Sinusoidal representation has been widely applied to speech modification, low bit rate speech and audio coding. Usually, speech signal is analyzed and synthesized using the overlap-add algorithm or the peak-picking algorithm. But the overlap-add algorithm is well known for high computational complexity and the peak-picking algorithm cannot track the transient and syllabic variation well. In this letter, both algorithms are applied to speech analysis/synthesis. Peaks are picked in the curve of power spectral density for speech signal; the frequencies corresponding to these peaks are arranged according to the descending orders of their corresponding power spectral densities. These frequencies are regarded as the candidate frequencies to determine the corresponding amplitudes and initial phases according to the least mean square error criterion. The summation of the extracted sinusoidal components is used to successively approach the original speech signal. The results show that the proposed algorithm can track the transient and syllabic variation and can attain the good synthesized speech signal with low computational complexity.

This paper proposes a scheme for reducing pilot interference in cell-free massive multiple-input multiple-output (MIMO) systems through scalable access point (AP) selection and efficient pilot allocation using the Grey Wolf Optimizer (GWO). Specifically, we introduce a bidirectional large-scale fading-based (B-LSFB) AP selection method that builds high-quality connections benefiting both APs and UEs. Then, we limit the number of UEs that each AP can serve and encourage competition among UEs to improve the scalability of this approach. Additionally, we propose a grey wolf optimization based pilot allocation (GWOPA) scheme to minimize pilot contamination. Specifically, we first define a fitness function to quantify the level of pilot interference between UEs, and then construct dynamic interference relationships between any UE and its serving AP sets using a weighted fitness function to minimize pilot interference. The simulation results shows that the B-LSFB strategy achieves scalability with performance similar to large-scale fading-based (LSFB) AP selection. Furthermore, the grey wolf optimization-based pilot allocation scheme significantly improves per-user net throughput with low complexity compared to four existing schemes.

In recent years, Graph Neural Networks has received enormous attention from academia for its huge potential of modeling the network traits such as macrostructure and single node attributes. However, prior mainstream works mainly focus on homogeneous network and lack the capacity to characterize the network heterogeneous property. Besides, most previous literature cannot the model latent influence link under microscope vision, making it infeasible to model the joint relation between the heterogeneity and mutual interaction within multiple relation type. In this letter, we propose a latent influence based self-attention framework to address the difficulties mentioned above. To model the heterogeneity and mutual interactions, we redesign the attention mechanism with latent influence factor on single-type relation level, which learns the importance coefficient from its adjacent neighbors under the same meta-path based patterns. To incorporate the heterogeneous meta-path in a unified dimension, we developed a novel self-attention based framework for meta-path relation fusion according to the learned meta-path coefficient. Our experimental results demonstrate that our framework not only achieves higher results than current state-of-the-art baselines, but also shows promising vision on depicting heterogeneous interactive relations under complicated network structure.

The asymmetrical halo and dual-material gate structure is used in the surrounding-gate metal-oxide-semiconductor field effect transistor (MOSFET) to improve the performance. By treating the device as three surrounding-gate MOSFETs connected in series and maintaining current continuity, a comprehensive drain current model is developed for it. The model incorporates not only channel length modulation and impact ionization effects, but also the influence of doping concentration and vertical electric field distributions. It is concluded that the device exhibits increased current drivability and improved hot carrier reliability. The derived analytical model is verified with numerical simulation.

The performance of integrated segmentation and recognition of handwritten numeral strings relies on the classification accuracy and the non-character resistance of the underlying character classifier, which is variable depending on the techniques of pattern normalization, feature extraction, and classifier structure. In this paper, we evaluate the effects of 12 normalization functions and four selected feature types on numeral string recognition. Slant correction (deslant) is combined with the normalization functions and features so as to create 96 feature vectors, which are classified using two classifier structures. In experiments on numeral string images of the NIST Special Database 19, the classifiers have yielded very high string recognition accuracies. We show the superiority of moment normalization with adaptive aspect ratio mapping and the gradient direction feature, and observed that slant correction is beneficial to string recognition when combined with good normalization methods.

As a next-generation video compression standard, High Efficiency Video Coding (HEVC) achieves enhanced coding performance relative to prior standards such as H.264/AVC. In the new standard, the improved intra prediction plays an important role in bit rate saving. Meanwhile, it also involves significantly increased complexity, due to the adoption of a highly flexible coding unit structure and a large number of angular prediction modes. In this paper, we present a low-complexity intra prediction algorithm for HEVC. We first propose a fast preprocessing stage based on a simplified cost model. Based on its results, a fast prediction unit selection scheme reduces the number of prediction unit (PU) levels that requires fine processing from 5 to 2. To supply PU size decision with appropriate thresholds, a fast training method is also designed. Still based on the preprocessing results, an efficient mode selection scheme reduces the maximum number of angular modes to evaluate from 35 to 8. This achieves further algorithm acceleration by eliminating the necessity to perform fine Hadamard cost calculation. We also propose a 32×32 PU compensation scheme to alleviate the mismatch of cost functions for large transform units, which effectively improves coding performance for high-resolution sequences. In comparison with HM 7.0, the proposed algorithm achieves over 50% complexity reduction in terms of encoding time, with the corresponding bit rate increase lower than 2.0%. Moreover, the achieved complexity reduction is relatively stable and independent to sequence characteristics.

