An instance of coincidence detection architecture relying on temporal coding

Although time and space are interrelated in every occurrence of real-world events, only spatial codes are used at the basic level of most computational architectures. Inspired by neurobiological facts and hypotheses that assign a primordial coding role to the temporal dimension, and developed to address both cognitive and engineering applications, guided propagation networks (GPNs) are aimed at a generic real-time machine, based on time-space coincidence testing. The involved temporal parameters are gradually introduced, in relation with complementary applications in the field of human-machine communication: sensori-motor modeling, pattern recognition and natural language processing.     

Comparing binary and real-valued coding in hybrid immune algorithm for feature selection and classification of ECG signals

The paper presents a new algorithm for feature selection and classification. The algorithm is based on an immune metaphor, and combines both negative and clonal selection mechanisms characteristic for B- and T-lymphocytes. The main goal of the algorithm is to select the best subset of features for classification. Two level evolution is used in the proposed system for detectors creation and feature selection. Subpopulations of evolving detectors (T-lymphocytes) are able to discover subsets of features well suited for classification. The subpopulations cooperate during evolution by means of a novel suppression mechanism which is compared to the traditional suppression mechanism. The proposed suppression method proved to be superior to the traditional suppression in both recognition performance and its ability to select the proper number of subpopulations dynamically. Some results in the task of ECG signals classification are presented. The results for binary and real coded T-lymphocytes are compared and discussed.     

Agent-based architecture for designing hybrid control systems

When designing very complex control strategy using hybrid technology, one usually faces the challenge of balancing effective realization of multi-control modeling with design simplicity. To better manage this difficulty we have used the agent paradigm as a simple and powerful bridge between asynchronous/distributed computation and Matlab environment. The proposed architecture has been used to design a complex hybrid control environment using multi-objective, fuzzy c-means, and genetic algorithms optimization to design hybrid control strategies suitable for the energy flows management on board of hybrid electric vehicles.     

The hybrid field-programmable architecture

The authors propose a new architecture that combines two existing technologies: lookup-table-based FPGAs and complex programmable logic devices based on PLA-like blocks. Their mapping results indicate that on average LUT-based FPGAs require 78% more area than their hybrid FPGA, while providing roughly the same circuit depth     

Real-time compression architecture for efficient coding in autostereoscopic displays

Integral imaging is a promising technique for delivering high-quality three-dimensional content. However, the large amounts of data produced during acquisition prohibits direct transmission of Integral Image data. A number of highly efficient compression architectures are proposed today that outperform standard two-dimensional encoding schemes. However, critical issues regarding real-time compression for quality demanding applications are a primary concern to currently existing Integral Image encoders. In this work we propose a real-time FPGA-based encoder for Integral Image and integral video content transmission. The proposed encoder is based on a highly efficient compression algorithm used in Integral Imaging applications. Real-time performance is achieved by realizing a pipelined architecture, taking into account the specific structure of an Integral Image. The required memory access operations are minimized by adopting a systolic concept of data flow through the core processing elements, further increasing the performance boost. The encoder targets, real-time, broadcast-type high-resolution Integral Image and video sequences and performs three orders of magnitude faster than the analogous software approach.     

Multilevel signals and trellis coding for data communication modems

The special bandwidth efficiency features of coded single carrier multilevel data communication signals pave the way for implementation of high speed data transmission via public analogue telephone networks. The minimum distance of nonredundant coded signals and the minimum free-distance of redundant coded signals are discussed and their influence on error rates is analysed. Security of data on public networks, where it is subject to noise and interference, is improved by advanced trellis coding. Combining the trellis with multilevel signalling provides high speed data modems with security against nearly all spurious signals, transient noise and circuit impairments. The signal structure and coding algorithm of CCITT's recent recommendations on 9600 bit/s and 14 400 bit/s modems for PSTN and leased lines are also discussed.     

A hybrid genetic-neural architecture for stock indexes forecasting

In this paper, a new approach for time series forecasting is presented. The forecasting activity results from the interaction of a population of experts, each integrating genetic and neural technologies. An expert of this kind embodies a genetic classifier designed to control the activation of a feedforward artificial neural network for performing a locally scoped forecasting activity. Genetic and neural components are supplied with different information: The former deal with inputs encoding information retrieved from technical analysis, whereas the latter process other relevant inputs, in particular past stock prices. To investigate the performance of the proposed approach in response to real data, a stock market forecasting system has been implemented and tested on two stock market indexes, allowing for account realistic trading commissions. The results pointed to the good forecasting capability of the approach, which repeatedly outperformed the “Buy and Hold” strategy.     

