Real-time pitch extraction of acoustical signals using windowing approach.

This paper presents a real-time signal processing technique based on a hardware interface using a microcontroller to process audio music signals for pitch extraction. A technique for transcribing music signals by extracting note parameters is described. The audio signal is divided to smaller sections known as windows to obtain samples of the signals for transcription. In general, two different approaches using static and dynamic window sizes to convert the voice samples for real-time processing are used. However, the transcription process involves complex calculations and in this paper we proposed a simple technique to estimate fundamental frequency of given sound signals. The transcribed data generated shows the feasibility of using microcontrollers for real-time MIDI generation hardware interface

Frequency shifting approach towards textual transcription of heartbeat sounds

Auscultation is an approach for diagnosing many cardiovascular problems. Automatic analysis of heartbeat sounds and extraction of its audio features can assist physicians towards diagnosing diseases. Textual transcription allows recording a continuous heart sound stream using a text format which can be stored in very small memory in comparison with other audio formats. In addition, a text-based data allows applying indexing and searching techniques to access to the critical events. Hence, the transcribed heartbeat sounds provides useful information to monitor the behavior of a patient for the long duration of time. This paper proposes a frequency shifting method in order to improve the performance of the transcription. The main objective of this study is to transfer the heartbeat sounds to the music domain. The proposed technique is tested with 100 samples which were recorded from different heart diseases categories. The observed results show that, the proposed shifting method significantly improves the performance of the transcription

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

Content-based image retrieval using colour and shape fused features

Multi-feature methods are able to contribute to a more effective method compared to single-feature methods since feature fusion methods will be able to close the gap that exists in the single-feature methods. This paper presents a feature fusion method, which focuses on extracting colour and shape features for content-based image retrieval (CBIR). The colour feature is extracted based on the proposed Multi-resolution Joint Auto Correlograms (MJAC), while the shape information is obtained through the proposed Extended Generalised Ridgelet-Fourier (EGRF). These features are fused together through a proposed integrated scheme. The feature fusion method has been tested on the SIMPLIcity image database, where several retrieval measurements are utilised to compare the effectiveness of the proposed method with few other comparable methods. The retrieval results show that the proposed Integrated Colour-shape (ICS) descriptor has successfully obtained the best overall retrieval performance in all the retrieval measurements as compared to the benchmark methods, which include precision (53.50%), precision at 11 standard recall levels (52.48%), and rank (17.40)

Effective keyword query structuring using NER for XML retrieval

Purpose: A more effective way for searching XML database is to use structured queries. However, using query languages to express queries prove to be difficult for most users since this requires learning a query language and knowledge of the underlying data schema. On the other hand, the success of web search engines has made many users to be familiar with keyword search and therefore they prefer to use a keyword search query interface to search XML data. The purpose of this paper is to propose and evaluate XKQSS, a query structuring method that relegates the task of generating structured queries from a user to a search engine while retaining the simple keyword search query interface. Design/methodology/approach: Existing query structuring approaches require users to provide structural hints in their input keyword queries even though their interface is keyword base. Other problems with existing systems include their inability to put keyword query ambiguities into consideration during query structuring and how to select the best generated structure query that best represents a given keyword query. To address these problems, this study allows users to submit a schema independent keyword query, use named Entity Recognition (NER) to categorize query keywords in order to resolve query ambiguities and compute semantic information for a node from its data content. Algorithms were proposed that find user search intentions and convert the intentions into a set of ranked structured queries. Findings: Experiments with Sigmod and IMDB datasets were conducted to evaluate the effectiveness of the method. The experimental result shows that the XKQSS is about 20% more effective than XReal in terms of return nodes identification, a state-of-art systems for XML retrieval. Originality/value: Existing systems do not take keyword query ambiguities into account. XKSS consists of two guidelines based on NER that help to resolve these ambiguities before converting the submitted query. It also include a ranking function computes a score for each generated query by using both semantic information and data statistic as opposed to data statistic only approach used by the existing approaches

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

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