Network-on-Chip (NoC) architectures have emerged as a better replacement of the traditional bus-based communication in the many-core era. However, continuous technology scaling has made aging mechanisms, such as Negative Bias Temperature Instability (NBTI) and electromigration, primary concerns in NoC design. In this work, a novel system-level aging model is proposed to model the effects of aging in NoCs, caused due to (a) asymmetric communication patterns between the network nodes, and (b) runtime traffic variations due to routing policies. This work observes a critical need of a holistic aging analysis, which when combined with power-performance optimization, poses a multi-objective design challenge. To solve this problem, two different aging-aware routing algorithms are proposed: (a) congestion-oblivious Mixed Integer Linear Programming (MILP)-based routing algorithm, and (b) congestion-aware adaptive routing algorithm and router micro-architecture. After extensive experimental evaluations, proposed routing algorithms reduce aging-induced power-performance overheads while also improving the system robustness.

Indiana University-Purdue University Indianapolis (IUPUI) / The Convolutional Neural Network (CNN) have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks. Capsule Networks have addressed the limitations of CNNs and have shown a great improvement by calculating the pose and transformation of the image. Deeper networks are more powerful than shallow networks but at the same time, more difficult to train. Residual Networks ease the training and have shown evidence that they can give good accuracy with considerable depth. Putting the best of Capsule Network and Residual Network together, we present Residual Capsule Network and 3-Level Residual Capsule Network, a framework that uses the best of Residual Networks and Capsule Networks. The conventional Convolutional layer in Capsule Network is replaced by skip connections like the Residual Networks to decrease the complexity of the Baseline Capsule Network and seven ensemble Capsule Network. We trained our models on MNIST and CIFAR-10 datasets and have seen a significant decrease in the number of parameters when compared to the Baseline models.

No description available.

This dissertation is submitted in fulfillment of the requirements for the degree of Master of Science, University of the Witwatersrand, Johannesburg, August 2018 / Abstract. The vehicle routing problem is a common combinatorial optimization, which is modelled to determine the best set routes to deploy a fleet of vehicles to customers, in order to deliver or collect goods efficiently. The vehicle routing problem has rich applications in design and management of distribution systems. Many combinatorial optimization algorithms which have been developed, were inspired through the study of vehicle routing problems. Despite the literature on vehicle routing problems, the existing techniques fail to perform well when n (the number of variables defining the problem) is very large, i.e., when n > 50. In this dissertation, we survey exact and inexact methods to solve large problems. Our attention is on the capacitated vehicle routing problem. For exact methods, we investigate only the Cutting Planes method which has recently been used in conjunction with other combinatorial optimization problem algorithms (like the Branch and Bound method) to solve large problems. In this investigation, we study the polyhedral structure of the capacitated vehicle routing problem. We compare two metaheuristics, viz., the Genetic Algorithm and the Ant Colony Optimization. In the genetic algorithm, we study the effect of four different crossover operators. Numerical results are presented and conclusion are drawn, based on our findings. / XL2019

