On Channel Capacity Estimation and Prediction for Rate-Adaptive Wireless Video

Packet drops caused by residue errors (MAC-layer errors) can severely deteriorate the wireless video quality. Prior studies have shown that this loss of quality can be circumvented by using forward error correction (FEC) to recover information from the corrupted packets. The performance of FEC encoded video streaming is critically dependent upon the choice of source and channel coding rates. In practice, the wireless channel conditions can vary significantly, thus altering the optimal rate choices. Thus, it is essential to develop an architecture which can estimate the channel capacity and utilize this estimate for rate allocation. In this paper we develop such a framework. Our contributions consist of two parts. In the first part we develop a prediction framework that leverages the received packets' signal to silence ratio (SSR) indications and MAC-layer checksum as side information to predict the operational channel capacity. In the second part, we use this prediction framework for rate allocation. The optimal rate allocation is dependent upon the channel capacity, the distribution of the (capacity) prediction error and the rate-distortion (RD) characteristics of the video source. Consequently, we propose a framework that utilizes the aforementioned statistics for RD optimal rate adaptation. We exhibit the efficacy of the proposed scheme by simulations using actual 802.11b wireless traces, an RD model for the video source and an ideal FEC model. Simulations using source RD models derived from five different popular video codecs (including H.264), show that the proposed framework provides up-to 5-dB improvements in peak signal-to-noise ratio (PSNR) when compared with conventional rate-adaptive schemes.      

New Results on Modal Participation Factors: Revealing a Previously Unknown Dichotomy

This paper presents a new fundamental approach to modal participation analysis of linear time-invariant systems, leading to new insights and new formulas for modal participation factors. Modal participation factors were introduced over a quarter century ago as a way of measuring the relative participation of modes in states, and of states in modes, for linear time-invariant systems. Participation factors have proved their usefulness in the field of electric power systems and in other applications. However, in the current understanding, it is routinely taken for granted that the measure of participation of modes in states is identical to that for participation of states in modes. Here, a new analysis using averaging over an uncertain set of system initial conditions yields the conclusion that these quantities (participation of modes in states and participation of states in modes) should not be viewed as interchangeable. In fact, it is proposed that a new definition and calculation replace the existing ones for state in mode participation factors, while the previously existing participation factors definition and formula should be retained but viewed only in the sense of mode in state participation factors. Several examples are used to illustrate the issues addressed and the results obtained.      

Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique

Many contour-based image corner detectors are based on the curvature scale-space (CSS). We identify the weaknesses of the CSS-based detectors. First, the “curvature” itself by its “definition” is very much sensitive to the local variation and noise on the curve, unless an appropriate smoothing is carried out beforehand. In addition, the calculation of curvature involves derivatives of up to second order, which may cause instability and errors in the result. Second, the Gaussian smoothing causes changes to the curve and it is difficult to select an appropriate smoothing-scale, resulting in poor performance of the CSS corner detection technique. We propose a complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation. The CPDA discrete curvature estimation technique is less sensitive to the local variation and noise on the curve. Moreover, it does not have the undesirable effect of the Gaussian smoothing. We provide a comprehensive performance study. Our experiments showed that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and localization error.      

Energetic and Informational Masking Effects in an Audiovisual Speech Recognition System

The paper presents a robust audiovisual speech recognition technique called audiovisual speech fragment decoding. The technique addresses the challenge of recognizing speech in the presence of competing nonstationary noise sources. It employs two stages. First, an acoustic analysis decomposes the acoustic signal into a number of spectro–temporall fragments. Second, audiovisual speech models are used to select fragments belonging to the target speech source. The approach is evaluated on a small vocabulary simultaneous speech recognition task in conditions that promote two contrasting types of masking: energetic masking caused by the energy of the masker utterance swamping that of the target, and informational masking, caused by similarity between the target and masker making it difficult to selectively attend to the correct source. Results show that the system is able to use the visual cues to reduce the effects of both types of masking. Further, whereas recovery from energetic masking may require detailed visual information (i.e., sufficient to carry phonetic content), release from informational masking can be achieved using very crude visual representations that encode little more than the timing of mouth opening and closure.      

