CONSTANT TIME DIGITAL GEOMETRY ALGORITHMS ON THE SCAN MODEL OF PARALLEL COMPUTATION

A computation model with automatic functions and relations as primitive operations

Parallel String Matching with Variable Length Don′t Cares

Reconfiguration is a powerful computational model in which the processors can be changed dynamically during the execution phase of the system. This paper presents dynamic reconfigurable register file allocation in hyper pipelined OR1200 (Open RISC) for single instruction multiple data (SIMD). The OR1200 instantly reconfigures the actual register file to the reconfigurable register file according to the requirement of the application. The unused general purpose registers obtained during the reconfiguration process can be used for hyper pipelining technique which improves overall performance of the single core processor system. Thus releasing the unused register reduces the power consumption and increases the execution speed of OR1200. This proposed reconfigurable technique is implemented using Verilog and it is tested using MediaBench multimedia benchmark dataset which ensures reduced register utilisation of 16.80% for multimedia dataset and power reduction up to 72.7% with reconfigurable modules. The proposed technique is configured in Virtex-6 field programmable gate array (FPGA) and results are analysed with the existing and proposed reconfigured OR1200.

The authors present a parallel algorithm for determining the correspondence between two sets of three-dimensional (3D) object feature points, referred to as frame 1 and frame 2, respectively. The points in frame 1 and frame 2 are obtained by observing the same dynamic scene (with multiple rigid objects), at two different instants of time. The parallel algorithm presented is adaptive, i.e. it works with a variable number of processors, and it uses a relatively weaker (and cheaper) model of parallel computation, namely, single-instruction multiple-data (SIMD) with shared memory blocks. The algorithm segments the scene based on difference in motion parameters. The approach is robust in the sense that it does not require the number of points in frame 1 and frame 2 to be identical. >

Recent advances in the design and synthesis of integrated circuits have prompted system architects to investigate computer aided design methods for systems that contain both application-specific and predesigned reprogrammable components. For the most part, we can apply high level synthesis techniques to synthesis of systems containing processors by treating the latter as a generalized resource. However, the problem is more complex, since the software on the processor implements system functionality in an instruction-driven manner with a statically allocated memory space, whereas ASICs operate as data driven reactive elements. Due to these differences in computational models and primitive operations in hardware and software, a new formulation of the problem of cosynthesis is needed. The authors present their cosynthesis approach. They specify system behavior using HardwareC, a hardware description language (HDL) that has a C-like syntax and supports timing and resource constraints. It also supports specification of unbounded and unknown delay operations that can arise from data-dependent decisions and external synchronization operations. The particular choice of a HDL to specify system functionality is immaterial for the cosynthesis formulation here, and other HDLs such as Verilog could be used.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The fasciola cinerea (FC) is a slender archicortical band at the posterior hippocampal tail, and its human morphology and network role are poorly defined. To generate a reproducible in vivo three-dimensional (3D) model of the FC, quantify its geometry, characterize structural and functional connectivity within posterior-medial memory networks, and test a tractography-constrained computational model in which the FC acts as a multiplicative gate. Open 7 T datasets, structural, diffusion, and resting-state functional magnetic resonance imaging (fMRI) were anchored to BigBrain and Julich-Brain priors. A semi-automated, atlas-guided pipeline was used to segment the FC and derive morphometrics (volume, thickness, width, curvature, and Laplace-Beltrami spectral shape). Reliability was assessed using the Dice, 95% Hausdorff distance, and test-retest intraclass correlation coefficient (ICC). Diffusion tractography was used to estimate the FC structural pathways toward retrosplenial (RSC), parahippocampal (PHC), posterior cingulate (PCC), and thalamic targets. Resting-state coupling was summarized using Fisher-z correlations and narrowband coherence. A Wilson-Cowan neural mass model, constrained by tractography, simulated FC-dependent FC-RSC coherence with morphometric scaling of gating gain. Segmentation was reliable (Dice = 0.78 ± 0.05; 95% Hausdorff = 1.62 ± 0.41mm; ICC_volume = 0.88; ICC_thickness = 0.82). Group morphometrics: volume 84.3 ± 17.9mm³, mean thickness 0.92 ± 0.15mm, width 1.86 ± 0.31mm, centerline length 14.2 ± 2.1mm. FC showed preferential connectivity: FC→RSC 0.21 ± 0.09; FC→PHC 0.18 ± 0.08; FC→PCC 0.11 ± 0.06; FC→Thalamus 0.06 ± 0.04. Resting-state coupling was strongest for FC-RSC (z = 0.24 ± 0.12) with a slow-band coherence enhancement. Thickness predicted the FC→RSC strength (β = 0.17 per 0.1mm) and FC-RSC z (β = 0.08 per 0.1mm), and higher curvature was negatively related. The gating model reproduced empirical FC-RSC coherence (r = 0.52 ± 0.11), and morphometric scaling improved the fit (Δr = + 0.06). We provide an anatomically grounded and mathematically validated 3D FC model that links microstructures to mesoscale connectivity. Preferential posterior-medial coupling and morphometry-dependent gating support the FC as a modulatory interface in human memory networks and yield testable markers for individualized mapping and clinical translation.

