Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets

An integrated understanding of molecular and developmental biology must consider the large number of molecular species involved and the low concentrations of many species in vivo. Quantitative stochastic models of molecular interaction networks can be expressed as stochastic Petri nets (SPNs), a mathematical formalism developed in computer science. Existing software can be used to define molecular interaction networks as SPNs and solve such models for the probability distributions of molecular species. This approach allows biologists to focus on the content of models and their interpretation, rather than their implementation. The standardized format of SPNs also facilitates the replication, extension, and transfer of models between researchers. A simple chemical system is presented to demonstrate the link between stochastic models of molecular interactions and SPNs. The approach is illustrated with examples of models of genetic and biochemical phenomena where the UltraSAN package is used to present results from numerical analysis and the outcome of simulations.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Abstract: Texture enhancement is an important component of image processing, with extensive application in science and engineering. The quality of medical images, quantified using the texture of the images, plays a significant role in the routine diagnosis performed by medical practitioners. Previously, image texture enhancement was performed using classical integral order differential mask operators. Recently, first order fractional differential operators were implemented to enhance images. Experiments conclude that the use of the fractional differential not only maintains the low frequency contour features in the smooth areas of the image, but also nonlinearly enhances edges and textures corresponding to high-frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we applied the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other fractional differential operators, our new algorithms provide higher signal to noise values, which leads to superior image quality.

Molecular biology: the key to personalised treatment in radiation oncology?

We know considerably more about what makes cells and tissues resistant or sensitive to radiation than we did 20 years ago. Novel techniques in molecular biology have made a major contribution to our understanding at the level of signalling pathways. Before the “New Biology” era, radioresponsiveness was defined in terms of physiological parameters designated as the five Rs. These are: repair, repopulation, reassortment, reoxygenation and radiosensitivity. Of these, only the role of hypoxia proved to be a robust predictive and prognostic marker, but radiotherapy regimens were nonetheless modified in terms of dose per fraction, fraction size and overall time, in ways that persist in clinical practice today. The first molecular techniques were applied to radiobiology about two decades ago and soon revealed the existence of genes/proteins that respond to and influence the cellular outcome of irradiation. The subsequent development of screening techniques using microarray technology has since revealed that a very large number of genes fall into this category. We can now obtain an adequately robust molecular signature, predicting for a radioresponsive phenotype using gene expression and proteomic approaches. In parallel with these developments, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) can now detect specific biological molecules such as haemoglobin and glucose, so revealing a 3D map of tumour blood flow and metabolism. The key to personalised radiotherapy will be to extend this capability to the proteins of the molecular signature that determine radiosensitivity.

Birdcall retrieval from environmental acoustic recordings using image processing

Acoustic recordings of the environment provide an effective means to monitor bird species diversity. To facilitate exploration of acoustic recordings, we describe a content-based birdcall retrieval algorithm. A query birdcall is a region of spectrogram bounded by frequency and time. Retrieval depends on a similarity measure derived from the orientation and distribution of spectral ridges. The spectral ridge detection method caters for a broad range of birdcall structures. In this paper, we extend previous work by incorporating a spectrogram scaling step in order to improve the detection of spectral ridges. Compared to an existing approach based on MFCC features, our feature representation achieves better retrieval performance for multiple bird species in noisy recordings.

Dual-DSP system for signal and image processing

The design of a dual-DSP microprocessor system and its application for parallel FFT and two-dimensional convolution are explained. The system is based on a master-salve configuration. Two ADSP-2101s are configured as slave processors and a PC/AT serves as the master. The master serves as a control processor to transfer the program code and data to the DSPs. The system architecture and the algorithms for the two applications, viz. FFT and two-dimensional convolutions, are discussed.

REMA: A computer-based mapping tool for analysis of restriction sites in multiple DNA sequences

REMA is an interactive web-based program which predicts endonuclease cut sites in DNA sequences. It analyses Multiple sequences simultaneously and predicts the number and size of fragments as well as provides restriction maps. The users can select single or paired combinations of all commercially available enzymes. Additionally, REMA permits prediction of multiple sequence terminal fragment sizes and suggests suitable restriction enzymes for maximally discriminatory results. REMA is an easy to use, web based program which will have a wide application in molecular biology research. Availability: REMA is written in Perl and is freely available for non-commercial use. Detailed information on installation can be obtained from Jan Szubert (jan.szubert@gmail.com) and the web based application is accessible on the internet at the URL http://www.macaulay.ac.uk/rema. Contact: b.singh@macaulay.ac.uk. (C) 2007 Elsevier B.V. All rights reserved.

Inexact floating-point adder for dynamic image processing

Power has become a key constraint in current nanoscale integrated circuit design due to the increasing demands for mobile computing and a low carbon economy. As an emerging technology, an inexact circuit design offers a promising approach to significantly reduce both dynamic and static power dissipation for error tolerant applications. Although fixed-point arithmetic circuits have been studied in terms of inexact computing, floating-point arithmetic circuits have not been fully considered although require more power. In this paper, the first inexact floating-point adder is designed and applied to high dynamic range (HDR) image processing. Inexact floating-point adders are proposed by approximately designing an exponent subtractor and mantissa adder. Related logic operations including normalization and rounding modules are also considered in terms of inexact computing. Two HDR images are processed using the proposed inexact floating-point adders to show the validity of the inexact design. HDR-VDP is used as a metric to measure the subjective results of the image addition. Significant improvements have been achieved in terms of area, delay and power consumption. Comparison results show that the proposed inexact floating-point adders can improve power consumption and the power-delay product by 29.98% and 39.60%, respectively.