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This paper introduces a novel algorithm for optimizing the coefficients of the digital filters used in incremental delta-sigma analog-to-digital converters (IDSC). This algorithm is modified from constrained linear least squares (LS) to improve the signal-to-noise-and-distortion ratio (SNDR) of IDSC and minimize the oversampling rate (OSR) of the modulator, which enhances system speed and reduces power consumption. In the case of the same SNDR, the first-order IDSC with the proposed filter can reduce the OSR of the modulator by 50%, at least compared to the IDSC with the conventional filter. The second-order IDSC with the proposed filter can reach a higher SNDR of 106 dB with an OSR of 64. Considering the nonlinearity of the integrator, the SNDR of IDSC with the proposed filter is also 10 dB greater than that of the conventional filters and 3dB greater than that of the IDSC using the near-optimal algorithm filter. The experimental results indicate that the proposed filter possesses an excellent figure of merit of 0.028 pJ / conv . [ABSTRACT FROM AUTHOR]

This work aims to further compensate for the weaknesses of feature sparsity and insufficient discriminative acoustic features in existing short-duration speaker recognition. To address this issue, we propose the Bark-scaled Gauss and the linear filter bank superposition cepstral coefficients (BGLCC), and the multidimensional central difference (MDCD) acoustic feature extracted method. The Bark-scaled Gauss filter bank focuses on low-frequency information, while linear filtering is uniformly distributed, therefore, the filter superposition can obtain more discriminative and richer acoustic features of short-duration audio signals. In addition, the multi-dimensional central difference method captures better dynamics features of speakers for improving the performance of short utterance speaker verification. Extensive experiments are conducted on short-duration text-independent speaker verification datasets generated from the VoxCeleb, SITW, and NIST SRE corpora, respectively, which contain speech samples of diverse lengths, and different scenarios. The results demonstrate that the proposed method outperforms the existing acoustic feature extraction approach by at least 10% in the test set. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods. [ABSTRACT FROM AUTHOR]

The statement about the stability of a linear filter built based on the Neyman–Pearson criterion is verified by performing falsifying experiments. No relationship is found between the number of small eigenvalues of the noise covariance matrix and network stability. [ABSTRACT FROM AUTHOR]

Parametric rolling is one of the dangerous dynamic phenomena. To discuss the safety of a vessel when a dangerous phenomenon occurs, it is important to estimate the probability of certain dynamical behavior of the ship with respect to a certain threshold level. In this paper, the moment values are obtained by solving the moment equations. Since the stochastic differential equation (SDE) is needed to obtain the moment equations, the autoregressive moving average (ARMA) filter is used. The effective wave is modeled using the 6th-order ARMA filter. In addition, the parametric excitation process is modeled using a non-memory transformation obtained from the relationship between GM and wave elevation. The resulting system of equations is represented by the 8th-order Itô stochastic differential equation, which consists of a second-order SDE for the ship motion and a 6th-order SDE for the effective wave. This system has nonlinear components. Therefore, the cumulant neglect closure method is used as higher-order moments need to be truncated. Furthermore, the probability density function of roll angle is determined using moment values obtained from the SDE and the moment equation. Here, two types of the probability density function are suggested and have a good agreement. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) reconstruction using line laser technology is a crucial aspect of optical measurement. The extraction of the laser stripe center plays a pivotal role in achieving high-precision 3D reconstruction in this field. When the photosensitive component captures images at lower exposure times, the line laser stripes are modulated by the object and affected by noise. This results in incomplete equality of stripe widths, directly impacting the accuracy of the final extracted optical stripe center coordinates. This paper proposed a high-precision laser stripe center detection algorithm for scenes captured under low-exposure conditions, based on adaptive weighted width extraction combined with the Hessian matrix. Firstly, an optimized 8th order Finite Impulse Response(FIR) filter is applied to the low-exposure laser stripe image to find the stripe pseudo-centers and extract the valid coordinate positions involved in the stripe width calculation. The width of the foreground pixel is then determined along its perpendicular direction using the directional template method at the pseudo-center point of the stripe. The Gaussian kernel is calculated based on the obtained weighted average width, and the corresponding Hessian matrix is derived to achieve precise extraction of the center of the low-exposure laser stripe. The experimental results demonstrate that, under various degrees of low-exposure conditions, the laser stripe center extraction method proposed in this paper outperforms the traditional method and exhibits higher measurement accuracy. • The extraction accuracy of laser fringe center based on Hessian matrix is closely related to the fringe width. • A low exposure reveals a narrower laser stripe. • Enhancing the accuracy of laser fringe center point extraction using the weighted average width. • Demonstrating the method's effectiveness in extracting laser fringe center points under low exposure camera conditions. [ABSTRACT FROM AUTHOR]

In this paper, the consensus problem of heterogeneous multi-agent systems under directed topology is investigated. Specifically, this system is composed of three classes of agents respectively described by first-order, second-order and third-order integrator dynamics. By the aid of linear filter, graph theory and matrix theory, the consensus problem is realized based on the two proposed consensus protocols. Moreover, group consensus can also be solved by adjusting parameters. Finally, some examples were presented to illustrate the theoretical results. Our study is expected to establish a more realistic model and provide a method to solve the consensus problem of heterogeneous multi-agent systems in more complex situations.

