Search

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.

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.