A hybrid similarity measure of contents for TV personalization

Similarity measure of contents plays an important role in TV personalization, e.g., TV content group recommendation and similar TV content retrieval, which essentially are content clustering and example-based retrieval. We define similar TV contents to be those with similar semantic information, e.g., plot, background, genre, etc. Several similarity measure methods, notably vector space model based and category hierarchy model based similarity measure schemes, have been proposed for the purpose of data clustering and example-based retrieval. Each method has advantages and shortcomings of its own in TV content similarity measure. In this paper, we propose a hybrid approach for TV content similarity measure, which combines both vector space model and category hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the hybrid similarity measure proposed here is superior to using either alone for TV content clustering and example-based retrieval.     

Distortion of neural signals by spike coding

Analog neural signals must be converted into spike trains for transmission over electrically leaky axons. This spike encoding and subsequent decoding leads to distortion. We quantify this distortion by deriving approximate expressions for the mean square error between the inputs and outputs of a spiking link. We use integrate-and-fire and Poisson encoders to convert naturalistic stimuli into spike trains and spike count and inter-spike interval decoders to generate reconstructions of the stimulus. The distortion expressions enable us to compare these spike coding schemes over a large parameter space. We verify that the integrate-and-fire encoder is more effective than the Poisson encoder. The disparity between the two encoders diminishes as the stimulus coefficient of variation (CV) increases, at which point, the variability attributed to the stimulus overwhelms the variability attributed to Poisson statistics. When the stimulus CV is small, the interspike interval decoder is superior, as the distortion resulting from spike count decoding is dominated by a term that is attributed to the discrete nature of the spike count. In this regime, additive noise has a greater impact on the interspike interval decoder than the spike count decoder. When the stimulus CV is large, the average signal excursion is much larger than the quantization step size, and spike count decoding is superior.     

A hybrid edge-cloud architecture for reducing on-demand gaming latency

The cloud was originally designed to provide general-purpose computing using commodity hardware and its focus was on increasing resource consolidation as a means to lower cost. Hence, it was not particularly adapted to the requirements of multimedia applications that are highly latency sensitive and require specialized hardware, such as graphical processing units. Existing cloud infrastructure is dimensioned to serve general-purpose workloads and to meet end-user requirements by providing high throughput. In this paper, we investigate the effectiveness of using this general-purpose infrastructure for serving latency-sensitive multimedia applications. In particular, we examine on-demand gaming, also known as cloud gaming, which has the potential to change the video game industry. We demonstrate through a large-scale measurement study that the existing cloud infrastructure is unable to meet the strict latency requirements necessary for acceptable on-demand game play. Furthermore, we investigate the effectiveness of incorporating edge servers, which are servers located near end-users (e.g., CDN servers), to improve end-user coverage. Specifically, we examine an edge-server-only infrastructure and a hybrid infrastructure that consists of using edge servers in addition to the cloud. We find that a hybrid infrastructure significantly improves the number of end-users served. However, the number of satisfied end-users in a hybrid deployment largely depends on the various deployment parameters. Therefore, we evaluate various strategies that determine two such parameters, namely, the location of on-demand gaming servers and the games that are placed on these servers. We find that, through both a careful selection of on-demand gaming servers and the games to place on these servers, we significantly increase the number of end-users served over the basic random selection and placement strategies.     

An architecture for generic extensions

We examine what is necessary to allow generic libraries to be used naturally in a multi-language, potentially distributed environment. Language-neutral library interfaces usually do not support the full range of programming idioms that are available when a library is used natively. We investigate how to structure the language bindings of the neutral interface to achieve a better expressibility and code re-use. We furthermore address how language-neutral interfaces can be extended with import bindings to recover the desired programming idioms. We also address the question of how these extensions can be organized to minimize the performance overhead that arises from using objects in manners not anticipated by the original library designers. Our approach is to treat a library as a software component and to view the problem as one of component extension. We use C++ as an example of a mature language, with libraries using a variety of patterns, and use the Standard Template Library as an example of a complex library for which efficiency is important. By viewing the library extension problem as one of component organization, we enhance software composibility, hierarchy maintenance and architecture independence.     

Unified development of algorithms used for linear predictive coding of speech signals

The requirement for low data rate voice transmission has resulted in a large number of algorithms being proposed for speech digitization at data rates of 2·4–4 kilobits/sec. Many of the proposed algorithms are quite complicated and have their origin in disciplines generally considered to be outside of the realm of the speech researcher or communication system designer. Additionally, the algorithms have been developed and presented in highly varying notation using various theoretical approaches. The result is a confusing array of equations, algorithms, and numerical analysis procedure. It is the goal of this paper to alleviate this problem by providing a unified tutorial development of the various algorithms used and proposed for speech data compression.Classical least squares estimation theory is used as the focal point of the discussion since it forms the basis for several of the more familiar speech digitization algorithms. The remainder of the algorithms, whether they have their basis in stochastic estimation theory or statistical regression theory, are related back to the more familiar least squares approach. The speech digitization techniques discussed are the covariance method, the autocorrelation method, the PARCOR method, a priori analysis, the sequential least squares method, the Kalman filter approach, the stochastic approximation method, and the general linear regression model. An effort has been made to provide sufficient theoretical background to establish the algorithm relationships without stressing mathematical rigor.     