Student Number : 9700136A - PhD thesis - School of Civil and Environmental Engineering - Faculty of Engineering and the Built Environment / A sediment budget model in which each steady discharge scours sediment along a trajectory towards ultimate target storage or deposits sediment towards the same ultimate target storage has been conceptualized and developed. The method is aimed at routing sediment in morphologically diverse bedrock-controlled channels in which sediment transport and storage is not a continuous process in space and time and mostly occurs in response to discrete discharges. The relative value of the ultimate stable scour depth (Huss) for each steady discharge with respect to the current scour depth after adding sediment supply determines the potential to scour or store sediment. Scour depths measured at discrete locations along the longitudinal profile of a laboratory pool at discrete times until changes in scour were not discernible for each steady discharge and sediment size have been integrated to provide the Huss and storage depletion curve. The experimentally established dependence of scour depth on critical flow depth, settling velocity and sediment supply formed the basis of generating dimensionless Huss and storage depletion curve from these parameters using the Buckingham π theorem. The optimization of experimental results to generate the storage depletion curve gave the exponent of time (φ) and the exponential decay factor (k) as 0.5 and 0.0040207 respectively. Regression fit of dimensionless Huss and critical flow intensity gave a linear relationship with a gradient of 0.90214, y-intercept of –1.4766 and R2 of 96%. The suitability of the model for budgeting sediment dynamics in a series of connected storage units, the validity of using the relative values of Huss and the current scour depth after adding sediment supply to determine scour potential and the existence of active storage associated with sediment supply for each steady discharge have been confirmed experimentally. Modelling with equivalent steady discharges computed from unit stream power principles on the rising and the falling limbs of the hydrograph resulted in scour on the rising limb of magnitude dependent on the magnitude and sequence of the flood event, and less or no scour on recession. The modelling concepts and approach have thus been validated and the potential to reasonably simulate sediment storage changes in bedrock-controlled rivers demonstrated.

Manyindustrialautomationsolutionsusewirelesscommunicationandrelyontheavail- ability and quality of the wireless channel. At the same time the wireless medium is highly congested and guaranteeing the availability of wireless channels is becoming increasingly difficult. In this work we show, that ad-hoc networking solutions can be used to provide new communication channels and improve the performance of mobile automation systems. These ad-hoc networking solutions describe different communi- cation strategies, but avoid relying on network infrastructure by utilizing the Peer-to- Peer (P2P) channel between communicating entities. This work is a step towards the effective implementation of low-range communication technologies(e.g. VisibleLightCommunication(VLC), radarcommunication, mmWave communication) to the industrial application. Implementing infrastructure networks with these technologies is unrealistic, since the low communication range would neces- sitate a high number of Access Points (APs) to yield full coverage. However, ad-hoc networks do not require any network infrastructure. In this work different ad-hoc net- working solutions for the industrial use case are presented and tools and models for their examination are proposed. The main use case investigated in this work are Automated Guided Vehicles (AGVs) for industrial applications. These mobile devices drive throughout the factory trans- porting crates, goods or tools or assisting workers. In most implementations they must exchange data with a Central Control Unit (CCU) and between one another. Predicting if a certain communication technology is suitable for an application is very challenging since the applications and the resulting requirements are very heterogeneous. The proposed models and simulation tools enable the simulation of the complex inter- action of mobile robotic clients and a wireless communication network. The goal is to predict the characteristics of a networked AGV fleet. Theproposedtoolswereusedtoimplement, testandexaminedifferentad-hocnetwork- ing solutions for industrial applications using AGVs. These communication solutions handle time-critical and delay-tolerant communication. Additionally a control method for the AGVs is proposed, which optimizes the communication and in turn increases the transport performance of the AGV fleet. Therefore, this work provides not only tools for the further research of industrial ad-hoc system, but also first implementations of ad-hoc systems which address many of the most pressing issues in industrial applica- tions. / Viele industrielle Automatisierungslösungen verwenden drahtlose Kommunikations- systeme und sind daher auf die Verfügbarkeit und Qualität des drahtlosen Kanals an- gewiesen. Gleichzeitig ist das drahtlose Medium stark belastet und die Gewährleis- tung der Verfügbarkeit der drahtlosen Kanäle wird zunehmends herrausfordernder. In dieser Arbeit wird gezeigt, dass Ad-hoc-Netzwerklösungen genutzt werden können, um neue Kommunikationskanäle bereitzustellen und die Leistung von mobilen Au- tomatisierungssystemen zu verbessern. Diese Ad-hoc-Netzwerklösungen können un- terschiedliche Kommunikationsstrategien bezeichnen. In all diesen Strategien wird der Peer-to-Peer (P2P)-Kanal zwischen zwei kommunizierenden Systemen verwendet statt Netzwerk-Infrastruktur. Diese Arbeit ist ein Schritt hin zur effektiven Implementierung von Kommunikations- technologien mit geringer Reichweite (z.B. Visible Light Communication (VLC), Radar- kommunikation, mmWave-Kommunikation) in der industriellen Anwendung. Die Im- plementierung von Infrastrukturnetzen mit diesen Technologien ist unrealistisch, da die geringe Kommunikationsreichweite eine hohe Anzahl von Access Points (APs) er- fordern würde um eine flächendeckende Bereitstellung von Kommunikationskanälen zu gewährleisten. Ad-hoc-Netzwerke hingegen benötigen keine Netzwerkinfrastruk- tur. In dieser Arbeit werden verschiedene Ad-hoc-Netzwerklösungen für den industri- ellenAnwendungsfallvorgestelltundWerkzeugeundModellefürderenUntersuchung vorgeschlagen. Der Hauptanwendungsfall, der in dieser Arbeit untersucht wird, sind Fahrerlose Trans- portSysteme (FTS) (fortführend als Automated Guided Vehicles (AGVs)) für industri- elle Anwendungen. Diese FTS fahren durch die Produktionsanlage um Kisten, Waren oder Werkzeuge zu transportieren oder um Mitarbeitern zu assistieren. In den meisten Implementierungen müssen sie Daten mit einer Central Control Unit (CCU) und unter- einander austauschen. Die Vorhersage, ob eine bestimmte Kommunikationstechnologie für eine Anwendung geeignet ist, ist sehr anspruchsvoll, da sowohl Anwendungen als auch Anforderungen sehr heterogen sind. Die präsentierten Modelle und Simulationswerkzeuge ermöglichen die Simulation der komplexen Interaktion von mobilen Robotern und drahtlosen Kommunikationsnetz- werken. Das Ziel ist die Vorhersage der Eigenschaften einer vernetzten FTS-Flotte. Mit den vorgestellten Werkzeugen wurden verschiedene Ad-hoc-Netzwerklösungen für industrielle Anwendungen mit FTS implementiert, getestet und untersucht. Die- se Kommunikationssysteme übertragen zeitkritische und verzögerungstolerante Nach- richten. Zusätzlich wird eine Steuerungsmethode für die FTS vorgeschlagen, die die KommunikationoptimiertunddamiteinhergehenddieTransportleistungderFTS-Flotte erhöht. Dieses Werk führt also nicht nur neue Werkzeuge ein um die Entwicklung in- dustrieller Ad-hoc Systeme zu ermöglichen, sondern schlägt auch einige Systeme für die kritischsten Kommunikationsprobleme industrieller Anwendungen vor.

