An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

4. generációs mobil rendszerek kutatása = Research on 4-th Generation Mobile Systems

A 3G mobil rendszerek szabványosítása a végéhez közeledik, legalábbis a meghatározó képességek tekintetében. Ezért létfontosságú azon technikák, eljárások vizsgálata, melyek a következő, 4G rendszerekben meghatározó szerepet töltenek majd be. Több ilyen kutatási irányvonal is létezik, ezek közül projektünkben a fontosabbakra koncentráltunk. A következőben felsoroljuk a kutatott területeket, és röviden összegezzük az elért eredményeket. Szórt spektrumú rendszerek Kifejlesztettünk egy új, rádiós interfészen alkalmazható hívásengedélyezési eljárást. Szimulációs vizsgálatokkal támasztottuk alá a megoldás hatékonyságát. A projektben kutatóként résztvevő Jeney Gábor sikeresen megvédte Ph.D. disszertációját neurális hálózatokra épülő többfelhasználós detekciós technikák témában. Az elért eredmények Imre Sándor MTA doktori disszertációjába is beépültek. IP alkalmazása mobil rendszerekben Továbbfejlesztettük, teszteltük és általánosítottuk a projekt keretében megalkotott új, gyűrű alapú topológiára épülő, a jelenleginél nagyobb megbízhatóságú IP alapú hozzáférési koncepciót. A témakörben Szalay Máté Ph.D. disszertációja már a nyilvános védésig jutott. Kvantum-informatikai módszerek alkalmazása 3G/4G detekcióra Új, kvantum-informatikai elvekre épülő többfelhasználós detekciós eljárást dolgoztunk ki. Ehhez új kvantum alapú algoritmusokat is kifejlesztettünk. Az eredményeket nemzetközi folyóiratok mellett egy saját könyvben is publikáltuk. | The project consists of three main research directions. Spread spectrum systems: we developed a new call admission control method for 3G air interfaces. Project member Gabor Jeney obtained the Ph.D. degree and project leader Sandor Imre submitted his DSc theses from this area. Application of IP in mobile systems: A ring-based reliable IP mobility mobile access concept and corresponding protocols have been developed. Project member Máté Szalay submitted his Ph.D. theses from this field. Quantum computing based solutions in 3G/4G detection: Quantum computing based multiuser detection algorithm was developed. Based on the results on this field a book was published at Wiley entitled: 'Quantum Computing and Communications - an engineering approach'

A Neural Network Approach to Identify Hyperspectral Image Content

A Hyperspectral is the imaging technique that contains very large dimension data with the hundreds of channels. Meanwhile, the Hyperspectral Images (HISs) delivers the complete knowledge of imaging; therefore applying a classification algorithm is very important tool for practical uses. The HSIs are always having a large number of correlated and redundant feature, which causes the decrement in the classification accuracy; moreover, the features redundancy come up with some extra burden of computation that without adding any beneficial information to the classification accuracy. In this study, an unsupervised based Band Selection Algorithm (BSA) is considered with the Linear Projection (LP) that depends upon the metric-band similarities. Afterwards Monogenetic Binary Feature (MBF) has consider to perform the ‘texture analysis’ of the HSI, where three operational component represents the monogenetic signal such as; phase, amplitude and orientation. In post processing classification stage, feature-mapping function can provide important information, which help to adopt the Kernel based Neural Network (KNN) to optimize the generalization ability. However, an alternative method of multiclass application can be adopt through KNN, if we consider the multi-output nodes instead of taking single-output node

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives