Sparse Nonlinear MIMO Filtering and Identification

In this chapter system identification algorithms for sparse nonlinear multi input multi output (MIMO) systems are developed. These algorithms are potentially useful in a variety of application areas including digital transmission systems incorporating power amplifier(s) along with multiple antennas, cognitive processing, adaptive control of nonlinear multivariable systems, and multivariable biological systems. Sparsity is a key constraint imposed on the model. The presence of sparsity is often dictated by physical considerations as in wireless fading channel-estimation. In other cases it appears as a pragmatic modelling approach that seeks to cope with the curse of dimensionality, particularly acute in nonlinear systems like Volterra type series. Three dentification approaches are discussed: conventional identification based on both input and output samples, semi–blind identification placing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulation

An adaptive detector implementation for MIMO-OFDM downlink

Cognitive radio (CR) systems require flexible and adaptive implementations of signal processing algorithms. An adaptive symbol detector is needed in the baseband receiver chain to achieve the desired flexibility of a CR system. This paper presents a novel design of an adaptive detector as an application-specific instruction-set processor (ASIP). The ASIP template is based on transport triggered architecture (TTA). The processor architecture is designed in such a manner that it can be programmed to support different suboptimal multiple-input multiple-output (MIMO) detection algorithms in a single TTA processor. The linear minimum mean-square error (LMMSE) and three variants of the selective spanning for fast enumeration (SSFE) detection algorithms are considered. The detection algorithm can be switched between the LMMSE and SSFE according to the bit error rate (BER) performance requirement in the TTA processor. The design can be scaled for different antenna configurations and different modulations. Some of the algorithm architecture co-optimization techniques used here are also presented. Unlike most other detector ASIPs, high level language is used to program the processor to meet the time-to-market requirements. The adaptive detector delivers 4.88 - 49.48 Mbps throughput at a clock frequency of 200 MHz on 90 nm technology

A Reconfigurable Platform For Cognitive Radio

Today¿s rigid spectrum allocation scheme creates a spectrum scarcity problem for future wireless communications. Measurements show that a wide range of the allocated frequency bands are rarely used. Cognitive radio is a novel approach to improve the spectrum usage, which is able to sense the spectrum and adapt its transmission while coexisting with the licensed spectrum user. A reconfigurable radio platform is required to provide enough adaptivity for cognitive radio. In this paper, we propose a cognitive radio system architecture and discuss its possible implementation on a heterogeneous reconfigurable radio platform

A time series feature of variability to detect two types of boredom from motion capture of the head and shoulders

Boredom and disengagement metrics are crucial to the correctly timed implementation of adaptive interventions in interactive systems. psychological research suggests that boredom (which other HCI teams have been able to partially quantify with pressure-sensing chair mats) is actually a composite: lethargy and restlessness. Here we present an innovative approach to the measurement and recognition of these two kinds of boredom, based on motion capture and video analysis of changes in head and shoulder positions. Discrete, three-minute, computer-presented stimuli (games, quizzes, films and music) covering a spectrum from engaging to boring/disengaging were used to elicit changes in cognitive/emotional states in seated, healthy volunteers. Interaction with the stimuli occurred with a handheld trackball instead of a mouse, so movements were assumed to be non-instrumental. Our results include a feature (standard deviation of windowed ranges) that may be more specific to boredom than mean speed of head movement, and that could be implemented in computer vision algorithms for disengagement detection

Cognitive Radio for Emergency Networks

In the scope of the Adaptive Ad-hoc Freeband (AAF) project, an emergency network built on top of Cognitive Radio is proposed to alleviate the spectrum shortage problem which is the major limitation for emergency networks. Cognitive Radio has been proposed as a promising technology to solve todayâ?~B??~D?s spectrum scarcity problem by allowing a secondary user in the non-used parts of the spectrum that aactully are assigned to primary services. Cognitive Radio has to work in different frequency bands and various wireless channels and supports multimedia services. A heterogenous reconfigurable System-on-Chip (SoC) architecture is proposed to enable the evolution from the traditional software defined radio to Cognitive Radio

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal