Self-triggered Stabilization of Contracting Systems under Quantization

We propose self-triggered control schemes for nonlinear systems with quantized state measurements. Our focus lies on scenarios where both the controller and the self-triggering mechanism receive only the quantized state measurement at each sampling time. We assume that the ideal closed-loop system without quantization or self-triggered sampling is contracting. Moreover, a growth rate of the open-loop system is assumed to be known. We present two control strategies that yield the closed-loop stability without Zeno behavior. The first strategy is implemented under logarithmic quantization and imposes no time-triggering condition other than setting an upper bound on inter-sampling times. The second one is a joint design of zooming quantization and periodic self-triggered sampling, where the adjustable zoom parameter for quantization changes based on inter-sampling times and is also used for the threshold of self-triggered sampling. In both strategies, we employ a trajectory-based approach for stability analysis, where contraction theory plays a key role.Comment: 26 pages, 10 figure

Neuromorphic Auditory Perception by Neural Spiketrum

Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems. To realize the promised brain-like intelligence, it needs to solve the challenges of the neuromorphic hardware architecture design of biological neural substrate and the hardware amicable algorithms with spike-based encoding and learning. Here we introduce a neural spike coding model termed spiketrum, to characterize and transform the time-varying analog signals, typically auditory signals, into computationally efficient spatiotemporal spike patterns. It minimizes the information loss occurring at the analog-to-spike transformation and possesses informational robustness to neural fluctuations and spike losses. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks. We further investigate the algorithm-hardware co-designs through a neuromorphic cochlear prototype which demonstrates that our approach can provide a systematic solution for spike-based artificial intelligence by fully exploiting its advantages with spike-based computation.Comment: This work has been submitted to the IEEE for possible publicatio

Contribution to reliable end-to-end communication over 5G networks using advanced techniques

5G cellular communication, especially with its hugely available bandwidth provided by millimeter-wave, is a promising technology to fulfill the coming high demand for vast data rates. These networks can support new use cases such as Vehicle to Vehicle and augmented reality due to its novel features such as network slicing along with the mmWave multi-gigabit-persecond data rate. Nevertheless, 5G cellular networks suffer from some shortcomings, especially in high frequencies because of the intermittent nature of channels when the frequency rises. Non-line of sight state is one of the significant issues that the new generation encounters. This drawback is because of the intense susceptibility of higher frequencies to blockage caused by obstacles and misalignment. This unique characteristic can impair the performance of the reliable transport layer widely deployed protocol, TCP, in attaining high throughput and low latency throughout a fair network. As a result, the protocol needs to adjust the congestion window size based on the current situation of the network. However, TCP cannot adjust its congestion window efficiently, which leads to throughput degradation of the protocol. This thesis presents a comprehensive analysis of reliable end-to-end communications in 5G networks and analyzes TCP’s behavior in one of the 3GPP’s well-known s cenarios called urban deployment. Furtherm ore, two novel TCPs bas ed on artificial intelligence have been proposed to deal with this issue. The first protocol uses Fuzzy logic, a subset of artificial intelligence, and the second one is based on deep learning. The extensively conducted simulations showed that the newly proposed protocols could attain higher performance than common TCPs, such as BBR, HighSpeed, Cubic, and NewReno in terms of throughput, RTT, and sending rate adjustment in the urban scenario. The new protocols' superiority is achieved by employing smartness in the conges tions control mechanism of TCP, which is a powerful enabler in fos tering TCP’s functionality. To s um up, the 5G network is a promising telecommunication infrastructure that will revolute various aspects of communication. However, different parts of the Internet, such as its regulations and protocol stack, will face new challenges, which need to be solved in order to exploit 5G capacity, and without intelligent rules and protocols, the high bandwidth of 5G, especially 5G mmWave will be wasted. Two novel schemes to solve the issues have been proposed based on an Artificial Intelligence subset technique called fuzzy and a machine learning-based approach called Deep learning to enhance the performance of 5G mmWave by improving the functionality of the transport layer. The obtained results indicated that the new schemes could improve the functionality of TCP by giving intelligence to the protocol. As the protocol works more smartly, it can make sufficient decisions on different conditions.La comunicació cel·lular 5G, especialment amb l’amplada de banda molt disponible que proporciona l’ona mil·limètrica, és una tecnologia prometedora per satisfer l’elevada demanda de grans velocitats de dades. Aquestes xarxes poden admetre casos d’ús nous, com ara Vehicle to Vehicle i realitat augmentada, a causa de les seves novetats, com ara el tall de xarxa juntament amb la velocitat de dades mWave de multi-gigabit per segon. Tot i això, les xarxes cel·lulars 5G pateixen algunes deficiències, sobretot en freqüències altes a causa de la naturalesa intermitent dels canals quan augmenta la freqüència. L’estat de no visió és un dels problemes significatius que troba la nova generació. Aquest inconvenient es deu a la intensa susceptibilitat de freqüències més altes al bloqueig causat per obstacles i desalineació. Aquesta característica única pot perjudicar el rendiment del protocol TCP, àmpliament desplegat, de capa de transport fiable en aconseguir un alt rendiment i una latència baixa en tota una xarxa justa. Com a resultat, el protocol ha d’ajustar la mida de la finestra de congestió en funció de la situació actual de la xarxa. Tot i això, TCP no pot ajustar la seva finestra de congestió de manera eficient, cosa que provoca una degradació del rendiment del protocol. Aquesta tesi presenta una anàlisi completa de comunicacions extrem a extrem en xarxes 5G i analitza el comportament de TCP en un dels escenaris coneguts del 3GPP anomenat desplegament urbà. A més, s'han proposat dos TCP nous basats en intel·ligència artificial per tractar aquest tema. El primer protocol utilitza la lògica Fuzzy, un subconjunt d’intel·ligència artificial, i el segon es basa en l’aprenentatge profund. Les simulacions àmpliament realitzades van mostrar que els protocols proposats recentment podrien assolir un rendiment superior als TCP habituals, com ara BBR, HighSpeed, Cubic i NewReno, en termes de rendiment, RTT i ajust d’índex d’enviament en l’escenari urbà. La superioritat dels nous protocols s’aconsegueix utilitzant la intel·ligència en el mecanisme de control de congestions de TCP, que és un poderós facilitador per fomentar la funcionalitat de TCP. En resum, la xarxa 5G és una prometedora infraestructura de telecomunicacions que revolucionarà diversos aspectes de la comunicació. No obstant això, diferents parts d’Internet, com ara les seves regulacions i la seva pila de protocols, s’enfrontaran a nous reptes, que cal resoldre per explotar la capacitat 5G, i sens regles i protocols intel·ligents, l’amplada de banda elevada de 5G, especialment 5G mmWave, pot ser desaprofitat. S'han proposat dos nous es quemes per resoldre els problemes basats en una tècnica de subconjunt d'Intel·ligència Artificial anomenada “difusa” i un enfocament basat en l'aprenentatge automàtic anomenat “Aprenentatge profund” per millorar el rendiment de 5G mmWave, millorant la funcionalitat de la capa de transport. Els resultats obtinguts van indicar que els nous esquemes podrien millorar la funcionalitat de TCP donant intel·ligència al protocol. Com que el protocol funciona de manera més intel·ligent, pot prendre decisions suficients en diferents condicionsPostprint (published version

