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To improve radio access capability, sky connections relying on satellites or unmanned aerial vehicles (UAV), as well as high-altitude platforms (HAP) will be exploited in 6G wireless communication systems, complementing terrestrial networks. For long-distance communication, a large smart antenna will be used that is characterized by high amounts of power consumed by digital beamformers. This paper focuses on reducing power consumption by relying on a thinned smart antenna (TSA). The performance of TSA is investigated in the sub-6 GHz band. The differential evolution (DE) algorithm is used to optimize excitation weights of the individual dipoles in the antenna array and these excitation weights are then used in TSA for beamforming, with signal processing algorithms deployed. The DE technique is used with the least mean square, recursive least square and sample matrix inversion algorithms. The proposed method offers almost the same directivity, simultaneously ensuring lower side lobes (SLL) and reduced power consumption. For a TSA of 20, 31, and 64 dipoles, the power savings are 20%, 19.4%, and 17.2%, respectively. SLL reductions achieved, in turn, vary from 5.2 dB to 8.1 dB. [ABSTRACT FROM AUTHOR]

The fast Fourier transformation algorithm (FFT) probably is the most important algorithm in the digital signal processing. It is an efficient algorithm to the discrete Fourier transformation which determines the frequency components of a discrete time-varying signal. Nowadays, it has a huge impact on the modern society because the FFT is running on more billion devices (e.g. smartphones) on the planet all the time and this tendency is continuously increasing. Moreover, this algorithm plays a key role in the computer science and engineering. Consequently, a well optimized algorithm can save tremendous resources (calculation capacity and memory). This paper presents a new relation between "twiddle factors" and gives an optimised form to the existing relations. In the paper the experimental results prove the efficiency of the proposed relations. By the new relations every radix-r and split-radix FFT will be more efficient because it accelerates the algorithm and/or saves memory. [ABSTRACT FROM AUTHOR]

This article investigates switched quantizers for speech signal depicted with Gaussian probability density function (PDF). Gaussian PDF is better for smaller frame lengths that are represented here. Companding technique results in constant Signal to Quantization Noise Ratio (SQNR). In this paper two approaches are present: quasi-logarithmic (QL) and piecewise uniform (PU) compandor. Simpler compandor directly affects the complexity of hardware realization and expense of given solution, but, on the other hand, also brings to weaker performances. Therefore, a smart choice has to be made. Usage of switched technique leads to better performances. This way, the quality of quantization is improved by dividing the dynamic range of variances into multiple subranges. For each subrange a separate quantizer is designed, with its support region's amplitude. The optimal amplitude is numerically determined, whereby a single criterion is obtaining the maximal SQNR. Bit rates of these quantizers don't depend on signal variance, as the fixed length codes are used. The performances of proposed quantizers are demonstrated on real speech signals from the reliable database. Comparison of obtained results with other recent solutions is done in order to show the efficiency of this model. [ABSTRACT FROM AUTHOR]

This article investigates switched quantizers for speech signal depicted with Gaussian probability density function (PDF). Gaussian PDF is better for smaller frame lengths that are represented here. Companding technique results in constant Signal to Quantization Noise Ratio (SQNR). In this paper two approaches are present: quasi-logarithmic (QL) and piecewise uniform (PU) compandor. Simpler compandor directly affects the complexity of hardware realization and expense of given solution, but, on the other hand, also brings to weaker performances. Therefore, a smart choice has to be made. Usage of switched technique leads to better performances. This way, the quality of quantization is improved by dividing the dynamic range of variances into multiple subranges. For each subrange a separate quantizer is designed, with its support region's amplitude. The optimal amplitude is numerically determined, whereby a single criterion is obtaining the maximal SQNR. Bit rates of these quantizers don't depend on signal variance, as the fixed length codes are used. The performances of proposed quantizers are demonstrated on real speech signals from the reliable database. Comparison of obtained results with other recent solutions is done in order to show the efficiency of this model. [ABSTRACT FROM AUTHOR]

