R.J.P. de Figueiredo

Fuzzy system design through fuzzy clustering and optimal predefuzzification

An approach to the design of fuzzy systems, assuming that the system specification is given in terms of a large number of sample I/O (input/output) pairs, that consists of two stages of processing is presented. First, K fuzzy relation patches are obtained by using a fuzzy clustering technique in the input-output joint universe of discourse. The number K of fuzzy clusters is selected and justified based on some cluster validity measure. Each fuzzy relation patch thus discovered then constitutes a fuzzy rule in the proposed system. Second, as in the case of the Takagi-Sugeno fuzzy model, a function is associated with each rule that can be regarded as a predefuzzifier for that rule. Each of these functions is obtained in an optimal way, so that an appropriately defined object function is minimized. An example is included to illustrate the approach.<<ETX>>

Automatic Detection and Diagnosis of Diabetic Retinopathy

A computer aided detection and diagnostic system has been developed for diabetic retinopathy (DR). The system detects the fovea, blood vessel network, optic disk, as well as bright and dark lesions associated with DR. The diagnosis is based on the number, type and location of abnormalities relative to the fovea. Detection of normal retinal components was done as part of the overall system development and the work has been reported in literature. Lesion detection is accomplished through the process of eliminating the normal retinal components: blood vessels, fovea and optic disk. Remaining objects in the retinal image include the background and abnormalities if present. The image is partitioned in two regions: fovea and non-fovea, which have different backgrounds. Filtering and statistical adaptive thresholding are applied throughout the remaining data. The diagnostics and final systems layer is a knowledge-based system.

A new design methodology for optimal interpolative neural networks with application to the localization and classification of acoustic transients

An evolutionary design methodology for neural networks based on the theory of optimal interpolation, (OI) is presented. A limited application of the OI net to the problems of localization and classification of acoustic transients is discussed. The modified recursive least squares (RLS) learning algorithm presented provides an avenue for the acquisition of an appropriate neural network configuration to solve a given pattern classification problem. The authors show that both OI and the back-propagation (BP) of comparable configurations perform satisfactorily in the simulations. The RLS OI method is preferred, however, because BP would occasionally run into some local minima and convergence could be very slow for the more complex decision boundaries between classes. The authors demonstrate that the OI net is particularly suited for application to the localization and classification of acoustic transients.<<ETX>>

A tunable pre-distorter for linearization of solid state power amplifier in mobile wireless OFDM

One of the major problems posed by orthogonal frequency division multiplexing (OFDM) is its high peak-to-average-power ratio (PAPR), which seriously limits the power efficiency of the solid state power amplifier (SSPA) because of the nonlinear distortion caused by high PAPR. In the present paper, we describe an analytical design approach that uses a sparse and yet accurate model for the pre-distorter (PD) based on the SSPA characteristics. Furthermore, the PD configuration is self-tuning, i.e., it can automatically re-adjust itself by tracking changes in the SSPA characteristics with very few (typically two) training symbols.

A Lowcomplexity Tree Algorithm for Pts-Based Papr Reduction in Wireless Ofdm

Orthogonal frequency division multiplexing (OFDM) has several attributes which make it a preferred modulation scheme for high speed wireless communications. However, its high peak-to-average-power ratio (PAPR) causes nonlinear distortion thus limiting the efficiency of the transmitters high power amplifier. Among the approaches proposed for PAPR mitigation, the partial transmit sequence (PTS) technique is very promising since it does not generate any signal distortion. However, its high complexity makes it difficult for use in high speed communication systems. We present a new low-complexity tree algorithm to implement the PTS approach, which seeks the best trade-off between performance and complexity. Simulation results show that this new techniques performance is similar to that of the optimum case, however with significantly lower complexity

Energy-Efficient Scheduling Optimization in Wireless Sensor Networks with Delay Constraints

Energy-efficiency in wireless sensor networks is a very critical design issue, because they usually operate with tiny batteries and take a long time for replacement. In this context, we analyze the optimized scheduling strategy to minimize the energy consumed by data fusion in wireless sensor network. For implementation purpose, a low-complexity fractional-inverse- log scheduling (FILS) algorithm is presented to reduce extra significant amount of energy consumption compared to previously designed protocols. Next, in order to eliminate the communication overhead in centralized scheduling protocols, the simplified distributed fractional-inverse-log scheduling is also provided, which is shown to be very efficient in energy saving especially for a large-scale wireless sensor network. With consideration of the peak power constraint in real circuit design, we update FILS to further constrict the transmission time. Simulation results show that with peak transmission power limitation, energy consumption is still substantially reduced by FILS, and it yields more energy saving for the system with high peak-to-average power ratio (PAPR).

Side Information Power Allocation for MIMO-OFDM PAPR Reduction by Selected Mapping

Multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) has been receiving a great deal of attention as a solution for high-quality service for next generation wireless communications. However one of main problems of orthogonal frequency division multiplexing (OFDM) is its high peak-to-average power ratio (PAPR) which seriously limits power efficiency of high power amplifier (HPA). In this paper, we present PAPR reduction technique of V-BLAST based MIMO-OFDM system. We use the selected mapping (SLM) technique as a PAPR reduction technique since it does not cause any signal distortion. As a special protection for side information (SI) of SLM technique, we propose SI power allocation technique. Simulation results show that proposed technique gives significantly better BER performance than ordinary SLM technique for MIMO-OFDM systems.

A localized nonlinear method for the contrast enhancement of images

This paper presents a number of concepts that are important contributions to the area of contrast enhancement. Among them are the idea of gray scale partitioning, the use of a tunable cubic polynomial for the contrast enhancement function, and, in the case of noisy images, the use of a tandem of pyramidal lowpass filters to remove the noise from homogeneous regions (regions of constant or near-constant intensity) while simultaneously preserving the edges. Gray scale partitioning is a unique and original concept. This idea is based upon the human perception of a set of gray chips. The human perception was quantified by a set of ten intensity values that were converted to reflectances. The essence of gray scale partitioning is to transform these reflectances into gray scale intensity values (assuming constant illumination). These intensity values form the endpoints of discrete subintervals and are therefore the basis for gray scale partitioning. This ensures that the enhancement process will preserve shades of gray. That is, the output gray value will be of the same shade of gray (will lie in the same subinterval) as the input gray value.

Topological dimensionality determination and dimensionality reduction based on minimum spanning trees

In the design of multidimensional systems for the analysis of complex data, an intelligent reduction of the data dimensionality is needed to enable its efficient and accurate processing without much loss of information. The underlying data transformation process can be implemented by nonlinearly mapping the high-dimensional data space onto a low-dimensional feature space where the mapping preserves the topological structure of the transformed data in the feature space as much as possible. This method is often referred to as Multidimensional Scaling (MDS). This paper describes a new MDS approach for feature extraction purposes. It consists of a fast hierachical Minimal-Spanning-Tree-based method which minimizes the Sammons criteria with a genetic algorithm. Results presented show that this new approach is promising for several applications.

Simultaneous object segmentation, multiple object tracking and alpha map generation

This paper presents an object-based video modeling. Motion segmentation is performed at the initial frame to identify different coherently moving regions, called motion-objects. These regions are grouped to form objects. Each motion-object is fitted a content-based mesh, and tracked subsequently to the next frame via mesh motion estimation and compensation. The uncovered background region(s), which emerges when objects move, will be segmented so as to identify the new objects or occluded parts of already existing objects. The mesh model is modified to reflect the changes in object boundaries.