Delays-induced Phase Transitions in Active Matter

We consider the patterns of collective motion emerging when many aligning, self-propelling units move in two dimensions while interacting through a repulsive potential and are also subject to delays and random perturbations. In this approach, delay plays the role analogous to reaction time so that a given particle is influenced by the information about the velocity and the position of the other particles in its vicinity with some time delay. To get insight into the involved complex flows and the transitions between them we use a simple model allowing, by fine-tuning of its few parameters, the observation and analysis of behaviours that are less accessible by experiments or analytic calculations and at the same time make the reproduction of experimental results possible. We report for the first time about a transition from an ordered, polarized collective motion to disorder as a function of the increasing time delay. For a fixed intermediate value of the delay similar transition (from order to disorder) is obtained as the repulsion radius is increased. Our simulations show a transition from total polarization to two kinds of states: fully disordered and a kind of state which is a mixture of patches of fully disordered motion in the background of orderly moving other particles. The transition occurs as the delay time is increased and is sharp, indicating that the nature of this order-disorder transition is either of first-order or is described by a sharply decreasing linear function. Our model is a simplified version of a practical situation of quickly growing interest because time delays are expected to play an increasingly important role when the traffic of many, densely distributed autonomous drones will move around in a quasi-two-dimensional air space

Efficient distributed load balancing for parallel algorithms

2009 - 2010With the advent of massive parallel processing technology, exploiting the power offered by hundreds, or even thousands of processors is all but a trivial task. Computing by using multi-processor, multi-core or many-core adds a number of additional challenges related to the cooperation and communication of multiple processing units. The uneven distribution of data among the various processors, i.e. the load imbalance, represents one of the major problems in data parallel applications. Without good load distribution strategies, we cannot reach good speedup, thus good efficiency. Load balancing strategies can be classified in several ways, according to the methods used to balance workload. For instance, dynamic load balancing algorithms make scheduling decisions during the execution and commonly results in better performance compared to static approaches, where task assignment is done before the execution. Even more important is the difference between centralized and distributed load balancing approaches. In fact, despite that centralized algorithms have a wider vision of the computation, hence may exploit smarter balancing techniques, they expose global synchronization and communication bottlenecks involving the master node. This definitely does not assure scalability with the number of processors. This dissertation studies the impact of different load balancing strategies. In particular, one of the key observations driving our work is that distributed algorithms work better than centralized ones in the context of load balancing for multi-processors (alike for multi-cores and many-cores as well). We first show a centralized approach for load balancing, then we propose several distributed approaches for problems having different parallelization, workload distribution and communication pattern. We try to efficiently combine several approaches to improve performance, in particular using predictive metrics to obtain a per task compute-time estimation, using adaptive subdivision, improving dynamic load balancing and addressing distributed balancing schemas. The main challenge tackled on this thesis has been to combine all these approaches together in new and efficient load balancing schemas. We assess the proposed balancing techniques, starting from centralized approaches to distributed ones, in distinctive real case scenarios: Mesh-like computation, Parallel Ray Tracing, and Agent-based Simulations. Moreover, we test our algorithms with parallel hardware such has cluster of workstations, multi-core processors and exploiting SIMD vectorial instruction set. Finally, we conclude the thesis with several remarks, about the impact of distributed techniques, the effect of the communication pattern and workload distribution, the use of cost estimation for adaptive partitioning, the trade-off fast versus accuracy in prediction-based approaches, the effectiveness of work stealing combined with sorting, and a non-trivial way to exploit hybrid CPUGPU computations. [edited by author]IX n.s

Computer vision for bird strike prevention

Collisions with birds cause damage to aircraft and in some cases can even cause air travel accidents. According to data from international organizations such as the Federal Aviation Administration (FAA), the radar-based tools currently used to address this problem do not solve it, as there is no indication of a decrease in the number of bird strikes. Early detection and notification to pilots of the presence of birds is key to trying to minimize the possibility that bird impacts can occur. The objective of this project is to improve bird detection capacity in the airport environment. To achieve this goal, this work proposes that the solution could be the use of artificial intelligence based devices and computer vision. To test this hypothesis, a model based on convolutional neural networks (CNN) is selected, trained and deployed on a device for testing. To do this, research is carried out on the different strategies used to solve problems with artificial intelligence and the performance of pre-trained classifier and detector models available. To select the computer board where the model will be deployed, a discussion of Raspberry Pi¿s market performance is made. A collection of bird images is made for training the model. The prototype will finally consist of deploying the model on a Raspberry Pi that through a script in Python programming language is able to automatically notice birds in the real world using a camera connected to the Raspberry Pi. If any detection occurs, the model is capable of making a notification that could serve to anticipate impacts and thus allow appropriate preventive measures to be taken beforehand. In conclusion, this technology shows great potential to support existing solutions today. Theoretical results with validation images show accuracy and recall parameters above 90% but experimental tests with the prototype do not allow for a conclusive judgment due to limitations regarding the training data set

Flexible high performance agent based modelling on graphics card hardware

Agent Based Modelling is a technique for computational simulation of complex interacting systems, through the specification of the behaviour of a number of autonomous individuals acting simultaneously. This is a bottom up approach, in contrast with the top down one of modelling the behaviour of the whole system through dynamic mathematical equations. The focus on individuals is considerably more computationally demanding, but provides a natural and flexible environment for studying systems demonstrating emergent behaviour. Despite the obvious parallelism, traditionally frameworks for Agent Based Modelling fail to exploit this and are often based on highly serialised mobile discrete agents. Such an approach has serious implications, placing stringent limitations on both the scale of models and the speed at which they may be simulated. Serial simulation frameworks are also unable to exploit multiple processor architectures which have become essential in improving overall processing speed. This thesis demonstrates that it is possible to use the parallelism of graphics card hardware as a mechanism for high performance Agent Based Modelling. Such an approach is in contrast with alternative high performance architectures, such as distributed grids and specialist computing clusters, and is considerably more cost effective. The use of consumer hardware makes the techniques described available to a wide range of users, and the use of automatically generated simulation code abstracts the process of mapping algorithms to the specialist hardware. This approach avoids the steep learning curve associated with the graphics card hardware's data parallel architecture, which has previously limited the uptake of this emerging technology. The performance and flexibility of this approach are considered through the use of benchmarking and case studies. The resulting speedup and locality of agent data within the graphics processor also allow real time visualisation of computationally and demanding high population models

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems