scholarly journals Atomic Network-Based DOA Estimation Using Low-Bit ADC

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 738
Author(s):  
Shuran Sheng ◽  
Peng Chen ◽  
Yuxuan Yao ◽  
Lenan Wu ◽  
Zhimin Chen
Keyword(s):  
Low Cost ◽  
Doa Estimation ◽  
Estimation Problem ◽  
Quantization Noise ◽  
Analog To Digital ◽  
Estimation Performance ◽  
Atomic Norm ◽  
Reconstruction Performance ◽  
Input Signals ◽  
Fully Connected

In the direction of arrival (DOA) estimation problem, when a low-bit analog to digital converter (ADC) is used, the estimation performance severely deteriorates. In this paper, the DOA estimation problem is considered in a low-cost direction finding system with low-bit ADC. To eliminate quantization noise, we propose a novel network ADCnet, which is a composition of fully connected layers and exponential linear unit (ELU) layers, and the input signals are the received signals using low-bit ADC. After the ADCnet, an AtomicNet is also proposed to estimate the DOA from the denoised signals, where atomic vectors are corresponding to the steer vectors. A loss function considering both the reconstruction performance and the sparsity is proposed in the AtomicNet. Different from the exiting atomic norm-based methods, the proposed method can avoid an optimization problem and estimate the DOA with lower computational complexity. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation performance using low-bit ADC.

scholarly journals Atomic Norm-Based DOA Estimation with Dual-Polarized Radar

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1056 ◽  
Author(s):  
Min Han ◽  
Wenbin Dou
Keyword(s):  
Doa Estimation ◽  
Spatial Domain ◽  
Estimation Problem ◽  
Semidefinite Program ◽  
Sparse Reconstruction ◽  
Reconstruction Problem ◽  
Estimation Performance ◽  
Atomic Norm ◽  
Dual Polarized ◽  
Definition Of

In the dual-polarized radar system, the horizontally and vertically polarized signals can be exploited to improve the direction of arrival (DOA) estimation performance. In this paper, the DOA estimation problem is considered in the dual-polarized radar. By exploiting the target sparsity in the spatial domain, the sparse-based method is proposed after formulating the DOA estimation problem as a sparse reconstruction problem. In the traditionally sparse methods using the compressed sensing (CS) theory, the spatial domain is discretized into grids to establish a dictionary matrix and solve the sparse reconstruction problem, but the off-grid error is introduced in the discretized grids. Therefore, we formulate a novel definition of atomic norm for the dual-polarized signals and give an atomic norm-based method to denoise the received signals. Then, an efficient semidefinite program (SDP) is derived, and the DOA is estimated by searching the peak values of the denoised signals. Simulation results show that the proposed method can significantly improve the DOA estimation performance in the dual-polarized radar. Additionally, compared with the state-of-art methods, the proposed method has better estimation performance with relatively low computational complexity.

scholarly journals Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2381
Author(s):  
Jaewon Lee ◽  
Hyeonjeong Lee ◽  
Miyoung Shin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Accidents ◽  
Physiological Signals ◽  
Superior Performance ◽  
Short Term ◽  
Input Signals ◽  
Skin Response ◽  
Novel Method ◽  
Fully Connected

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

Spatial Filtering Based on Differential Spectrum for Improving ML DOA Estimation Performance

2016 ◽  
Vol 23 (12) ◽  
pp. 1811-1815 ◽  
Author(s):  
Rodrigo Pinto Lemos ◽  
Hugo Vinicius Leao e Silva ◽  
Edna Lucia Flores ◽  
Jonas Augusto Kunzler ◽  
Diego Fernando Burgos
Keyword(s):  
Spatial Filtering ◽  
Doa Estimation ◽  
Differential Spectrum ◽  
Estimation Performance

scholarly journals Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1914
Author(s):  
Jian Xie ◽  
Qiuping Wang ◽  
Yuexian Wang ◽  
Xin Yang
Keyword(s):  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Array Signal Processing ◽  
Elevation Angle ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rank Reduction ◽  
Communication Signals ◽  
Estimation Performance ◽  
Direction Cosines

Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such as mutual coupling, may significantly deteriorate the estimation performance. To deal with this problem, a robust real-valued estimator for rectilinear sources was developed to alleviate unknown mutual coupling in URAs. An augmented covariance matrix was built up by extracting the real and imaginary parts of observations containing the circularity and noncircularity of signals. Then, the actual steering vector considering mutual coupling was reparameterized to make the rank reduction (RARE) property available. To reduce the computational complexity of two-dimensional (2D) spectral search, we individually estimated y-axis and x-axis direction-cosines in two stages following the principle of RARE. Finally, azimuth and elevation angle estimates were determined from the corresponding direction-cosines respectively. Compared with existing solutions, the proposed method is more computationally efficient, involving real-valued operations and decoupled 2D spectral searches into twice those of one-dimensional searches. Simulation results verified that the proposed method provides satisfactory estimation performance that is robust to unknown mutual coupling and close to the counterparts based on 2D spectral searches, but at the cost of much fewer calculations.

scholarly journals Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

Inventions ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 70
Author(s):  
Elena Solovyeva ◽  
Ali Abdullah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Computational Cost ◽  
High Accuracy ◽  
Activation Functions ◽  
Fully Connected ◽  
Fully Connected Networks

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.

scholarly journals Superresolution 2D DOA Estimation for a Rectangular Array via Reweighted Decoupled Atomic Norm Minimization

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Ming-Ming Liu ◽  
Chun-Xi Dong ◽  
Yang-Yang Dong ◽  
Guo-Qing Zhao
Keyword(s):  
Computational Complexity ◽  
Optimization Problem ◽  
Toeplitz Matrices ◽  
Doa Estimation ◽  
Rank Minimization ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Surrogate Function

This paper proposes a superresolution two-dimensional (2D) direction of arrival (DOA) estimation algorithm for a rectangular array based on the optimization of the atomic l0 norm and a series of relaxation formulations. The atomic l0 norm of the array response describes the minimum number of sources, which is derived from the atomic norm minimization (ANM) problem. However, the resolution is restricted and high computational complexity is incurred by using ANM for 2D angle estimation. Although an improved algorithm named decoupled atomic norm minimization (DAM) has a reduced computational burden, the resolution is still relatively low in terms of angle estimation. To overcome these limitations, we propose the direct minimization of the atomic l0 norm, which is demonstrated to be equivalent to a decoupled rank optimization problem in the positive semidefinite (PSD) form. Our goal is to solve this rank minimization problem and recover two decoupled Toeplitz matrices in which the azimuth-elevation angles of interest are encoded. Since rank minimization is an NP-hard problem, a novel sparse surrogate function is further proposed to effectively approximate the two decoupled rank functions. Then, the new optimization problem obtained through the above relaxation can be implemented via the majorization-minimization (MM) method. The proposed algorithm offers greatly improved resolution while maintaining the same computational complexity as the DAM algorithm. Moreover, it is possible to use a single snapshot for angle estimation without prior information on the number of sources, and the algorithm is robust to noise due to its iterative nature. In addition, the proposed surrogate function can achieve local convergence faster than existing functions.

Low-cost thin-film passive RFID circuits and detector system

2020 ◽  
Vol 12 (5) ◽  
pp. 356-366
Author(s):  
Salma El-Sawy ◽  
Wasim Nawaz ◽  
Mohamed Osama ◽  
Ahmet Tekin
Keyword(s):  
Printed Circuit Board ◽  
Light Emitting Diode ◽  
Low Cost ◽  
Flexible Substrate ◽  
Circuit Board ◽  
Detection Methods ◽  
Single Chip ◽  
Analog To Digital ◽  
Inductively Coupled ◽  
Passive Rfid

AbstractThis paper discusses the design of chip-less RFID tags of a standard pocket size of 69 mm by 156 mm. These tags are based on lumped elements of copper metal traces constructed on a thin polyamide flexible substrate. Moreover, a low-cost single-chip Bluetooth detector circuit system is demonstrated. Two different detection methods: variable coil load coupling and optical light intensity detection were combined to yield 256 unique ID codes. In the first method, by utilizing simple 4 MHz digital drivers and an integrated analog to digital converter (ADC) in the reader controller; various inductively coupled resonant loads corresponding to multiple distinct tags could be differentiated, yielding eight different (3-bit) ID codes. The additional via-based hole pattern reflectometer method creates additional 32 distinct levels (5-bit) utilizing 650 nm visible light-emitting diode and a simple trans-impedance operational along with the same analog ADC pins of a Bluetooth controller. The printed circuit board trace coil on the two-layer low-cost FR-4 waterproof sealed detector unit is simultaneously used as a Qi wireless power receiver to charge the120 mAh 2450 Lithium Polymer (LiR) battery. The device could remain operational for more than a month with a single charge; remaining connected with a mobile device and enabling 10 readouts daily.

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