A fast parallel (23, 12, 7) QR decoder based on syndrome-weight determination

In this paper, a time-area efficient hardware decoder of the (23, 12, 7) quadratic residue code, or Golay code, is presented. The key feature of this proposed algorithm lies in fast determination of error positions through the analysis on the weight of syndromes without large operations in finite fields. Comparing with the algorithm using one syndrome, the proposed algorithm is more suitable for the parallel hardware design by using two syndromes. Based on an Altera Cyclone II field-programmable gate array (FPGA) device (EP2C35F484C6), the area cost and the time delay of the complete system are reduced by up to 86.4% and 22.5%, respectively. Using the Taiwan Semiconductor Manufacturing Company (TSMC) 0.18-μm complementary metal-oxide-semiconductor (CMOS) standard cell library, the proposed decoder is 91.8% smaller and 8.3% faster.     

Additive noise removal using a novel fuzzy-based filter

The paper proposes a new fuzzy-based two-step filter for restoring images corrupted with additive noise. The goal of the first step is to compute the difference between the central pixel and its neighborhood in a selected window and to compute a fuzzy membership degree for each difference value using a Gaussian membership function. Computed fuzzy membership values are appropriately utilized as weights for each pixel and then computes the weighted average representing the modified value for the current central pixel. The second step is used as an augmented step to the first one and its goal is to improve the result obtained in the first step by reducing the noise in the color component differences without destroying the fine details of the image. The experimental analysis shows that the proposed method gives better results compared to existing advanced filters for additive noise reduction. Both visual, quantitative and qualitative analysis have been done to prove the efficiency and effectiveness of the proposed method.     

An approximation method of origin–destination flow traffic from link load counts

Traffic matrix (TM) is a key input of traffic engineering and network management. However, it is significantly difficult to attain TM directly, and so TM estimation is so far an interesting topic. Though many methods of TM estimation are proposed, TM is generally unavailable in the large-scale IP backbone networks and is difficult to be estimated accurately. This paper proposes a novel method of TM estimation in large-scale IP backbone networks, which is based on the generalized regression neural network (GRNN), called GRNN TM estimation (GRNNTME) method. Firstly, building on top of GRNN, we present a multi-input and multi-output model of large-scale TM estimation. Because of the powerful capability of learning and generalizing of GRNN, the output of our model can sufficiently capture the spatio-temporal correlations of TM. This ensures that the estimation of TM can accurately be attained. And then GRNNTME uses the procedure of data posttreating further to make the output of our model closer to real value. Finally, we use the real data from the Abilene Network to validate GRNNTME. Simulation results show that GRNNTME can perform well the accurate and fast estimation of TM, track its dynamics, and holds the stronger robustness and lower estimation errors.     

Crypt analysis of two time pads in case of compressed speech

Keystream reuse, also known as the two time pad problem, is a well known weakness in stream ciphers. The implementers of the cryptographic algorithms are still underestimating this threat. The keystream reuse exploitation techniques presented so far assume the underlying plaintext to be textual data and all the heuristics presented previously are based on the language characteristics of the underlying text based data, which fail when compression is applied on the plaintext before encryption. This paper presents exploitation techniques for two time pads in case of stream ciphered digitized and compressed speech signals. In this paper we show that how an adversary can automatically recover the digitized speech signals encrypted under the same keystream provided the language (e.g. English) and digital encoding/compression scheme details of the underlying speech signals are known. Our technique of cryptanalysis is based on the modeling of the speech parameters by exploiting the inter frame correlations between each components of the speech vectors in different frames and then using these models to decode the two speech signals in the keystream reuse scenario. The technique is flexible enough to incorporate all modern speech coding schemes based on parameter or hybrid encoding and compression techniques. The simulation experiments have showed promising results for most of the present day speech digitization and compression techniques.     

Cell design in bacteria as a convex optimization problem

In this paper, we investigate the prediction of the cell composition of bacteria with respect to their medium. By modeling the bacterium as an interconnection of subsystems, the problem is written as a non-smooth convex optimization problem equivalent to a Linear Programming feasibility problem. We then obtain a new method, called Resource Balance Analysis (RBA), predicting the distribution of the available resources in the medium among the various cellular subsystems. Beyond its predictive capability, the proposed approach grasps some fundamental aspects of the bacterium physiology by including a refined model. This method reveals the existence of an intrinsic bottleneck in the system resource distribution of the bacterium, leading to the existence of a structural limitation of its growth rate which can be predicted. RBA is also able to predict the configuration of the metabolic network for a given medium at steady-state regimen which nicely fits the available experimental results for the gram-positive model bacterium Bacillus subtilis.     

Optimality and convergence of adaptive optimal control by reinforcement synthesis

Adaptive Optimal Control (AOC) by reinforcement synthesis is proposed to facilitate the application of optimal control theory in feedback controls. Reinforcement synthesis uses the critic–actor architecture of reinforcement learning to carry out sequential optimization. Optimality conditions for AOC are formulated using the discrete minimum principle. A proof of the convergence conditions for the reinforcement synthesis algorithm is presented. As the final time extends to infinity, the reinforcement synthesis algorithm is equivalent to the Dual Heuristic dynamic Programming (DHP) algorithm, a version of approximate dynamic programming. Thus, formulating DHP with the AOC approach has rigorous proofs of optimality and convergence. The efficacy of AOC by reinforcement synthesis is demonstrated by solving a linear quadratic regulator problem.     

Opacity of discrete event systems and its applications

In this paper, we investigate opacity of discrete event systems. We define two types of opacities: strong opacity and weak opacity. Given a general observation mapping, a language is strongly opaque if all strings in the language are confused with some strings in another language and it is weakly opaque if some strings in the language are confused with some strings in another language. We show that security and privacy of computer systems and communication protocols can be investigated in terms of opacity. In particular, two important properties in security and privacy, namely anonymity and secrecy, can be studied as special cases of opacity. We also show that by properly specifying the languages and the observation mapping, three important properties of discrete event systems, namely observability, diagnosability, and detectability, can all be reformulated as opacity. Thus, opacity has a wide range of applications. Also in this paper we provide algorithms for checking strong opacity and weak opacity for systems described by regular languages and having a generalized projection as the observation mapping.     

Car side window kinematics

It is a demanding and time-consuming task to construct the side window mechanism of a car such that the deflections along the seals are minimized. The reason for this is that the window surface S delivered by the stylist, will in general not be movable in a proper way: It is required that the surface tightly moves along the seals. Deflections will necessarily occur no matter how much effort the engineer invests. This article describes how to find a spatial motion that minimizes seal deflection for some given automobile side window surface. Necessary but acceptable modification of the stylist’s surface S is also addressed, as well as the lifting mechanism design. It is argued that our systematic approach offers considerable savings compared to the prevailing trial-and-error approach.