Acknowledge-Based Non-Congestion Estimation: An Indirect Queue Management Approach for Concurrent TCP and UDP-Like Flows

This paper presents a new approach for indirect Active Queue Management (indirect AQM) technique called Acknowledge-based Non-Congestion Estimation (ANCE), which employs end-to-end queue management along a network instead to use Explicit Congestion Notification (ECN) bit or to drop packets in the queue. The ANCE performance was compared with Random Early Detection (RED), Control Delay (CoDel), Proportional Integral controller Enhanced (PIE), Explicit Non-Congestion Notification (ENCN), TCP-Jersey and E-DCTCP schemes in a daisychain and in a dumbbell cenario, with TCP flows and UDP-like Networked Control Systems (NCS) flow sharing the same network topology. On the other hand, this paper presents a method for modeling, simulation and verification of communication systems and NCS, using UPPAAL software tool, on which, all network components (channels, routers, transmitters, receivers, plants, and Controllers) were modeled using timed automata making easy a formal verification of the whole modeled system. Simulations and statistical verification show that despite using fewer resources (since ANCE does not need the ECN bit) ANCE presents a very close performance  to ENCN overcoming Drop Tail, RED, CoDel, PIE and E-DCTCP in terms of Integral Time Absolute Error (ITAE) for NCS and fairness for TCP flows. ANCE also attains better performance than RED, PIE, TCP-Jersey and E-DCTCP in terms of throughput for TCP flows

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Non-classical computing: feasible versus infeasible

Physics sets certain limits on what is and is not computable. These limits are very far from having been reached by current technologies. Whilst proposals for hypercomputation are almost certainly infeasible, there are a number of non classical approaches that do hold considerable promise. There are a range of possible architectures that could be implemented on silicon that are distinctly different from the von Neumann model. Beyond this, quantum simulators, which are the quantum equivalent of analogue computers, may be constructable in the near future

Cellular Automata Can Reduce Memory Requirements of Collective-State Computing

Various non-classical approaches of distributed information processing, such as neural networks, computation with Ising models, reservoir computing, vector symbolic architectures, and others, employ the principle of collective-state computing. In this type of computing, the variables relevant in a computation are superimposed into a single high-dimensional state vector, the collective-state. The variable encoding uses a fixed set of random patterns, which has to be stored and kept available during the computation. Here we show that an elementary cellular automaton with rule 90 (CA90) enables space-time tradeoff for collective-state computing models that use random dense binary representations, i.e., memory requirements can be traded off with computation running CA90. We investigate the randomization behavior of CA90, in particular, the relation between the length of the randomization period and the size of the grid, and how CA90 preserves similarity in the presence of the initialization noise. Based on these analyses we discuss how to optimize a collective-state computing model, in which CA90 expands representations on the fly from short seed patterns - rather than storing the full set of random patterns. The CA90 expansion is applied and tested in concrete scenarios using reservoir computing and vector symbolic architectures. Our experimental results show that collective-state computing with CA90 expansion performs similarly compared to traditional collective-state models, in which random patterns are generated initially by a pseudo-random number generator and then stored in a large memory.Comment: 13 pages, 11 figure

Urban land expansion model based on SLEUTH, a case study in Dongguan city, China

The SLEUTH urban model is developed with sets of predefined growing rules involving Spontaneous Growth, New Spreading Center Growth, Edge Growth, Road Influenced Growth and Self-modification. They are applied continuously to lead the urban simulation to a specific morphology. A SLEUTH land use model was set up to simulate urban growth trajectory of Dongguan city from 1997 to 2009. The accuracy of localized parameters was evaluated to illuminate the growth pattern of Dongguan. Two different scenarios were set to predict the urban development from 2022 to 2030. Edge Growth is the dominant force of Dongguan's urbanization: regions adjacent to growth centers are more likely to be urbanized than remote area in general. Rapid urban expansion takes up large amount of other land types, around 2030, urbanization will reach the critical state in spatial. Unlike excessive growth rate in scenario 1, the urbanization speed is obviously more reasonable and sustainable in scenario 2, which confirms SLEUTH urban model is a good assistant of urban planning to avoid willful expansion with a scenario forecast. To protect ecological environment and promoting sustainable development of the region, relevant decision makers should take effective strategies to control urban sprawl. By the set of forecast scenarios, SLEUTH can certainly predict future urban development as an auxiliary to urban planners and government.Dongguan is under rapid urbanization in these decades. SLEUTH is an urban land use model named after the six input layers (Slope, Land use, Excluded, Urban, Transportation and Hill shade), and it is applied for simulating how surrounding land use changes due to urban expansion. A SLEUTH model was coupled with multi-source GIS (Geographic Information Systems) and RS (Remote Sensing) data to simulate urban growth trajectory of Dongguan city from 1997 to 2009. The accuracy of localized parameters was evaluated to illuminate the growth pattern of Dongguan. Based on the hypothesis that the urbanization process is as fast as before, a historical scenario from 2010 to 2050 was built up to choose the suitable study periods. In order to prove SLEUTH is able to offer reasonable outcomes for urban plan, two different scenarios were set to predict the urban development from 2022 to 2030, which shows SLEUTH is able to offer reasonable outcomes to government policy makers. Finally, the dynamic mechanism of urban growth combined with local characteristics was discussed. Some suggestions were also proposed for future urban planning and policy making in this study

Predicting Sets of Automata: Architecture, Evolution, Examples of Prognosis, and Applications

This chapter describes the sets of interacting automata constructed on the cascades of wavelet coefficients of input signal. The basic principles of the evolution of automata during the processing of incoming cascades and the vector of processes consisting of segments of cascades of constant length are described. The main principles of constructing the family of automata are determined from the internal symmetry of incoming cascades and the definition of symmetry groups of vector processes and their isotropy groups. The trajectories of states are defined on nontrivial topological spaces, the so-called degeneration spaces of the characteristic functional. The family of evolving automata with tunable communications architecture is designed to predict the state of engineering objects and identify predictors, early predictors, and hidden predictors of failure. This chapter provides examples of the work of predictive automata in various fields of engineering and medicine. It demonstrates the operation of the automaton in spaces with a nontrivial topology of input cascades, algorithms of the predictor search, and estimations. The family of evolving automata with reconstructing architecture of connections is designed to predict the state of engineering objects and medicine and identify predictors, early predictors, and hidden predictors of failure. The architecture and functional properties of automata are determined from the results and main conclusions

Hypoxic Cell Waves around Necrotic Cores in Glioblastoma: A Biomathematical Model and its Therapeutic Implications

Glioblastoma is a rapidly evolving high-grade astrocytoma that is distinguished pathologically from lower grade gliomas by the presence of necrosis and microvascular hiperplasia. Necrotic areas are typically surrounded by hypercellular regions known as "pseudopalisades" originated by local tumor vessel occlusions that induce collective cellular migration events. This leads to the formation of waves of tumor cells actively migrating away from central hypoxia. We present a mathematical model that incorporates the interplay among two tumor cell phenotypes, a necrotic core and the oxygen distribution. Our simulations reveal the formation of a traveling wave of tumor cells that reproduces the observed histologic patterns of pseudopalisades. Additional simulations of the model equations show that preventing the collapse of tumor microvessels leads to slower glioma invasion, a fact that might be exploited for therapeutic purposes.Comment: 29 pages, 9 figure