Chen and Bhanu proposed “Contour Matching for 3D Ear Recognition”. In this, ICP algorithm is to match a data shape to a model shape by iteratively minimizing the distance between corresponding pairs. In the training phase, the helixes of model ears in 3D images are extracted and saved. In the testing phase, the helix of the test ear in 3D image is detected. For each model ear helix, we run the ICP algorithm to find the initial rigid transformation which aligns a model ear helix with the test ear helix. After this, we have a set of rigid transformations for each model-test pair. Then by applying the initial rigid transformation obtained previously to selected control points of the model ear, we run ICP to improve the transformation which brings model ear and test ear into best alignment, for every model-test pair. The root meansquare (RMS) registration error is used as the matching error criterion. The model ear with the minimum RMS error is declared as the recognized ear .
indoor communication by tracking the nodes and reducing the energy consumption. Author providing the solution of Service jam and provided the effective Service modulation. Author applied the estimation using Least Square (LS) and MMSE (MinimumMeanSquare Error) methods. The Communication conditions are observed and later on applied the improve the Service balancing. Author has defined a CMA algorithm for blind Communication Distribution. Author analyzed the Service respective to inclusive noise and relatively provided the Communication estimation and correction. Author has used the indoor positioning based Communication improving by setting up the optimized sequence and setting up the estimation method. Author defined the Communication modeling with error identification and its structural observation to identify the Service noise. The algorithm specification and transition is applied to improve the Service strength.
population mean has been proposed under simple random sampling without replacement. The properties of the proposed estimator (Bias and MeanSquare Error) have been obtained up to first order of approximation using Taylor’s Series Expansion and the condition for its efficiency over some existing estimators have been established. The minimum value of the meansquare error of the proposed estimator has also been derived. The efficiency of proposed estimator based on optimal value of the constant, exhibit significant improvement over the existing estimators considered in the study. Empirical studies were conducted with natural populations to demonstrate the performance of the proposed estimator comparing with some existing estimators. The results of the empirical study show that the proposed estimator performs better than the existing estimators.
This problem has been solved subsequently and resulted in less complex suboptimal multi- user detection algorithms such as the decorrelating detector, minimummeansquare error detector (linear detectors) and other sub-optimal detectors. Because of the significant advantages which multi-user detection offers CDMA based wireless systems, in terms of capacity improvements and near-far resistance.[3-6]
This section provides an overview of related works in the field of 3D microarray gene expression data analysis, in particular, the work related to the Greedy based clustering and biclustering. To propose various potential modifications to the OAC-triclustering algorithms based on the prime operators (Arnold, 2016). To perform slight modifications based on clustering procedures to optimize the performance of the specialist-generalist using classification system (Gnatyshak, 2014).A pattern-driven local search operator is inbuilt with the binary Particle Swarm Optimization (PSO) algorithm is used to improve the search efficiency (Yangyang Li, 2014 ).BPSO encoding gene-to-class sensitivity (GCS) mainly used to perform gene selection. GCS is used to extract the samples with the help of extreme learning machine (ELM).ELM, K. nearest neighbour (KNN) and support vector machine (SVM) classifiers are used for prediction with high accuracy for microarray data, it gives the efficiency and effectiveness for gene selection method (FeiHan, 2015). The Multi-Objective Particle Swarm Optimization is used for gene expression data to extract the bicluster. The main purpose of this technique is to cover all elements of the gene expression matrix amongst the overlapping bicluster (Mohsen lashkargir, 2009). Biclustering algorithm is used to identify the coherent bicluster with minimum MSR (MeanSquare Residue) and with maximum row variance for gene expression data.
Fig. 4 Illustrates that it also has better sum-rate performance and also lower BER performance. The sum rate in bits/s/Hz is computed by ∑ log2(1 + SINR ) Here, the channel is the IEEE 802.11n channel model A, which is a ﬂat-fading MIMO channel with average path gain of 0 dB, angular spreads AS =40° at the transmitter and receiver, mean angle of departure is 45° and mean angle of arrival is 45°.
