Synthesis and electrochemical,in situ spectroelectrochemical,electrical and gas sensing properties of ball-type homo- and hetero-dinuclear phthalocyanines with four [1a,8b-dihydronaphtho[b]naphthofuro[3,2-d]furan-7,10-diyl] bridges

The new mono-nuclear Fe^II 2 and ball-type homo-dinuclear Fe^II–Fe^II 3 phthalocyanines have been synthesized from the corresponding 4,4′-(1a,8b-dihydronaphtho[b]naphthofuro[3,2-d]-furan-7,10-diyl)bis(oxy)diphthalonitrile 1 while ball-type hetero-dinuclear Fe^II–Co^II phthalocyanine 4 was synthesized from 2. The compounds have been characterized by elemental analysis, UV–vis, IR and MALDI-TOF-mass spectroscopies. The redox behaviours of the complexes were identified by controlled-potential coulometry, cyclic voltammetry and differential pulse voltammetry measurements on Pt in dimethylsulfoxide containing tetrabutylammonium perchlorate. The assignments of the redox processes and the understanding of the interactions between the metal phthalocyanine units in 3 and 4 were achieved by the combined evaluation of the voltammetric and in situ spectroelectrochemical outcomes. Complex 3 showed ring-based mixed-valence behaviour as a result of the considerable interaction between the phthalocyanine rings. On the other hand, the interactions between the two metal phthalocyanine units of 4 were found to be much weaker than those in 3. The potential application of molecular organic semiconductors needs the control adjustment of conductivity. Ac and dc conductivity measurements were performed with the applied external electric filed. At high frequency, the conduction follows the universal power law and conduction mechanism can be explained by classical hopping barriers mechanism for the system.     

An optimal energy‐efficient clustering method in wireless sensor networks using multi‐objective genetic algorithm

In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi‐objective two‐nested genetic algorithm is presented. The top level algorithm is a multi‐objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two‐tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA‐based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Rakeness with block sparse Bayesian learning for efficient ZigBee‐based EEG telemonitoring

Future healthcare systems are shifted toward long‐term patient monitoring using embedded ultra‐low power devices. In this paper, the strengths of both rakeness‐based compressive sensing (CS) and block sparse Bayesian learning (BSBL) are exploited for efficient electroencephalogram (EEG) transmission/reception over wireless body area networks. A binary sensing matrix based on the rakeness concept is used to find the most energetic signal directions. A balance is achieved between collecting energy and enforcing restricted isometry property to capture the underlying signal structure. Correct presentation of the EEG oscillatory activity, EEG wave shape, and main signal characteristics is provided using the discrete cosine transform based BSBL, which models the intra‐block correlation. The IEEE 802.15.4 wireless communication technology (ZigBee) is employed, since it targets low data rate communications in an energy efficient manner. To alleviate noise and channel multipath effects, a recursive least square based equalizer is used, with an adaptation algorithm that continually updates the filter weights using successive input samples. For the same compression ratio (CR), results indicate that the proposed system permits a higher reconstruction quality compared with the standard CS algorithm. For higher CRs, lower dimensional projections are allowed, meanwhile guaranteeing a correct reconstruction. Thus, low computational high quality data compression/reconstruction are achieved with minimal energy expenditure at the sensors nodes.     

A comparison between reliability analyses based primarily on disjointness or statistical independence: The case of the generalized indra network

A switching expression is readily convertible to a reliability expression if (a) all ORed terms are disjoint, and (b) all ANDed sums are statistically independent. The usual approach of system reliability analysis makes a primary use of (a) and a secondary use of (b). An alternative approach reverses the roles of (a) and (b). Symbolic reliability expressions for the source-to-terminal reliability of a generalized Indra network (GIN) with nonidentical components are derived by the two approaches. For this particular case, the second approach leads to a shorter, more elegant derivation and simpler novel results. Typical plots of the GIN reliability functions are presented and their properties are discussed.     

Generalized carrier to interference ratio analysis for shotgun cellular systems in multiple dimensions over composite Rayleigh–Lognormal (Suzuki) fading

Telecommunication Systems - This paper analyzes the carrier-to-interference ratio (CIR) of the so-called shotgun cellular systems (SCSs) in $$\tau $$ dimensions ( $$\tau =1, 2,$$ and 3). SCSs are...     

Improving of Wavelength Division Multiplexing Based on Free Space Optical Communication via Power Comparative System

Wireless Personal Communications - The names of the second and third authors in the initial online publication were not correctly typeset. The original article has been corrected.     

J‐divergence‐based Power Allocation in Wireless Sensor Networks with Distributed Detection under Correlated Noises

In this paper, the power allocation problem in a wireless sensor network (WSN) with binary distributed detection is considered. It is assumed that the sensors independently transmit their local decisions to a fusion center (FC) through a slow fading orthogonal multiple access channel (OMAC), where, in every channel, the interferences from other devices are considered as correlated noises. In this channel, the associated power allocation optimization problem with equal power constraint is established between statistical distributions under different hypotheses by using the Jeffrey divergence (J‐divergence) as a performance criterion. It is shown that this criterion for the power allocation problem is more efficient compared to other criteria such as mean square error (MSE). Moreover, several numerical simulations and examples are presented to illustrate the effectiveness of the proposed approach.