Evolving Fuzzy Rules for Relaxed-Criteria Negotiation

In the literature on automated negotiation, very few negotiation agents are designed with the flexibility to slightly relax their negotiation criteria to reach a consensus more rapidly and with more certainty. Furthermore, these relaxed-criteria negotiation agents were not equipped with the ability to enhance their performance by learning and evolving their relaxed-criteria negotiation rules. The impetus of this work is designing market-driven negotiation agents (MDAs) that not only have the flexibility of relaxing bargaining criteria using fuzzy rules, but can also evolve their structures by learning new relaxed-criteria fuzzy rules to improve their negotiation outcomes as they participate in negotiations in more e-markets. To this end, an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules was developed. Implementing the idea in a testbed, two kinds of experiments for evaluating and comparing EvEMDAs (MDAs with relaxed-criteria rules that are evolved using the evolutionary algorithm) and EMDAs (MDAs with relaxed-criteria rules that are manually constructed) were carried out through stochastic simulations. Empirical results show that: 1) EvEMDAs generally outperformed EMDAs in different types of e-markets and 2) the negotiation outcomes of EvEMDAs generally improved as they negotiated in more e-markets.      

A Discrete-Time Robust Extended Kalman Filter for Uncertain Systems With Sum Quadratic Constraints

This technical note outlines the formulation of a novel discrete-time robust extended Kalman filter for uncertain systems with uncertainties described in terms of Sum Quadratic Constraints. The robust filter is an approximate set-valued state estimator which is robust in the sense that it can handle modeling uncertainties in addition to exogenous noise. Riccati and filter difference equations are obtained as an approximate solution to a reverse-time optimal control problem defining the set-valued state estimator. In order to obtain a solution to the set-valued state estimation problem, the discrete-time system dynamics are modeled backwards in time.      

Fast-Mesh: A Low-Delay High-Bandwidth Mesh for Peer-to-Peer Live Streaming

Peer-to-peer (P2P) technology has emerged as a promising scalable solution for live streaming to a large group. In this paper, we address the design of an overlay mesh which achieves low source-to-peer delay, accommodates asymmetric and diverse uplink bandwidth, and continuously improves delay based on an existing pool of peers. By considering a streaming mesh as an aggregation of data flows along multiple spanning trees, the peer delay in the mesh is then its longest delay (including both propagation and scheduling delay) among all the trees. Clearly, such delay can be very high if the mesh is not designed well. In this paper, we propose and study a mesh protocol called Fast-Mesh, which optimizes such delay while meeting a certain streaming bandwidth requirement. Fast-Mesh is particularly suitable for a mildly dynamic network consisting of proxies, supernodes, or content distribution servers.      

Optimization of Power Allocation for Interference Cancellation With Particle Swarm Optimization

In code division multiple access (CDMA) systems a significant degradation in detection performance due to multiuser interference can be avoided by the utilization of interference cancellation methods. Further enhancement can be obtained by optimizing the power allocation of the users. The resulting constrained single-objective optimization problem is solved here by means of particle swarm optimization (PSO). It is shown that the maximum number of users for a CDMA system can be increased significantly if an optimized power profile is employed. Furthermore, an extensive study of PSO control parameter settings using three different neighborhood topologies is performed on the basis of the power allocation problem, and two constraint-handling techniques are evaluated. Results from the parameter study are compared with examinations from the literature. It is shown that the von-Neumann neighborhood topology performs consistently better than gbest and lbest. However, strong interaction effects and conflicting recommendations for parameter settings are found that emphasize the need for adaptive approaches.      

Text-Like Segmentation of General Audio for Content-Based Retrieval

Automatic detection of (semantically) meaningful audio segments, or audio scenes, is an important step in high-level semantic inference from general audio signals, and can benefit various content-based applications involving both audio and multimodal (multimedia) data sets. Motivated by the known limitations of traditional low-level feature-based approaches, we propose in this paper a novel approach to discover audio scenes, based on an analysis of audio elements and key audio elements, which can be seen as equivalents to the words and keywords in a text document, respectively. In the proposed approach, an audio track is seen as a sequence of audio elements, and the presence of an audio scene boundary at a given time stamp is checked based on pair-wise measuring the semantic affinity between different parts of the analyzed audio stream surrounding that time stamp. Our proposed model for semantic affinity exploits the proven concepts from text document analysis, and is introduced here as a function of the distance between the audio parts considered, and the co-occurrence statistics and the importance weights of the audio elements contained therein. Experimental evaluation performed on a representative data set consisting of 5 h of diverse audio data streams indicated that the proposed approach is more effective than the traditional low-level feature-based approaches in solving the posed audio scene segmentation problem.      