This paper deals with aliasing distortion in digital audio signal synthesis of classic periodic waveforms with infinite Fourier series, for electronic musical instruments. When these waveforms are generated in the digital domain then the aliasing appears due to its unlimited bandwidth. There are several techniques for the synthesis of these signals that have been designed to avoid or reduce the aliasing distortion. However, these techniques have high computing demands. One can say that today's computers have enough computing power to use these methods. However, we have to realize that today's computer-aided music production requires tens of multi-timbre voices generated simultaneously by software synthesizers and the most of the computing power must be reserved for harddisc recording subsystem and real-time audio processing of many audio channels with a lot of audio effects. Trivially generated classic analog synthesizer waveforms are therefore still effective for sound synthesis. We cannot avoid the aliasing distortion but spectral components produced by the aliasing can be masked with harmonic components and thus made inaudible if sufficient oversampling ratio is used. This paper deals with the assessment of audible aliasing distortion with the help of a psychoacoustic model of simultaneous masking and compares the computing demands of trivial generation using oversampling with those of other methods. [ABSTRACT FROM AUTHOR]

This paper proposes a new model of switching scalar quantization that besides the usual adaptation to the variance uses a switching technique for adaptation to the distribution of the speech signal source. By applying the new model a smaller quantization errors achieved in comparison to the conventional models with a usual adaptation. [ABSTRACT FROM AUTHOR]

Fault diagnosis of the marine power station is essential to ensure the normal electric supply for the whole ship. In this paper, a new faults diagnosis technique for the power station using the data fusion technique has been proposed. The vibration signals of the power station were recorded by the multi-channel sensors. The independent component analysis (ICA) was adopted as the data fusion approach to find the characteristic vibration signals of the power station faults. Then the wavelet packet was employed to extract the feature vector of the fused vibration signals. In addition, the oil particle features has been extracted using the oil analysis. Lastly, the least square support vector machines (LS-SVM) was used to recognize the fault patterns of the power station. Moreover, the improved particle swarm optimization (PSO) was employed to enhance the learning ability of the LS-SVM. The experimental tests were implemented in a real ship to evaluate the effectiveness of the proposed diagnosis approach. The diagnosis results have shown that distinguished fault features have been extracted and the fault identification accuracy is acceptable. In addition, the classification rate of the proposed method is superior to the traditional SVM based method. [ABSTRACT FROM AUTHOR]

The IEEE International Symposium on Biomedical Imaging (ISBI) is a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, across all scales of observation. It fosters knowledge transfer among different imaging communities and contributes to an integrative approach to biomedical imaging. ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS). The 2018 meeting will include tutorials, and a scientific program composed of plenary talks, invited special sessions, challenges, as well as oral and poster presentations of peer-reviewed papers. High-quality papers are requested containing original contributions to the topics of interest including image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological, and statistical modeling. Accepted 4-page regular papers will be published in the symposium proceedings published by IEEE and included in IEEE Xplore. To encourage attendance by a broader audience of imaging scientists and offer additional presentation opportunities, ISBI 2018 will continue to have a second track featuring posters selected from 1-page abstract submissions without subsequent archival publication. [ABSTRACT FROM AUTHOR]

• We have applied machine learning to identify 6 finger gestures from EMG signals. • Time/frequency features, ANN, SVM and k-NN algorithms were used for classification. • Fine and Weighted k-NN showed a better performance with accuracy of 97%. • The gestures were turned into a joint trajectory using interpolation methods. • We then reconstruct the finger kinematics and simulate the dynamics. • Experiments were carried out to create an EMG database from 20 control subjects. • The correlation between the joint trajectories and the tracked hand-motion was 0.91. The recovery of hand motion is one of the most challenging aspects in stroke rehabilitation. This paper presents an initial approach to robot-assisted hand-motion therapies. Our goal was twofold: firstly, we have applied machine learning methods to identify and characterize finger motion patterns from healthy individuals. To this purpose, Electromyographic (EMG) signals have been acquired from flexor and extensor muscles in the forearm using surface electrodes. Time and frequency features were used as inputs to machine learning algorithms for recognition of six hand gestures. In particular, we compared the performance of Artificial Neural Networks (ANN), Support Vector Machines (SVM) and k-Nearest Neighbor (k-NN) algorithms for classification. Secondly, each identified gesture was turned into a joint reference trajectory by applying interpolation methods. This allowed us to reconstruct the hand/finger motion kinematics and to simulate the dynamics of each motion pattern. Experiments were carried out to create an EMG database from 20 control subjects, and a VICON camera tracking system was used to validate the accuracy of the proposed system. The average correlation between the EMG-based generated joint trajectories and the tracked hand-motion was 0.91. Furthermore, statistical analysis applied to 14 different SVM, ANN and k-NN configurations showed that Fine k-NN and Weighted k-NN have a better performance for the classification of gestures (98% of accuracy). In a future, the trajectories controlled by EMG signals could be applied to an exoskeleton or hand-robotic prosthesis for rehabilitation. [ABSTRACT FROM AUTHOR]