Region-based wavelet coding methods for digital mammography

Spatial resolution and contrast sensitivity requirements for some types of medical image techniques, including mammography, delay the implementation of new digital technologies, namely, computer-aided diagnosis, picture archiving and communications systems, or teleradiology. In order to reduce transmission time and storage cost, an efficient data-compression scheme to reduce digital data without significant degradation of medical image quality is needed. In this study, we have applied two region-based compression methods to digital mammograms. In both methods, after segmenting the breast region, a region-based discrete wavelet transform is applied, followed by an object-based extension of the set partitioning in hierarchical trees (OB-SPIHT) coding algorithm in one method, and an object-based extension of the set partitioned embedded block (OB-SPECK) coding algorithm in the other. We have compared these specific implementations against the original SPIHT and the new standard JPEG 2000, both using reversible and irreversible filters, on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bit per pixel (bbp). Distortion was evaluated for all images and compression rates by the peak signal-to-noise ratio. For all images, OB-SPIHT and OB-SPECK performed substantially better than the traditional SPIHT and JPEG 2000, and a slight difference in performance was found between them. A comparison applying SPIHT and the standard JPEG 2000 to the same set of images with the background pixels fixed to zero was also carried out, obtaining similar implementation as region-based methods. For digital mammography, region-based compression methods represent an improvement in compression efficiency from full-image methods, also providing the possibility of encoding multiple regions of interest independently.     

Language Simplification through Error-Correcting and Grammatical Inference Techniques

In many language processing tasks, most of the sentences generally convey rather simple meanings. Moreover, these tasks have a limited semantic domain that can be properly covered with a simple lexicon and a restricted syntax. Nevertheless, casual users are by no means expected to comply with any kind of formal syntactic restrictions due to the inherent spontaneous nature of human language. In this work, the use of error-correcting-based learning techniques is proposed to cope with the complex syntactic variability which is generally exhibited by natural language. In our approach, a complex task is modeled in terms of a basic finite state model, F, and a stochastic error model, E. F should account for the basic (syntactic) structures underlying this task, which would convey the meaning. E should account for general vocabulary variations, word disappearance, superfluous words, and so on. Each natural user sentence is thus considered as a corrupted version (according to E) of some simple sentence of L(F). Adequate bootstrapping procedures are presented that incrementally improve the structure of F while estimating the probabilities for the operations of E. These techniques have been applied to a practical task of moderately high syntactic variability, and the results which show the potential of the proposed approach are presented.     

Yeast two-hybrid systems and protein interaction mapping projects for yeast and worm

The availability of complete genome sequences necessitates the development of standardized functional assays to analyse the tens of thousands of predicted gene products in high-throughput experimental settings. Such approaches are collectively referred to as 'functional genomics'. One approach to investigate the properties of a proteome of interest is by systematic analysis of protein-protein interactions. So far, the yeast two-hybrid system is the most commonly used method for large-scale, high-throughput identification of potential protein-protein interactions. Here, we discuss several technical features of variants of the two-hybrid systems in light of data recently obtained from different protein interaction mapping projects for the budding yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans.     

Learning finite-state models for machine translation

In formal language theory, finite-state transducers are well-know models for simple “input-output” mappings between two languages. Even if more powerful, recursive models can be used to account for more complex mappings, it has been argued that the input-output relations underlying most usual natural language pairs can essentially be modeled by finite-state devices. Moreover, the relative simplicity of these mappings has recently led to the development of techniques for learning finite-state transducers from a training set of input-output sentence pairs of the languages considered. In the last years, these techniques have lead to the development of a number of machine translation systems. Under the statistical statement of machine translation, we overview here how modeling, learning and search problems can be solved by using stochastic finite-state transducers. We also review the results achieved by the systems we have developed under this paradigm. As a main conclusion of this review we argue that, as task complexity and training data scarcity increase, those systems which rely more on statistical techniques tend produce the best results. This work was partially supported by the European Union project TT2 (IST-2001-32091) and by the Spanish project ITEFTE (TIC 2003-08681-C02-02). Editor: Georgios Paliouras and Yasubumi Sakakibara     

Sliding mode control based buck-boost bidirectional converter to drive piezoelectric loads