Segmentation, Indexing, and Retrieval for Environmental and Natural Sounds

We propose a method for characterizing sound activity in fixed spaces through segmentation, indexing, and retrieval of continuous audio recordings. Regarding segmentation, we present a dynamic Bayesian network (DBN) that jointly infers onsets and end times of the most prominent sound events in the space, along with an extension of the algorithm for covering large spaces with distributed microphone arrays. Each segmented sound event is indexed with a hidden Markov model (HMM) that models the distribution of example-based queries that a user would employ to retrieve the event (or similar events). In order to increase the efficiency of the retrieval search, we recursively apply a modified spectral clustering algorithm to group similar sound events based on the distance between their corresponding HMMs. We then conduct a formal user study to obtain the relevancy decisions necessary for evaluation of our retrieval algorithm on both automatically and manually segmented sound clips. Furthermore, our segmentation and retrieval algorithms are shown to be effective in both quiet indoor and noisy outdoor recording conditions.      

Color-Coating Production Scheduling for Coils in Inventory in Steel Industry

This paper studies a large-scale scheduling problem in iron and steel industry, called Color-Coating Production Scheduling for Coils in Inventory (CCPSCI). The problem is to select steel coils from those in the coil yard and to create a production schedule so that the productivity and product quality are maximized, while the production cost and other penalties are minimized. A tabu search (TS) algorithm is proposed for this problem. Results on real production instances show that the proposed method is much more effective and efficient than manual scheduling.      

Beamforming With a Maximum Negentropy Criterion

In this paper, we address a beamforming application based on the capture of far-field speech data from a single speaker in a real meeting room. After the position of the speaker is estimated by a speaker tracking system, we construct a subband-domain beamformer in generalized sidelobe canceller (GSC) configuration. In contrast to conventional practice, we then optimize the active weight vectors of the GSC so as to obtain an output signal with maximum negentropy (MN). This implies the beamformer output should be as non-Gaussian as possible. For calculating negentropy, we consider the $Gamma$ and the generalized Gaussian (GG) pdfs. After MN beamforming, Zelinski postfiltering is performed to further enhance the speech by removing residual noise. Our beamforming algorithm can suppress noise and reverberation without the signal cancellation problems encountered in the conventional beamforming algorithms. We demonstrate this fact through a set of acoustic simulations. Moreover, we show the effectiveness of our proposed technique through a series of far-field automatic speech recognition experiments on the Multi-Channel Wall Street Journal Audio Visual Corpus (MC-WSJ-AV), a corpus of data captured with real far-field sensors, in a realistic acoustic environment, and spoken by real speakers. On the MC-WSJ-AV evaluation data, the delay-and-sum beamformer with postfiltering achieved a word error rate (WER) of 16.5%. MN beamforming with the $Gamma$ pdf achieved a 15.8% WER, which was further reduced to 13.2% with the GG pdf, whereas the simple delay-and-sum beamformer provided a WER of 17.8%. To the best of our knowledge, no lower error rates at present have     

Metasynthesis: M-Space, M-Interaction, and M-Computing for Open Complex Giant Systems

The studies of complex systems have been recognized as one of the greatest challenges for current and future science and technology. Open complex giant systems (OCGSs) are a family of specially complex systems with system complexities such as openness, human involvement, societal characteristic, and intelligence emergence. They greatly challenge multiple disciplines such as system sciences, system engineering, cognitive sciences, information systems, artificial intelligence, and computer sciences. As a result, traditional problem-solving methodologies can help deal with them but are far from a mature solution methodology. The theory of qualitative-to-quantitative metasynthesis has been proposed as a breakthrough and effective methodology for the understanding and problem solving of OCGSs. In this paper, we propose the concepts of M-Interaction, M-Space, and M-Computing which are three key components for studying OCGS and building problem-solving systems. M-Interaction forms the main problem-solving mechanism of qualitative-to-quantitative metasynthesis; M-Space is the OCGS problem-solving system embedded with M-Interactions, while M-Computing consists of engineering approaches to the analysis, design, and implementation of M-Space and M-Interaction. We discuss the theoretical framework, problem-solving process, social cognitive evolution, intelligence emergence, and pitfalls of certain types of cognitions in developing M-Space and M-Interaction from the perspectives of cognitive sciences and social cognitive interaction. These can help one understand complex systems and develop effective problem-solving methodologies.      

Neural-Network-Based State Feedback Control of a Nonlinear Discrete-Time System in Nonstrict Feedback Form

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.      

