Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Synthetic aperture radar imaging with fractional Fourier transform and channel equalization

This paper investigates the Range-Doppler Algorithm based on the Fractional Fourier Transform (RDA-FrFT) to obtain High-Resolution (HR) images for targets in Synthetic Aperture Radar (SAR) imaging. A mathematical framework for the RDA-FrFT is developed in this paper with closed-form expressions for the range and azimuth compression. The channel effect is considered in this paper for the first time with three inverse techniques to reduce this effect; inverse filter deconvolution, Linear Minimum Mean Square Error (LMMSE) deconvolution, and regularized deconvolution. The performance of the RDA-FrFT is compared with the classical RDA, which is based on the Fourier Transform (FT). Simulation results reveal that the RDA-FrFT offers better focusing capabilities and greater side-lobe reduction ratios. The reflectivity profile obtained with the RDA-FrFT demonstrates a superior performance to the classical RDA. Results show also that the RDA-FrFT gives low Peak Side-Lobe (PSL) and Integrated Side-Lobe (ISL) levels after range and azimuth compression for the detected targets. Finally, the results reveal that the proposed regularized deconvolution technique enhances the performance of the RDA-FrFT significantly if the channel effect is considered.     

Simultaneous measurement of temperature and strain with long period grating pairs using low resolution detection

An investigation showing the performance of compact long period grating (LPG) pairs under demanding experimental conditions, making comparisons with the use of single LPGs for the simultaneous measurement of strain and temperature is presented. In this work, a LPG pair (LPGP) sensor comprising two weak (2.5 dB each) LPGs, 13 mm apart, was compared against the performance of two single LPG sensors (LPG1 & LPG2) with coupling strengths of 2.5 dB and 12.5 dB, respectively. All the LPG sensors used were subjected to extreme conditions arising from the fabrication, by choosing a high amplitude mask period with short physical length LPGs, and the high temperature annealing processes. In addition, a low resolution detection instrument was used to capture the spectra under study. Results have shown that the LPGP is 40 times better than the first single LPG used (LPG1) and three times better than the second LPG (LPG2). Using the error analysis methodology of Brady et al., the temperature and strain errors due to the uncertainty of the wavelength measurement for each of the LPG1, LPG2, and LPGP were compared with the typical performance of various fibre Bragg grating (FBG)-based sensor schemes under similar testing conditions. It was found that LPGPs offer the best performance especially when using low resolution detection instrumentation.     

The provision of online public goods: Examining social structure in an electronic network of practice

Electronic networks of practice are computer-mediated social spaces where individuals working on similar problems self-organize to help each other and share knowledge, advice, and perspectives about their occupational practice or common interests. These interactions occur through message postings to produce an on-line public good of knowledge, where all participants in the network can then access this knowledge, regardless of their active participation in the network. Using theories and concepts of collective action and public goods, five hypotheses are developed regarding the structural and social characteristics that support the online provision and maintenance of knowledge in an electronic network of practice. Using social network analysis, we examine the structure of message contributions that produce and sustain the public good. We then combine the results from network analysis with survey results to examine the underlying pattern of exchange, the role of the critical mass, the quality of the ties sustaining participation, the heterogeneity of resources and interests of participants, and changes in membership that impact the structural characteristics of the network. Our results suggest that the electronic network of practice chosen for this study is sustained through generalized exchange, is supported by a critical mass of active members, and that members develop strong ties with the community as a whole rather than develop interpersonal relationships. Knowledge contribution is significantly related to an individual's tenure in the occupation, expertise, availability of local resources and a desire to enhance one's reputation, and those in the critical mass are primarily responsible for creating and sustaining the public good of knowledge. Finally, we find that this structure of generalized exchange is stable over time although there is a high proportion of member churn in the network.     

Target coverage in camera networks for manufacturing workplaces

In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given set of target points of interest are each k-covered with a predefined minimum spatial resolution. Since the problem is NP-complete, and even NP-hard to approximate, a novel method based on Simulated Annealing is presented to solve the optimisation problem. A new neighbourhood generation function is proposed to handle the discrete nature of the problem. The visual coverage is modelled using realistic and coherent assumptions of camera intrinsic and extrinsic parameters making it suitable for many real world camera based applications. Task-specific quality of coverage measure is proposed to assist selecting the best among the set of camera network placements with equal coverage. A 3D CAD of the monitored space is used to examine physical occlusions of target points. The results show the accuracy, efficiency and scalability of the presented solution method; which can be applied effectively in the design of practical camera networks.     

Geometrical approach of planar hyper-redundant manipulators: Inverse kinematics,path planning and workspace

A new method for inverse kinematics for hyper-redundant manipulators is proposed in this paper to plan the path of the end-effector. The basic idea is that for a given smooth path consisting of points close enough to each other; computing the inverse kinematics for these points is carried out geometrically using the proposed method. In this method, the angles between the adjacent links are set to be the same, which makes lining up of two or more joint axes impossible; therefore, avoiding singularities. The manipulability index has been used to show how far the manipulator from the singularity configuration is. The determination of the workspace of the manipulator using the proposed method has been presented in this paper. The simulation results have been carried out on a planar and a three dimensional manipulators. The effectiveness of the proposed method is clearly demonstrated by comparing its result with results calculated by the well-known method of measuring manipulability which is used for singularity avoidance for the last two decades.     

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.