The present work deals with the development of a converter to drive piezoelectric actuators to be used in automotive and aerospace applications and work under mechanical frequencies up to 2 kHz with rectangular, triangular and sinusoidal reference signals. In order to maximize the field in the piezoelectric ceramic important voltages (up to 2,000 V) are needed. To fulfill such requirements with a limited input voltage a bidirectional buck-boost converter is proposed. Such a converter shows voltage reduction and elevation capability. This advantage overcomes its intrinsic control non-linearity and the drawbacks involved. The converter is controlled by means of a sliding mode control strategy based on two different linked sliding surfaces which are defined as the energy error of the converter for charging and discharging operation modes, so that each surface controls one of the two switches employed. The state-space evolution of the voltage and current is investigated in order to analyze the converter limitations. The proposed converter and control strategy have been simulated and have proved to be appropriate and robust for different voltage references and values of the equivalent capacitance C _P .     

TWO GRAMMATICAL INFERENCE APPLICATIONS IN MUSIC PROCESSING

This article reviews our work in the field of music processing (MP) using grammatical inference (GI), where regular grammars are used for modeling musical style. These models can be used to generate automatic composition (AC) and classify music by style (musical style identification) with their resulting applications. The latter, for instance, would improve content-based retrieval in multimedia databases, joining indexing by musical style to other suitable indexes. In this work, several GI techniques are used to learn from examples of melodies, stochastic grammars for different musical styles. Then, each of the learned grammars is used to generate new melodies (composition) or to classify test melodies (style identification). Our studies in this field show the need of proper music coding schemes, so different coding schemes are presented and compared. Results from our previous studies have been improved, achieving in style identification a classification error rate that ranges from 0.5 to 1.7%, depending on the corpus used.     

A multicore solution to Block–Toeplitz linear systems of equations

There exist algorithms, also called “fast” algorithms, which exploit the special structure of Toeplitz matrices so that, e.g., allow to solve a linear system of equations in O(n ²) flops. However, some implementations of classical algorithms that do not use this structure (O(n ³) flops) highly reduce the time to solution when several cores are available. That is why it is necessary to work on “fast” algorithms so that they do not lose track of the benefits of new hardware/software. In this work, we propose a new approach to the Generalized Schur Algorithm, a very known algorithm for the solution of Toeplitz systems, to work on a Block–Toeplitz matrix. Our algorithm is based on matrix–matrix multiplications, thus allowing to exploit an efficient implementation of this operation if it exists. Our algorithm also makes use of the thread level parallelism featured by multicores to decrease execution time.     

To be human is to be creative

We will assert than in the era of Ubiquitous Technology to be human is to be creative. Small children are experimental and creative actors. The socialisation process in modern societies, both at home and at educational institutions, does not enhance and develop their creativity. On the contrary, their creativity is discouraged in many ways. We conceptualise creativity developmentally: It is possible to use activities, teaching methods, motivation and procedures to enhance and develop creativity, even in older people. This paper gives some guides that can be used both at home and at work to explore, enhance and develop ones own creativity and the creativity of others. Each suggestion is presented from a practical viewpoint and then related to some of the tools and concepts that scientists and artists use in their creative endeavours.     

A multi-objective control strategy for grid connection of DG (distributed generation) resources

This paper presents a flexible control technique for connection of DG (distributed generation) resources to distribution networks, especially during ride-through on faulty grid. This strategy is derived from the abc/αβ and αβ/dq transformations of the ac system variables. The active and reactive currents injected by the DG source are controlled in the synchronously rotating orthogonal dq reference frame. The transformed variables are used to control the VSI (voltage source inverter) which connects the DG to the distribution network. Using a P.L.L. (phase locked loop) in circuit of proposed control technique, the angle of positive sequence has been detected, in order to synchronize the currents to the distribution network. The proposed control technique has the capability of providing active and reactive powers and harmonic currents to nonlinear loads with a fast dynamic response. Simulation results and mathematical analysis have been completed in order to achieve a reduced THD (total harmonic distortion), increased power factor and compensated load’s active and reactive powers. The analyses show the high performance of this control strategy in DG applications in comparison with other existing strategies.     

Diffraction-free and diffraction-limited propagation of light in graded-index planar waveguides with hyperbolic secant refractive index profile

Abstract

A study of diffraction-free and diffraction-limited propagation of light with transverse amplitude in sech through a planar waveguide with hyperbolic secant refractive index profile is presented. Analytical solutions by the stationary phase method are obtained. A particular case is analysed.