On the design of space-time and space-frequency codes for MIMO frequency-selective fading channels

The authors introduced an algebraic design framework for space-time coding in flat-fading channels . We extend this framework to design algebraic codes for multiple-input multiple-output (MIMO) frequency-selective fading channels. The proposed codes strive to optimally exploit both the spatial and frequency diversity available in the channel. We consider two design approaches: The first uses space-time coding and maximum likelihood decoding to exploit the multi-path nature of the channel at the expense of increased receiver complexity. Within this time domain framework, we also propose a serially concatenated coding construction which is shown to offer a performance gain with a reasonable complexity iterative receiver in some scenarios. The second approach utilizes the orthogonal frequency division multiplexing technique to transform the MIMO multipath channel into a MIMO flat block fading channel. The algebraic framework is then used to construct space-frequency codes (SFC) that optimally exploit the diversity available in the resulting flat block fading channel. Finally, the two approaches are compared in terms of decoder complexity, maximum achievable diversity advantage, and simulated frame error rate performance in certain representative scenarios.     

Facial feature extraction using a probabilistic approach

Facial features such as lip corners, eye corners and nose tip are critical points in a human face. Robust extraction of such facial feature locations is an important problem which is used in a wide range of applications. In this work, we propose a probabilistic framework and several methods which can extract critical points on a face using both location and texture information. The new framework enables one to learn the facial feature locations probabilistically from training data. The principle is to maximize the joint distribution of location and apperance/texture parameters. We first introduce an independence assumption which enables independent search for each feature. Then, we improve upon this model by assuming dependence of location parameters but independence of texture parameters. We model location parameters with a multi-variate Gaussian and the texture parameters are modeled with a Gaussian mixture model which are much richer as compared to the standard subspace models like principal component analysis. The location parameters are found by solving a maximum likelihood optimization problem. We show that the optimization problem can be solved using various search strategies. We introduce local gradient-based methods such as gradient ascent and Newton's method initialized from independent model locations both of which require certain non-trivial assumptions to work. We also propose a multi-candidate coordinate ascent search and a coarse-to-fine search strategy which both depend on efficiently searching among multiple candidate points. Our framework is compared in detail with the conventional statistical approaches of active shape and active appearance models. We perform extensive experiments to show that the new methods outperform the conventional approaches in facial feature extraction accuracy.     

Using the Stochastic Collocation Method for the Uncertainty Quantification of Drug Concentration Due to Depot Shape Variability

Numerical simulations entail modeling assumptions that impact outcomes. Therefore, characterizing, in a probabilistic sense, the relationship between the variability of model selection and the variability of outcomes is important. Under certain assumptions, the stochastic collocation method offers a computationally feasible alternative to traditional Monte Carlo approaches for assessing the impact of model and parameter variability. We propose a framework that combines component shape parameterization with the stochastic collocation method to study the effect of drug depot shape variability on the outcome of drug diffusion simulations in a porcine model. We use realistic geometries segmented from MR images and employ level-set techniques to create two alternative univariate shape parameterizations. We demonstrate that once the underlying stochastic process is characterized, quantification of the introduced variability is quite straightforward and provides an important step in the validation and verification process.     

Comparing between estimation approaches: admissible and dominating linear estimators

We treat the problem of evaluating the performance of linear estimators for estimating a deterministic parameter vector x in a linear regression model, with the mean-squared error (MSE) as the performance measure. Since the MSE depends on the unknown vector x, a direct comparison between estimators is a difficult problem. Here, we consider a framework for examining the MSE of different linear estimation approaches based on the concepts of admissible and dominating estimators. We develop a general procedure for determining whether or not a linear estimator is MSE admissible, and for constructing an estimator strictly dominating a given inadmissible method so that its MSE is smaller for all x. In particular, we show that both problems can be addressed in a unified manner for arbitrary constraint sets on x by considering a certain convex optimization problem. We then demonstrate the details of our method for the case in which x is constrained to an ellipsoidal set and for unrestricted choices of x. As a by-product of our results, we derive a closed-form solution for the minimax MSE estimator on an ellipsoid, which is valid for arbitrary model parameters, as long as the signal-to-noise-ratio exceeds a certain threshold.     

Neuro-fuzzy modeling and control

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed     

A categorization of multiscale-decomposition-based image fusionschemes with a performance study for a digital camera application

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a single image which is more suitable for human and machine perception or further image-processing tasks. In this paper, a generic image fusion framework based on multiscale decomposition is studied. This framework provides freedom to choose different multiscale decomposition methods and different fusion rules. The framework includes all of the existing multiscale-decomposition-based fusion approaches we found in the literature which did not assume a statistical model for the source images. Different image fusion approaches are investigated based on this framework. Some evaluation measures are suggested and applied to compare the performance of these fusion schemes for a digital camera application. The comparisons indicate that our framework includes some new approaches which outperform the existing approaches for the cases we consider     

Adaptive appearance modeling for video tracking: survey and evaluation

Long-term video tracking is of great importance for many applications in real-world scenarios. A key component for achieving long-term tracking is the tracker's capability of updating its internal representation of targets (the appearance model) to changing conditions. Given the rapid but fragmented development of this research area, we propose a unified conceptual framework for appearance model adaptation that enables a principled comparison of different approaches. Moreover, we introduce a novel evaluation methodology that enables simultaneous analysis of tracking accuracy and tracking success, without the need of setting application-dependent thresholds. Based on the proposed framework and this novel evaluation methodology, we conduct an extensive experimental comparison of trackers that perform appearance model adaptation. Theoretical and experimental analyses allow us to identify the most effective approaches as well as to highlight design choices that favor resilience to errors during the update process. We conclude the paper with a list of key open research challenges that have been singled out by means of our experimental comparison.     

TCP Over Optical Burst Switching: To Split or Not to Split?

TCP-based applications account for a majority of data traffic in the Internet; thus, understanding and improving the performance of TCP over optical burst switching (OBS) network is critical. In this paper, we identify the ill effects of implementing TCP over a hybrid network (IP-access and OBS-core). We propose a Split-TCP framework for the hybrid IP-OBS network to improve TCP performance. We propose two Split-TCP approaches, namely, 1:1:1 and N:1:N. We evaluate the performance of the proposed approaches over an IP-OBS hybrid network. Based on the simulation results, N:1:N Split-TCP approach outperforms all other approaches. We also develop an analytical model for end-to-end Split-TCP throughput and verify it with simulations.     

Multimedia goes mobile in broadcast networks

In digital broadcasting systems, like Eureka 147 DAB, it is possible to separate the actual service from the transmission system. Such a separation enables the distribution of any type of digital data to stationary, portable, or mobile terminals. However, a radio channel for mobile reception has certain characteristics to which existing multimedia services must adapt. We present a multimedia system model for digital broadcasting. We also discuss implementing this model within the broadcasting framework     

Variation-Tolerant Dynamic Power Management at the System-Level

The power characteristics of system-on-chips (SoCs) in nanoscale technologies are significantly impacted by manufacturing process variations, making it important to consider these effects during system-level power analysis and optimization. In this paper, we identify and address the problem of designing effective power management schemes in the presence of such variations. In particular, we demonstrate that conventional power management schemes, which are designed without considering the impact of variations, can result in substantial power wastage. We therefore propose two approaches to variation-aware power management, namely, design-specific and chip-specific approaches. In each of these approaches, the goal is to consider the impact of variations while deriving power management policy parameters, in order to optimize metrics that are relevant under variations. We motivate and introduce these metrics, and present both exact and heuristic approaches to optimize them. The methods are designed and implemented in the context of two power management frameworks, namely an ideal oracle-based framework and a timeout-based framework. We experimentally evaluate the proposed ideas using an ARM946 processor core model. For the oracle-based framework, variation-aware power management can result in improvements of upto 59% for $mu +sigma$ , and upto 55% for 95th percentile of the energy distribution, over conventional power management schemes that do not consider variations. For the timeout-based framework, we obtain reductions of upto 43% in $mu+sigma$ and upto 55% in the 99th percentile of the energy distribution.      

MDSI: Signal Integrity Interconnect Fault Modeling and Testing for SoCs

Unacceptable loss of signal integrity may cause permanent or intermittent harm to the functionality and performance of SoCs. In this paper, we present an abstract model and a new test pattern generation method of signal integrity problems on interconnects. This approach is achieved by considering the effects for testing inputs and parasitic RLC elements of interconnects. We also develop a framework to deal with arbitrary interconnection topology. Experimental results show that the proposed signal integrity fault model is more exact and more powerful for long interconnects than previous approaches.     

Progressive framework for deep neural networks:from linear to non-linear

We propose a novel progressive framework to optimize deep neural networks. The idea is to try to combine the stability of linear methods and the ability of learning complex and abstract internal representations of deep learning methods. We insert a linear loss layer between the input layer and the first hidden non-linear layer of a traditional deep model. The loss objective for optimization is a weighted sum of linear loss of the added new layer and non-linear loss of the last output layer. We modify the model structure of deep canonical correlation analysis (DCCA), i.e., adding a third semantic view to regularize text and image pairs and embedding the structure into our framework, for cross-modal retrieval tasks such as text-to-image search and image-to-text search. The experimental results show the performance of the modified model is better than similar state-of-art approaches on a dataset of National University of Singapore (NUS-WIDE). To validate the generalization ability of our framework, we apply our framework to RankNet, a ranking model optimized by stochastic gradient descent. Our method outperforms RankNet and converges more quickly, which indicates our progressive framework could provide a better and faster solution for deep neural networks.     

