Treatment-Based Classi?cation in Residential Wireless Access Points

IEEE 802.11 wireless access points (APs) act as the central communication hub inside homes, connecting all networked devices to the Internet. Home users run a variety of network applications with diverse Quality-of-Service requirements (QoS) through their APs. However, wireless APs are often the bottleneck in residential networks as broadband connection speeds keep increasing. Because of the lack of QoS support and complicated configuration procedures in most off-the-shelf APs, users can experience QoS degradation with their wireless networks, especially when multiple applications are running concurrently. This dissertation presents CATNAP, Classification And Treatment iN an AP , to provide better QoS support for various applications over residential wireless networks, especially timely delivery for real-time applications and high throughput for download-based applications. CATNAP consists of three major components: supporting functions, classifiers, and treatment modules. The supporting functions collect necessary flow level statistics and feed it into the CATNAP classifiers. Then, the CATNAP classifiers categorize flows along three-dimensions: response-based/non-response-based, interactive/non-interactive, and greedy/non-greedy. Each CATNAP traffic category can be directly mapped to one of the following treatments: push/delay, limited advertised window size/drop, and reserve bandwidth. Based on the classification results, the CATNAP treatment module automatically applies the treatment policy to provide better QoS support. CATNAP is implemented with the NS network simulator, and evaluated against DropTail and Strict Priority Queue (SPQ) under various network and traffic conditions. In most simulation cases, CATNAP provides better QoS supports than DropTail: it lowers queuing delay for multimedia applications such as VoIP, games and video, fairly treats FTP flows with various round trip times, and is even functional when misbehaving UDP traffic is present. Unlike current QoS methods, CATNAP is a plug-and-play solution, automatically classifying and treating flows without any user configuration, or any modification to end hosts or applications

On I/O Performance and Cost Efficiency of Cloud Storage: A Client\u27s Perspective

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service provider’s data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage. In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a client’s perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors of cloud storage from the client side. Through extensive experiments, we have obtained several critical findings and useful implications for system optimization. We then design a client cache framework, called Pacaca, to further improve end-to-end performance of cloud storage. Pacaca seamlessly integrates parallelized prefetching and cost-aware caching by utilizing the parallelism potential and object correlations of cloud storage. In addition to improving system performance, we have also made efforts to reduce the monetary cost of using cloud storage services by proposing a latency- and cost-aware client caching scheme, called GDS-LC, which can achieve two optimization goals for using cloud storage services: low access latency and low monetary cost. Our experimental results show that our proposed client-side solutions significantly outperform traditional methods. Our study contributes to inspiring the community to reconsider system optimization methods in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary storage layer

Planning and dynamic spectrum management in heterogeneous mobile networks with QoE optimization

The radio and network planning and optimisation are continuous processes that do not end after the network has been launched. To achieve the best trade-offs, especially between quality and costs, operators make use of several coverage and capacity enhancement methods. The research from this thesis proposes methods such as the implementation of cell zooming and Relay Stations (RSs) with dynamic sleep modes and Carrier Aggregation (CA) for coverage and capacity enhancements. Initially, a survey is presented on ubiquitous mesh networks implementation scenarios and an updated characterization of requirements for services and applications is proposed. The performance targets for the key parameters, delay, delay variation, information loss and throughput have been addressed for all types of services. Furthermore, with the increased competition, mobile operator’s success does not only depend on how good the offered Quality of Service (QoS) is, but also if it meets the end user’s expectations, i.e., Quality of Experience (QoE). In this context, a model for the mapping between QoS parameters and QoE has been proposed for multimedia traffic. The planning and optimization of fixed Worldwide Interoperability for Microwave Access (WiMAX) networks with RSs in conjunction with cell zooming has been addressed. The challenging case of a propagation measurement-based scenario in the hilly region of Covilhã has been considered. A cost/revenue function has been developed by taking into account the cost of building and maintaining the infrastructure with the use of RSs. This part of the work also investigates the energy efficiency and economic implications of the use of power saving modes for RSs in conjunction with cell zooming. Assuming that the RSs can be switched-off or zoomed out to zero in periods when the traffic exchange is low, such as nights and weekends, it has been shown that energy consumption may be reduced whereas cellular coverage and capacity, as well as economic performance may be improved. An integrated Common Radio Resource Management (iCRRM) entity is proposed that implements inter-band CA by performing scheduling between two Long Term Evolution – Advanced (LTE-A) Component Carriers (CCs). Considering the bandwidths available in Portugal, the 800 MHz and 2.6 GHz CCs have been considered whilst mobile video traffic is addressed. Through extensive simulations it has been found that the proposed multi-band schedulers overcome the capacity of LTE systems without CA. Result shown a clear improvement of the QoS, QoE and economic trade-off with CA

