Efficient parallel processing with optical interconnections

With the advances in VLSI technology, it is now possible to build chips which can each contain thousands of processors. The efficiency of such chips in executing parallel algorithms heavily depends on the interconnection topology of the processors. It is not possible to build a fully interconnected network of processors with constant fan-in/fan-out using electrical interconnections. Free space optics is a remedy to this limitation. Qualities exclusive to the optical medium are its ability to be directed for propagation in free space and the property that optical channels can cross in space without any interference. In this thesis, we present an electro-optical interconnected architecture named Optical Reconfigurable Mesh (ORM). It is based on an existing optical model of computation. There are two layers in the architecture. The processing layer is a reconfigurable mesh and the deflecting layer contains optical devices to deflect light beams. ORM provides three types of communication mechanisms. The first is for arbitrary planar connections among sets of locally connected processors using the reconfigurable mesh. The second is for arbitrary connections among N of the processors using the electrical buses on the processing layer and N2 fixed passive deflecting units on the deflection layer. The third is for arbitrary connections among any of the N2 processors using the N2 mechanically reconfigurable deflectors in the deflection layer. The third type of communication mechanisms is significantly slower than the other two. Therefore, it is desirable to avoid reconfiguring this type of communication during the execution of the algorithms. Instead, the optical reconfiguration can be done before the execution of each algorithm begins. Determining a right configuration that would be suitable for the entire configuration of a task execution is studied in this thesis. The basic data movements for each of the mechanisms are studied. Finally, to show the power of ORM, we use all three types of communication mechanisms in the first O(logN) time algorithm for finding the convex hulls of all figures in an N x N binary image presented in this thesis

Simulations and Algorithms on Reconfigurable Meshes With Pipelined Optical Buses.

Recently, many models using reconfigurable optically pipelined buses have been proposed in the literature. A system with an optically pipelined bus uses optical waveguides, with unidirectional propagation and predictable delays, instead of electrical buses to transfer information among processors. These two properties enable synchronized concurrent access to an optical bus in a pipelined fashion. Combined with the abilities of the bus structure to broadcast and multicast, this architecture suits many communication-intensive applications. We establish the equivalence of three such one-dimensional optical models, namely the LARPBS, LPB, and POB. This implies an automatic translation of algorithms (without loss of speed or efficiency) among these models. In particular, since the LPB is the same as an LARPBS without the ability to segment its buses, their equivalence establishes reconfigurable delays (rather than segmenting ability) as the key to the power of optically pipelined models. We also present simulations for a number of two-dimensional optical models and establish that they possess the same complexity, so that any of these models can simulate a step of one of the other models in constant time with a polynomial increase in size. Specifically, we determine the complexity of three two-dimensional optical models (the PR-Mesh, APPBS, and AROB) to be the same as the well known LR-Mesh and the cycle-free LR-Mesh. We develop algorithms for the LARPBS and PR-Mesh that are more efficient than existing algorithms in part by exploiting the pipelining, segmenting, and multicasting characteristics of these models. We also consider the implications of certain physical constraints placed on the system by restricting the distance over which two processors are able to communicate. All algorithms developed for these models assume that a healthy system is available. We present some fundamental algorithms that are able to tolerate up to N/2 faults on an N-processor LARPBS. We then extend these results to apply to other algorithms in the areas of image processing and matrix operations

Efficient registration for precision inspection of free-form surfaces

Precision inspection of free-form surface is difficult with current industry practices that rely on accurate fixtures. Alternatively, the measurements can be aligned to the part model using a geometry-based registration method, such as the iterative closest point (ICP) method, to achieve a fast and automatic inspection process. This paper discusses various techniques that accelerate the registration process and improve the efficiency of the ICP method. First, the data structures of approximated nearest nodes and topological neighbor facets are combined to speed up the closest point calculation. The closest point calculation is further improved with the cached facets across iteration steps. The registration efficiency can also be enhanced by incorporating signal-to-noise ratio into the transformation of correspondence sets to reduce or remove the noise of outliers. Last, an acceleration method based on linear or quadratic extrapolation is fine-tuned to provide the fast yet robust iteration process. These techniques have been implemented on a four-axis blade inspection machine where no accurate fixture is required. The tests of measurement simulations and inspection case studies indicated that the presented registration method is accurate and efficient.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45849/1/170_2005_Article_370.pd

Efficient Molecular Dynamics Simulation on Reconfigurable Models with MultiGrid Method

