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The selective delayless subband structure stands out as a promising algorithmic choice for the multi-channel active control of vehicle interior noise, particularly in the context of road noise. This type of algorithm reduces the eigenvalue spread of the autocorrelation matrix of the signal by decomposing the signal into subbands, and the desired subbands are activated selectively, thus achieving a significant performance improvement while consuming less computational resources compared with the traditional fullband algorithms. Nevertheless, the effectiveness of the subband algorithm appears to be closely tied to the configuration of the primary disturbance signal. In cases where the primary disturbance signal encompasses both broadband and tonal components, the performance advantage of the subband algorithm becomes constrained. In this paper, we conduct a thorough comparative analysis of two extensively employed subband algorithms, namely the Morgan and the Milani methods, through simulations employing data obtained from a multi-channel active noise control (ANC) headrest system. The results indicate that the Morgan method outperforms the Milani method when configured optimally. Subsequently, we propose an enhanced version of the subband algorithm based on the Morgan method. The enhanced algorithm incorporates an additional sinusoidal noise canceller (SNC) subsystem and a narrowband active noise control (NANC) subsystem based on local secondary path modeling to address tonal components, while the selective subband structure is employed for controlling broadband components. In addition, the computational complexity of the algorithm is analyzed. The effectiveness of the proposed algorithm is validated through numerous simulations and the ANC tests conducted on an actual vehicle utilizing the multi-channel ANC headrest system. The performance of the proposed algorithm surpasses that of traditional selective subband and fullband algorithms, and balanced binaural noise reduction is achieved. [ABSTRACT FROM AUTHOR]

Satisfiability of word equations problem is: Given two sequences consisting of letters and variables decide whether there is a substitution for the variables that turns this equation into true equality. The exact computational complexity of this problem remains unknown, with the best lower and upper bounds being, respectively, NP and PSPACE. Recently, the novel technique of recompression was applied to this problem, simplifying the known proofs and lowering the space complexity to (non-deterministic) O (n log ⁡ n). In this paper we show that satisfiability of word equations is in non-deterministic linear space, thus the language of satisfiable word equations is context-sensitive. We use the known recompression-based algorithm and additionally employ Huffman coding for letters. The proof, however, uses analysis of how the fragments of the equation depend on each other as well as a new strategy for non-deterministic choices of the algorithm. [ABSTRACT FROM AUTHOR]

Active sound profiling (ASP) is gaining ever-increasing attention owing to its powerful ability to balance the sound pressure levels of certain frequency points or frequency bands of target noise for sound quality enhancement. Most of the current ASP algorithms, however, only focus on narrowband or broadband noise alone and the computational efficiency is very low. To alleviate this problem, a feedforward time–frequency-domain hybrid active sound profiling (TFD-HASP) algorithm is proposed in this paper. The algorithm consists of three subsystems, in which the narrowband ASP (NASP) subsystem utilizes the time-domain internal model FXLMS algorithm along with the local secondary path estimation approach, the broadband ASP (BASP) subsystem is based on the delayless frequency-domain internal model FXLMS algorithm, and the sinusoidal noise canceller (SNC) subsystem is designed to eliminate narrowband interference to the BASP subsystem. In this way, the presented TFD-HASP algorithm not only has good spectral reshaping capability for both narrowband and broadband noise components, but also requires notably low computational effort. A series of simulations are performed to evaluate the performance of the TFD-HASP algorithm. In addition, the comfortable sound quality of a real vehicle interior noise at high speed is designed and realized. Experimental results demonstrate that the proposed algorithm achieves satisfactory active sound profiling for noise with both narrowband and broadband characteristics. [ABSTRACT FROM AUTHOR]

Counting objects is a fundamental but challenging problem. In this paper, we propose diffusion-based, geometry-free, and learning-free methodologies to count the number of objects in images. The main idea is to represent each object by a unique index value regardless of its intensity or size, and to simply count the number of index values. First, we place different vectors, refer to as seed vectors, uniformly throughout the mask image. The mask image has boundary information of the objects to be counted. Secondly, the seeds are diffused using an edge-weighted harmonic variational optimization model within each object. We propose an efficient algorithm based on an operator splitting approach and alternating direction minimization method, and theoretical analysis of this algorithm is given. An optimal solution of the model is obtained when the distributed seeds are completely diffused such that there is a unique intensity within each object, which we refer to as an index. For computational efficiency, we stop the diffusion process before a full convergence, and propose to cluster these diffused index values. We refer to this approach as Counting Objects by Diffused Index (CODI). We explore scalar and multi-dimensional seed vectors. For Scalar seeds, we use Gaussian fitting in histogram to count, while for vector seeds, we exploit a high-dimensional clustering method for the final step of counting via clustering. The proposed method is flexible even if the boundary of the object is not clear nor fully enclosed. We present counting results in various applications such as biological cells, agriculture, concert crowd, and transportation. Some comparisons with existing methods are presented. [ABSTRACT FROM AUTHOR]

