Some Optimally Adaptive Parallel Graph Algorithms on EREW PRAM Model

The study of graph algorithms is an important area of research in computer science, since graphs offer useful tools to model many real-world situations. The commercial availability of parallel computers have led to the development of efficient parallel graph algorithms. Using an exclusive-read and exclusive-write (EREW) parallel random access machine (PRAM) as the computation model with a fixed number of processors, we design and analyze parallel algorithms for seven undirected graph problems, such as, connected components, spanning forest, fundamental cycle set, bridges, bipartiteness, assignment problems, and approximate vertex coloring. For all but the last two problems, the input data structure is an unordered list of edges, and divide-and-conquer is the paradigm for designing algorithms. One of the algorithms to solve the assignment problem makes use of an appropriate variant of dynamic programming strategy. An elegant data structure, called the adjacency list matrix, used in a vertex-coloring algorithm avoids the sequential nature of linked adjacency lists. Each of the proposed algorithms achieves optimal speedup, choosing an optimal granularity (thus exploiting maximum parallelism) which depends on the density or the number of vertices of the given graph. The processor-(time)2 product has been identified as a useful parameter to measure the cost-effectiveness of a parallel algorithm. We derive a lower bound on this measure for each of our algorithms

A Pattern-based deadlock-freedom analysis strategy for concurrent systems

Local analysis has long been recognised as an effective tool to combat the state-space explosion problem. In this work, we propose a method that systematises the use of local analysis in the verification of deadlock freedom for concurrent and distributed systems. It combines a strategy for system decomposition with the verification of the decomposed subsystems via adherence to behavioural patterns. At the core of our work, we have a number of CSP refinement expressions that allows the user of our method to automatically verify all the behavioural restrictions that we impose. We also propose a prototype tool to support our method. Finally, we demonstrate the practical impact our method can have by analysing how it fares when applied to some examples

Graph-based analysis of brain structural MRI data in Multiple System Atrophy

Il lavoro che ho sviluppato presso l’unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull’analisi dei dati strutturali di risonanza magnetica mediante l’utilizzo della graph theory, con lo scopo di valutare eventuali differenze tra un campione di pazienti affetti da Atrofia Multi Sistemica (MSA) e uno di controlli sani (HC). L’MSA è una patologia neurodegenerativa sporadica e progressiva. Essa si divide in due sottotipi: MSA-P ed MSA-C. Circa un terzo delle persone affette da MSA sperimentano una particolare apnea respiratoria, chiamata Stridor. Nello studio sono stati confrontati tra loro tre coppie di gruppi: HC vs MSA, No-stridor vs Stridor, e MSA-C vs MSA-P. I grafi sono strutture matematiche definite da nodi e links, in campo neurologico, la graph theory è usata con lo scopo di comprendere il funzionamento del cervello visto come network. L'approccio qui utilizzato è bastato sulla correlazione volumetriche tra le diverse regioni del cervello. Per costruire un grafo per ogni gruppo il primo step è stato ottenere la parcellizzazione delle immagini cerebrali, in seguito sono stati valutati i volumi delle regioni cerebrali, e in fine le correlazioni tra esse. Una volta costruiti i grafi è stato possibile calcolare i parametri topologici che ne caratterizzano struttura ed organizzazione. Nei vari confronti fatti non sono state riscontrate differenze nelle proprietà globali del network. L’analisi regionale invece ha evidenziato un'alterazione tra MSA e HC relativa a regioni che appartengono al network centrale autonomico, particolarmente colpito dalla malattia. Sono state inoltre riscontrate alterazioni nella organizzazione modulare dei gruppi presi in esame. Questa analisi ha mostrato la possibilità di indagare la funzionalità dei network cerebrali e della loro architettura modulare con misure strutturali quali la covarianza dei volumi delle varie regioni cerebrali in gruppi di soggetti

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

A clinical patient vital signs parameter measurement, processing and predictive algorithm using ECG

