An energy management circuit is proposed for self-powered ubiquitous sensor modules using vibration-based energy. With the proposed circuit, the sensor modules work with low duty cycle operation. Moreover, a two-tank circuit as a part of the energy management circuit is utilized to solve the problem that the average power density of ambient energy always varies with time while the power consumption of the sensor modules is constant and larger than it. In addition, the long start-up time problem is also avoided with the timing control of the proposed energy management circuit. The CMOS implementation and silicon verification results of the proposed circuit are also presented. Its validity is further confirmed with a vibration-based energy generation. The sensor module is used to supervise the vibration of machines and transfer the vibration signal discontinuously. A piezoelectric element acts as the vibration-to-electricity converter to realize battery-free operation.

This letter proposes a new adaptive power management mechanism (APM2) which takes into account the remaining energy in an IEEE 802.16e system. Benefits of the mechanism are the reduction of frame response delay in a state with sufficient remaining energy, and an increase in the life of a station in a state of insufficient remaining energy. An analytical model for sleep mode operation is developed, and the proposed mechanism is validated by computer simulation.

Due to the limited resources of sensor nodes, an energy-efficient routing algorithm of Wireless Sensor Networks is considered as one of the most important issues in improving network lifetime. To resolve this issue, several routing algorithms have been suggested, but the published studies have mainly focused on minimizing distances between sensor nodes or the number of hops. These researches have also assumed that all the sensor nodes participate in the sensing task. In this paper, we propose a new cluster head selection algorithm focusing on both the density of sensor nodes and sensing-awareness that has not been considered yet in other existing researches on cluster-based routing scheme. In the real sensor network environment, only a part of sensor nodes participates in data reporting, so consideration of sensing-awareness in a routing algorithm will have effect on network efficiency. Moreover, the density of sensor nodes that has resulted from geographical location of sensor nodes can be an important factor in cluster head selection. The simulation results show that the proposed algorithm, by considering these 2 factors simultaneously, reduces energy consumption and enhances network lifetime.

CORDIC (COordinate Rotation DIgital Computer) is a well known algorithm using simple adders and shifters to evaluate various elementary functions. Thus, CORDIC is suitable for the design of high performance chips using VLSI technology. In this paper, a complete analysis of the computation error of both the (conventional) CORDIC algorithm and the CORDIC algorithm with expanded convergence range is derived to facilitate the design task. The resulting formulas regarding the relative and absolute approximation errors and the truncation error are summarized in the tabular form. As the numerical accuracy of CORDIC processors is determined by the word length of operands and the number of iterations, three reference tables are constructed for the optimal choice of these numbers. These tables can be used to facilitate the design of cost-effective CORDIC processors in terms of areas and performances. In addition, two design examples: singular value decomposition (SVD) and lattice filter for digital signal processing systems are given to demonstrate the goal and benefit of the derived numerical analysis of CORDIC.

We prove that the Variational distance (and its positive multiples) is the only f-divergence that satisfies both the identity of indiscernibles and the triangle inequality. Therefore it is the unique f-divergence which serves as a metric. This point is interpreted as a fundamental confliction of the convexity for f(x) with the metric properties for its associated f-divergence. Therefore, we relax the convexity of f(x) and replace it with other constraints to create new metrics.

The article describes recent adaptive estimation algorithms over distributed networks. The algorithms rely on local collaborations and exploit the space-time structure of the data. Each node is allowed to communicate with its neighbors in order to exploit the spatial dimension, while it also evolves locally to account for the time dimension. Algorithms of the least-mean-squares and least-squares types are described. Both incremental and diffusion strategies are considered.

Sensor network deployment is very challenging due to the hostile and unpredictable nature of environments. The field coverage of wireless sensor networks (WSNs) can be enhanced and consequently network lifetime can be prolonged by optimizing the sensor deployment with a finite number of mobile sensors. In this paper, we introduce a comprehensive taxonomy for WSN self-deployment in which three sensor relocation algorithms are proposed to match the mobility degree of sensor nodes, particle swarm optimization based algorithm (PSOA), relay shift based algorithm (RSBA) and energy efficient fuzzy optimization algorithm (EFOA). PSOA regards the sensors in the network as a swarm, and reorganizes the sensors by the particle swarm optimization (PSO) algorithm, in the full sensor mobility case. RSBA and EFOA assume relatively limited sensor mobility, i.e., the movement distance is bounded by a threshold, to further reduce energy consumption. In the zero mobility case, static topology control or scheduling schemes can be used such as optimal cluster formation. Simulation results show that our approaches greatly improve the network coverage as well as energy efficiency compared with related works.