The uplink channel estimation in massive MIMO-OFDM systems is done with frequency selective channels. An efficient Distributed MinimumMeanSquare Error (DMMSE) algorithm  was proposed to achieve nearly optimal channel estimates at low complexity by makes use of the strong spatial correlation among antenna array elements. Through repetitive sharing of information among neighbouring array of elements the MMSE problem is reduced. A channel estimation algorithm for multi-antenna Radio Frequency (RF) energy transfer system  based on the received power measurements was proposed. Here energy beam-forming technique is used for resolving low energy transfer efficiency problem by estimating the channel gains between transmit and receive antennas. Receiver measures the received RF power signal and send it back to the transmitter but this leads to linear estimation problem. This problem can be solved by using least squares method and the Kalman filter-based algorithm.
The main results of this paper are inscribed in the context of both centralized and distributed attack construction problems. The setting assumes that the state variables are described by a multivariate Gaussian process and that the operator per- forms minimum-mean-square-error (MMSE) estimation over the measurements. The trade-off between the damage to the network, e.g., the excess distortion term, and the ability to remain hidden to the network operator, e.g, to keep the probability of attack detection under a given threshold is studied in both scenarios. In the former, all attackers are sufficiently coordinated to be considered as a single entity and thus, classical tools from matrix theory and optimization theory are used to determine the optimal attack. The distributed scenario considers that attackers are fully distributed. That being the case, tools from game theory are used to determine optimal individual behaviors and the resulting distributed attack construction strategies. A novel utility function that models the features of the dynamic between the attackers and the operator is proposed. The game resulting from the implementation of this utility function is studied analytically and numerically. Specifically, existence results and bounds on the number of Nash Equilibria (NEs) of the game are provided. The next section describes the system model, including the estimation and detection procedures. Centralized attack construction strategies are discussed in Section III. The de- centralized case and the properties of the resulting game are analyzed in Section IV. Section V presents simulations of the attack strategies in IEEE Test Systems. The paper ends with concluding remarks in Section VI.
power control, perfect channel estimation and time delay estimation are assumed. Rayleigh flat fading channel is considered. We define one frame as one packet of 20 symbols. The channel coefficients are modeled with an independent zero mean complex Gaussian random vari- able with variance 0.5 per dimension. The channel coef- ficients are constant during one frame transmission. Gold sequences with 31 processing gain are utilized. Each chip of the spreading sequences is sampled at 5 samples/chip. Timing delays are generated randomly to realize an asynchronous model.
The graphs considered here are simple, finite and undirected. Let V G ( ) denote the vertex set and E G ( ) de- note the edge set of G. For detailed survey of graph labeling we refer to Gallian . For all other standard ter- minology and notations we follow Harary . The concept of mean labeling on degree splitting graph was in- troduced in . Motivated by the authors we study the root squaremean labeling on degree splitting graphs. Root squaremean labeling was introduced in  and the root squaremean labeling of some standard graphs was proved in -. The definitions and theorems are useful for our present study.
The LMS is an iterative beamforming algorithm that uses the estimate of the gradient vector from the available data. This algorithm makes successive corrections to the weight vector in the direction of the negative of the gradient vector which finally concludes to minimum MSE. This successive correction to the weight vector is the point at which optimum value is obtained that relies on autocorrelation matrix R and cross correlation matrix P of the filter.
Daily 615.33 771.54 Weekly -11.19 6.94 Monthly 1.64 -11.91 Quarterly 11.37 -0.98 From Table 1, we can see that the ARIMA model can represent monthly and quarterly series, whereas the ARMA model can represent daily and weekly series. Then, the time horizon of forecasting were calculated and short terms forecasting were chosen by considering the smallest value of Root MeanSquare Error (RMSE). Forecasting results are summarized in Table 2.
In this paper, we develop two types of attack-resistant location estimation techniques to tolerate the malicious attacks against range-based location discovery in wireless sensor networks. Our first technique, named attack-resistant MinimumMeanSquare Estimation, is based on the observation that malicious location references introduced by attacks are intended to mislead a sensor node about its location, and thus are usually inconsistent with the benign ones. To exploit this observation, our method identifies malicious location references by examining the inconsistency among location references (indicated by the meansquare error of estimation) and defeats malicious attacks by removing such malicious data. Three variants are developed to identify malicious location references: the brute- force algorithm, the greedy algorithm and the enhanced greedy algorithm. The brute- force algorithm tries every combination of location references to identify the largest set of consistent location references. It introduces high computation overhead at sensor nodes. The greedy algorithm is developed to reduce the computation overhead. It works in rounds and remove the most suspicious location reference in each round. The enhanced greedy algorithm is developed to improve the performance of the greedy algorithm by adopting a more efficient way to identify the most suspicious location reference.