In the field of biology, MD simulations are continuously used to investigate biological studies. A Molecular Dynamics (MD) system is defined by the position and momentum of particles and their interactions. The dynamics of a system can be evaluated by an N-body problem and the simulation is continued until the energy reaches equilibrium. Thus, solving the dynamics numerically and evaluating the interaction is computationally expensive even for a small number of particles in the system. We are focusing on long-ranged interactions, since the calculation time is O(N^2) for an N particle system. In this dissertation, we are proposing two research directions for the MD simulation. First, we design a new variation of Multigrid (MG) algorithm called Multi-level charge assignment (MCA) that requires O(N) time for accurate and efficient calculation of the electrostatic forces. We apply MCA and back interpolation based on the structure of molecules to enhance the accuracy of the simulation. Our second research utilizes reconfigurable models to achieve fast calculation time. We have been working on exploiting two reconfigurable models. We design FPGA-based MD simulator implementing MCA method for Xilinx Virtex-IV. It performs about 10 to 100 times faster than software implementation depending on the simulation accuracy desired. We also design fast and scalable Reconfigurable mesh (R-Mesh) algorithms for MD simulations. This work demonstrates that the large scale biological studies can be simulated in close to real time. The R-Mesh algorithms we design highlight the feasibility of these models to evaluate potentials with faster calculation times. Specifically, we develop R-Mesh algorithms for both Direct method and Multigrid method. The Direct method evaluates exact potentials and forces, but requires O(N^2) calculation time for evaluating electrostatic forces on a general purpose processor. The MG method adopts an interpolation technique to reduce calculation time to O(N) for a given accuracy. However, our R-Mesh algorithms require only O(N) or O(logN) time complexity for the Direct method on N linear R-Mesh and N¡¿N R-Mesh, respectively and O(r)+O(logM) time complexity for the Multigrid method on an X¡¿Y¡¿Z R-Mesh. r is N/M and M = X¡¿Y¡¿Z is the number of finest grid points

Computing convexity properties of images on a pyramid computer

We present efficient parallel algorithms for using a pyramid computer to determine convexity properties of digitized black/white pictures and labeled figures. Algorithms are presented for deciding convexity, identifying extreme points of convex hulls, and using extreme points in a variety of fashions. For a pyramid computer with a base of n simple processing elements arranged in an n 1/2 × n 1/2 square, the running times of the algorithms range from Θ(log n ) to find the extreme points of a convex figure in a digitized picture, to Θ( n 1/6 ) to find the diameter of a labeled figure, Θ( n 1/4 log n ) to find the extreme points of every figure in a digitized picture, to Θ( n 1/2 ) to find the extreme points of every labeled set of processing elements. Our results show that the pyramid computer can be used to obtain efficient solutions to nontrivial problems in image analysis. We also show the sensitivity of efficient pyramid-computer algorithms to the rate at which essential data can be compressed. Finally, we show that a wide variety of techniques are needed to make full and efficient use of the pyramid architecture.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41351/1/453_2005_Article_BF01759066.pd

Predictive Analytics Lead to Smarter Self-Organizing Directional Wireless Backbone Networks

Directional wireless systems are becoming a cost-effective approach towards providing a high-speed, reliable, broadband connection for the ubiquitous mobile wireless devices in use today. The most common of these systems consists of narrow-beam radio frequency (RF) and free-space-optical (FSO) links, which offer speeds between 100Mbps and 100Gbps while offering bit-error-rates comparable to fixed fiber optic installations. In addition, spatial and spectral efficiencies are accessible with directional wireless systems that cannot be matched with broadcast systems. The added benefits of compact designs permit the installation of directional antennas on-board unmanned autonomous systems (UAS) to provide network availability to regions prone to natural disasters, in maritime situations, and in war-torn countries that lack infrastructure security. In addition, through the use of intelligent network-centric algorithms, a flexible airborne backbone network can be established to dodge the scalability limitations of traditional omnidirectional wireless networks. Assuring end-to-end connectivity and coverage is the main challenge in the design of directional wireless backbone (DWB) networks. Conflating the duality of these objectives with the dynamical nature of the environment in which DWB networks are deployed, in addition to the standardized network metrics such as latency-minimization and throughput maximization, demands a rigorous control process that encompasses all aspects of the system. This includes the mechanical steering of the directional point-to-point link and the monitoring of aggregate network performance (e.g. dropped packets). The inclusion of processes for topology control, mobility management, pointing, acquisition, and tracking of the directional antennas, alongside traditional protocols (e.g. IPv6) provides a rigorous framework for next-generation mobile directional communication networks. This dissertation provides a novel approach to increase reliability in reconfigurable beam-steered directional wireless backbone networks by predicating optimal network reconfigurations wherein the network is modeled as a giant molecule in which the point-to-point links between two UASs are able to grow and retract analogously to the bonds between atoms in a molecule. This cross-disciplinary methodology explores the application of potential energy surfaces and normal mode analysis as an extension to the topology control optimization. Each of these methodologies provides a new and unique ability for predicting unstable configurations of DWB networks through an understanding of second-order principle dynamics inherent within the aggregate configuration of the system. This insight is not available through monitoring individual link performance. Together, the techniques used to model the DWB network through molecular dynamics are referred to as predictive analytics and provide reliable results that lead to smarter self-organizing reconfigurable beam-steered DWB networks. Furthermore, a comprehensive control architecture is proposed that complements traditional network science (e.g. Internet protocol) and the unique design aspects of DWB networks. The distinct ability of a beam-steered DWB network to adjust the direction of its antennas (i.e. reconfigure) in response to degraded effects within the atmosphere or due to an increased separation of nodes, is not incorporated in traditional network processes such re-routing mechanism, and therefore, processes for reconfiguration can be abstracted which both optimize the physical interconnections while maintaining interoperability with existing protocols. This control framework is validated using network metrics for latency and throughput and compared to existing architectures which use only standard re-routing mechanisms. Results are shown that validate both the analogous molecular modeling of a reconfigurable beam-steered directional wireless backbone network and a comprehensive control architecture which coalesces the unique capabilities of reconfiguration and mobility of mobile wireless backbone networks with existing protocols for networks such as IPv6