The growing usage of computer networks is requiring improvements in network technologies and management techniques so users will receive high quality service. As more individuals transmit data through a computer network, the quality of service received by the users begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing. A more effective method for routing data through a computer network can assist with the new problems being encountered with today's growing networks. Effective routing algorithms use various techniques to determine the most appropriate route for transmitting data. Determining the best route through a wide area network (WAN), requires the routing algorithm to obtain information concerning all of the nodes, links, and devices present on the network. The most relevant routing information involves various measures that are often obtained in an imprecise or inaccurate manner, thus suggesting that fuzzy reasoning is a natural method to employ in an improved routing scheme. The neural network is deemed as a suitable accompaniment because it maintains the ability to learn in dynamic situations. Once the neural network is initially designed, any alterations in the computer routing environment can easily be learned by this adaptive artificial intelligence method. The capability to learn and adapt is essential in today's rapidly growing and changing computer networks. These techniques, fuzzy reasoning and neural networks, when combined together provide a very effective routing algorithm for computer networks. Computer simulation is employed to prove the new fuzzy routing algorithm outperforms the Shortest Path First (SPF) algorithm in most computer network situations. The benefits increase as the computer network migrates from a stable network to a more variable one. The advantages of applying this fuzzy routing algorithm are apparent when considering the dynamic nature of modern computer networks. / Ph. D.

No description available.

No description available.

No description available.