Low-Power Content-Based Video Acquisition for Super-Resolution Enhancement

Motivated by the optimization of power and improvement of video resolution, this paper proposes a content-based adaptive sampling system for video acquisition. Blind sampling suffers from the lack of resolution and blurring. However, use of À priori knowledge can provide intelligent sampling function that will reduce the blur artifacts. This paper proposes an information theoretic criteria-based sampling function. Higher sampling is proposed at high motion and edge regions while lower sampling at the low-frequency regions. This helps in providing better resolution with lower power consumption. Previous researches focus on enhancing the coding performance after the video acquisition stage. The proposed adaptive sampling scheme naturally performs super resolution without requiring extensive postprocessing. The proposed scheme has been tested on ten exemplary video sequences. Quality of the proposed adaptive sampling method is over 10–16 dB better than the coarsely sampled video. The power savings is ${approx}30hbox{--}40hbox{%}$ compared to acquiring the full resolution video.      

Nonlinear Complementary Filters on the Special Orthogonal Group

This paper considers the problem of obtaining good attitude estimates from measurements obtained from typical low cost inertial measurement units. The outputs of such systems are characterized by high noise levels and time varying additive biases. We formulate the filtering problem as deterministic observer kinematics posed directly on the special orthogonal group $SO(3)$ driven by reconstructed attitude and angular velocity measurements. Lyapunov analysis results for the proposed observers are derived that ensure almost global stability of the observer error. The approach taken leads to an observer that we term the direct complementary filter. By exploiting the geometry of the special orthogonal group a related observer, termed the passive complementary filter, is derived that decouples the gyro measurements from the reconstructed attitude in the observer inputs. Both the direct and passive filters can be extended to estimate gyro bias online. The passive filter is further developed to provide a formulation in terms of the measurement error that avoids any algebraic reconstruction of the attitude. This leads to an observer on $SO(3)$, termed the explicit complementary filter, that requires only accelerometer and gyro outputs; is suitable for implementation on embedded hardware; and provides good attitude estimates as well as estimating the gyro biases online. The performance of the observers are demonstrated with a set of experiments performed on a robotic test-bed and a radio controlled unmanned aerial vehicle.      

Modeling and Optimization of Meta-Caching Assisted Transcoding

The increase of aggregate Internet bandwidth and the rapid development of 3G wireless networks demand efficient delivery of multimedia objects to all types of wireless devices. To handle requests from wireless devices at runtime, the transcoding-enabled caching proxy has been proposed to save transcoded versions to reduce the intensive computing demanded by online transcoding. Constrained by available CPU and storage, existing transcoding-enabled caching schemes always selectively cache certain transcoded versions, expecting that many future requests can be served from the cache. But such schemes treat the transcoder as a black box, leaving no room for flexible control of joint resource management between CPU and storage. In this paper, we first introduce the idea of meta-caching by looking into a transcoding procedure. Instead of caching certain selected transcoded versions in full, meta-caching identifies intermediate transcoding steps from which certain intermediate results (called metadata) can be cached so that a fully transcoded version can be easily produced from the metadata with a small amount of CPU cycles. Achieving big saving in caching space with possibly small sacrifice on CPU load, the proposed meta-caching scheme provides a unique method to balance the utilization of CPU and storage resources at the proxy. We further construct a model to analyze the meta-caching scheme. Based on the analysis, we propose AMTrac, Adaptive Meta-caching for Transcoding, which adaptively applies meta-caching based on the client request patterns and available resources. Experimental results show that AMTrac can significantly improve the system throughput over existing approaches.      

Low-Complexity Iris Coding and Recognition Based on Directionlets

A novel iris recognition method is presented. In the method, the iris features are extracted using the oriented separable wavelet transforms (directionlets) and they are compared in terms of a weighted Hamming distance. The feature extraction and comparison are shift-, size-, and rotation-invariant to the location of iris in the acquired image. The generated iris code is binary, whose length is fixed (and therefore commensurable), independent of the iris image, and comparatively short. The novel method shows a good performance when applied to a large database of irises and provides reliable identification and verification. At the same time, it preserves conceptual and computational simplicity and allows for a quick analysis and comparison of iris samples.      

Global Solution for the Optimal Feedback Control of the Underactuated Heisenberg System

We present a global solution for an optimal feedback controller of the underactuated Heisenberg system or nonholonomic integrator. Employing a recently developed technique based on generating functions appearing in the Hamilton-Jacobi theory, we circumvent a singularity caused by underactuation to develop a nonlinear optimal feedback control in an implicitly analytical form. The systematic procedure to deal with underactuation indicates that generating functions should be effective tools for solving general underactuated optimal control problems.