Probability of Potential Model Pruning in Monte-Carlo Go

SVD-based redundancy removal in 1-D CNNs for acoustic scene classification

Due to the growing importance of multichannel modulation, there has been great interest in the design of high-performance transmultiplex systems. In this paper, a new cosine-modulated transmultiplex structure is proposed based on a prototype filter designed with the frequency-response masking (FRM) approach. This new structure leads to substantial reduction in the computational complexity (number of multiplications per output sample) of the prototype filters having sharp transition band and equivalently small roll-off values. The relation between the interpolation factor used in the FRM prototype filter and the decimation factor in the subbands leads to distinct structures. Examples included indicate that the reduction in computational complexity can be higher than 50% of the current state-of-art designs, whereas the reduction on the number of distinct coefficients of the prototype filter can be reduced even further (over 75%). As a result, the proposed approach allows the design of very selective subfilters for transmultiplexes with a very large number of subchannels.

We propose a novel Minimum Distance guided Genetic Algorithm (MDGA) for Multi-User Detection (MUD) in a synchronous Multi-Carrier Code Division Multiple Access (MC-CDMA) broadband wireless system. In contrast to conventional GAs, our MDGA exploits adequately the output from a bank of Matched Filters as guidance. It starts with a balanced ratio of exploration and exploitation which is maintained throughout the process. A novel replacement strategy is proposed which increases dramatically the convergence rate as compared to the conventional GAs. This allows us to use the simplest form of genetic operators to gain significant reduction in computational complexity as well as near-optimum results. The simulation results demonstrate that our scheme achieves 99.54% and 50+% reduction in computational complexity as compared to the MUD schemes using exhaustive search and conventional GA respectively.

Motion estimation typically consumes 50% to 70% of total power in video encode application. Optimizing the power consumption of motion estimation process is of great importance to low power video applications. Power dissipation increases with computational complexity. Reduction in motion estimation complexity is usually associated with increase in bit rate and a loss of quality. We explore a set of algorithms that reduce the complexity of motion estimation by adaptively changing the matching complexity based on macro-block features yet have only a modest cost in terms of bit rate increase and quality loss. The adaptive techniques are applied to the global elimination algorithm, which is a well known motion estimation algorithm. The global elimination algorithm uses fixed partition sizes and shapes irrespective of the nature of the macro-block. We show that by adapting the partition sizes and shapes according to the macro-block features such as variance and Hadamard coefficients, the computational complexity of global elimination algorithm can be significantly reduced with only a small increase in bit rate. We also propose a novel center-biased search order that uses early termination method designed to work with the global elimination algorithm. The adaptive match and center-biased search together result in around 57% reduction in computational complexity and 50% reduction in power dissipation compared to the original global elimination algorithm.

A merging procedure joining search pattern and variable block size motion estimation for H.264/AVC is proposed in this paper. The principal purpose of the proposed methods is the reduction of the computational complexity for block matching module. In fact, there are numerous contributions in the literature aiming the reduction of the computational cost needed for motion estimation. The best solution from a qualitative point of view is the full search that considers every possible detail. The computational effort required is enormous and this makes motion estimation by far the most important computational bottleneck in video coding systems. Our approach invests and exploits the center-biased characteristics of the real world video sequences, aiming to achieve an acceptable image quality while independently targeting the reduction of the computational complexity. The simulations results demonstrated that the proposal performs well.

A low-complexity and robust minimum variance beamformer for ultrasound imaging systems using beamspace dominant mode rejection

The brief presents the results of synthesizing efficient algorithms for implementing the basic data-processing macro operations used in tessarine-valued neural networks. These macro operations primarily include the macro operation of multiplication of two tessarines: the macro operation of calculating the inner product of two tessarine-valued vectors and the macro operation of multiple multiplications of one tessarine by the set of different tessarines. When synthesizing the discussed algorithms, we use the fact that tessarine multiplications can be interpreted as matrix-vector products. In each of these cases, the matrices have a specific block structure, which allows them to be efficiently factorized. This factorization provides a reduction in the multiplicative complexity of computing the product of two tessarines. In what follows, we use the optimized tessarine multiplication algorithm to synthesize reduced-complexity algorithms for the inner product of two tessarine-valued vectors as well as to compute multiple tessarine multiplication. Also, to further decrease the computational complexity of the inner product of two tessarine-valued vectors and multiple tessarine multiplication, we take into account that the selective sets of operations in the computations of all partial matrix-vector multiplications participating in these macro operations are the same. Accounting for this fact provides an additional reduction in computation complexity.