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the modern clinical and healthcare setting, the electronic collection and analysis of patient related vital signs and parameters are a fundamental part of the relevant treatment plan and positive patient response. Modern analytical techniques combined with readily available computer software today allow for the near real time analysis of digitally acquired measurements. In the clinical context, this can directly relate to patient survival rates and treatment success. The processing of clinical parameters, especially the Electrocardiogram (ECG) in the critical care setting has changed little in recent years and the analytical processes have mostly been managed by highly trained and experienced cardiac specialists. Warning, detection and measurement techniques are focused on the post processing of events relying heavily on averaging and analogue filtering to accurately capture waveform morphologies and deviations. This Ph.D. research investigates an alternative and the possibility to analyse, in the digital domain, bio signals with a focus on the ECG to determine if the feasibility of bit by bit or near real time analysis is indeed possible but more so if the data captured has any significance in the analysis and presentation of the wave patterns in a patient monitoring environment. The research and experiments have shown the potential for the development of logical models that address both the detection and short term predication of possible follow-on events with a focus on Myocardial Ischemic (MI) and Infraction based deviations. The research has shown that real time waveform processing compared to traditional graph based analysis, is both accurate and has the potential to be of benefit to the clinician by detecting deviations and morphologies in a real time domain. This is a significant step forward and has the potential to embed years of clinical experience into the measurement processes of clinical devices, in real terms. Also, providing expert analytical and identification input electronically at the patient bedside. The global human population is testing the healthcare systems and care capabilities with the shortage of clinical and healthcare providers in ever decreasing coverage of treatment that can be provided. The research is a moderate step in further realizing this and aiding the caregiver by providing true and relevant information and data, which assists in the clinical decision process and ultimately improving the required standard of patient care

Parallel Algorithms for Constructing Convex Hulls.

For a given set of planar points S, the convex hull of S, CH(S), is defined to be a list of ordered points which represents the smallest convex polygon that contains all of the points. The convex hull problem, one of the most important problems in computational geometry, has many applications in areas such as computer graphics, simulation and pattern recognition. There are two strategies used in designing parallel convex hull algorithms. One strategy is the divide-and-conquer paradigm. The disadvantage to this strategy is that the recursive merge step is complicated and difficult to implement on current parallel machines. The second strategy is to parallelize sequential convex hull algorithms. The algorithms designed using the second strategy are often iterative algorithms which can be more easily implemented on the current parallel machines. This research focuses on designing parallel convex hull algorithms using the second strategy because we intend to facilitate the implementation of the newly designed algorithms on massively parallel machines. We first design a sequential algorithm for constructing a convex hull of a simple polygon, which is a special case of a set of planar points. This optimal algorithm is extended to handle a set of planar points without increasing the time complexity. Next, the sequential algorithm is converted for linear array and two or more dimensional mesh-array architectures. The algorithms for the case where the number of points is greater than the number of processors is also addressed. Each of the algorithms developed is optimal. To analyze the performance of the algorithms compared to previous algorithms, a system called the Parallel Convex Hull Simulation System was developed. The results of the analysis indicate that the new algorithms exhibit better performance than previous algorithms

Hardware Considerations for Signal Processing Systems: A Step Toward the Unconventional.

As we progress into the future, signal processing algorithms are becoming more computationally intensive and power hungry while the desire for mobile products and low power devices is also increasing. An integrated ASIC solution is one of the primary ways chip developers can improve performance and add functionality while keeping the power budget low. This work discusses ASIC hardware for both conventional and unconventional signal processing systems, and how integration, error resilience, emerging devices, and new algorithms can be leveraged by signal processing systems to further improve performance and enable new applications. Specifically this work presents three case studies: 1) a conventional and highly parallel mix signal cross-correlator ASIC for a weather satellite performing real-time synthetic aperture imaging, 2) an unconventional native stochastic computing architecture enabled by memristors, and 3) two unconventional sparse neural network ASICs for feature extraction and object classification. As improvements from technology scaling alone slow down, and the demand for energy efficient mobile electronics increases, such optimization techniques at the device, circuit, and system level will become more critical to advance signal processing capabilities in the future.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116685/1/knagphil_1.pd

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design