Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition

Image fusion has been receiving increasing attention in the research community with the aim of investigating general formal solutions to a wide spectrum of applications. The objective of this work is to formulate a method that can efficiently fuse multifocus as well as multispectral images for context enhancement and thus can be used by different applications. We propose a novel pixel fusion rule based on multiresolution decomposition of the source images using wavelet, wavelet-packet, and contourlet transform. To compute fused pixel value, we take weighted average of the source pixels, where the weight to be given to the pixel is adaptively decided based on the significance of the pixel, which in turn is decided by the corresponding children pixels in the finer resolution bands. The fusion performance has been extensively tested on different types of images viz. multifocus images, medical images (CT and MRI), as well as IR ? visible surveillance images. Several pairs of images were fused to compare the results quantitatively as well as qualitatively with various recently published methods. The analysis shows that for all the image types into consideration, the proposed method increases the quality of the fused image significantly, both visually and quantitatively, by preserving all the relevant information. The major achievement is average 50% reduction in artifacts.     

Design and performance of a product code turbo encoding-decoding prototype

This paper presents the latest results on a block turbo decoder design. We propose a block turbo decoder circuit for the error protection of small data blocks such asAtm cells on anAwgn (additive white Gaussian noise) channel with a code rate close to 0.5. A prototype was developed atEnst Bretagne. It allowsBer (bit error rate) measurements down to 10^?9 and uses programmable gate arrays (Fpga Xilinx circuits). The elementary extendedBch code and the data block size can be modified to fit specifications of different applications.     

Highly Parallelable Bidimensional Median Filter for Modern Parallel Programming Models

The median filter is a non-linear filter used for removal of Salt & Pepper noise from images, where each pixel of the image is replaced by the median of its surrounding elements, which is calculated by sorting the data. The complexity of the sorting algorithms used for the median filters are $O(n^2)$ or $O(n)$ , depending on the kernel size. These algorithms were formulated for scalar single processor computers, with few of them successfully adapted and implemented for computers with a parallel architecture. In this paper we greatly improve the results of our earlier work, in which by means of a novel sorting algorithm, based on the Complementary Cumulative Distribution function, with $O(n)$ computational complexity and a highly parallelable structure, we presented a 2D median filter that achieved $O(1)$ or $O(n)$ computational complexity, depending memory constraints. The improvements are twofold: we propose a trade-off between $O(1)$ complexity and $O(n)$ complexity in order to improve the overall throughput; additionally we make use of the Salt & Pepper noise model to improve the image reconstruction quality with a small performance impact. The proposed algorithm have been implemented in three parallel programming models: SIMD Intel, Multicore Intel with SIMD, and SIMT (CUDA), achieving a peak throughput of 27.0, 100.1 and 91.6 megapixels per second respectively.     

Image Fusion Processing for IKONOS 1-m Color Imagery

Many image fusion techniques have been developed. However, most existing fusion processes produce color distortion in 1-m fused IKONOS images due to nonsymmetrical spectral responses of IKONOS imagery. Here, we proposed a fusion process to minimize this spectral distortion in IKONOS 1-m color images. The 1-m fused image is produced from a 4-m multispectral (MS) and 1-m panchromatic (PAN) image, maintaining the relations of spectral responses between PAN and each band of the MS images. To obtain this relation, four spectral weighting parameters are added with the pixel value of each band of the original MS image. Then, each pixel value is updated using a steepest descent method to reflect the maximum spectral response on the fused image. Comparison among the proposed technique and existing processes [intensity hue saturation (IHS) image fusion, Brovey transform, principal component analysis, fast IHS image fusion] has been done. Our proposed technique has succeeded to generate 1-m fused images where spectral distortion has been reduced significantly, although some block distortions appeared at the edge of the fused images. To remove this block distortion, we also proposed a sharpening process using a wavelet transform, which removed block distortion without significant change in the color of the entire image.     

ATM and AAL layer issues concerning multimedia applications

This paper presents an overview of the facilities that should be provided by theAtm layer and the adaptation layer (Aal) in order to support multimedia communications. From the analysis of data flows generated by well known multimedia applications and the services that could be offered byAtm networks both in the public and the private domain, we propose solutions enabling the availability of such applications onAtm networks taking into account both economical andQos parameters.     

Multi-focus image fusion by local optimization over sliding windows

This article presents a technique to solve the problem of multi-focus image fusion. This technique is based on the maximization of a linear function with spatial coherence constraints. The final fused image is computed as the sum of the source images using a segmentation map. We can compute the segmentation map using the Simplex method, where the objective function includes one variable associated with each pixel. The Simplex method requires a huge amount of memory resources to produce it. We present an algorithm called CPW-S, which uses some strategies to solve the problem in a context with fewer variables; images are split into regions, thus reducing the computational effort. We present results for two pairs of synthetic images in order to quantify the results, obtaining more than \(98\%\) of pixel accuracy for the segmentation map. We also present results for several pairs of real images (widely used in the literature) and a triad of multi-focus images. The resulting fused images are qualitatively good for all the real images included in the experiments.     