High efficiency video coding (HEVC) is the newest video coding standard to greatly increase the coding efficiency of its ancestor H.264/AVC with the aids of its new features such as the quadtree-based coding unit (CU) partitioning, a simple deblocking filter, and other advanced coding techniques. However, HEVC delivers a highly increased computation complexity, which is mainly due to the exhaustive rate distortion optimization search of quadtree-based CU partitioning. Firstly, a feature reduction approach is proposed on a fuzzy support vector machine (SVM) based CU size decision method. The proposed feature reduction approach with rate control (RC) can reduce computational complexity by eliminating some correlated features of a fuzzy SVM-based CU size decision method under a similar coding efficiency. According to the empirical results, our approach can achieve up to 3% of complexity reduction under the same rate distortion (RD) performance over a fuzzy SVM-based approach. Secondly, instead of machine learning (ML) based fast algorithm approach, a CU partitioning pattern optimization method based on genetic algorithm (GA) is proposed to save the computational complexity of a hierarchical quadtree-based CU partitioning. The required coding unit partitioning pattern for an exhaustive partitioning and the rate distortion cost are efficiently considered as the chromosome and the fitness function of the genetic algorithm, respectively. To reduce the computational time, CU partitioning patterns of the key frame is searched and shared to other consecutive frames by taking into account the highly temporal correlation. Our evaluation results show that the proposed method can achieve 62.5% and 16.7% computational complexity reduction on average at 8 Mbps with a negligible average quality degradation compared with HM16.5 and state-of-the-art support vector machine-based fast algorithm, respectively, under low delay P configuration with rate control while 64.1% and 15.1% under low delay configuration with rate control.

Aiming at problems that the eigenvalue based spectrum sensing algorithms don’t perform well in the situation of low SNR, small sample and need high computational complexity with eigenvalue decomposition, based on the difference value between maximum and minimum eigenvalue spectrum sensing algorithm (DMM), a difference value between the arithmetic mean and geometric mean eigenvalue spectrum sensing algorithm (DAG) with low computational complexity and dynamic threshold was proposed, which via the power method. Simulation results show that the DAG can improve performance over the classical algorithms in situation of low SNR, small samples and increased second users without reduction of computational complexity.

Integral equation based techniques, such as the method of moments (MOM), have been widely used to analyze time harmonic surface scattering phenomena. Unfortunately, time domain counterparts of the MOM, often referred to as marching-on-in-time (MOT) schemes, have historically received scant attention. Classical MOT schemes (i) have long been conceived as intrinsically unstable, and (ii) suffer from a high computational complexity. It is well-known that the computational complexity of the MoM can be reduced by the fast multipole method. A similar time domain algorithm, namely the plane wave time domain (PWTD) algorithm, has been introduced. The PWTD algorithm is designed to be used in tandem with the conventional MOT scheme in either a two-level or a multilevel setting. This paper describes the practical implementation of the two-level scheme and demonstrates the reduction in computational complexity achieved by using the PWTD algorithm. Although the PWTD scheme is applicable to a variety of integral equations arising from the analysis of wave phenomena, this paper illustrates its application in the context of scalar wave scattering from surfaces on which Neumann boundary conditions are enforced-a problem of interest in many disciplines.