The more advanced multi-view extension, MV-HEVC, effectively exploits visual similarities between multi-view videos and enables high compression efficiency. Each view in the multi-view sequence depends on the captured scene, the distance between cameras and recording angles. Increasing the distance between dependent viewpoints generates an inter-view disparity. This impacts the inter-view similarities, affects the disparity estimation and further increases the computational complexity of the MV-HEVC encoder. In this paper, an efficient earlier disparity estimation is proposed for low complexity MV-HEVC. This algorithm is based on reducing the complexity of disparity estimation by eliminating the inter-view offset. Moreover, the inter-view similarities are controlled by considering the reliability of each coding unit size in the search range. This reliability is estimated by reducing the number of searching points within a new limited window. For reliable motion estimation, we further proposed an earlier decision of coding units splitting in the dependent views according to those in the reference views. Experimental results show that the proposed algorithm can achieve an average encoding time saving of 20.37%–40,61% with marginal performance degradation. • Visual adjustment between views can be improved by eliminating inter-view offsets. • Eliminating inter-view offsets reduces search time in the disparity estimation. • The visual adjustment allows a trade-off between encoding time and video quality. • An earlier decision of coding units splitting for reliable motion estimation. [ABSTRACT FROM AUTHOR]

• A cost-efficient LFE-ASQC algorithm with similar or even better control accuracy than the ANE and ASP algorithms is proposed. • The proposed algorithm reduces the computational complexity more than one order of magnitude compared to the ANE or ASP algorithms. • The proposed LSP modeling methods A and B have significantly higher modeling accuracy. • Simulated and experimental studies confirm the equalization capability of the proposed algorithm in both stationary and non-stationary cases. Current active noise equalization or active sound profiling algorithms with a parallel structure are typically used for sound quality improvement of multi-harmonic noise. The computational efficiency and system performance may, however, be severely degraded due to an increase in the length of estimated secondary path or the number of controlled frequencies. To alleviate this problem, a local secondary path estimation and filtered-error structure based active sound quality control (LFE-ASQC) algorithm is proposed in this paper. The algorithm utilizes a set of low-order local secondary path (LSP) models to perform filtering operations and uses the filtered pseudo-error signals instead of usual filtered reference signals to update control filter weights, thus it not only has good spectral reshaping capability but also requires notably low computational effort. Two new LSP modeling methods are also proposed. Moreover, a computational complexity analysis of the proposed LFE-ASQC algorithm is provided. Numerical simulations are conducted to evaluate the accuracy of the LSP modeling methods and the performance of the LFE-ASQC algorithm. In addition, the powerful sound quality of a real vehicle interior noise during acceleration is designed and realized. Experimental results demonstrate that the proposed algorithm achieves satisfactory active sound quality control for stationary and non-stationary multi-tonal noises. [ABSTRACT FROM AUTHOR]

• A novel NANC system based on FEWT and recursive third-order Chebyshev expansion is proposed. • The FEWT with order-statistic filtering is applied to stabilize non-stationary noise. • A RTOCN filter is presented for compensating the higher-order nonlinear distortions. • The channel-reduced diagonal structure is designed to optimize the RTOCN filter. • Experiment results indicate that proposed method outperforms conventional methods. Active noise control (ANC) has been widely studied to eliminate low-frequency noise. However, efficiently controlling higher-order distortions of nonlinear primary and secondary path is still a major challenge. To address this, this paper proposes a novel nonlinear ANC algorithm based on the fast empirical wavelet transform and recursive Chebyshev nonlinear filter. In this method, the fast empirical wavelet transform based on order-statistic filtering is first applied to decompose non-stationary noise into empirical mode functions (EMFs) which are more stationary and easy to control. Furthermore, a novel third-order Chebyshev nonlinear filter including the non-recursive subsection and recursive one is proposed to individually expand EMFs for compensating higher-order nonlinear distortions from primary path and secondary path. Moreover, the strategy of channel-reduced diagonal structure is designed to optimise the number of expanded channels, and the computational complexity of proposed algorithm is also investigated. Experimental results demonstrate that proposed method performs the highest noise reduction over the conventional algorithms but also has a fast convergence rate. [ABSTRACT FROM AUTHOR]

A real number x is absolutely normal if, for every base b ≥ 2 , every two equally long strings of digits appear with equal asymptotic frequency in the base- b expansion of x. This paper presents an explicit algorithm that generates the binary expansion of an absolutely normal number x , with the n th bit of x appearing after n polylog (n) computation steps. This speed is achieved by simultaneously computing and diagonalizing against a martingale that incorporates Lempel-Ziv parsing algorithms in all bases. [ABSTRACT FROM AUTHOR]

Model checking (MC) for Halpern and Shoham's interval temporal logic HS has been recently shown to be decidable. An intriguing open question is its exact complexity for full HS : it is at least EXPSPACE -hard, and the only known upper bound, which exploits an abstract representation of Kripke structure paths (descriptor), is non-elementary. In this paper, we provide a uniform framework to MC for full HS and meaningful fragments of it, with a specific type of descriptor for each fragment. Then, we devise a general MC alternating algorithm, parameterized by the descriptor's type, which has a polynomially bounded number of alternations and whose running time is bounded by the length of minimal representatives of descriptors (certificates). We analyze its complexity and give tight bounds on the length of certificates. For two types of descriptor, we obtain exponential upper and lower bounds; for the other ones, we provide non-elementary lower bounds. [ABSTRACT FROM AUTHOR]