Minimum payment collaborative sensing network using mobile phones

Mobile phones with embedded sensors have been applied in various collaborative sensing applications. To encourage mobile phone users to perform collaborative sensing, the data demanders usually pay mobile phone users for required data. In this paper, we study the Minimum Payment of Attaining the Required Data with mobile phones (MPARD) problem in collaborative sensing network: given sensing regions \(R = \{R_1, R_2, \ldots , R_m\}\) , the set of requisite data \(D_i\) for each sensing region \(R_i\) and a set of mobile phones \(M\) , the \(MPARD\) problem studies how to select mobile phones to obtain all the required data such that the data demanders’ total payment to mobile phone users is minimized. In reality, some systems need the fresh sensing data from mobile phones at each pre-determined time slot, and others don’t require the real-time data and the sensing data from previous time slots is also deemed useful. Based on the above two different requirements of data timeliness, we first define two subproblems derived from \(MPARD\) problem: \(MPARD_t\) and \(MPARD_p\) . After that, for each subproblem, we propose an approximation algorithm for the situation where the trajectories of mobile phones are determinate and a heuristic for the situation where trajectories are unknown. Simulation results demonstrate that our algorithms are efficient.     

Quark: A Lightweight Hash

The need for lightweight (that is, compact, low-power, low-energy) cryptographic hash functions has been repeatedly expressed by professionals, notably to implement cryptographic protocols in RFID technology. At the time of writing, however, no algorithm exists that provides satisfactory security and performance. The ongoing SHA-3 Competition will not help, as it concerns general-purpose designs and focuses on software performance. This paper thus proposes a novel design philosophy for lightweight hash functions, based on the sponge construction in order to minimize memory requirements. Inspired by the stream cipher Grain and by the block cipher KATAN (amongst the lightest secure ciphers), we present the hash function family Quark, composed of three instances: u-Quark, d-Quark, and s-Quark. As a sponge construction, Quark can be used for message authentication, stream encryption, or authenticated encryption. Our hardware evaluation shows that Quark compares well to previous tentative lightweight hash functions. For example, our lightest instance u-Quark conjecturally provides at least 64-bit security against all attacks (collisions, multicollisions, distinguishers, etc.), fits in 1379 gate-equivalents, and consumes on average 2.44 μW at 100 kHz in 0.18 μm ASIC. For 112-bit security, we propose s-Quark, which can be implemented with 2296 gate-equivalents with a power consumption of 4.35 μW.     

A novel fuzzy system for edge detection in noisy image using bacterial foraging

Bio-inspired edge detection using fuzzy logic has achieved great attention in the recent years. The bacterial foraging (BF) algorithm, introduced in Passino (IEEE Control Syst Mag 22(3):52–67, 2002) is one of the powerful bio-inspired optimization algorithms. It attempts to imitate a single bacterium or groups of E. Coli bacteria. In BF algorithm, a set of bacteria forages towards a nutrient rich medium to get more nutrients. A new edge detection technique is proposed to deal with the noisy image using fuzzy derivative and bacterial foraging algorithm. The bacteria detect edge pixels as well as noisy pixels in its path during the foraging. The new fuzzy inference rules are devised and the direction of movement of each bacterium is found using these rules. During the foraging if a bacterium encounters a noisy pixel, it first removes the noisy pixel using an adaptive fuzzy switching median filter in Toh and Isa (IEEE Signal Process Lett 17(3):281–284, 2010). If the bacterium does not encounter any noisy pixel then it searches only the edge pixel in the image and draws the edge map. This approach can detect the edges in an image in the presence of impulse noise up to 30%.     

P-SIGMA : security aware paging in end-to-end mobility management scheme

Mobile users need to maintain end-to-end connections when changing subnets. Handoff across networks often causes delay and loss of packets and make end users vulnerable to security threats that may lead to disruptions. In this paper, we design P-SIGMA , a delay and security-hardened, disruption-tolerant and signalling cost-effective end-to-end mobility management scheme by incorporating paging in SIGMA , an internet protocol diversity-based end-to-end transport layer mobility management scheme. P-SIGMA can detect and mitigate disruptions against two most common security threats: session hijacking and freeloading. We also propose a signalling cost analysis model for paging and a recovery-time model for attacks. To demonstrate performance improvement, we compare P-SIGMA with SIGMA and show the speed at which P-SIGMA can detect attacks (security breaches). We observe that P-SIGMA has lower signalling than SIGMA while maintaining all the benefits of a transport layer mobility management scheme. P-SIGMA can also detect paging related security threats with a small detection time without incurring any additional signalling.     

