Estudi de la coautoria de publicacions científiques entre UPC i cinc universitats dels Estats Units : Caltech, Stanford University, UC Davis, UC Irvine i UCLA

S'analitza la coautoria de la UPC amb autors vinculats a institucions acadèmiques dels Estats Units, per totes les àrees temàtiques, de gener de 2009 a juny de 2014.Postprint (published version

Estudi comparatiu de la publicació científica de la UPC i l’ETSETB vs. altres universitats (2006-2016)

L'informe es centra en la publicació científica especialitzada en l'àmbit temàtic propi de l'ETSETB: l'enginyeria de telecomunicacions i l'electrònica. Es comparen indicadors bibliomètrics de la UPC i l'ETSETB amb els d'altres universitats nacionals, europees i internacionals amb activitat de recerca notable en l'àrea de les telecomunicacions i l'electrònica.Postprint (published version

Energy-efficient resource allocation scheme based on enhanced flower pollination algorithm for cloud computing data center

Cloud Computing (CC) has rapidly emerged as a successful paradigm for providing ICT infrastructure. Efficient and environmental-friendly resource allocation mechanisms, responsible for allocatinpg Cloud data center resources to execute user applications in the form of requests are undoubtedly required. One of the promising Nature-Inspired techniques for addressing virtualization, consolidation and energyaware problems is the Flower Pollination Algorithm (FPA). However, FPA suffers from entrapment and its static control parameters cannot maintain a balance between local and global search which could also lead to high energy consumption and inadequate resource utilization. This research developed an enhanced FPA-based energy efficient resource allocation scheme for Cloud data center which provides efficient resource utilization and energy efficiency with less probable Service Level Agreement (SLA) violations. Firstly, an Enhanced Flower Pollination Algorithm for Energy-Efficient Virtual Machine Placement (EFPA-EEVMP) was developed. In this algorithm, a Dynamic Switching Probability (DSP) strategy was adopted to balance the local and global search space in FPA used to minimize the energy consumption and maximize resource utilization. Secondly, Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) algorithm was developed. In this algorithm, Local Neighborhood Search (LNS) and Pareto optimisation strategies were combined with Clustering algorithm to avoid local trapping and address Cloud service providers conflicting objectives such as energy consumption and SLA violation. Lastly, Energy-Aware Multi-Cloud Flower Pollination Optimization (EAM-FPO) scheme was developed for distributed Multi-Cloud data center environment. In this scheme, Power Usage Effectiveness (PUE) and migration controller were utilised to obtain the optimal solution in a larger search space of the CC environment. The scheme was tested on MultiRecCloudSim simulator. Results of the simulation were compared with OEMACS, ACS-VMC, and EA-DP. The scheme produced outstanding performance improvement rate on the data center energy consumption by 20.5%, resource utilization by 23.9%, and SLA violation by 13.5%. The combined algorithms have reduced entrapment and maintaned balance between local and global search. Therefore, based on the findings the developed scheme has proven to be efficient in minimizing energy consumption while at the same time improving the data center resource allocation with minimum SLA violation

A New Media Access Control Protocol For VANET: Priority R-ALOHA (PR-ALOHA)

More practical applications of Media Access Control (MAC) protocols arise as the world turns increasingly wireless. Low delay, high throughput and reliable communication are essential requirements for standard performance in safety applications (e.g., lane changes warning, pre-crash warning and electronic brake lights). In particular, multi-priority protocols are important in Vehicular Ad Hoc Networks (VANETs), specifically in Inter-Vehicle Communication (IVC) where safety messages are given higher priority and transmitted faster than normal messages. The R-ALOHA protocol is considered one of the few promising protocols for VANETs because it is simple to implement and suitable for medium access control in Ad Hoc wireless networks. However, R-ALOHA lacks the property of prioritizing the different messages. In this dissertation, a new two-level priority MAC protocol called Priority R-ALOHA (PR-ALOHA) is presented to overcome the lack of priority problem in R-ALOHA. The two levels are low priority and high priority where priority is introduced by reserving specific time slots in the frame exclusively for high priority messages. This effectively increases the number of slots that a high priority message may compete for and thus decreases its delay. A two-dimensional Markov model coupled with Monte Carlo simulation is introduced to investigate the dynamic behavior of PR-ALOHA in steady and transient states. Modeling and simulation results of PR-ALOHA show that PR-ALOHA improves the performance of high priority traffic with limited effect on normal network traffic. Then, a dynamic slot allocation algorithm is introduced to PR-ALOH to optimize slot usage. Finally, a mobility model is introduced to emulate the behavior of the vehicles on the road where the performance of the PR-ALOHA with variable parameters, such as the length of the highway, the vehicle transmission range and the number of vehicles on the road have been investigated. Based on the findings of this dissertation, PR-ALOHA combined with dynamic slot allocation and mobility has a potential in applications like IVC where it can prevent car accidents through faster channel access and rapid transfer of warning messages to surrounding vehicles

Scalable Parallel Optimization of Digital Signal Processing in the Fourier Domain

The aim of the research presented in this thesis is to study different approaches to the parallel optimization of digital signal processing algorithms and optical coherence tomography methods. The parallel approaches are based on multithreading for multi-core and many-core architectures. The thesis follows the process of designing and implementing the parallel algorithms and programs and their integration into optical coherence tomography systems. Evaluations of the performance and the scalability of the proposed parallel solutions are presented. The digital signal processing considered in this thesis is divided into two groups. The first one includes generally employed algorithms operating with digital signals in Fourier domain. Those include forward and inverse Fourier transform, cross-correlation, convolution and others. The second group involves optical coherence tomography methods, which incorporate the aforementioned algorithms. These methods are used to generate cross-sectional, en-face and confocal images. Identifying the optimal parallel approaches to these methods allows improvements in the generated imagery in terms of performance and content. The proposed parallel accelerations lead to the generation of comprehensive imagery in real-time. Providing detailed visual information in real-time improves the utilization of the optical coherence tomography systems, especially in areas such as ophthalmology