Real-time segmentation of heart sound pattern with amplitude reconstruction

This paper presents a new idea for real-time segmentation of heart sound using amplitude reconstruction. Biomedical signal processing usually uses recorded information as the inputs. Real-time processing systems are challenging fields of engineering including biomedical signal processing. Segmentation of heart sound means that, system receives an audio stream and it separates the given signal into cycles that includes heart sound pulses, first and second heart sound. For implementing real-time heart sound segmentation, a fast method with low complexity is required. In the proposed system, the heart sound is filtered on frequency domain, and then it is processed on amplitude domain to extract the cycles. Although this technique is implemented without any complex calculation such as Furrier or wavelet transforms, the absorbed results showed its feasibility as a real-time segmentation method

Utilization of fuzzy controller for laboratory scale convective fruit dryers

In the present study, a fruit dryer system that is controlled based on fuzzy logic is presented. A laboratory scale cabinet was developed which includes four sensors in different lengths for monitoring the cabin temperature and humidity. Fuzzy base controller is a new monitoring technique in food industrial machines that utilize sensors captured values as its input parameters to make a suitable decision according to temperature values. Furthermore, to implement the fuzzy system, a microcontroller base monitoring system is developed. Microcontroller captured temperature samples and converted them in to digital values. Output of the fuzzy controller will control the speed of the fan and power of the heater. Several performed results indicated the amenability of the proposed monitoring system as a drying machine main controller in different drying curves. Fluctuation of the cabin temperature with fuzzy control was smoother than non-fuzzy control. Nevertheless, fuzzy control has a significant influence on the power consumption as well

Effective predicate identification algorithm for XML retrieval

Query structuring systems are keyword search systems recently used for effective retrieval of XML documents. Existing systems fail to put keyword query ambiguity problems into consideration during query preprocessing. Thus, the systems return irrelevant user search intentions. A search intention consists of entity nodes and predicate nodes of XML data. In this paper, an entity based query segmentation (EBQS) method which interprets a user query as a list of keywords and/or named entities to resolve ambiguity. Then, segment terms proximity scorer (STPS) that assigns relevance scores to XML fragments that contains query keywords is proposed. Fragments containing the keywords as interpreted by EBQS are assigned higher scores. Finally, an effective predicate identification algorithm (EPIA) which uses EBQS and STPS to return relevant predicates is introduced. The effectiveness of the algorithm is demonstrated through experimental performance study on some real world XML documents

Multi-resolution shape-based image retrieval using Ridgelet transform

Complicated shapes can be effectively characterized using multi-resolution descriptors. One popular method is the Ridgelet transform which has enjoyed very little exposure in describing shapes for Content-based Image Retrieval (CBIR). Many of the existing Ridgelet transforms are only applied on images of size M×M. For M×N sized images, they need to be segmented into M×M sub-images prior to processing. A different number of orientations and cut-off points for the Radon transform parameters also need to be utilized according to the image size. This paper presents a new shape descriptor for CBIR based on Ridgelet transform which is able to handle images of various sizes. The utilization of the ellipse template for better image coverage and the normalization of the Ridgelet transform are introduced. For better retrieval, a template-option scheme is also introduced. Retrieval effectiveness obtained by the proposed method has shown to be higher compared to several previous descriptors

Generalized Ridgelet-Fourier for M×N images: determining the normalization criteria

Ridgelet transform (RT) has gained its popularity due to its capability in dealing with line singularities effectively. Many of the existing RT however is only applied to images of size M×M or the M×N images will need to be pre-segmented into M×M sub-images prior to processing. The research presented in this article is aimed at the development of a generalized RT for content-based image retrieval so that it can be applied easily to any images of various sizes. This article focuses on comparing and determining the normalization criteria for Radon transform, which will aid in achieving the aim. The Radon transform normalization criteria sets are compared and evaluated on an image database consisting of 216 images, where the precision and recall and Averaged Normalized Modified Retrieval Rank (ANMRR) are measured

Heart sounds clustering using a combination of temporal, spectral and geometric features

Heart murmurs are the first sign of heart valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds from heart sounds with murmurs using various types of audio features. In this study, the feasibility of using a combination of temporal, spectral and geometric features in clustering five types of physiological and pathological heart sounds is shown. Thirty six heart sound recordings comprising normal and abnormal heart sounds were collected from training CDs and online resources. The proposed combination of features exhibits a promising discriminatory power in phonocardiographic signals clustering

Automatic object segmentation using perceptual grouping of regions with contextual constraints

Image segmentation is still considered a very challenging subject despite years of research effort poured into the field. The problem is exacerbated when there is need for specific object detection. Since objects can be visually non-homogeneous, techniques that attempt to segment images into visually uniform regions using only the bottom-up cues, tend to fail. We propose a novel two-step model that incorporates both bottom-up information and top-down object constraints. Firstly, a set of uniform regions are generated using an extension of contour detection, seeded region growing, and graph-based methods. The second step applies co-occurrence constraints on the image regions in order to perceptually group regions into objects. This unsupervised segmentation process models each object using higher-level knowledge in the form of visual co-occurrences of its constituent parts. Experiments on the horse and ImageCLEF databases show that the proposed technique performs comparably well with existing state-of-the-art techniques

Invariant Generalised Ridgelet-Fourier for shape-based image retrieval

A new shape descriptor called the Invariant Generalised Ridgelet-Fourier is defined for the application of Content-based Image Retrieval (CBIR). The proposed spectral-based method is invariant to rotation, scaling, and translation (RST) as well as able to handle images of arbitrary size. The implementation of Ridgelet transform on the ellipse containing the shape and the normalisation of the Radon transform is introduced. The 1D Wavelet transform is then applied to the Radon slices. In order to extract the rotation invariant feature, Fourier transform is implemented in the Ridgelet domain. The performance of the proposed method is accessed on a standard MPEG-7 CE-1 B dataset in terms of few objective evaluation criteria. From the experiments, it is shown that the proposed method provides promising results compared to several previous methods

The effect of noise on RWTSAIRS classifier.

Artificial Immune Recognition System (AIRS) is an immune inspired classifier that competes with famous classifiers. One of the most important components of AIRS is resource competition. The goal of resource competition is the development of the fittest individuals. Resource competition phase removes weakest individuals and selects strongest (apparently better) individuals. However, with this type of selection, there is a high selective pressure with a loss of diversity. It may generate premature memory cells and decrease the accuracy of classifier. In a previous study, the Real World Tournament Selection (RWTS) method was incorporated into the resource competition phase of AIRS to prevent this problem. The new classifier, named RWTSAIRS, obtained higher accuracy than AIRS in standard datasets from UCI machine learning repository. Real-world data is not perfect and contains noise that may impact the models created from data and decision made based on data. In this study, the performance of RWTSAIRS is evaluated in noisy environments. For this purpose, class and attribute noise are injected into some datasets

Impact of acoustical voice activity detection on spontaneous filled pause classification

Filled pause detection is imperative for spontaneous speech recognition as it may degrade speech recognition rate. However, filled pause is commonly confused with elongation as they shared the same acoustical properties. Few attempts of classifying filled pause and elongation employed Hidden Markov model. Our proposed method of utilizing Neural Network as a classifier achieved 96% precision rate. We also proved that voice activity detection (VAD) affects the performance of speech recognition. Three acoustical-based VAD are compared and the best precision rate is achieved by incorporating volume and first-order difference features. Experiments are conducted using Malay language spontaneous speeches of Malaysia Parliamentary Debate sessions