MediaSync: Handbook on Multimedia Synchronization

This book provides an approachable overview of the most recent advances in the fascinating field of media synchronization (mediasync), gathering contributions from the most representative and influential experts. Understanding the challenges of this field in the current multi-sensory, multi-device, and multi-protocol world is not an easy task. The book revisits the foundations of mediasync, including theoretical frameworks and models, highlights ongoing research efforts, like hybrid broadband broadcast (HBB) delivery and users' perception modeling (i.e., Quality of Experience or QoE), and paves the way for the future (e.g., towards the deployment of multi-sensory and ultra-realistic experiences). Although many advances around mediasync have been devised and deployed, this area of research is getting renewed attention to overcome remaining challenges in the next-generation (heterogeneous and ubiquitous) media ecosystem. Given the significant advances in this research area, its current relevance and the multiple disciplines it involves, the availability of a reference book on mediasync becomes necessary. This book fills the gap in this context. In particular, it addresses key aspects and reviews the most relevant contributions within the mediasync research space, from different perspectives. Mediasync: Handbook on Multimedia Synchronization is the perfect companion for scholars and practitioners that want to acquire strong knowledge about this research area, and also approach the challenges behind ensuring the best mediated experiences, by providing the adequate synchronization between the media elements that constitute these experiences

Human-Machinic Assemblages: Technologies, Bodies, and the Recuperation of Social Reproduction in the Crisis Era

This dissertation argues that class composition, as defined and theorised by Operaismo and Autonomist thinkers, has had both a major and a minoritarian form. In fact class composition in its major form has always been subtended by a minor current. I examine both historical (the 1905 Russian Soviets, the 1919 Turin factory councils, the Italian social movements of the 1970s) and contemporary examples (the occupation of Tahrir Square in Egypt, the Indignados movement in Spain, and Occupy Wall Street in 2011, as well as the 2012 Quebec student strike) of class composition. From these examples I then argue that the minor current of class composition is rooted in social reproduction – both its crisis and its recuperation. And further that this minor current expands throughout history, growing to command greater attention within social and labour movements. Further, this dissertation argues that contemporary social movements appear today as an assemblage, a human-machinic assemblage, which enact social reproduction in crisis and recuperation through both embodied and technologized forms. I demonstrate the ways in which technologies of communication are implicated in forms of securitised and commodified social reproduction, but also open up new and powerful possibilities for autonomous and liberatory social reproduction. This dissertation relies on a merger of conceptual, theoretical, and field research and benefits from the author’s direct involvement in social and political struggles

Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903

Técnicas avanzadas de aprendizaje profundo para la detección y análisis de tos en pacientes respiratorios