Abstract —In this paper, we propose two simple signal detectors that are based on successive interference cancellation (SIC) for time-reversal space-time block codes to combat intersymbol interference in frequency- selective fading environments. The main idea is to treat undetected symbols and noise together as Gaussian noise with matching mean and variance and use the already-detected symbols to help current signal recovery. The first scheme is a simple SIC signal detector whose ordering is based on the channel powers. The second proposed SIC scheme, which is denoted parallel arbitrated SIC (PA-SIC), is a structure that concatenates in paral- lel a certain number of SIC detectors with different ordering sequences and then combines the soft output of each individual SIC to achieve performance gains. For the proposed PA-SIC, we describe the optimal or- dering algorithm as a combinatorial problem and present a low-complexity ordering technique for signal decoding. Simulations show that the new schemes can provide a performance that is very close to maximum- likelihood sequence estimation (MLSE) decoding under time-invariant conditions. Results for frequency-selective and doubly selective fading channels show that the proposed schemes significantly outperform the conventional minimummeansquare error-(MMSE) like receiver and that the new PA-SIC performs much better than the proposed conventional SIC and is not far in performance from the MLSE. The computational complexity of the SIC algorithms is only linear with the number of transmit antennas and transmission rates, which is very close to the MMSE and much lower than the MLSE. The PA-SIC also has a complexity that is linear with the number of SIC components that are in parallel, and the optimum tradeoff between performance and complexity can be easily determined according to the number of SIC detectors.
The purpose of speech enhancement is to improve the naturalness and perceptual quality for the proposed speech signal in order to reduce the fatigue of human listeners. It also aims at achieving a better intelligibility of the proposed speech for listeners or to increase the accuracy of a speech recognition system operating in a noisy environment. Many effective algorithms, such as spectral subtraction, hard or soft threshold, and minimummeansquare error (MMSE) estimation and Wiener filtering, have been proposed, implemented, and reported in the last three decades [1-4]. However, the problem of speech enhancement remains open.
Abstract: - In this paper, we present performance of pilot based channel estimation techniques such as Least Squares (LS) and Linear MinimumMeanSquare Error (LMMSE) for different interpolation methods and modulation schemes. The performance is evaluated using the mean squared error (MSE) as the performance metric of interest for downlink LTE system. Simulation results show that for low SNR environment, applying QPSK modulation for any interpolation method produces the best MSE performance, but for high SNR environment, linear interpolation produces the worst MSE performance for QPSK and 16-QAM modulation schemes.
This section presents the simulation results illustrating the performance of the proposed filter. The test image employed here is the true color image “parrot” with 290×290 pixels. For the addition of noise, the source image was corrupted by additive Gaussian noise with standard deviation σ =10, 20,30 and 40. The noise model was computer simulated. The performance of the zed filter is compared with the traditional mean filter shown in Table (1). All filters considered operate using 3×3 processing window. The performance of filters was evaluated by computing the meansquare error (MSE) between the original image and filtered image as follow:
is modeled as independent flat fading channel. The analysis and results in – show that the STS scheme constitutes an at- tractive transmit diversity scheme, which is capable of attaining the maximal achievable transmit diversity gain without using extra spreading codes and without an increased transmit power. In this contribution, the MIMO SCDMA system using STS is investigated, when considering the uplink transmis- sion in a cellular-style system, since DS-CDMA has been mainly deployed in cellular systems. Specifically, the perfor- mance of a range of linear single-user and multiuser detectors (MUDs), namely, correlation, decorrelating, and minimummean-square error (MMSE) detectors , for the synchronous MIMO SCDMA systems is investigated, when communicating over multipath Rayleigh-fading channels. Furthermore, it is well-recognized that, when multipath fading channel is consid- ered, the DS-CDMA system using orthogonal spreading codes performs not as good as the system using some other types of nonorthogonal spreading codes, such as Gold sequences . This is because, except the purely synchronous case, orthogonal spreading codes have an unsatisfactory correlation properties. Therefore, instead of using orthogonal spreading codes for the STS in the context of downlink case, in this contribution Gold sequences  are used for the STS in the considered MIMO SCDMA system.