Detection of abandoned objects in crowded environments

With concerns about terrorism and global security on the rise, it has become vital to have in place efficient threat detection systems that will identify potentially dangerous situations, and alert the authorities to take appropriate action. Of particular relevance is the case of abandoned objects in highly crowded areas. This thesis describes a general framework that recognizes the event of someone leaving an object unattended in forbidden areas. Our approach involves the recognition of four sub-events that characterize the activity of interest. When an unaccompanied object is found, the system analyzes its history to determine its most likely owner(s). Through subsequent frames, the system keeps a lookout for the owner, whose presence in or disappearance from the scene defines the status of the object and determines the appropriate course of action. The system was successfully implemented and tested on several standardized datasets.Electrical and Computer Engineerin

Proceedings of the 2012 Workshop on Ambient Intelligence Infrastructures (WAmIi)

This is a technical report including the papers presented at the Workshop on Ambient Intelligence Infrastructures (WAmIi) that took place in conjunction with the International Joint Conference on Ambient Intelligence (AmI) in Pisa, Italy on November 13, 2012. The motivation for organizing the workshop was the wish to learn from past experience on Ambient Intelligence systems, and in particular, on the lessons learned on the system architecture of such systems. A significant number of European projects and other research have been performed, often with the goal of developing AmI technology to showcase AmI scenarios. We believe that for AmI to become further successfully accepted the system architecture is essential

Proceedings of the 2012 Workshop on Ambient Intelligence Infrastructures (WAmIi)

This is a technical report including the papers presented at the Workshop on Ambient Intelligence Infrastructures (WAmIi) that took place in conjunction with the International Joint Conference on Ambient Intelligence (AmI) in Pisa, Italy on November 13, 2012. The motivation for organizing the workshop was the wish to learn from past experience on Ambient Intelligence systems, and in particular, on the lessons learned on the system architecture of such systems. A significant number of European projects and other research have been performed, often with the goal of developing AmI technology to showcase AmI scenarios. We believe that for AmI to become further successfully accepted the system architecture is essential