Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features

The recently proposed tensor-based recursive least-squares dichotomous coordinate descent algorithm, namely RLS-DCD-T, was designed for the identification of multilinear forms. In this context, a high-dimensional system identification problem can be efficiently addressed (gaining in terms of both performance and complexity), based on tensor decomposition and modeling. In this paper, following the framework of the RLS-DCD-T, we propose a regularized version of this algorithm, where the regularization terms are incorporated within the cost functions. Furthermore, the optimal regularization parameters are derived, aiming to attenuate the effects of the system noise. Simulation results support the performance features of the proposed algorithm, especially in terms of its robustness in noisy environments.

Convex Optimization for Distributed Acoustic Beamforming

<p>Beamforming filter optimization can be performed over a distributed wireless sensor network, but the output calculation remains either centralized or linked in time to the weights optimization. We propose a distributed method for calculating the beamformer output which is independent of the filter optimization. The new method trades a small decrease in signal to noise performance for a large decrease in transmission power. Background is given on distributed convex optimization and acoustic beamforming. The new model is described with analysis of its behaviour under independent noise. Simulation results demonstrate the desirable properties of the new model in comparison with centralized output computation.</p>

Convex Optimization for Distributed Acoustic Beamforming

<p>Beamforming filter optimization can be performed over a distributed wireless sensor network, but the output calculation remains either centralized or linked in time to the weights optimization. We propose a distributed method for calculating the beamformer output which is independent of the filter optimization. The new method trades a small decrease in signal to noise performance for a large decrease in transmission power. Background is given on distributed convex optimization and acoustic beamforming. The new model is described with analysis of its behaviour under independent noise. Simulation results demonstrate the desirable properties of the new model in comparison with centralized output computation.</p>

Low-power 25Gb/s 16:1 Multiplexer for 400Gb/s Ethernet PHY

A lower power 25Gb/s 16:1 multiplexer using 65nm CMOS technology for 400Gb/s Ethernet (400GbE) physical layer (PHY) interface was presented. CMOS+CML mixed logic is adopted to achieve hierarchical architecture, avoiding the high clock requirement of one-step structure and improving the transmission speed. In order to reduce power while achieving high data rate, multiplexing structure is also optimized by utilizing multi-frequency multi-phase technology which not only ensures the requirement of the phase stabilization, but also leaves out some flip-flops. For CMOS-CML conversion circuit, transmission gate and cross-coupled CMOS inverter are used to match the delay of CMOS inverter, suppressing the effect of common-mode noise. Simulation results show that the multiplexer works correctly and jitter of output signal is less than 0.1UI. When voltage is 1.2V, the total power is 32.7mW at 25Gb/s.

MEMS microphone intensity array for cabin noise measurements

Aircraft cabin noise measurements in flight are used toto quantify the noise level, and to identify the entry point of acoustic energy into the cabin. Sound intensity probes are the state-of-the-art measurement technique for this task. During measurements, additional sound absorbing material is used to ease the rather harsh acoustic measurement environment inside the cabin. In order to decrease the expensive in-flight measurement time, an intensity array approach was chosen. This intensity probe consists of 512 MEMS-Microphones. Depending on the frequency, these microphones can be combined as an array of hundreds of 3D- intensity probes. The acoustic velocity is estimated using a high order 3D finite difference stencil. At low frequencies, a larger spacing is used to reduce the requirement of accurate phase match of the microphone sensors. Measurements were conducted in the ground-based Dornier 728 cabin noise simulation as well as in-flight.

A Deep Learning-Based Electromagnetic Signal for Earthquake Magnitude Prediction

The influence of earthquake disasters on human social life is positively related to the magnitude and intensity of the earthquake, and effectively avoiding casualties and property losses can be attributed to the accurate prediction of earthquakes. In this study, an electromagnetic sensor is investigated to assess earthquakes in advance by collecting earthquake signals. At present, the mainstream earthquake magnitude prediction comprises two methods. On the one hand, most geophysicists or data analysis experts extract a series of basic features from earthquake precursor signals for seismic classification. On the other hand, the obtained data related to earth activities by seismograph or space satellite are directly used in classification networks. This article proposes a CNN and designs a 3D feature-map which can be used to solve the problem of earthquake magnitude classification by combining the advantages of shallow features and high-dimensional information. In addition, noise simulation technology and SMOTE oversampling technology are applied to overcome the problem of seismic data imbalance. The signals collected by electromagnetic sensors are used to evaluate the method proposed in this article. The results show that the method proposed in this paper can classify earthquake magnitudes well.

Negative updating applied to the best-of-n problem with noisy qualities

The ability to perform well in the presence of noise is an important consideration when evaluating the effectiveness of a collective decision-making framework. Any system deployed for real-world applications will have to perform well in complex and uncertain environments, and a component of this is the limited reliability and accuracy of evidence sources. In particular, in swarm robotics there is an emphasis on small and inexpensive robots which are often equipped with low-cost sensors more prone to suffer from noisy readings. This paper presents an exploratory investigation into the robustness of a negative updating approach to the best-of-n problem which utilises negative feedback from direct pairwise comparison of options and opinion pooling. A site selection task is conducted with a small-scale swarm of five e-puck robots choosing between $$n=7$$ n = 7 options in a semi-virtual environment with varying levels of sensor noise. Simulation experiments are then used to investigate the scalability of the approach. We now vary the swarm size and observe the behaviour as the number of options n increases for different error levels with different pooling regimes. Preliminary results suggest that the approach is robust to noise in the form of noisy sensor readings for even small populations by supporting self-correction within the population.