Automatic analysis in soccer scenes is still a difficult task in the absence of soccer field information. The first and most important step in almost all analysis, is soccer field line recognition and homography extraction. The aim of this paper is introducing a novel approach for automatic detection and recognition of soccer field lines and arcs by minimal information. A simple camera model and perspective map is assumed to reduce unknown parameters. An accurate method is utilized for detecting line pixels. The side of playfield area is determined based on the orientation of lines and arcs. Based on the detected playfield area side, an initial perspective map is obtained. An optimization algorithm then adjusts the parameters of perspective transform and camera. The proposed method needs only some minimal information in theory and practice. It is applied to some typical soccer videos. The achieved results demonstrate its robustness and accuracy.

Interlayer information fusion-based and dual-attention improved U-Net for ABVS image sequence intelligent tumor segmentation

A family of functions F that map [0,m-1] into [0,n-1] is said to be $\h$-wise independent if any tuple of $\h$ distinct points in $[0,m-1]$ have a corresponding image, for a randomly selected $f\in F$, that is uniformly distributed in $[0,n-1]^{\h}$. This paper shows that for suitably fixed $\epsilon < 1$ and any $\h < m^\epsilon$, there are families of $\h$-wise independent functions that can be evaluated in constant time for the standard random access model of computation. It is also proven that any such family requires a storage array of $m^\delta$ random seeds for a suitable $\delta<1$. These seeds can be pseudorandom values precomputed from an initial $O(\h)$ random seeds. A simple adaptation yields $n^\epsilon$-wise independent functions that require $n^\delta$ storage in many cases where $m\gg n$. Lower bounds are presented to show that neither storage requirement can be materially reduced. Previous constructions of random functions having constant evaluation time and sublinear storage exhibited only a constant degree of independence. Unfortunately, the explicit randomized constructions, while requiring a constant number of operations, are far too slow for any practical application. However, nonconstructive existence arguments are given, which suggest that this factor might be eliminated. The problem of eliminating this factor is shown to be equivalent to a fundamental open question in graph theory. As a consequence of these constructions, many probabilistic algorithms---from traditional hashing to Ranade's emulation of common PRAM algorithms---can for the first time be shown to achieve, up to constant factors, their expected asymptotic performance for a programmable, albeit formal and currently impractical, model of computation, and a research direction is now available that may eventually lead to implementations that are fast and provably sound.

ANSV problem on BSRs

The author presents a parallel algorithm for packing N>or=n/sup 2/ pieces in a massively parallel computing environment of n/sup 2/ processors operating in single-instruction multiple-data (SIMD) mode. The heuristic approach that is developed utilizes a grid topology that structures the input to increase packing efficiency without sorting the data set. The results of implementing this algorithm on a Connection Machine 2 system are compared with the results obtained from parallel versions of two traditional bin packing algorithms. It is concluded that the use of data partitioning to initialize the packing appears to be a practical method of allocating the packing workload to a set of parallel processors while maintaining the integrity of the packing algorithm. The results support the contention that parallelism offers opportunity for improvement in the efficiency of packing algorithms and that the SIMD model of computation with its data parallel programming approach is appropriate for bin packing problems. >

Object matching is an important issue for machine vision, object recognition and image analysis. A Hausdorff distance is one of commonly used measures for object matching because it is simple and insensitive to changes of image characteristics. But the conventional Hausdorff distances require high computational complexity and are not suited to the practical applications. In this work, we propose a new algorithm combining an improved partial Hausdorff distance, genetic algorithm and simulated annealing algorithm for higher computing efficiency and better matching results than the conventional Hausdorff measures. In this proposed method, the GA provides a global search and the SA algorithm provides local search. Analysis found the hybrid GA-SA conducts parallel analyses that increase the probability of finding an optimal solution while reducing computation time for object matching. Theoretical analysis and simulation results show that the new algorithm is very effective and robust under several kinds of noise conditions.

