Bandwidth-constrained queries in sensor networks

Sensor networks consist of battery-powered wireless devices that are required to operate unattended for long periods of time. Thus, reducing energy drain is of utmost importance when designing algorithms and applications for such networks. Aggregate queries are often used by monitoring applications to assess the status of the network and detect abnormal behavior. Since radio transmission often constitutes the biggest factor of energy drain in a node, in this paper we propose novel algorithms for the evaluation of bandwidth- constrained queries over sensor networks. The goal of our techniques is, given a target bandwidth utilization factor, to program the sensor nodes in a way that seeks to maximize the accuracy of the produced query results at the monitoring node, while always providing strong error guarantees to the monitoring application. This is a distinct difference of our framework from previous techniques that only provide probabilistic guarantees on the accuracy of the query result. Our algorithms are equally applicable when the nodes have ample power resources, but bandwidth consumption needs to be minimized, for instance in densely distributed networks, to ensure proper operation of the nodes. Our experiments with real sensor data show that bandwidth-constrained queries can substantially reduce the number of messages in the network while providing very tight error bounds on the query result.     

Substrate current and degradation of n-channel polycrystalline silicon thin-film transistors

Experimental investigation of the substrate current I_sub as a function of the gate voltage has been performed in n-channel polycrystalline silicon thin-film transistors (polysilicon TFTs), considering the drain voltage as a parameter of the study. At low gate voltages, I_sub exhibits a peak located close to the threshold voltage of the transistor due to hot-carriers generated by impact ionization. At higher gate voltages, I_sub increases monotonically with increasing the gate voltage, which is attributed to the temperature rise owing to self-heating. The degradation behavior of polysilicon TFTs, stressed under two different gate and drain bias conditions that cause the same substrate current due to hot-carrier and self-heating effects, is investigated.     

An Updated Review of Meat Authenticity Methods and Applications

Adulteration of foods is a serious economic problem concerning most foodstuffs, and in particular meat products. Since high-priced meat demand premium prices, producers of meat-based products might be tempted to blend these products with lower cost meat. Moreover, the labeled meat contents may not be met. Both types of adulteration are difficult to detect and lead to deterioration of product quality. For the consumer, it is of outmost importance to guarantee both authenticity and compliance with product labeling. The purpose of this article is to review the state of the art of meat authenticity with analytical and immunochemical methods with the focus on the issue of geographic origin and sensory characteristics. This review is also intended to provide an overview of the various currently applied statistical analyses (multivariate analysis (MAV), such as principal component analysis, discriminant analysis, cluster analysis, etc.) and their effectiveness for meat authenticity.     

A model reduction approach for constructing compact dynamic thermal models of IGBT-modules of inverters

This paper presents a model reduction approach for constructing lumped RC thermal networks of IGBT-modules of inverters for which heat and subsequent temperature increases vary with time on different scales ranging from nanosecond to second. It was observed that the time-dependent heat and temperature profiles of transistors and diodes of IGBT-modules of inverters oscillate at two frequencies, one in the range 0.1–50 Hz corresponding to the load current modulation, and the other in the range 1–20 kHz corresponding to the switching frequency. The reduction approach consisted of decomposing the module into different elements, each being described with a number of RC cells selected according to the time-constant of the element with regard to the module. The lumped RC thermal networks were found in good agreement with the continuous model by offering a considerably lower computational time on the different time scales. For simplicity, the reduction approach is presented for one-dimensional heat flow through the cross-plane direction of the module.     

Econazole-releasing porous space maintainers for fungal periprosthetic joint infection

While antibiotic-eluting polymethylmethacrylate space maintainers have shown efficacy in the treatment of bacterial periprosthetic joint infection and osteomyelitis, antifungal-eluting space maintainers are associated with greater limitations for treatment of fungal musculoskeletal infections including limited elution concentration and duration. In this study, we have designed a porous econazole-eluting space maintainer capable of greater inhibition of fungal growth than traditional solid space maintainers. The eluted econazole demonstrated bioactivity in a concentration-dependent manner against the most common species responsible for fungal periprosthetic joint infection as well as staphylococci. Lastly, these porous space maintainers retain compressive mechanical properties appropriate to maintain space before definitive repair of the joint or bony defect.