The problem of linear interpolation in the context of a multivariate time series having multiple (possibly non-consecutive) missing values is studied. A concise formula for the optimal interpolating filter is derived, and illustrations using two simple models are provided. [ABSTRACT FROM AUTHOR]

Aiming at utilizing artificial neural networks to enhance intelligent filtering for interfered wireless communication signal in harsh environments, a new method named convolutional neural filtering is designed and presented in this paper. This method is based on model-driven deep learning princeple, by analyzing the theoretical connection between the filter model and the convolutional neural layer, it attempts to use one-dimensional convolution kernels to learn a matched or bandpass filter. Moreover, the model introduces a kernel-wise attention mechanism between different convolution kernels to selectively emphasize informative filters. The results show that in terms of interference and noise suppression for received wireless signal, the filtering method has highlighted dynamic adaptability to variation of signals and interference, and it also reveals that the performance is affected by the initialization parameters and the number of convolution kernels. Based on this method an embeddable filtering unit fully based on neural network is provided, which can be easily integrated into a deep learning network targeting such as wireless signal detection and recognition applications, avoiding complex preprocessing for end-to-end wireless signal learning.

Abstract A linear denoising filter is usually of lowpass type, and the main parameter in a lowpass filter is the cutoff frequency. The lowpass filters are normally shift invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain. This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model. An example of such a linear filter is presented for low-dose X-ray computed tomography (CT).

Recently, edge-preserving filters have achieved great success in infrared (IR) and visible (VI) image fusion field. However, most edge-preserving filters are complex. In this paper, with the side window filtering technology by which most filters can improve their edge-preserving capabilities, we propose a general perceptual IR and VI image fusion framework with simple linear filter. Firstly, the source images are decomposed into edge feature components, hybrid components and base components by using linear filter and its side window version. Then, these components are combined by max-absolute fusion rule and improved max-absolute fusion rule. Finally, the fused image is reconstructed by adding all the fused components. In our experiments, two popular linear filters, i.e., box filter and Gaussian filter, are used to verify the effectiveness of the proposed framework. Experimental results show that the proposed fusion framework can obtain better perceptual fusion results than compared methods.

In this paper, a block based systematic approach for synchronous design of linear filters and oscillators through full design space exploration is presented, which exploits inherent hierarchical property of tree. To bridge the gap between system specification and hardware structure of analog system, transfer function of linear filter and characteristic equation of oscillator have been modeled using a rooted tree, formed based on the characteristics of four basic building blocks. A hardware conversion algorithm has been used to map the paths of the tree into operational transconductance amplifier (OTA) based circuits, implementing both programmable linear filter and oscillator. Twenty seven distinct architectures for filters and oscillators are generated. The proposed approach is also compatible with Field Programmable Analog Array (FPAA), as demonstrated by mapping the generated circuits into a hexagonal FPAA. [ABSTRACT FROM AUTHOR]

This study employs the data filtering technique to investigate the recursive identification problems for a non‐linear exponential autoregressive model with moving average noise, i.e. the ExpARMA model. Whitening the ExpARMA model by a linear filter, the original identification model is divided into a filtered identification model and a coloured noise model, then a filtering‐based extended stochastic gradient algorithm is derived. In order to improve the parameter estimation accuracy, the multi‐innovation identification theory is used to develop a filtering‐based multi‐innovation extended stochastic gradient algorithm for the ExpARMA model. A simulation example is given to demonstrate the superiority of the proposed filtering‐based multi‐innovation algorithm over the existing algorithms. [ABSTRACT FROM AUTHOR]

Image segmentation of magnetic resonance image (MRI) is a crucial process for visualisation and examination of abnormal tissues, especially during clinical analysis. Complexity and variations of the tumour structure magnify the challenges in the automated detection of a brain tumour in MRIs. This study presents an automatic lesion recognition method in the MRI followed by classification. In the proposed multistage image segmentation method, the intent region initialisation is performed using low‐level information by the keypoint descriptors. A set of the linear filter is used to transform low‐level information into higher‐level image features. The set of features and filter training data are accomplished to track the tumour region. The authors adopt a possibilistic model for region growing, and disparity map for the refinement process to grave consist boundary. Further, the features are extracted using the Fisher vector and autoencoder. A set of handcrafted features is also extracted using a segmentation‐based localised region to train and test the support vector machine and multilayer perceptron classifiers. The experiments that are performed using five MRI datasets confirm the superiority of proposal as that of the state‐of‐the‐art methods. It reports 94.5 and 91.76%, average accuracy of segmentation and classification, respectively. [ABSTRACT FROM AUTHOR]