Multimedia communication in wireless sensor networks

The technological advances in Micro ElectroMechanical Systems (Mems) and wireless communications have enabled the realization of wireless sensor networks (Wsn) comprised of large number of low-cost, low-power, multifunctional sensor nodes. These tiny sensor nodes communicate in short distances and collaboratively work toward fulfilling the application specific objectives ofWsn. However, realization of wide range of envisionedWsn applications necessitates effective communication protocols which can address the unique challenges posed by theWsn paradigm. Since many of these envisioned applications may also involve in collecting information in the form of multimedia such as audio, image, and video; additional challenges due to the unique requirements of multimedia delivery overWsn, e.g., diverse reliability requirements, time constraints, high bandwidth demands, must be addressed as well. Thus far, vast majority of the research efforts has been focused on addressing the problems of conventional data communication inWsn. Therefore, there exists an urgent need for research on the problems of multimedia communication inWsn. In this paper, a survey of the research challenges and the current status of the literature on the multimedia communication inWsn is presented. More specifically, the multimediaWsn applications, factors influencing multimedia delivery overWsn, currently proposed solutions in application, transport, and network layers, are pointed out along with their shortcomings and open research issues.     

Image distortion analysis based on normalized perceptual information distance

Image distortion analysis is a fundamental issue in many image processing problems, including compression, restoration, recognition, classification, and retrieval. Traditional image distortion evaluation approaches tend to be heuristic and are often limited to specific application environment. In this work, we investigate the problem of image distortion measurement based on the theory of Kolmogorov complexity, which has rarely been studied in the context of image processing. This work is motivated by the normalized information distance (NID) measure that has been shown to be a valid and universal distance metric applicable to similarity measurement of any two objects (Li et al. in IEEE Trans Inf Theory 50:3250–3264, 2004). Similar to Kolmogorov complexity, NID is non-computable. A useful practical solution is to approximate it using normalized compression distance (NCD) (Li et al. in IEEE Trans Inf Theory 50:3250–3264, 2004), which has led to impressive results in many applications such as construction of phylogeny trees using DNA sequences (Li et al. in IEEE Trans Inf Theory 50:3250–3264, 2004). In our earlier work, we showed that direct use of NCD on image processing problems is difficult and proposed a normalized conditional compression distance (NCCD) measure (Nikvand and Wang, 2010), which has significantly wider applicability than existing image similarity/distortion measures. To assess the distortions between two images, we first transform them into the wavelet transform domain. Assuming stationarity and good decorrelation of wavelet coefficients beyond local regions and across wavelet subbands, the Kolmogorov complexity may be approximated using Shannon entropy (Cover et al. in Elements of information theory. Wiley-Interscience, New York, 1991). Inspired by Sheikh and Bovik (IEEE Trans Image Process 15(2):430–444, 2006), we adopt a Gaussian scale mixture model for clusters of neighboring wavelet coefficients and a Gaussian channel model for the noise distortions in the human visual system. Combining these assumptions with the NID framework, we derive a novel normalized perceptual information distance measure, where maximal likelihood estimation and least square regression are employed for parameter fitting. We validate the proposed distortion measure using three large-scale, publicly available, and subject-rated image databases, which include a wide range of practical image distortion types and levels. Our results demonstrate the good prediction power of the proposed method for perceptual image distortions.     

LDPC-based space-time coded OFDM systems: Turbo-EM receiver design with channel state information guessing algorithms

In this paper we study some turbo receiver architectures employing low-density parity check (Ldpc) codes together with orthogonal frequency division multiplexing (Ofdm) for high data rate wireless transmissions. Different demodulation schemes based on expectation-maximization (Em) algorithm are studied along with the channel impulse response (Em) algorithms. We studied differentCir guessing algorithms including the EM-based algorithms such as a space-alternating generalized expectation-maximization algorithm (Sage). It is shown that the proposed turbo-Em receiver employing a soft maximum a posteriori (Map)Em demodulator and a belief propagationLdpc decoder can perform within 1 dB from the ergodic capacity of the studiedMimo ofdm channels. Besides, we find that a suboptimum structure based on a soft interference cancellationMmse filtering demodulator exhibits negligible loss in non-correlated fadingMimo channels but suffer extra performance loss in highly correlatedMimo channels.     

Web-based interorganizational information systems for logistics outsourcing

This article examines how web-based interorganizational information systems(ios)can efficiently support coordination mechanisms between outsourcer and third party logistics (3PL). First, we review the literature on coordination mechanisms andios. Second, we report on the methodology used for gathering information on outsourcers and 3PLs. Third, armed with limited but quality data, we identified two fundamental dimensions: 3PL involvement andios impacts on logistics outsourcing decisions. By combining the two dimensions, we propose a conceptual framework that highlights four main categories ofios that we characterize as neutralios, supply chainios, strategicios and dynamicios. After characterizing and analyzing each category, we discuss how these web basedios support outsourcers and 3PL along the supply chain. Finally, the article discusses the framework’s relevancy and its limits.     