This paper describes a new method for lossy audio signal compression via compressive sensing (CS). In this method, a structured shrinkage operator is employed to decompose the audio signal into three layers, with two sparse layers, tonal and transient, and additive noise, and then, both the tonal and transient layers are compressed using CS. Since the shrinkage operator is able to take into account the structure information of the coefficients in the transform domain, it is able to achieve a better sparse approximation of the audio signal than traditional methods do. In addition, we propose a sparsity allocation algorithm, which adjusts the sparsity between the two layers, thus improving the performance of CS. Experimental results demonstrated that the new method provided a better compression performance than conventional methods did.

The characteristics of antenna array, like sensor location, gain and phase response are rarely perfectly known in realistic situations. Location errors usually have a serious impact on the DOA (direction of arrival) estimation. In this paper, a novel array location calibration method of MUSIC (multiple signal classification) algorithm based on the virtual interpolated array is proposed. First, the paper introduces the antenna array positioning scheme. Then, the self-calibration algorithm of FIR-Winner filter based on virtual interpolation array is derived, and its application restriction are also analyzed. Finally, by simulating the different location errors of antenna array, the effectiveness of the proposed method is validated.

Motion compensation is widely used in many video coding standards. Due to its bandwidth requirement and complexity, motion compensation is one of the most challenging parts in the design of high definition video decoder. In this paper, we propose a high performance and low bandwidth motion compensation design, which supports H.264/AVC, MPEG-1/2 and Chinese AVS standards. We introduce a 2-Dimensional cache that can greatly reduce the external bandwidth requirement. Similarities among the 3 standards are also explored to reduce hardware cost. We also propose a block-pipelining strategy to conceal the long latency of external memory access. Experimental results show that our motion compensation design can reduce the bandwidth by 74% in average and it can real-time decode 1920x1088@30 fps video stream at 80 MHz.

We have developed a low-power, high-performance MPEG-4 codec LSI for mobile video applications. This codec LSI is capable of up to CIF 30-fps encoding, making it suitable for various visual applications. The measured power consumption of the codec core was 9 mW for QCIF 15-fps codec operation and 38 mW for CIF 30-fps encoding. To provide an error-robust MPEG-4 codec, we implemented an error-resilience function in the LSI. We describe the techniques that have enabled low power consumption and high performance and discuss our test results.

H.264/AVC encoder employs rate control to adaptively adjust quantization parameter (QP) to enable coded video to be transmitted over a constant bit-rate (CBR) channel. In this topic, bit allocation is crucial since it is directly related with actual bit generation and the coding quality. Meanwhile, the rate-distortion-optimization (RDO) based mode-decision technique also affects performance a lot for the strong relation among mode, bits, and quality. This paper presents a multi-stage rate control scheme for R-D optimized H.264/AVC encoders under CBR video transmission. To enhance the precision of the complexity estimation and bit allocation, a frequency-domain parameter named mean-absolute-transform-difference (MATD) is adopted to represent frame and macroblock (MB) residual complexity. Second, the MATD ratio is utilized to enhance the accuracy of frame layer bit prediction. Then, by considering the bit usage status of whole sequence, a measurement combining forward and backward bit analysis is proposed to adjust the Lagrange multiplier λMODE on frame layer to optimize the mode decision for all MBs within the current frame. On the next stage, bits are allocated on MB layer by proposed remaining complexity analysis. Computed QP is further adjusted according to predicted MB texture bits. Simulation results show the PSNR improvement is up to 1.13 dB by using our algorithm, and the stress of output buffer control is also largely released compared with the recommended rate control in H.264/AVC reference software JM13.2.

In this paper, we present an area-efficient 4/8/16/32-point inverse discrete cosine transform (IDCT) architecture for a HEVC decoder. Compared with previous work, this work reduces the hardware cost from two aspects. First, we reduce the logical costs of 1D IDCT by proposing a reordered parallel-in serial-out (RPISO) scheme. By using the RPISO scheme, we can reduce the required calculations for butterfly inputs in each cycle. Secondly, we reduce the area of transpose architecture by proposing a cyclic data mapping scheme that can achieve 100% I/O utilization of each SRAM. To design a fully pipelined 2D IDCT architecture, we propose a pipelining schedule for row and column transform. The results show that the normalized area by maximum throughput for the logical IDCT part can be reduced by 25%, and the memory area can be reduced by 62%. The maximum throughput reaches 1248 Mpixels/s, which can support real-time decoding of a 4K × 2K 60fps video sequence.