Access to the electromagnetic spectrum is an ever-growing challenge for radar. Future radar will be required to mitigate RF interference from other RF sources, relocate to new frequency bands while maintaining quality of service, and share frequency bands with other RF systems. The spectrum sensing, multioptimization (SS-MO) technique was recently investigated as a possible solution to these challenges. Prior results have indicated significant improvement in the signal-to-interference plus noise ratio at the cost of a high computational complexity. However, the optimization computational cost must be manageable in real time to address the dynamically changing spectral environment. In this paper, a bioinspired filtering technique is investigated to reduce the computational complexity of SS-MO. The proposed technique is analogous to the processing of the thalamus in the human brain in that the number of samples input to SS-MO is significantly decreased, thus, resulting in a reduction in computational complexity. The performance and computational complexity of SS-MO and the proposed technique are investigated. Both techniques are used to process a variety of measured spectral data. The results indicate a significant decrease in computational complexity for the proposed approach while maintaining performance of the SS-MO technique.

This paper presents a novel significance driven inverse distance weighted (SDIDW) filter for the impulsive noise removal in the X-ray images. The proposed SDIDW filter restores the noisy pixel using minimum number of nearest noise-free pixels to achieve good estimation while exhibiting low computational complexity. In the proposed filter, higher priority (weight) is given to nearest pixels compared to distant pixels and only sufficient nearest noise free pixels are determined to estimate the value of noisy pixel. A high level analysis of the computation complexity at varying noise density is done which shows that proposed SDIDW filter provides significant reduction in computation complexity over the adaptive median filters. Finally, the performance of the proposed filter is evaluated and compared over the state-of-the-art impulse noise removal techniques for varying noise density (wide range 10–90% and very high noise density range 91–99%). The experimental results on medical images demonstrate significant improvement in filtered images quality by the proposed filter over the state-of-the-art filters at each sample of noise density with small computational complexity.

The major issue in orthogonal frequency division multiplexing (OFDM) system is a high peak-to-average power ratio (PAPR) of transmitting signal. Selective mapping (SLM) is the promising technique to reduce PAPR in OFDM system. However, it suffers from high computational complexity. In our proposed method, to achieve low computational complexity, the input data sequences are partitioned into two sub blocks in such a way that the candidate signals are generated by performing circular convolution for the first sub block and by conventional SLM in the second sub block. The candidate signals of modified SLM are obtained by combining two sub block candidate signals. The proposed method reduces PAPR marginally along with reduction in computational complexity. To further reduce PAPR, different phase sequences namely Riemann, New Riemann and chaotic sequences are used in C-SLM. The Matlab simulation shows that, the modified SLM with new Riemann sequence results in high PAPR reduction than other phase sequences. In addition, it provides significant reduction computational complexity compared to C-SLM.

The new emerging video coding standard H.264 employs variable block size motion compensation using multiple references with quarter-pel motion vector accuracy. This scheme is a key feature to accomplish higher coding gain, however, also a decisive factor that increases overall computational complexity. To overcome this, we propose a novel fast mode decision scheme suited for variable block sizes by classifying coding modes based on rate-distortion cost. The experimental results show that the proposed method provides significant reduction in computational complexity without any noticeable coding loss and additional operations.

Massive multiple-input multiple-output (MIMO) is a cornerstone technology in beyond 5G (B5G) communication systems due to its ability to achieve exceptional power and spectral efficiency. The development of low-complexity detectors for massive MIMO remains a key area of research, driven by the need to strike a balance between performance and computational complexity, especially as the number of antennas increases at both the transmitter and receiver. In this paper, we propose efficient initialization methods to address these challenges. Instead of the conventional diagonal matrix, we employ the stair matrix and the band matrix in the initialization of the proposed detector based on accelerated overrelaxation. We also employ successive overrelaxation, Gauss-Seidel, and Jacobi methods to improve the performance of the proposed detector. The initialization scaling factors are based on the spectral radius of the iteration matrix. The proposed detectors are evaluated using diverse massive MIMO configurations and multiple modulation schemes and under both perfect and imperfect channel state information (CSI). Extensive simulations show that the proposed detectors achieve significant performance enhancements accompanied by a remarkable reduction in computational complexity, making them highly suitable for practical large-scale systems.