In ad-hoc networks, mobile nodes are limited by a range of radio coverage and have an irregular source of power due to their battery. In ad-hoc networks, there are a lot of situations that all mobile nodes need to agree on their key not at the same time but in part and then merge themselves subsequently. This is because ad-hoc networks have specific features such as mobility and allow various conditions during configuration. In this thesis, we propose MCP (Merging Clusters Protocol), a simple key agreement scheme that can effectively deal with merging different adjacent clusters in mobile ad-hoc networks. When nodes of each cluster have already agreed on their own group keys and intend to merge themselves for further secure communications, MCP can be used in an efficient and secure way. In addition, it can be utilized for efficient group key agreement in a large ad-hoc network. We analyze the security and efficiency of MCP and discuss the experimental results according to practical implementation scenarios.

We propose a novel clustering scheme considering non-uniform correlation distribution derived by experimental environment property. Firstly, we investigate the entropy property of actual environment, and then show that its spatial correlation is not uniformly distributed. Based on this result, we present the clustering strategy which provides the efficient data aggregation. Through the simulation under the non-uniform correlation distribution, we show the advantage of the proposed scheme in terms of the energy consumption property per node and the network lifetime.

This paper proposes effective energy feature normalization methods for robust speech recognition in noisy environments. We first develop an energy subtraction method and a modified method for the Log-energy Dynamic Range Normalization (ERN) using inverse function. We then present the hybrid method combining the energy subtraction and the modified ERN. Using Aurora2.0 database for representative evaluations, a significant performance improvement over the ERN method is demonstrated.

Spatial multiplexing (SM) offers a linear increase in transmission rate without bandwidth expansion or power increase. In SM systems, the LMMSE receiver establishes a good tradeoff between the complexity and performance. The performance of the LMMSE receiver would be degraded by MIMO channel estimation errors. This letter focus on obtaining the asymptotic convergence of output interference power and SIR performance for the LMMSE receiver with channel uncertainty. Exactly matched simulation results verify the validity of analysis in the large-system assumption. Furthermore, we find that the analytical results are also valid in the sense of average results for limited-scale system in spite of the asymptotic assumption used in derivation.

This letter proposes a broadcast scheme for use in ad hoc networks using variable-range transmission power. Preserving energy and ensuring a high delivery ratio of broadcast packets are crucial tasks for broadcasting in ad hoc networks. Using individual broadcast relaying nodes to dynamically vary the transmission range can help saving power and reduce interference during communication. We analyzed the performance of the proposed scheme and compared it to other prevalent broadcast schemes for wireless ad hoc networks based on common-range transmission power.

We compare the convergence performance of different numerical schemes for computing the fundamental matrix from point correspondences over two images. First, we state the problem and the associated KCR lower bound. Then, we describe the algorithms of three well-known methods: FNS, HEIV, and renormalization. We also introduce Gauss-Newton iterations as a new method for fundamental matrix computation. For initial values, we test random choice, least squares, and Taubin's method. Experiments using simulated and real images reveal different characteristics of each method. Overall, FNS exhibits the best convergence properties.

With the advances of micro-electronic and wireless communication technology, deploying a large number of low cost, small-sized sensor nodes over a vast area for environment monitoring is becoming more practical. Setting up gradients for delivering queries or data reports by flooding the sensor networks consumes a great deal of energy especially for large scale sensor networks. In this paper, we propose an energy conserving observer-initiated data dissemination protocol, called Grid Map Data Query Protocol (GMDQP), for multiple mobile sinks on a large scale sensor network. It conserves communication energy by employing a grid map data query technique to avoid full network events or queries flooding. A data source only announces the existence of data within a local area and a sink collects data by sending query message in a grid map. Nodes at cross point of grid check and query the data. It conserves energy by avoiding full network flooding in setting up data forwarding path. Simulations show that the proposed architecture is quite energy efficient.