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Hierarchically compressed wavelet synopses

The wavelet decomposition is a proven tool for constructing concise synopses of large data sets that can be used to obtain fast approximate answers. Existing research studies focus on selecting an optimal set of wavelet coefficients to store so as to minimize some error metric, without however seeking to reduce the size of the wavelet coefficients themselves. In many real data sets the existence of large spikes in the data values results in many large coefficient values lying on paths of a conceptual tree structure known as the error tree. To exploit this fact, we introduce in this paper a novel compression scheme for wavelet synopses, termed hierarchically compressed wavelet synopses, that fully exploits hierarchical relationships among coefficients in order to reduce their storage. Our proposed compression scheme allows for a larger number of coefficients to be stored for a given space constraint thus resulting in increased accuracy of the produced synopsis. We propose optimal, approximate and greedy algorithms for constructing hierarchically compressed wavelet synopses that minimize the sum squared error while not exceeding a given space budget. Extensive experimental results on both synthetic and real-world data sets validate our novel compression scheme and demonstrate the effectiveness of our algorithms against existing synopsis construction algorithms. This work has been funded by the project PENED 2003. The project is co-financed 75% of public expenditure through EC—European Social Fund, 25% of public expenditure through Ministry of Development—General Secretariat of Research and Technology and through private sector, under measure 8.3 of OPERATIONAL PROGRAMME “COMPETITIVENESS" in the 3rd Community Support Programme.     

Gray-Level Reduction Using Local Spatial Features

This paper proposes a new method for reduction of the number of gray-levels in an image. The proposed approach achieves gray-level reduction using both the image gray-levels and additional local spatial features. Both gray-level and local feature values feed a self-organized neural network classifier. After training, the neurons of the output competition layer of the SOFM define the gray-level classes. The final image has not only the dominant image gray-levels, but also has a texture approaching the image local characteristics used. To split the initial classes further, the proposed technique can be used in an adaptive mode. To speed up the entire multithresholding algorithm and reduce memory requirements, a fractal scanning subsampling technique is adopted. The method is applicable to any type of gray-level image and can be easily modified to accommodate any type of spatial characteristic. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented.     

Atlas: Storing, updating and querying RDF(S) data on top of DHTs

The RDF(S) data model has been proposed for encoding metadata about Web resources. As more and more Web resources are annotated using RDF(S), there is an urgent need for efficiently dealing with this large volume of data. In this paper, we present Atlas, a peer-to-peer system for storing, updating and querying RDF(S) data. The Atlas system has been built using the distributed hash table Bamboo. Atlas was developed in the context of project OntoGrid, where it was used as a distributed repository for RDF(S) metadata describing Grid services and resources. The development of Atlas continues in other projects that our group participates currently. This paper gives an overview of the most recent version of Atlas and discusses a representative application.     

Adaptive and reliable video transmission over UMTS for enhanced performance

This paper proposes a mechanism for the congestion control for video transmission over universal mobile telecommunications system (UMTS). Our scheme is applied when the mobile user experiences real‐time multimedia content and adopts the theory of a widely accepted rate control method in wired networks, namely equation‐based rate control. In this approach, the transmission rate of the multimedia data is determined as a function of the packet loss rate, the round trip time and the packet size and the server explicitly adjusts its sending rate as a function of these parameters. Furthermore, we examine the performance of the UMTS for real‐time video transmission using real‐time protocols. Through a number of experiments, we measure performance parameters such as end‐to‐end delay, delay in radio access network, delay jitter and throughput in the wireless link. Copyright © 2006 John Wiley & Sons, Ltd.     

Augmentative Pharmacological Strategies in Treatment-Resistant Major Depression: A Comprehensive Review

Treatment resistant depression (TRD) is associated with poor outcomes, but a consensus is lacking in the literature regarding which compound represents the best pharmacological augmentation strategy to antidepressants (AD). In the present review, we identify the available literature regarding the pharmacological augmentation to AD in TRD. Research in the main psychiatric databases was performed (PubMed, ISI Web of Knowledge, PsychInfo). Only original articles in English with the main topic being pharmacological augmentation in TRD and presenting a precise definition of TRD were included. Aripiprazole and lithium were the most investigated molecules, and aripiprazole presented the strongest evidence of efficacy. Moreover, olanzapine, quetiapine, cariprazine, risperidone, and ziprasidone showed positive results but to a lesser extent. Brexpiprazole and intranasal esketamine need further study in real-world practice. Intravenous ketamine presented an evincible AD effect in the short-term. The efficacy of adjunctive ADs, antiepileptic drugs, psychostimulants, pramipexole, ropinirole, acetyl-salicylic acid, metyrapone, reserpine, testosterone, T3/T4, naltrexone, SAMe, and zinc cannot be precisely estimated in light of the limited available data. Studies on lamotrigine and pindolol reported negative results. According to our results, aripiprazole and lithium may be considered by clinicians as potential effective augmentative strategies in TRD, although the data regarding lithium are somewhat controversial. Reliable conclusions about the other molecules cannot be drawn. Further controlled comparative studies, standardized in terms of design, doses, and duration of the augmentative treatments, are needed to formulate definitive conclusions.