Antecedentes: La tos es un mecanismo de defensa y expulsión del aparato respiratorio que provoca una respuesta refleja y sonora. En la actualidad, el análisis de la tos como marcador sintomático del avance de una enfermedad se apoya en instrumentos poco adecuados para el seguimiento en escenarios de la vida real. Algunos solo se han evaluado en ambientes silenciosos y controlados, otros se diseñaron para resolver un problema más general que la detección de la tos o se enfocan en una población muy concreta. Asimismo, algunos enfoques no se han concebido con la eficiencia requerida para operar en un smartphone. Por estos motivos, los métodos de análisis de audio empleados en estos dispositivos no son capaces de manejar ambientes ruidosos, como el caso de un paciente que use su smartphone en el bolsillo como dispositivo de captura de datos. Objetivo: Este Trabajo de Fin de Máster (TFM) tiene como propósito emplear técnicas de aprendizaje profundo (Deep Learning) para diseñar un sistema de “audición máquina” (Machine Hearing) que procese espectrogramas de señales acústicas y los clasifique de acuerdo a su contenido. Específicamente, se pretende reconocer los espectrogramas que contienen tos y los que no, y además clasificar cada tos a partir de sus propiedades espectrales según la enfermedad respiratorias asociada a la tos o el tipo de tos. Métodos: Para llevar a cabo el proyecto, adquirimos 36866 señales de audio contaminadas por ruido de 20 pacientes respiratorios con distintas afecciones. La mitad de estas señales correspondieron a episodios de tos, mientras que la otra mitad no contenía ningún sonido de tos. Estas señales de audio se someten a un preprocesamiento en tres etapas. Primero, las señales de audio originales (señales de tos y no tos) se segmentan para que cada segmento dure un segundo. En segundo lugar, se transforman las señales 1D temporales en imágenes (señales 2D) mediante tres métodos. Los dos primeros métodos transforman cada clip de audio, que son señales de tiempo (1D), en señales de tiempo-frecuencia (imágenes 2D) realizando un espectrograma logarítmico o un espectrograma de mel. El tercer método aplica a los audios la técnica de ventanas deslizadas cambiando la forma del vector y transformándolo en una matriz. Posteriormente, los datos se normalizan para poder alimentar a una red neuronal recurrente convolucional (C-LSTM). La red neuronal convolucional (Convolutional Neural Network, CNN) extrae características de los espectrogramas de audio automáticamente para identificar “patrones” espectrales o temporales. Luego, se alimenta a una red neuronal recurrente de memoria a corto plazo (Long Short-Term Memory, LSTM), que predice el frame actual haciendo referencia a los frames adyacentes. De esta manera, primero detecta si el clip de audio contiene tos o no, y en caso afirmativo, procedemos a realizar un análisis posterior con el objetivo de detectar el tipo de tos o la enfermedad subyacente. Resultados: El sistema de audiodetección de tos que obtuvo una especificidad mas alta presenta sensibilidad del 86,23% y una especificidad del 93,90 %. Por otro lado, el método de clasificación de tos que obtuvo la mayor exactitud fue el que discriminó entre tos de pacientes con COVID-19 y tos de pacientes que tiene síntomas pero sin diagnóstico de COVID-19, que obtuvo un 58,21 %. Conclusiones: Los resultados de este TFM abren la posibilidad de crear un dispositivo cómodo y no invasivo, con una mínima interferencia en las actividades cotidianas, capaz de detectar con carácter temprano enfermedades respiratorias, beneficiando a pacientes, profesionales sanitarios y sistemas nacionales de salud.Background: Coughing is a defense mechanism and expulsion mechanism of the respiratory system that causes a reflexive and audible response. Currently, the analysis of cough as a symptomatic marker of disease progression relies on instruments that are poorly suited for monitoring in real-life scenarios. Some have only been evaluated in quiet and controlled environments, while others were designed to solve a problem more general than cough detection or focus on a very specific population. Additionally, some approaches have not been conceived with the required efficiency to operate on a smartphone. For these reasons, the audio analysis methods used in these devices are not capable of handling noisy environments, such as the case of a patient using their smartphone in their pocket as a data capture device. Objective: The purpose of this Master’s Thesis is to employ deep learning techniques to design a “machine hearing” system that processes spectrograms of acoustic signals and classifies them according to their content. Specifically, the aim is to recognize spectrograms that contain coughing and those that do not, as well as classify the disease associated with each cough based on their spectral properties. Methods: To carry out the project, audio signals contaminated with noise from twenty patients with various respiratory conditions were used, along with 18,433 audio signals recorded during cough episodes and 18,433 audio signals recorded during non-cough episodes. These audio signals undergo preprocessing in three stages. First, the original audio signals (cough and non-cough signals) are segmented to have a duration of one second each. Secondly, the temporal 1D signals are transformed into images (2D signals) using three methods. The first two methods transform each audio, which are time-domain signals, into time-frequency signals by performing a logarithmic spectrogram or a mel spectrogram. The third method applies sliding windows to the audios, changing the vector shape and transforming it into a matrix. Subsequently, the data is normalized to feed into a Convolutional Long Short-Term Memory (C-LSTM) neural network. The Convolutional Neural Network (CNN) automatically extracts features from the audio spectrograms to identify spectral or temporal “patterns”. Finally, the processed data is fed into a Long Short-Term Memory (LSTM) recurrent neural network, which predicts the current frame by referencing adjacent frames. In this way, it first detects if the audio contains a cough or not, and if it does, it proceeds to diagnose the respiratory disease. Results: The audio detection system for coughs that achieved the highest specificity had a sensitivity of 86.23% and a specificity of 93.90 %. On the other hand, the cough classification method with the highest accuracy was the one that discriminated between coughs from COVID-19 patients and coughs from patients with symptoms but without a COVID-19 diagnosis, which achieved 58.21 %. Conclusions: The results of this Master’s Thesis open up the possibility of creating a comfortable and noninvasive device with minimal interference in daily activities, capable of early detecting respiratory diseases, benefiting patients, healthcare professionals, and national health systems.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Ingeniería de Telecomunicació