Feature-based hybrid inspection planning for complex mechanical parts

Globalization and emerging new powers in the manufacturing world are among many challenges, major manufacturing enterprises are facing. This resulted in increased alternatives to satisfy customers\u27 growing needs regarding products\u27 aesthetic and functional requirements. Complexity of part design and engineering specifications to satisfy such needs often require a better use of advanced and more accurate tools to achieve good quality. Inspection is a crucial manufacturing function that should be further improved to cope with such challenges. Intelligent planning for inspection of parts with complex geometric shapes and free form surfaces using contact or non-contact devices is still a major challenge. Research in segmentation and localization techniques should also enable inspection systems to utilize modern measurement technologies capable of collecting huge number of measured points. Advanced digitization tools can be classified as contact or non-contact sensors. The purpose of this thesis is to develop a hybrid inspection planning system that benefits from the advantages of both techniques. Moreover, the minimization of deviation of measured part from the original CAD model is not the only characteristic that should be considered when implementing the localization process in order to accept or reject the part; geometric tolerances must also be considered. A segmentation technique that deals directly with the individual points is a necessary step in the developed inspection system, where the output is the actual measured points, not a tessellated model as commonly implemented by current segmentation tools. The contribution of this work is three folds. First, a knowledge-based system was developed for selecting the most suitable sensor using an inspection-specific features taxonomy in form of a 3D Matrix where each cell includes the corresponding knowledge rules and generate inspection tasks. A Travel Salesperson Problem (TSP) has been applied for sequencing these hybrid inspection tasks. A novel region-based segmentation algorithm was developed which deals directly with the measured point cloud and generates sub-point clouds, each of which represents a feature to be inspected and includes the original measured points. Finally, a new tolerance-based localization algorithm was developed to verify the functional requirements and was applied and tested using form tolerance specifications. This research enhances the existing inspection planning systems for complex mechanical parts with a hybrid inspection planning model. The main benefits of the developed segmentation and tolerance-based localization algorithms are the improvement of inspection decisions in order not to reject good parts that would have otherwise been rejected due to misleading results from currently available localization techniques. The better and more accurate inspection decisions achieved will lead to less scrap, which, in turn, will reduce the product cost and improve the company potential in the market

Three Branch Diversity Systems for Multi-Hop IoT Networks

Internet of Things (IoT) is an emerging technological paradigm connecting numerous smart objects for advanced applications ranging from home automation to industrial control to healthcare. The rapid development of wireless technologies and miniature embedded devices has enabled IoT systems for such applications, which have been deployed in a variety of environments. One of the factors limiting the performance of IoT devices is the multipath fading caused by reflectors and attenuators present in the environment where these devices are deployed. Leveraging polarization diversity is a well-known technique to mitigate the deep signal fades and depolarization effects caused by multipath. However, neither experimental validation of the performance of polarization diversity antenna with more than two branches nor the potency of existing antenna selection techniques on such antennas in practical scenarios has received much attention. The objectives of this dissertation are threefold. First, to demonstrate the efficacy of a tripolar antenna, which is specifically designed for IoT devices, in harsh environments through simulations and experimental data. Second, to develop antenna selection strategies to utilize polarized signals received at the antenna, considering the restrictions imposed due to resource limitations of the IoT devices. Finally, to conduct comparative analyses on the existing standard diversity techniques and proposed approaches, in conjunction with experimental data. Accordingly, this dissertation presents the testing results of tripolar antenna integrated with Arduino based IoT devices deployed in environments likely to be experienced by IoT devices in real life applications. Both simulation and experimental results from single point-to-point wireless links demonstrate the advantage of utilizing tripolar antennas in harsh propagation conditions over single branch antenna. Motivated by these empirical results, we deploy a small-scale IoT network with tripolar antenna based nodes to analyze the impact of tripolar antenna on neighbor nodes performance as well as to investigate end-to-end network performance. This work illustrates that the selection of antenna branches, while considering network architecture and the level of congestion on the repeater nodes, minimizes excessive antenna switching and energy consumption. Similar results are shown for IoT networks with predetermined and dynamic routing protocols, where the proposed techniques yielded lower energy consumption than the conventional diversity schemes. Furthermore, a probabilistic, low complexity antenna selection approach based on Hidden Markov model is proposed and implemented on wireless sensor nodes aiming to reduce energy consumption and improve diversity gain. Finally, we develop a dual-hop based technique where a node selects the antenna element for optimal performance based on its immediate network neighbors antenna configuration status during selection. The performance of the proposed technique, which is verified through simulation and measured data, illustrates the importance of considering network-wide evaluations of antenna selection techniques