Method for Designing Efficient Mixed Radix Multipliers

The multiplication of two signed inputs, \(A {\times } B\) , can be accelerated by using the iterative Booth algorithm. Although high radix multipliers require summing a smaller number of partial products, and consume less power, its performance is restricted by the generation of the required hard multiples of B ( \(\pm \phi B\) terms). Mixed radix architectures are presented herein as a method to exploit the use of several radices. In order to implement efficient multipliers, we propose to overlap the computation of the \(\pm \phi B\) terms for higher radices with the addition of the partial products associated to lower radices. Two approaches are presented which have different advantages, namely a combinatory design and a synchronous design. The best solutions for the combinatory mixed radix multiplier for \(64\times 64\) bits require \(8.78\) and \(6.55~\%\) less area and delay in comparison to its counterpart radix-4 multiplier, whereas the synchronous solution for \(64\times 64\) bits is almost \(4{\times }\) smaller in comparison with the combinatory solution, although at the cost of about \(5.3{\times }\) slowdown. Moreover, we propose to extend this technique to further improve the multipliers for residue number systems. Experimental results demonstrate that best proposed modulo \(2^{n}{-}1\) and \(2^{n}{+}1\) multiplier designs for the same width, \(64{\times }64\) bits, provide an Area-Delay-Product similar for the case of the combinatory approach and \(20~\%\) reduction for the synchronous design, when compared to their respective counterpart radix-4 solutions.     

EnhancingIEEE 802.11 performance in congested environments

IEEE 802.11 is the most deployed wireless local area networking standard nowadays. It uses carrier sense multiple access with collision avoldance (CSMA/CA) to, resolve contention between nodes. Contention windows (CW) change dynamically to adapt to the contention level: Upon each collision a node doubles itsCW to, reduce further collision risks. Upon a successful transmission theCW is reset, assuming the contention level dropped. However, contention level is more likely to change slowly, and resetting theCW causes new collisions and retransmissions before reaching the optimal value again. This wastes bandwidth and increases delays. In this paper we propose simple slowCW decrease functions and compare their performances to the legacy standard. We analyze them through simulation and show their considerable enhancement at all congestion levels and transient phases.     

Distribution of traffic among applications as measured in the French METROPOLIS project

We investigate in this paper the time evolution and the composition in terms of applications of traffic in two different networks, namely the Renater network, dedicated to the French academic and research community, and the France Télécom backbone network supporting commercial traffic. For each network, we present the time evolution of traffic in terms of applications, the associated pie charts for global results, as well as, for each detected application, its flow size distribution, that should have an impact on the traffic nature (self-similarity or long range dependence due to the heavy tail of flow size distribution). Based on these results, this paper presents a discussion on the differences between academic and commercial traffic in terms of usage, as well as possible solutions against lrd and its associated degradation of network performance. For traffic analysis, we propose a new method of classifying traffic according to applications, which relies on applicative protocols recognition instead on the iana ports numbers.     

Design of An Arcak-Type Generalized H_2 Filter for Delayed Static Neural Networks

In this paper, an Arcak-type generalized \(H_2\) filter is designed for a class of static neural networks with time-varying delay. By employing some inequalities and constructing a suitable Lyapunov functional, a delay-dependent condition is derived by means of linear matrix inequalities such that the filtering error system is globally asymptotically stable and a prescribed generalized \(H_2\) performance is achieved. It is shown that the design of such a desired filter for a delayed static neural network is successfully transformed into solving a convex optimization problem subject to some linear matrix inequalities. It is thus facilitated readily by some standard algorithms. A numerical example is finally provided to show the effectiveness of the developed approach. A comparison on the generalized \(H_2\) performance for different gain parameters of the activation function is also given.     

Mobility-awareness in group key management protocols within MANETs

Several sensitive applications deployed within wireless networks require group communications. A high level of security is often required in such applications, like military or public security applications. The most suitable solution to ensure security in these services is the deployment of a group key management protocol, adapted to the characteristics ofManets, especially to mobility of nodes. In this paper, we present theOmct (Optimized Multicast Cluster Tree) algorithm for dynamic clustering of multicast group, that takes into account both nodes localization and mobility, and optimizes the energy and bandwidth consumptions. Then, we show how we integrateOmct within our group key management protocolBalade, in a sequential multi-source model. The integration ofBalade andOmct allows an efficient and fast key distribution process, validated through simulations, by applying various models of mobility (individual mobility and group mobility). The impact of the mobility model on the performance and the behaviour of the group key management protocolBalade coupled withOmct, is also evaluated.