This paper describes the design of a random access memory (RAM) bank with a 0.35-µm CMOS process for column-parallel analog/digital converters (ADC) utilized in CMOS imagers. A dynamic latch is utilized that expends neither input DC nor drain current during the monitoring phase. Accuracy analysis of analog/digital conversion error in the RAM bank is discussed to ensure low power consumption of a counter buffer circuit. Moreover, the counter buffer utilizes a combination of NMOS and CMOS buffers to reduce power consumption. Total power consumption of a 10-bit 800-column 40 MHz RAM bank is 2.9 mA for use in an imager.

Gaussian pulse has no beginning point, so has no Laplace transform and is non-physical. We propose sinnt pulse (referred to as pseudo-Gaussian pulse or PGP) as an approximation of the Gaussian pulse. PGP has the Laplace transform and approaches the Gaussian pulse as n→∞. The propagation of PGP-modulated wave packet in the highly anomalous dispersion band of a Lorentz medium is investigated by numerical inversion of Laplace transform. Our results are greatly different from the conventional results obtained by the saddle point method. Our results show that the velocity of a Gaussian wave packet cannot be explained only by the concept of the group velocity as has been done so far.

This paper is intended to provide an alternative approach for the design of FIR filters by using a Hopfield Neural Network (HNN). The proposed approach establishes the error function between the amplitude response of the desired FIR filter and the designed one as a Lyapunov energy function to find the HNN parameters. Using the framework of HNN, the optimal filter coefficients can be obtained from the output state of the network. With the advantages of local connectivity, regularity and modularity, the architecture of the proposed approach can be applied to the design of differentiators and Hilbert transformer with significantly reduction of computational complexity and hardware cost. As the simulation results illustrate, the proposed neural-based method is capable of achieving an excellent performance for filter design.

In this paper, we propose TB-MAC (Threshold-Based MAC), which has been designed to consider various network traffic conditions while providing energy efficiency in a wireless sensor networks. Existing MAC protocols for sensor networks attempt to solve the energy consumption problem caused by idle listening using an active/sleep duty cycle. Since there are various traffic conditions, however, they may not always provide improvements in energy consumption. Hence, we propose a MAC protocol algorithm that stores data in a buffer and transmits data when the buffer exceeds a threshold value so that energy efficiency is always guaranteed for any network traffic condition. The analytical results show that our proposed algorithm enables significant improvements in energy consumption compared to the existing MAC protocols for sensor networks.

An iterative reconstruction algorithm of accelerating the estimation of the complex relative permittivity of a cylindrical dielectric object based on the multigrid optimization method (MGOM) is presented. A cost functional is defined by the norm of a difference between the scattered electric fields measured and calculated for an estimated contrast function, which is expressed as a function of the complex relative permittivity of the object. Then the electromagnetic inverse scattering problem can be treated as an optimization problem where the contrast function is determined by minimizing the cost functional. We apply the conjugate gradient method (CGM) and the frequency-hopping technique (FHT) to the minimization of the cost functional, and also employ the multigrid method (MGM) with a V-cycle to accelerate the rate of convergence for getting the reconstructed profile. The reconstruction scheme is called the multigrid optimization method. Computer simulations are performed for lossy and inhomogeneous dielectric circular cylinders by using single-frequency or multifrequency scattering data. The numerical results demonstrate that the rate of convergence of the proposed metod is much faster than that of the conventional CGM for both noise-free and noisy cases.

We determine the annealing dynamics of AsGa antisite defects in As ion-implanted GaAs. An Arrhenius plot of the carrier decay rate or the defect density vs. the annealing temperature in the high temperature regime gives an energy EPA, which is different from true activation energy. The annealing time dependence of EPA obtained by the two diffusion models (self diffusion of AsGa antisite defects and VGa vacancy assisted diffusion of AsGa antisite defects) are compared with EPA's obtained from already published works. The results prove that the diffusion of AsGa antisite defects is assisted by the VGa vacancy defects that exist in a high density.