Mohammed Albekairi (21615782), Meshari D. Alanazi (21615785), Turki M. Alanazi (17990269) et al. · Figshare · 2025
<div> Human-robot interaction has gained significant attention in various domains, including healthcare, customer service, and industrial automation. High computational cost, inefficient service matching, and elevated failure rates in dynamic service contexts are some primary disadvantages of existing query-processing systems. This research introduces a Hybrid Intelligent Computing Model (HICM) to improve robots’ ability to process inquiries autonomously. The goal is to make robots better at responding to human questions in real time with efficient, personalized, and context-specific solutions. Using self-organized computing approaches, robotic agents can reliably provide end-users with services suited to their demands. Due to their autonomous nature, robots must be able to calculate quickly and accurately to provide timely services. To meet these needs, the proposed HICM incorporates a sophisticated decision-support system to handle human questions and find the appropriate services. Within this decision-making framework, the model evaluates the characteristics and relevance of questions about accessible services by combining annealing and Tabu Search approaches. To avoid addressing queries incompatibly, the Tabu Search technique approaches query resolution as a non-convergent optimization issue. Comparing HICM’s performance to other models reveals significant improvements over CDS, DGTA, and CCS. In particular, HICM reduced calculation time by 8.67%, service time by 15.09%, and failure rates by 7.87%. In terms of important metrics, HICM fared better than the competing models. Its success factor was 11.8% higher, its matching ratio was 14.88% higher, and its failure rates were 6.22% lower. These findings demonstrate the model’s efficiency and reliability in terms of robotic query processing and real-time service delivery. </div>
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Jan Krajícek · 2016
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Mee Seong Im, Venkat Dasari · Contemporary mathematics - American Mathematical Society · 2026
Deep neural networks (DNN) have been widely used and play a major role in the field of computer vision and autonomous navigation. However, these DNNs are computationally complex and their deployment over resource-constrained platforms is difficult without additional optimizations and customization. In this manuscript, we provide an overview of DNN architectures and propose methods to reduce computational complexity, thereby accelerating training and inference speed for deployment on edge computing platforms with limited resources.
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原辰次, SHINJI HARA · Tokyo Tech Research Repository (Tokyo Institute of Technology) · 2006
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Beckmann, Arnold · Cronfa (Swansea University) · 2008
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Arnold, Beckmann · Cronfa (Swansea University) · 2008
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A. Guez, Moshe Kam, James L. Eilbert · IEEE Transactions on Systems Man and Cybernetics · 1989
An important class of neural models is described as a set of coupled nonlinear differential equations with state variables corresponding to the axon hillock potential of neurons. Through a nonlinear transformation, these models can be converted to an equivalent system of differential equations whose state variables correspond to firing rates. The firing rate formulation has certain computational advantages over the potential formulation of the model. The computational and storage burdens per cycle in simulations are reduced, and the resulting equations become quasilinear in a large significant subset of the state space. Moreover, the dynamic range of the state space is bounded, alleviating the numerical stability problems in network simulation. These advantages are demonstrated through an example, using the authors' model for the so-called neural solution to the traveling salesman problem proposed by J.J. Hopfield and D.W. Tank (1985).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Klaus Aehlig, Arnold Beckmann · Annals of Pure and Applied Logic · 2009
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L. Singh, Wasim Arif, Anand Jee · 2024
This paper proposes a new receiver architecture for Power Domain Non-Orthogonal Multiple Access (PD-NOMA) systems in downlink communication with improved computational time complexity. The analytical model of the proposed architecture is established and discussed in detail. The experimental results are obtained and compared with the conventional model. The average computation time of all users is recorded and discussed. The proposed model records a significant improvement in time complexity, providing an average improvement of 26.44%, 76.87%, 84.84%,88.70%, and so on for the QPSK PD-NOMA receiver, and -8.17%, 65.50%, 78.73%, 84.60%, and so on for the 16-QAM PD-NOMA receiver for each user, compared to the conventional model, while offering identical BER performance. It is also observed that, unlike conventional PD-NOMA receivers, the computational time complexity of the proposed model decreases significantly as the number of users increases.
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Wlodzimierz M. Baranski, Andrzej Wytyczak-partyka, Tomasz Walkowiak · 2008
Abstract: Face recognition is one of the most common recognition tasks. Algorithms have improved significantly over the last decade and nowadays give a satisfying recognition rate. In this work we will try to look at one of the classical face recognition algorithms [2] from a computational complexity point of view and try to suggest a way to reduce the complexity, so the algorithm could be successfully implemented in environments with seriously limited resources (mobile devices, embedded systems).
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温 臼井 · Medical Entomology and Zoology · 2013
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Yutaka Usui · Tottori University research result repository (Tottori University) · 2013
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Kaito Masaki, Yukihiro Kamiya · 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE) · 2022
This paper investigates the reduction of computational complexity of the log-geometric mean for real-time serial-to-parallel converter (Log-GMRS), a signal analysis method suitable for edge computing architecture in Internet of Things (IoT). Log-GMRS is advantageous in terms of the computational complexity while it achieves high resolution in a low frequency band. This paper quantitatively evaluates the computational complexity reduction of Log-GMRS comparing with GMRS.