Cooperative, hybrid agent architecture for real-time traffic signal control

This paper presents a new hybrid, synergistic approach in applying computational intelligence concepts to implement a cooperative, hierarchical, multiagent system for real-time traffic signal control of a complex traffic network. The large-scale traffic signal control problem is divided into various subproblems, and each subproblem is handled by an intelligent agent with a fuzzy neural decision-making module. The decisions made by lower-level agents are mediated by their respective higher-level agents. Through adopting a cooperative distributed problem solving approach, coordinated control by the agents is achieved. In order for the multiagent architecture to adapt itself continuously to the dynamically changing problem domain, a multistage online learning process for each agent is implemented involving reinforcement learning, learning rate and weight adjustment as well as dynamic update of fuzzy relations using an evolutionary algorithm. The test bed used for this research is a section of the Central Business District of Singapore. The performance of the proposed multiagent architecture is evaluated against the set of signal plans used by the current real-time adaptive traffic control system. The multiagent architecture produces significant improvements in the conditions of the traffic network, reducing the total mean delay by 40% and total vehicle stoppage time by 50%.     

A hybrid P2P communications architecture for zonal MMOGs

Distributed Virtual Environments are becoming more popular in today’s computing and communications among people. Perhaps the most widely used form of such environments is Massively Multiplayer Online Games (MMOG), which are in the form of client/server architecture that requires considerable server resources to manage a large number of distributed players. Peer-to-peer communication can achieve scalability at lower cost but may introduce other difficulties. Synchronous communication is a prime concern for multi-user collaborative applications like MMOGs where players need frequently interaction with each other to share their game states. In this article, we present a hybrid MMOG architecture called MM-VISA (Massively Multiuser VIrtual Simulation Architecture). In this architecture, servers and peers are coupled together to take the inherent advantages of the centralized architecture and the scalability of distributed systems. As the virtual world is decomposed into smaller manageable zones, the players’ random movement causes reorganization at the P2P overlay structure. The frequent nature of movements along with unintelligent zone crossing approaches, currently implemented in MMOGs, breaks synchronous communication. To limit such problem, we consider players’ gaming characteristics to intelligently define routing paths. A graph-theoretic framework is incorporated for overlay oriented real-time distributed virtual environments. We shall show that interest-driven zone crossing, dynamic shared region between adjacent zones, and clustering of entities based on their attributes significantly decrease unstable overlay situations. The effectiveness of the presented system is justified through simulation.     

A global security architecture for operated hybrid WLAN mesh networks

Hybrid Wireless Mesh Network (HWMN) is a new wireless networking paradigm. Unlike traditional wireless networks, in HWMNs, hosts may rely on each other to keep the network connected. Operators and wireless internet service providers are choosing HWMNs to offer Internet connectivity, as it allows fast, easy and affordable network deployments. One main challenge in design of these networks is their vulnerability to security attacks. In this paper, we investigate the main security issues focusing on the most vulnerable part of the hybrid WLAN mesh infrastructure which concerns the ad hoc network part. Through our proposed architecture, Security Architecture for Operator’s Hybrid WLAN Mesh Network (SATHAME), we identify the new challenges and opportunities posed by this emerging networking environment and explore approaches to secure users, data and communications. From the analysis of strengths and weaknesses of secured routing protocols, we designed a new robust routing structure called MacroGraph (MG). MG structure is extracted from the mesh ad hoc network for each communication to be established between a source and a destination. Especially, MG is a robust structure based on node-disjoint path routing scheme and dynamic trust management that can be adapted to respond to applications’ security requirements. We present a performance analysis of our efficient, robust and scalable multipath reactive secured routing protocol. We investigate the behavior of our proposed scheme under two attack scenarios: Packet Dropping and Route Error attacks in dense network configurations.     

组合结构设计的双频带数字预失真方法

双频带射频功放的数字预失真技术(DPD)要求支持更宽的带宽.为了保证功放输出的线性指标并降低预失真系统的实现开销,设计一种组合结构的双频带宽带数字预失真方法.该方法采用一维整频带数字预失真模型与四个一维分频带数字预失真模型的组合结构来综合处理双频带的发射信号.仿真实验结果表明,采用90 MHz的双频带LTE信号,临道泄露比(ACLR)指标较改进的二维记忆多项式DPD有1 dB提升,与二维DPD有0.5 dB差别,满足系统指标要求.