In this paper has been considered the main content and distinctive features of the “Geometric Modeling” training course for the “Mathematics and Computer Science” training program 02.03.01 (“Mathematical and Computer Modeling” specialization).&#x0D; The goal of the “Geometric Modeling” course study is the assimilation of mathematical methods for construction of geometric objects with complex curved shapes, and techniques for their computer visualization by using polygons of curves and surfaces. Methods for construction of structures’ curved shapes using spline representations, as well as techniques for construction of surfaces and volumetric geometries using motion operations and basic logical operations on geometric objects are considered. The spline representations include linear and bilinear splines, Hermite cubic splines and Hermite surfaces, natural cubic and bicubic interpolation splines, Bezier curves and surfaces, rational Bezier splines, B-splines and B-spline surfaces, NURBS-curves and NURBS-surfaces, transfinite interpolation methods, and splines of surfaces with triangular form. Logical operations for intersection of two spline curves, and intersection of two parametric surfaces are considered. The principles of scientific visualization and computer animation are considered in this course as well.&#x0D; Some examples for visualization of initial data and results of curves and surfaces construction in two- and three-dimensional spaces through the software shell developed by authors and used by students while doing tests have been demonstrated. The software shell has a web interface with the WebGL library graphic support. Tasks for four practical studies in a computer classroom, as well as several variations of homework are represented.&#x0D; The problems occurring in preparation materials for some course sections are discussed, as well as the practical importance of acquired knowledge for the further progress of students.&#x0D; The paper may be interesting for teachers of “Geometric Modeling” and “Computer Graphics” courses aimed to students with a specialization in mathematics and information, as well as to those who independently develop software interfaces for algorithms of geometric modeling.

Probabilistic computing, with its efficient and non‐deterministic computational model, holds great potential and is driving a paradigm shift in computation. However, traditional CMOS circuits face issues such as a high device count, significant energy consumption, and pseudo‐random behavior. Additionally, commonly used probabilistic computing devices like LBNM and sMTJ suffer from problems related to volatility and initialization. This paper explores the application of non‐volatile combined bit (c‐bit) devices, exhibiting back‐hopping‐like phenomena, in logical operations, arithmetic computations, and data backup and recovery. Logical reverse inferences are successfully implemented for AND, OR, XOR, and XNOR operations. Importantly, the logical reverse inference of full and half adders is achieved, as well as the decomposition of addends, verifying the feasibility of c‐bit devices in complex arithmetic operations. Furthermore, by utilizing multiple c‐bit devices working in tandem, efficient data backup and reliable recovery are achieved, offering a novel solution for data security. This research not only enriches the theoretical framework of probabilistic computing but also provides new insights and directions for the future development of computational models.

This paper proposes a computational modeling for image filtering processes based on the Cartesian Genetic Programming CGP methodology, suitable for hardware devices. A computational system named ALIF-CGP Automatic Learning of Image Filters Using Cartesian Genetic Programming was designed as a simulator for automatically constructing a sequence of operators, mainly morphological and logical, which can filter a particular shape of image. ALIF-CGP is a convenient option for executing the non-trivial task, usually manually done by human experts, of selecting the sequence of nonlinear operators to be used in morphological filters. ALIF-CGP has already a built-in pool of morphological and logical operators, which can be used by default. The user, however, has the flexibility of choosing only those operators which are of interest or then, conveniently introduce new ones. The system expects as input a pair of images input-target. The flexibility given by the CGP-based computational modeling used by ALIF-CGP as well as its efficiency and satisfactory results, obtained in various image processing case studies, recommend its use when developing a hardware implementation for the purposes of image filtering. A few case studies using ALIF-CGP are presented and comparatively analyzed in relation to previous results available in the literature.