A linear denoising filter is usually of lowpass type, and the main parameter in a lowpass filter is the cutoff frequency. The lowpass filters are normally shift invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain. This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model. An example of such a linear filter is presented for low-dose X-ray computed tomography (CT). [ABSTRACT FROM AUTHOR]

• Improving short-duration speaker recognition performance. • Proposing a filter combination-based acoustic feature extraction method. • Proposing a multi-dimensional central difference dynamic feature extraction method. • Proposing an attention-enhanced Dense-Res2Block network architecture. Speech authentication in smart services typically involves short utterances. However, due to the short duration of these utterances (e.g. less than 3 s) and the limited enrolment and/or test data available, it is difficult to learn enough information to accurately distinguish the person. As a result, speaker recognition from short utterances is very challenging. In this paper, in the acoustic end. we propose a novel Bark-scaled Gaussian and linear filter bank cepstral coefficients (BGLCC) and multi-dimensional central difference (MDCD) acoustic features extraction method. Also, to enhance the discriminative embedding, in the network end, a novel attention-enhanced Dense-Res2Block network is proposed. First, the rich low-frequency information is extracted based on the high distribution density of the Bark-scaled Gaussian filter bank in the low-frequency domain, and more high-frequency information is extracted based on the linear filter bank uniformly distributed in the high-frequency domain. This means that the combined filters can produce more discriminative and richer acoustic features from short-duration audio signals. In addition, the multi-dimensional central difference method captures better speaker dynamic features in the relative BGLCC domain to improve the performance of short utterance speaker recognition. Finally, an attention-enhanced Dense-Res2Block architecture can obtain a variety of feature expressions of different scale combinations to enhance features. Extensive analysis of a variety of datasets, which speech samples of different types, diverse lengths, etc., demonstrate the superiority of the proposed feature extraction method and model over existing acoustic feature extraction methods and speaker recognition models, including those based on MFCCs, LPCCs, and fusion features, and X-vector-PLDA, BLSTM-ResNet, ResNet34-SP, ECAPA-TDNN respectively. The experimental results show that the proposed method achieves the best performance compared to the existing approach. [ABSTRACT FROM AUTHOR]

Hammerstein system identification is difficult because there exist the product items of the parameters between the non‐linear block and the linear block. This study presents a novel parameter separation based recursive least squares (PS‐RLS) identification algorithm for resolving this problem. Its basic idea is to use a linear filter to filter the output data and the noise, and then to obtain two new identification submodels in each of which the output is linear in the corresponding parameter vector. Compared with the over‐parametrisation based recursive least squares method, the proposed algorithm can avoid estimating the redundant parameters and has a higher computational efficiency. The simulation results show that the proposed PS‐RLS algorithm can generate highly accurate parameter estimates with less computational effort. [ABSTRACT FROM AUTHOR]

In the paper, we present a method estimating the linear Gaussian filter kernel by means of photo-response non-uniformity (PRNU) noise. In the past decade, a large number of works have proved that PRNU noise is a reliable fingerprint for digital image. Accordingly, PRNU noise has been widely used in such fields as camera identification and image manipulation detection. Specifically, in order to detect whether the query image is a Gaussian filtered image and estimate the filter kernel of the Gaussian filtered image, the proposed paper takes fully the camera reference PRNU noise as the identification fingerprint, and we correlate the identification fingerprint with the noise residual extracted from clean image and noise residual extracted from filtered image, respectively. Mathematically, the linear correlation between the two cross-correlation can be determined. In addition, due to the unknown of clean image, how to access to a clean image without additive noise is crucial in this application. Consequently, we propose to use the method of recapturing the query image instead of randomly shooting a large number of content-irrelevant images. It exhibits better estimation result compared to the existing techniques. Moreover, we observe that the proposed method is robust to JPEG compression. [ABSTRACT FROM AUTHOR]

Multiple laser stripe measurement (MLSM) is a vital technique in optical three-dimensional (3D) measurement. The accurate extraction of the laser stripe centerlines plays a decisive role in achieving high measurement accuracy. However, complex 3D measurement scenarios pose challenges due to various interferences, such as optical interference caused by dense stripes and no-ideal distribution caused by non-uniform reflectivity. To address these challenges, we propose an adaptive step size gradient centerline extraction method based on an 2D linear filter. This method incorporates local adaptive variable parameters for fine-tuning while utilizing global parameters. We validate its performance by conducting comprehensive comparative experiments with two commonly used methods. The results demonstrate that our proposed method enables more precise centerline extraction, exhibits enhanced stability, and yields lower measurement errors in complex 3D measurement conditions and highlight the effectiveness and superior performance of our method. [ABSTRACT FROM AUTHOR]