A Turbo Detection and Sphere-Packing-Modulation-Aided Space-Time Coding Scheme

Arecently proposed space-time block-coding (STBC) signal-construction method that combines orthogonal design with sphere packing (SP), referred to here as STBC-SP, has shown useful performance improvements over Alamouti’s conventional orthogonal design. In this contribution, we demonstrate that the performance of STBC-SP systems can be further improved by concatenating SP-aided modulation with channel coding and performing demapping as well as channel decoding iteratively. We also investigate the convergence behavior of this concatenated scheme with the aid of extrinsic-information-transfer charts. The proposed turbo-detected STBC-SP scheme exhibits a “turbo-cliff” at Eb/N0 = 2.5 dB and provides Eb/N0 gains of approximately 20.2 and 2.0 dB at a bit error rate of 10?5 over an equivalent throughput uncoded STBC-SP scheme and a turbo-detected quadrature phase shift keying (QPSK) modulated STBC scheme, respectively, when communicating over a correlated Rayleigh fading channel. Index Terms—EXIT charts, iterative demapping, multidimensional mapping, space-time coding, sphere packing, turbo detection

Concatenated Space Time Block Codes and TCM, Turbo TCM Convolutional as well as Turbo Codes

Space-time block codes provide substantial diversity advantages for multiple transmit antenna systems at a low decoding complexity. In this paper, we concatenate space-time codes with Convolutional Codes (CC), Turbo Convolutional codes (TC), Turbo BCH codes (TBCH), Trellis Coded Modulation (TCM) and Turbo Trellis Coded Modulation (TTCM) schemes for achieving a high coding gain. The associated performance and complexity of the coding schemes is compared

The classical-quantum divergence of complexity in modelling spin chains

The minimal memory required to model a given stochastic process - known as the statistical complexity - is a widely adopted quantifier of structure in complexity science. Here, we ask if quantum mechanics can fundamentally change the qualitative behaviour of this measure. We study this question in the context of the classical Ising spin chain. In this system, the statistical complexity is known to grow monotonically with temperature. We evaluate the spin chain's quantum mechanical statistical complexity by explicitly constructing its provably simplest quantum model, and demonstrate that this measure exhibits drastically different behaviour: it rises to a maximum at some finite temperature then tends back towards zero for higher temperatures. This demonstrates how complexity, as captured by the amount of memory required to model a process, can exhibit radically different behaviour when quantum processing is allowed.Comment: 9 pages, 3 figures, comments are welcom

Using a mobile robot to test a theory of cognitive mapping

This paper describes using a mobile robot, equipped with some sonar sensors and an odometer, to test navigation through the use of a cognitive map. The robot explores an office environment, computes a cognitive map, which is a network of ASRs [36, 35], and attempts to find its way home. Ten trials were conducted and the robot found its way home each time. From four random positions in two trials, the robot estimated the home position relative to its current position reasonably accurately. Our robot does not solve the simultaneous localization and mapping problem and the map computed is fuzzy and inaccurate with much of the details missing. In each homeward journey, it computes a new cognitive map of the same part of the environment, as seen from the perspective of the homeward journey. We show how the robot uses distance information from both maps to find its way home. © 2007 Springer-Verlag Berlin Heidelberg

Computing a network of ASRs using a mobile robot equipped with sonar sensors

This paper presents a novel algorithm for computing absolute space representations (ASRs) [1]-[2] for mobile robots equipped with sonar sensors and an odometer. The robot is allowed to wander freely (i.e. without following any fixed path) along the corridors in an office environment from a given start point to an end point. It then wanders from the end point back to the start point. The resulting ASRs computed in both directions are shown. © 2006 IEEE

A split & merge approach to metric-topological map-building

We present a novel split and merge based method for dividing a given metric map into distinct regions, thus effectively creating a topological map on top of a metric one. The initial metric map is obtained from range data that are convened to a geometric map consisting of linear approximations of the indoor environment. The splitting is done using an objective function that computes the quality of a region, based on criteria such as the average region width (to distinguish big rooms from corridors) and overall direction (which accounts for sharp bends). A regularization term is used in order to avoid the formation of very small regions, which may originate from missing or unreliable sensor data. Experiments based on data acquired by a mobile robot equipped with sonar sensors are presented, which demonstrate the capabilities of the proposed method. © 2006 IEEE

Spatial information extraction for cognitive mapping with a mobile robot

When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting "cognitive map" be used for localization? In this paper, we present an approach using a mobile robot to generate a "cognitive map", the main focus being on experiments conducted in large spaces that the robot cannot apprehend at once due to the very limited range of its sensors. The robot computes a "cognitive map" and uses distance and orientation information for localization. © Springer-Verlag Berlin Heidelberg 2007

Surveying structural complexity in quantum many-body systems

Quantum many-body systems exhibit a rich and diverse range of exotic behaviours, owing to their underlying non-classical structure. These systems present a deep structure beyond those that can be captured by measures of correlation and entanglement alone. Using tools from complexity science, we characterise such structure. We investigate the structural complexities that can be found within the patterns that manifest from the observational data of these systems. In particular, using two prototypical quantum many-body systems as test cases - the one-dimensional quantum Ising and Bose-Hubbard models - we explore how different information-theoretic measures of complexity are able to identify different features of such patterns. This work furthers the understanding of fully-quantum notions of structure and complexity in quantum systems and dynamics.Comment: 9 pages, 5 figure

Using a mobile robot for cognitive mapping

When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting “cognitive map” be used to return home? In this paper, we presented a novel approach using a mobile robot to do cognitive mapping. Our robot computes a “cognitive map” and uses distance and orientation information to find its way home. The process developed provides interesting insights into the nature of cognitive mapping and encourages us to use a mobile robot to do cognitive mapping in the future, as opposed to its popular use in robot mapping