Linear predictive receivers for fading channels

The authors discuss maximum likelihood sequence detection (MLSD) based on prediction techniques for linearly modulated digital signals transmitted over fading channels. Efficient implementations of the sequence detector are investigated and a general formulation for computing the prediction coefficients is derived. Furthermore, the equivalence of different existing prediction-based receivers is shown     

Hydrogen sorption sites in the gallium containing hybrid catalysts used for the aromatization of light alkanes

Gallium species which were incorporated initially in the oxide form, were all in the (+3) oxidation state in the hybrid catalyst and exhibited two types of hydrogen chemisorption sites, s_hw and s_hs. The stronger sorption sites shs which predominated at higher gallium dispersion, corresponded probably to the most effective surface configuration for gallium in the aromatization ofn-butane.     

Preparation of water‐repellent silks by a reaction with octadecenylsuccinic anhydride

Silk fibers and membranes were acylated with octadecenylsuccinic anhydride (ODSA) at 75°C for different times. Swelling [N,N‐dimethylformamide (DMF) and dimethyl sulfoxide (DMSO)] and nonswelling (xylene) solvent media were used for the reaction. Silk membranes that reacted in DMF or DMSO displayed faster reaction kinetics and attained higher weight‐gain values than fibers. The effect of the solvent on the reaction yield was in the following order: DMSO > DMF ? xylene. The Fourier transform infrared spectra of acylated silk samples showed the characteristic absorption bands of the anhydride at 2990, 2852, 1780–1700, and 1170 cm^?1. The intensity of the latter band, which increased linearly with the weight gain, was used as a marker for evaluating the reaction kinetics of the samples acylated in the nonswelling medium. The moisture regain and water retention of silk fibers acylated with ODSA decreased significantly, regardless of the solvent system used. Accordingly, the water repellency increased. Acylation induced an increase in the thermal stability of the silk fibers and membranes. Fine particles adhering to the surfaces of the silk fibers acylated in xylene were detected by scanning electron microscopy. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 324–332, 2003     

Detecting similarities in virtual machine behavior for cloud monitoring using smoothed histograms

The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions consider each Virtual Machine (VM) as a black box with independent characteristics, we embrace a new perspective where VMs with similar behaviors in terms of resource usage are clustered together. We argue that this new approach has the potential to address scalability issues in cloud monitoring and management. In this paper, we propose a technique to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. This innovative technique models VMs behavior exploiting the probability histogram of their resources usage, and performs smoothing-based noise reduction and selection of the most relevant information to consider for the clustering process. Through extensive evaluation, we show that our proposal achieves high and stable performance in terms of automatic VM clustering, and can reduce the monitoring requirements of cloud systems.     

Formal verification of security protocol implementations: a survey

Automated formal verification of security protocols has been mostly focused on analyzing high-level abstract models which, however, are significantly different from real protocol implementations written in programming languages. Recently, some researchers have started investigating techniques that bring automated formal proofs closer to real implementations. This paper surveys these attempts, focusing on approaches that target the application code that implements protocol logic, rather than the libraries that implement cryptography. According to these approaches, libraries are assumed to correctly implement some models. The aim is to derive formal proofs that, under this assumption, give assurance about the application code that implements the protocol logic. The two main approaches of model extraction and code generation are presented, along with the main techniques adopted for each approach.     

An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

Constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O₂ consumption observed in “Crabtree-positive” and “Crabtree-negative” yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into “Crabtree-positive” and “Crabtree-negative” clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.     

Taxonomy-based relaxation of query answering in relational databases

Traditional information search in which queries are posed against a known and rigid schema over a structured database is shifting toward a Web scenario in which exposed schemas are vague or absent and data come from heterogeneous sources. In this framework, query answering cannot be precise and needs to be relaxed, with the goal of matching user requests with accessible data. In this paper, we propose a logical model and a class of abstract query languages as a foundation for querying relational data sets with vague schemas. Our approach relies on the availability of taxonomies, that is, simple classifications of terms arranged in a hierarchical structure. The model is a natural extension of the relational model in which data domains are organized in hierarchies, according to different levels of generalization between terms. We first propose a conservative extension of the relational algebra for this model in which special operators allow the specification of relaxed queries over vaguely structured information. We also study equivalence and rewriting properties of the algebra that can be used for query optimization. We then illustrate a logic-based query language that can provide a basis for expressing relaxed queries in a declarative way. We finally investigate the expressive power of the proposed query languages and the independence of the taxonomy in this context.     

Aromatic bisphenol A-based elastomers via hydrosilylation chemistry

The preparation of highly aromatic elastomers from a bisphenol A-based divinyl-terminated resin and polymerization with various aromatic silane containing compounds utilizing a room temperature hydrosilylation reaction is demonstrated. The polymers exhibit high thermal and oxidative stability with 5% weight losses around 430 and 350°C and char yields ranging from 35% to 40%. The thermosets maintained their elastomeric properties with good hardness and mechanical properties as measured by elongation measurements. The toughness of the thermosets was not improved with the inclusion of aromatic moieties but the hardness did appear to increase with the addition of more aromatic groups.     

Multi-target tracking on confidence maps: An application to people tracking

We propose a generic online multi-target track-before-detect (MT-TBD) that is applicable on confidence maps used as observations. The proposed tracker is based on particle filtering and automatically initializes tracks. The main novelty is the inclusion of the target ID in the particle state, enabling the algorithm to deal with unknown and large number of targets. To overcome the problem of mixing IDs of targets close to each other, we propose a probabilistic model of target birth and death based on a Markov Random Field (MRF) applied to the particle IDs. Each particle ID is managed using the information carried by neighboring particles. The assignment of the IDs to the targets is performed using Mean-Shift clustering and supported by a Gaussian Mixture Model. We also show that the computational complexity of MT-TBD is proportional only to the number of particles. To compare our method with recent state-of-the-art works, we include a postprocessing stage suited for multi-person tracking. We validate the method on real-world and crowded scenarios, and demonstrate its robustness in scenes presenting different perspective views and targets very close to each other.