriptors is the basis of numerous computer vision applications. In the past decade, many local image descriptors such as SIFT and SURF have been proposed, yet algorithms requiring low memory and computation complexity are still preferred. Binary descriptors such as BRIEF have been suggested to satisfy this demand, showing a comparable performance but much faster computation speed. In this paper, we propose a novel local image descriptor, LIPID, which employs intensity permutation and interval division to yield an effective performance in terms of speed and recognition. Our method is inspired by LUCID, proposed by Ziegler and Christiansen [8]. An extensive evaluation on the well-known benchmark datasets reveals the robustness and effectiveness of LIPID as well as its capability to handle illumination changes and texture images.

A new multistage Hough transform approach to the problem of ellipse detection in images is presented. The proposed approach is based on the polar and pole definition of conics and its advantage lies in avoiding the propagation of parameter estimation error over the successive stages of the Hough transform. The proposed method is capable of detecting partially visible ellipses, overlapping ellipses, and groups of concentric ellipses. A set of experimental results is presented to demonstrate these capabilities of the suggested approach.

A data fusion model consisting of several levels of parallel decision fusions is considered. Global optimization of such a model is discussed to obtain the fusion rules for overall optimal performance. The reliability analysis of the proposed model is carried out to establish its superiority over the existing parallel and serial fusion models. >

An evolutionary solution for optimal forecasting the movement of a patient is proposed in this paper. As the great changes of society and environment in modern life, high living pressure results in more and more mental disorders. The safety issue regarding the mental illness patients has risen. According to the statistics of patient safety repost system from the Ministry of the interior in Taiwan, the accidents happened in all the patients have over 85% from the schizophrenias during their treatment period. It indicates the potential dangerous of the psychotics while they are in the treatment period. Therefore, to prevent the accidents will need to focus on the understanding of patient movement in order to issue the warning before the accident happened. It is a practical problem in health care. Moving object prediction addresses the problem of locating a moving object correctly. Therefore, how to effectively forecast or predict the location of moving objects can lead to valuable design for real world applications regarding the issues of moving objects. To address the solution for forecasting problem, we applied evolutionary algorithm (EA) as the target algorithm. The algorithm has powerful techniques to helping stochastic search on the fittest solutions for the problems. Hence, the objective of this paper is to devise the methods that apply EA to solve the practical problems. A case study is a good means to broaden the understanding of the methods how EA can solve problems. Moreover, a case analysis to apply the model for the accident prevention of inpatients is presented to identify the potential of solving the practical problem.

Translational biomedical research involves a diverse group of researchers, data sources and data types voluminous quantities. A collaborative research application is needed to connect these diverse elements. Two approaches are used to integrate biomedical research data; a central data warehouse approach in what all the information is imported into it and a mediator approach which integrates the data on demand. We propose the Beaumont BioBank Integration Management System (BIMS), a collaborative research architecture based on the mediator approach utilizing the Honest Broker System. Our system provides collaborative, flexible and secure environment capable of accelerating biomedical research.

It was reported that between 44,000 and 98,000 deaths occur annually as a consequence of medical errors within American hospitals alone [1] and the US National Association of Boards of Pharmacy reports that as many as 7,000 deaths occur in the US each year because of incorrect prescriptions [2] Therefore, a great desire to improve access to new healthcare methods, and the challenge of delivering healthcare becomes significant nowadays.

A model based two-dimensional object recognition system capable of performing under occlusion and geometric transformation is described in this paper. The system is based on the concept of associative search using overlapping local features. During the training phase, the local features are hashed to set up the associations between the features and models. In the recognition phase, the same hashing procedure is used to retrieve associations that participate in a voting process to determine the identity of the shape. Two associative retrieval techniques for discrete and continuous features, respectively, are described in the paper. The performance of the system is studied using a test set of 1,000 shapes that are corrupted versions of 100 models in the shape database. It is shown that the incorporation of a verification phase to confirm the retrieved associations can provide zero error performance with a small reject rate.

This paper describes Data Modeling for unstructured data of Diffusion Tensor Imaging (DTI). Data Modeling is an essential first step for data preparation in any data management and data mining procedure. Conventional Entity- Relational (E-R) data modeling is lossy, irreproducible, and time-consuming especially when dealing with unstructured image data associated with complex systems like the human brain. We propose a methodological framework for more objective E-R data modeling with unlimited query support by eliminating the structured content-dependent metadata associated with the unstructured data. The proposed method is applied to DTI data and a minimum system is implemented accordingly. Eventually supported with navigation, data fusion, and feature extraction modules, the proposed system provides a content-based support environment (C-BASE). Such an environment facilitates an unlimited query support with a reproducible and efficient database schema. Switching between different modalities of data, while confining the feature extractors within the object(s) of interest, we supply anatomically specific query results. The price of such a scheme is relatively large storage and in some cases high computational cost. The data modeling and its mathematical framework, behind the scene of query executions and the user interface of the system are presented in this paper.