Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers

In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate with the input signal, manifesting into power-supply induced intermodulation distortion (PS-IMD) and due to the memory effects of the system, there exist asymmetries in the PS-IMDs. In this paper, a new behavioral modeling based on the Elman Wavelet Neural Network (EWNN) is proposed to study the nonlinear distortion of the CDPAs. In EWNN model, the Morlet wavelet functions are employed as the activation function and there is a normalized operation in the hidden layer, the modification of the scale factor and translation factor in the wavelet functions are ignored to avoid the fluctuations of the error curves. When there are 30 neurons in the hidden layer, to achieve the same square sum error (SSE) $\epsilon_{min}=10^{-3}$, EWNN needs 31 iteration steps, while the basic Elman neural network (BENN) model needs 86 steps. The Volterra-Laguerre model has 605 parameters to be estimated but still can't achieve the same magnitude accuracy of EWNN. Simulation results show that the proposed approach of EWNN model has fewer parameters and higher accuracy than the Volterra-Laguerre model and its convergence rate is much faster than the BENN model

Effect of saline stress on the physiology and growth of maize hybrids and their related inbred lines

Salinity is one major abiotic stress that restrict plant growth and crop productivity. In maize (Zea mays L), salt stress causes significant yield loss each year. However, indices of maize response to salt stress are not completely explored and a desired method for maize salt tolerance evaluation is still not established. A Chinese leading maize variety Jingke968 showed various resistance to environmental factors, including salt stress. To compare its salt tolerance to other superior maize varieties, we examined the physiological and growth responses of three important maize hybrids and their related inbred lines under the control and salt stress conditions. By compar- ing the physiological parameters under control and salt treatment, we demonstrated that different salt tolerance mechanisms may be involved in different genotypes, such as the elevation of superoxide dismutase activity and/ or proline content. With Principal Component Analysis of all the growth indicators in both germination and seedling stages, along with the germination rate, superoxide dismutase activity, proline content, malondialdehyde content, relative electrolyte leakage, we were able to show that salt resistance levels of hybrids and their related inbred lines were Jingke968 > Zhengdan958 > X1132 and X1132M > Jing724 > Chang7-2 > Zheng58 > X1132F, respectively, which was consistent with the saline field observation. Our results not only contribute to a better understanding of salt stress response in three important hybrids and their related inbred lines, but also this evaluation system might be applied for an accurate assessment of salt resistance in other germplasms and breeding material

Grouped Knowledge Distillation for Deep Face Recognition

Compared with the feature-based distillation methods, logits distillation can liberalize the requirements of consistent feature dimension between teacher and student networks, while the performance is deemed inferior in face recognition. One major challenge is that the light-weight student network has difficulty fitting the target logits due to its low model capacity, which is attributed to the significant number of identities in face recognition. Therefore, we seek to probe the target logits to extract the primary knowledge related to face identity, and discard the others, to make the distillation more achievable for the student network. Specifically, there is a tail group with near-zero values in the prediction, containing minor knowledge for distillation. To provide a clear perspective of its impact, we first partition the logits into two groups, i.e., Primary Group and Secondary Group, according to the cumulative probability of the softened prediction. Then, we reorganize the Knowledge Distillation (KD) loss of grouped logits into three parts, i.e., Primary-KD, Secondary-KD, and Binary-KD. Primary-KD refers to distilling the primary knowledge from the teacher, Secondary-KD aims to refine minor knowledge but increases the difficulty of distillation, and Binary-KD ensures the consistency of knowledge distribution between teacher and student. We experimentally found that (1) Primary-KD and Binary-KD are indispensable for KD, and (2) Secondary-KD is the culprit restricting KD at the bottleneck. Therefore, we propose a Grouped Knowledge Distillation (GKD) that retains the Primary-KD and Binary-KD but omits Secondary-KD in the ultimate KD loss calculation. Extensive experimental results on popular face recognition benchmarks demonstrate the superiority of proposed GKD over state-of-the-art methods.Comment: 9 pages, 2 figures, 7 tables, accepted by AAAI 202

Underwater broadband acoustic scattering modelling based on FDTD

A modified finite-difference time-domain (FDTD) method is described in this paper. The absorption coefficient which is frequency-dependent is considered, and it is used to compute broadband acoustic scattering model of underwater complex object. The perfectly matched layer (PML) absorbing boundary condition (ABC) is applied to this work. Considering computation and accuracy comprehensively, PML boundary layer number and the attenuation coefficient is set at proper values. Computer Graphics are applied to mesh-generating of the irregular object. A pulse of LFM signal is used to simulate wide-band acoustic scattering field of a circle in 2D and a complex object in 3D. And the scattered acoustic pressure waveforms of some certain points are computed in the calculation field. Results obtained from simulation confirm the high accuracy of the proposed method.This research was supported in part by the Foundation of Key Laboratory of China’s Education Ministry and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.http://www.eejournal.ktu.lt/index.php/eltam201

Numerical Analysis of Modeling Based on Improved Elman Neural Network

A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance

FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data

Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.Comment: 10 pages, 1 figure, WACV 2024 Workshop

Edge-centric queries stream management based on an ensemble model

The Internet of things (IoT) involves numerous devices that can interact with each other or with their environment to collect and process data. The collected data streams are guided to the cloud for further processing and the production of analytics. However, any processing in the cloud, even if it is supported by improved computational resources, suffers from an increased latency. The data should travel to the cloud infrastructure as well as the provided analytics back to end users or devices. For minimizing the latency, we can perform data processing at the edge of the network, i.e., at the edge nodes. The aim is to deliver analytics and build knowledge close to end users and devices minimizing the required time for realizing responses. Edge nodes are transformed into distributed processing points where analytics queries can be served. In this paper, we deal with the problem of allocating queries, defined for producing knowledge, to a number of edge nodes. The aim is to further reduce the latency by allocating queries to nodes that exhibit low load (the current and the estimated); thus, they can provide the final response in the minimum time. However, before the allocation, we should decide the computational burden that a query will cause. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query. The complexity class, thus, can be matched against the current load of every edge node. We discuss our scheme, and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results

Synchronization of a class of complex dynamical networks with time-varying delay couplings

Dimirovski, Georgi M. (Dogus Author) -- Conference Location: Saint PetersburgThis paper investigates synchronization dynamics of a complex delayed dynamical network as well as the effects of time-varying delay. Following the approach via Razumikhin theorem, simple delay-dependent synchronization criteria are derived in terms of linear matrix inequalities, which can be verified via the interior-point algorithm. The proposed criteria can deal with a fast time-varying delay in coupling term and enabled removing the restriction on the derivative of the time-varying delay. The effectiveness of the proposed synchronization scheme and the theoretical results are illustrated by a numerical example

Decentralized adaptive synchronization of an uncertain complex delayed dynamical network

Dimirovski, Georgi M. (Dogus Author)In this paper, we investigate the locally and globally adaptive synchronization problem for an uncertain complex dynamical network with time-varying coupling delays based on the decentralized control. The coupling terms here are bounded by high-order polynomials with known gains that are ubiquitous in a large class of complex dynamical networks. We generalize the usual technology of searching for an appropriate coordinates transformation to change the network dynamics into a series of decoupled lower-dimensional systems. Several adaptive synchronization criteria are derived by constructing the Lyapunov-Krasovskii functional and Barbalat lemma, and the proposed criteria are simple in form and convenient for the practical engineering design. Numerical simulations illustrated by a nearest-neighbor coupling network verify the effectiveness of the proposed synchronization scheme