Generation of wave digital structures for networks containing multiport elements

Most standard wave digital filters are derived from passive reference circuits, where only parallel and serial connections between one-port elements occur. In this paper, a framework for the automated generation of the wave digital structures is presented, where the reference circuit is assumed to comprise arbitrary connection types. It is shown how the representation of the underlying graph by its so-called SPQR-tree is related to a suitable adaptor structure and how this concept can be generalized to also cope with networks containing certain multiport elements. We propose two different novel approaches to finding a valid tree representation from a given reference circuit. The first approach relies on the usage of apt replacement graphs for the multiport elements. The second one is based on searches for circles on suitably constructed graph representations and generates wave digital structures with minimum implementation effort even in presence of nonreciprocal elements. In both approaches, standard separation algorithms with known efficient implementations can be applied.     

Specification and analysis of power-managed systems

Dynamic power management encompasses several techniques for reducing energy dissipation in electronic systems by selective slowdown or shutdown of components. We present a theoretical framework for explaining and classifying different approaches to power management. Within this framework, we model power-manageable components, workloads, and controllers as discrete-event systems (DESs). The structure of these DESs is specified in terms of physical states (representing operation modes) and events (triggering state transitions), while system behavior is specified in terms of next-event and next-state functions. In particular, nondeterministic next-event and next-state functions are modeled by conditional probability distributions, according to generalized semi-Markov processes (GSMPs). The modeling framework provides a general denotational model for system specification and a rigorous execution semantics that enables event-driven simulation. We introduce a modeling framework, built on top of MathWork's Simulink, supporting the specification and execution of our model. In particular, we present templates for the Simulink simulator to execute GSMP models, and we describe how to use such templates for specifying, analyzing, and optimizing dynamic power-managed systems. Finally, we demonstrate the expressive power and versatility of the proposed approach by using the modeling framework and the simulator for the analysis of representative real-life case studies, including the Intel Xscale processor architecture, a multitasking real-time system, and a sensor network.     

Lightening the Software Production Process in a CMM Level 5 Framework

The Capability Maturity Model (CMM) is a framework for judging the maturity of the software processes of an organization. This model is often misapplied in practice, being used as a bulwark for rigid approaches to software development. This usually leads to project failure when requirements are set in a rapidly changing environment, or when they are vaguely defined. Agile methodologies behave very well in this kind of environment. In this article we show that Agile methodologies can be applied in a CMM context, specifying how every goal of each Key Process Area of the model can be fulfilled when an Agile methodology such as Xp@Scrum is being used. We also show some metrics from pilote projects at a Motorola Software Centre backing our aiming, and showing the benefits introduced by Agile approaches to software development.     

Evolution equations for continuous-scale morphological filtering

Multiscale signal analysis has emerged as a useful framework for many computer vision and signal processing tasks. Morphological filters can be used to develop nonlinear multiscale operations that have certain advantages over linear multiscale approaches in that they preserve important signal features such as edges. The authors discuss several nonlinear partial differential equations that model the scale evolution associated with continuous-space multiscale morphological erosions, dilations, openings, and closings. These equations relate the rate of change of the multiscale signal ensemble as scale increases to a nonlinear operator acting on the space of signals. The nonlinear operator is characterized by the shape and dimensionality of the structuring element used by the morphological operators, generally taking the form of a nonlinear function of certain partial differential operators     

A Refinement-Based Compositional Reasoning Framework for Pipelined Machine Verification

We present a refinement-based compositional framework for showing that pipelined machines satisfy the same safety and liveness properties as their non-pipelined specifications. Our framework consists of a set of convenient, easily applicable, and complete compositional proof rules. We show how to apply our compositional framework in the context of microprocessor verification to verify both abstract, term-level models and executable, bit-level models. Our framework enables us to verify machine models that are significantly more complex than the kinds of models that can be verified using current state-of-the-art automated decision procedures. For example, using our framework, we can verify a 32-bit, 10-stage, executable pipelined machine model. In addition, our compositional framework offers drastic improvements in the context of design debugging over monolithic approaches, in part because bugs are isolated to particular steps in the compositional proof and because the counter examples generated are much smaller.     

Hybrid Analysis of Nonlinear Circuits: DAE Models with Indices Zero and One

We extend in this paper some previous results concerning the differential-algebraic index of hybrid models of electrical and electronic circuits. Specifically, we present a comprehensive index characterization which holds without passivity requirements, in contrast to previous approaches, and which applies to nonlinear circuits composed of uncoupled, one-port devices. The index conditions, which are stated in terms of the forest structure of certain digraph minors, do not depend on the specific tree chosen in the formulation of the hybrid equations. Additionally, we show how to include memristors in hybrid circuit models; in this direction, we extend the index analysis to circuits including active memristors, which have been recently used in the design of nonlinear oscillators and chaotic circuits. We also discuss the extension of these results to circuits with controlled sources, making our framework of interest in the analysis of circuits with transistors, amplifiers, and other multiterminal devices.