Effects of feeding an aquatic weed Lemna trisulaca as partial replacement of fish meal on the performance of growing ducklings

Growing ducklings were fed diets containing an aquatic weed Lemna trisulaca meal (LTM) replacing, on a protein basis, either 40, 60 or 80 g kg^?1 of the fish meal (FM) from a control diet which contained 120 g FM kg^?1. Partial replacement of FM (40 and 60 g kg^?1 of the FM) by LTM on a protein basis showed good growth and low food consumption but food conversion efficiency was found to be comparable. It was concluded that LTM could be considered as a protein feed supplement for growing ducklings and also as a part replacement of animal protein (FM) in the nutrition of growing ducklings without deleterious effect on performance.     

Reliability and Mobility Load Balancing in Next Generation Self-organized Networks: Using Stochastic Learning Automata

Self-organizing networking (SON) is an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster. Most current self-organization networking functions apply rule-based recommended systems to control network resources which seem too complicated and time-consuming to design in practical conditions. This research proposes a cognitive cellular network empowered by an efficient self-organization networking approach which enables SON functions to separately learn and find the best configuration setting. An effective learning approach is proposed for the functions of the cognitive cellular network, which exhibits how the framework is mapped to SON functions. One of the main functions applied in this framework is mobility load balancing. In this paper, a novel Stochastic Learning Automata has been suggested as the load balancing function in which approximately the same quality level is provided for each subscriber. This framework can also be effectively extended to cloud-based systems, where adaptive approaches are needed due to unpredictability of total accessible resources, considering cooperative nature of cloud environments. The results demonstrate that the function of mobility robustness optimization not only learns to optimize HO performance, but also it learns how to distribute excess load throughout the network. The experimental results demonstrate that the proposed scheme minimizes the number of unsatisfied subscribers (N_us) by moving some of the edge users served by overloaded cells towards one or more adjacent target cells. This solution can also guarantee a more balanced network using cell load sharing approach in addition to increase cell throughput outperform the current schemes.

     

Dependability evaluation of Altera FPGA-based embedded systems subjected to SEUs

Dependability evaluation of embedded systems due to the integration of hardware and software parts is difficult to analyze. In this paper, we have proposed an experimental method to determine sensitivity to soft errors in an embedded system exploiting Altera SRAM-based FPGAs. The evaluation is performed using both the hardware and software parts of the embedded system in a single framework. To do this, the HDL hardware model of the target system as well as the C-written software codes of the target system, are required. Both permanent and transient faults are injected into the partially- or fully-synthesizable hardware of the target system and this can be performed during the design cycle of the system. The fault injection is composed of injecting SEUs into user design memory, and used configuration memory of the exploited FPGA. Using the experimental results, the sensitivity of Altera FPGAs to SEU faults are analyzed and derived. The analytical results reveal that the configuration memory is more significant than design memory to the SEUs due to the relative number of SRAM bits. Moreover, in this framework, in the case of injecting SEUs into user memory, the fault injection experiments are accelerated by the cooperation between a simulator and the FPGA.     

An efficient data dissemination model for wireless sensor networks

Wireless Networks - In a wireless sensor network, one of the most important constraints on sensor nodes is their power source, which is a battery. Sensor nodes carry a limited and generally...     

Flexible Prime‐Field Genus 2 Hyperelliptic Curve Cryptography Processor with Low Power Consumption and Uniform Power Draw

This paper presents an energy‐efficient (low power) prime‐field hyperelliptic curve cryptography (HECC) processor with uniform power draw. The HECC processor performs divisor scalar multiplication on the Jacobian of genus 2 hyperelliptic curves defined over prime fields for arbitrary field and curve parameters. It supports the most frequent case of divisor doubling and addition. The optimized implementation, which is synthesized in a 0.13 μm standard CMOS technology, performs an 81‐bit divisor multiplication in 503 ms consuming only 6.55 μJ of energy (average power consumption is 12.76 μW). In addition, we present a technique to make the power consumption of the HECC processor more uniform and lower the peaks of its power consumption.     

Optimal rate allocation in successively structured Gaussian CEO problem

We consider the Chief Executive Officer (CEO) problem in which agents encode their observations without collaborating with each other and send through rate constrained noiseless channels to a fusion center (FC).We apply the successive coding strategy into this problem and determine the closed-form expressions for optimal rates in order to achieve the minimum distortion under a sum-rate constraint. We show that the optimal sum-rate distortion performance for the Gaussian CEO problem is achievable using the successive coding strategy which is inherently a low complexity approach of obtaining a prescribed distortion. We also determine the optimal rate allocation region for the successively structured Gaussian CEO problem.     

An improved cluster formation process in wireless sensor network to decrease energy consumption

Wireless sensor network has special features and many applications, which have attracted attention of many scientists. High energy consumption of these networks, as a drawback, can be reduced by a hierarchical routing algorithm. The proposed algorithm is based on the Low Energy Adaptive Clustering Hierarchy (LEACH) and Quadrant Cluster based LEACH (Q-LEACH) protocols. To reduce energy consumption and provide a more appropriate coverage, the network was divided into several regions and clusters were formed within each region. In selecting the cluster head (CH) in each round, the amount of residual energy and the distance from the center of each node were calculated by the base station (including the location and residual energy of each node) for all living nodes in each region. In this regard, the node with the largest value had the highest priority to be selected as the CH in each network region. The base station calculates the CH due to the lack of energy constraints and is also responsible for informing it throughout the network, which reduces the load consumption and tasks of nodes in the network. The information transfer steps in this protocol are similar to the LEACH protocol stages. To better evaluate the results, the proposed method was implemented with LEACH LEACH-SWDN, and Q-LEACH protocols using MATLAB software. The results showed better performance of the proposed method in network lifetime, first node death time, and the last node death time.

     

Multiresolution fMRI activation detection using translation invariant wavelet transform and statistical analysis based on resampling

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods.     

Diffraction by a wide double wedge

Following an approximate technique by Karp and Russek for the diffraction by a wide slit, an asymptotic expression for the far diffracted field to a plane wave incident on two semi-infinite wedges is derived in terms of the unperturbed field due to the two wedges in isolation plus an interaction term due to an equivalent inhomogeneous line source at each edge. Numerical results for the diffraction pattern, transmission coefficient, equivalent line source intensity, and interaction term are presented and are in good agreement with previous results.     

Speech frame recognition based on less shift sensitive wavelet filter banks

The wavelet transform possesses multi-resolution property and high localization performance; hence, it can be optimized for speech recognition. In our previous work, we show that redundant wavelet filter bank parameters work better in speech recognition task, because they are much less shift sensitive than those of critically sampled discrete wavelet transform (DWT). In this paper, three types of wavelet representations are introduced, including features based on dual-tree complex wavelet transform (DT-CWT), perceptual dual-tree complex wavelet transform, and four-channel double-density discrete wavelet transform (FCDDDWT). Then, appropriate filter values for DT-CWT and FCDDDWT are proposed. The performances of the proposed wavelet representations are compared in a phoneme recognition task using special form of the time-delay neural networks. Performance evaluations confirm that dual-tree complex wavelet filter banks outperform conventional DWT in speech recognition systems. The proposed perceptual dual-tree complex wavelet filter bank results in up to approximately 9.82 % recognition rate increase, compared to the critically sampled two-channel wavelet filter bank.