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Nelson Francisco, Nuno M. M. Rodrigues, Eduardo A. B. da Silva et al. · 2011
The Multidimensional Multiscale Parser algorithm was originally proposed as a generic lossy data compression algorithm. An high degree of adaptivity and versatility allowed it to outperform state-of-the-art transform-based compression methods for a wide range of applications, from still images, compound documents, or even ECG's, just to name a few. However, as other pattern matching algorithms, it presents a high computational complexity. In this paper, we investigated several techniques that allowed to considerably reduce both the encoder's and the decoder's computational complexity, with marginal R-D performance losses. The most important reduction was achieved on the decoder, that reduced up to 95% the time required by the previous method. These improvements contribute to affirm MMP as an alternative to traditional transform-based encoders, approaching its computational complexity with that of transform-based algorithms.
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J. Ravindrababu, Dasi Swathi, J. V. Ravi Teja et al. · WSEAS TRANSACTIONS ON COMMUNICATIONS · 2024
Multiple Access Interference (MAI) is one of the major problems that limit the system capacity of Direct Sequence-Code Division Multiple Access (DS-CDMA) Wireless Communication Systems. The reduction of MAI to improve system capacity without increasing computational Complexity is the motivation to do this work. The objective of this paper is to develop efficient multiuser detection algorithms that improve the DS-CDMA system performance i.e., to achieve a low Bit Error Rate (BER) and high Signal to Noise Ratio (SNR) with less computational complexity. The BER performance of the multistage multiuser detection schemes using Kasami spreading sequences and MMSE detector is found to be better than that of a conventional Matched Filter detector in a single-stage multiuser detection scheme but the Computational Complexity increases with the number of stages and the number of users. The BER performance of the multistage multiuser PIC detection scheme is found to be better than that of a single-stage multiuser PIC detection scheme but at the cost of computational complexity increasing with a number of stages and users. It is found that the BER performance of the multistage multiuser PPIC detection scheme is better than that of the multistage multiuser PIC detection scheme but the computational complexity increases. Though the computational complexity is found reduced in the multistage multiuser DPIC when compared to the multistage multiuser PIC, the BER performance remains almost the same as that in multistage multiuser PIC.
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Mostafa Rizk, Amer Baghdadi, Michel Jézéquel · Journal of Circuits Systems and Computers · 2019
High data rates and error-rate performance approaching close to theoretical limits are key trends for evolving digital wireless communication applications. To address the first requirement, multiple-input multiple-output (MIMO) techniques are adopted in emergent wireless communication standards and applications. On the other hand, turbo concept is used to alleviate the destructive effects of the channel and ensure error-rate performance close to theoretical limits. At the receiver side, the incorporation of MIMO techniques and turbo processing leads to increased complexity that has a severe impact on computation speed, power consumption and implementation area. Because of its increased complexity, the detector is considered critical among all receiver components. Low-complexity algorithms are developed at the cost of decreased performance. Minimum mean-squared error (MMSE) solution with iterative detection and decoding shows an acceptable tradeoff. In this paper, the complexity of the MMSE algorithm in turbo detection context is investigated thoroughly. Algorithmic computations are surveyed to extract the characteristics of all involved parameters. Consequently, several decompositions are applied leading to enhanced performance and to a significant reduction of utilized computations. The complexity of the algorithm is evaluated in terms of real-valued operations. The proposed decompositions save an average of [Formula: see text] and [Formula: see text] of required operations for 2 [Formula: see text] 2 and 4 [Formula: see text] 4 MIMO systems, respectively. In addition, the hardware implementation designed applying the devised simplifications and decompositions outperforms available state-of-the-art implementations in terms of maximum operating frequency, execution time, and performance.
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Jing Gao, Jinkuan Wang, Zhibin Xie et al. · 2008
One main drawback in OFDM system is its high peak-to-average power ratio (PAPR). To solve this problem, a novel PTS algorithm based on modified simulated annealing optimization (MSA-PTS) is proposed in this paper. Specifically, the modified SA optimization approach is applied to search the optimal combination of phase factors, which can avoid the search being trapped in a local optimum solution, and achieve the signal with minimum PAPR without changing the simulated annealing process. Meanwhile, a threshold value is set, which can improve the convergence rate and reduce the computational complexity significantly. Simulations show that MSA-PTS algorithm can achieve good PAPR reduction while obtain low computational complexity.
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Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2009
Computational complexity reduction in the spatial scalable video coding encoder by Enming Luo thesis 2009 xiii, 69 p. : ill. ; 30 cm Scalable Video Coding (SVC) has been approved as the extension of the H.264/AVC video coding standard. This…Read more ›
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Enming Luo · 2009
Computational complexity reduction in the spatial scalable video coding encoder by Enming Luo thesis 2009 xiii, 69 p. : ill. ; 30 cm Scalable Video Coding (SVC) has been approved as the extension of the H.264/AVC video coding standard. This…Read more ›
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Shusuke Narieda · 2017
This paper presents a computationally efficient cyclostationarity detection based spectrum sensing in cognitive radio. Traditionally, several cyclostationarity detection based spectrum sensing techniques with a low computational complexity have been presented, e.g., peak detector (PD) or maximum cyclic autocorrelation selection (MCAS), and so on. Compared to both techniques, PD can be affected the noise uncertainty because PD requires the noise floor estimation whereas MCAS does not require the measurement. Furthermore, a computational complexity of MCAS is greater than that of PD because MCAS must compute some statistics for signal detection instead of the measurement unnecessary whereas PD must compute only one statistic. In the presented techniques that is based on MCAS, only one statistic must be computed unlike MCAS. Presented obtains other necessary statistics from the computation procedure of the statistic to be computed, and presented does not require the noise floor estimation as PD. Some numerical examples are shown to validate the effectiveness of the presented technique.
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