MSE

Secreted cysteine proteases are relevant actors in parasite biology, taking part in critical host colonization roles such as traversing tissue barriers, immune evasion and nutrient digestion. In the trematode Fasciola hepatica, the initial step to successful infection of the ...

Fixed-point Fast Fourier Transform (FFT) units are widely used in digital communication systems. The twiddle multipliers required for realizing large FFTs are typically implemented with the Coordinate Rotation Digital Computer (CORDIC) algorithm to restrict memory requirements. Recent approaches aiming to optimize the bit-widths of FFT units while satisfying a given maximum bound on Mean-Square- Error (MSE) mostly focus on the architectures with integer multipliers. They ignore the quantization error of coefficients, disabling them to analyze the exact error defined as the difference between the fixed-point circuit and the reference floating-point model. This paper presents an efficient analysis of MSE as well as an optimization algorithm for CORDIC- based FFT units, which is applicable to other Linear-Time- Invariant (LTI) circuits as well.

This paper mainly aims to study the performance of objective assessment methods of image quality. It take into consideration the correlations between each objective assessment and the subjective assessment in order to determine objective test performance. Three objective assessment methods used in this study are the Structural Similarity (SSIM) index, the Peak Signal-to-Noise Ratio (PSNR) and the Mean Squared Error (MSE) calculating algorithm. The resulting data indicate what type of objective assessment was most suitable for which type of impairment imposed upon an image. This is clarified using the Pearson Correlation Coefficient as described in the paper. As an overall, SSIM index had the best correlation characteristics to the subjective assessment, followed by the MSE calculating algorithm. From this study, a better understanding of the requirements for developing an efficient image quality assessment method was gained.

The majority of laryngectomees use the electrolarynx as their primary mode of verbal communication after total laryngectomy surgery. However, the archetypal electrolarynx suffers from a monotonous tone and the inconvenience of requiring manual control. This paper presents the potential of pattern recognition to support electrolarynx use by predicting fundamental frequency (F0) and voicing state (VS) from surface EMG of the infrahyoid and suprahyoid muscles, as well as from a respiratory trace. In this study, surface EMG signals from the infrahyoid and suprahyoid muscle groups and respiratory trace were collected from 10 able-bodied, adult males (18-60 years old). Participants performed three kinds of vocal tasks – tones, legatos and phrases. Signal features were extracted from the EMG and respiratory trace, and a Support Vector Machine (SVM) classifier with radial basis function kernels was employed to predict F0 and voicing state. An average root mean squared error of 2.81 ± 0.6 se...

This paper deals with comparative performance analysis of popularly used trained/supervised/non-blind algorithms i.e. LMS, NLMS, APA, and RLS. EMSE curve has been simulated for quick comparison. Complexity of abovementioned discussed. Different algorithm defining parameters like EMSE, MSE, and MSD have been analyzed and compared. SNR=25dB. Step-size µ=0.10 & forgetting factor Ł=0.99 have been taken. RLS stands out in complexity as well as convergence.

This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR.

The paper introduces a frequentist's alternative to the recently developed hierarchical Bayes methods for small area estimation with binary data. Specifically, the best predictor (BP) and empirical best predictor (EBP) of small area specific random effect are developed in the context of a mixed logistic model and different asymptotic properties of the proposed BP and EBP are studied. An approximation to the mean squared error (MSE) of the proposed EBP correct up to the order o(m −1) is obtained, where m denotes the number of small areas. The asymptotic behavior of the relative savings loss (RSL) demonstrates the superiority of the proposed EBP over the usual small area proportion.

This paper addresses the area of data compression as it is applicable to image processing. An analysis of several image compression strategies are examined for their relative effectiveness. Several topics concerning image compression are examined in this study ...

The problem of reducing the initialization time of the echo cancellers and the channel equalizers in modems is extremely important for high-speed full-duplex data transmission over two-wire lines. In a previous publication Chen et al. (see IEEE Trans. Information Theory, vol.41, no.5, 1995) proposed a full-duplex fast training procedure for simultaneously estimating the echo and channel responses. It reduces the tap-setting time to half of that required by the traditional half-duplex fast training schemes. However, its estimation accuracy may be degraded by the frequency offsets in both the far echo and far signal that are caused by the analog carrier network. We extend the previous work and develop a new algorithm that can compensate for the frequency offsets in both the far echo and far signal. The performance of the new method is analyzed in terms of the mean-square error. Simulation results are presented to confirm the analysis

To provide security in data communication networks various forms of cryptographic and steganographic
algorithms have been proposed. Though both of these techniques serve the same purpose i.e. to secure data
but have some drawbacks. Using cryptography might raise some suspicion whereas in steganography the
distortion in cover can attract an adversary. The combinations of the two as proposed by various
researchers can be very fruitful in terms of brute force search time and robustness against attacks. A
popular method of combination is using pseudorandom Least Significant Bit (LSB) substitution along with
stream cipher or block cipher. The problem with pseudorandom LSB substitution technique is with the
random pixel selection process. It inserts the cipher text on randomly chosen pixels using random interval
method having certain period. Now if this period of random interval number generations is large, it
increases time complexity. On the other hand if it is too low then the cipher text will not be evenly
distributed in all the colour planes. This paper proposes an adaptive method on the basis of size of secret
data for random pixel selection for data embedding. The results show that using such an approach not only
increases brute force search time but also reduces the time complexity of the overall process.

Synthetic Aperture Radar (SAR) imagery to monitor oil spills are some methods that have been proposed for the West African sub-region. With the increase in the number of oil exploration companies in Ghana (and her neighbors) and the rise in the coastal activities in the sub-region, there is the need for proper monitoring of the environmental impact of these socio-economic activities on the environment.Detection and near real-time information about oil spills are fundamental in reducing oil spill environmental impact. SAR images are prone to some noise, which is predominantly speckle noise around the coastal areas. This paper evaluatesthe performance of the mean and median filters used in the preprocessing filtering to reduce speckle noise in SAR images for most image processing algorithms.