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Reconfigurable intelligent surface (RIS) is one of the promising technologies for sixth generation communications due to its advantages including energy saving, high spectral efficiency, etc. However, the non-convex joint beamforming design is a challenge, especially in the multi-hop RIS-assisted communication system. This paper proposes a deep learning-based joint beamforming (DLBF) design, aiming to maximize the system data rate for multi-hop RIS-aided communication systems. The proposed DLBF design consists of the reflection matrices design of all RISs and the transmit beamforming design at the base station, which has a reduced computational complexity. Numerical results show that the proposed DLBF can achieve 1.8 bit/s/Hz sum rate gain compared to the conventional beamforming method for the two-user scenario, which can be enhanced by large-scale users. The sum rate performance can be improved by increasing the number of RISs due to the reflection gain, and corresponding results provide a guidance of the multi-hop number selection for further investigation. [ABSTRACT FROM AUTHOR]

The point cloud provides viewers with intuitive geometric understanding but requires a huge amount of data. Moving Picture Experts Group (MPEG) has developed video‐based point‐cloud compression in the range of 300–700. As the compression rate increases, the complexity increases to the extent that it takes 101.36 s to compress one frame in an experimental environment using a personal computer. To realize real‐time point‐cloud compression processing, the direct patch projection (DPP) method proposed herein simplifies the complex patch segmentation process by classifying and projecting points according to their geometric positions. The DPP method decreases the complexity of the patch segmentation from 25.75 s to 0.10 s per frame, and the entire process becomes 8.76 times faster than the conventional one. Consequently, this proposed DPP method yields similar peak signal‐to‐noise ratio (PSNR) outcomes to those of the conventional method at reduced times (4.7–5.5 times) at the cost of bitrate overhead. The objective and subjective results show that the proposed DPP method can be considered when low‐complexity requirements are required in lightweight device environments. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attacks disrupt service availability, leading to significant financial setbacks for individuals and businesses. This paper introduces Eye-Net, a deep learning-based system optimized for DDoS attack detection that combines feature selection, balancing methods, Multilayer Perceptron (MLP), and quantization-aware training (QAT) techniques. An Analysis of Variance (ANOVA) algorithm is initially applied to the dataset to identify the most distinctive features. Subsequently, the Synthetic Minority Oversampling Technique (SMOTE) balances the dataset by augmenting samples for under-represented classes. Two distinct MLP models are developed: one for the binary classification of flow packets as regular or DDoS traffic and another for identifying six specific DDoS attack types. We store MLP model weights at 8-bit precision by incorporating the quantization-aware training technique. This adjustment slashes memory use by a factor of four and reduces computational cost similarly, making Eye-Net suitable for Internet of Things (IoT) devices. Both models are rigorously trained and assessed using the CICDDoS2019 dataset. Test results reveal that Eye-Net excels, surpassing contemporary DDoS detection techniques in accuracy, recall, precision, and F1 Score. The multiclass model achieves an impressive accuracy of 96.47% with an error rate of 8.78%, while the binary model showcases an outstanding 99.99% accuracy, maintaining a negligible error rate of 0.02%. [ABSTRACT FROM AUTHOR]

The point cloud provides viewers with intuitive geometric understanding but requires a huge amount of data. Moving Picture Experts Group (MPEG) has developed video-based point-cloud compression in the range of 300-700. As the compression rate increases, the complexity increases to the extent that it takes 101.36 s to compress one frame in an experimental environment using a personal computer. To realize real-time point-cloud compression processing, the direct patch projection (DPP) method proposed herein simplifies the complex patch segmentation process by classifying and projecting points according to their geometric positions. The DPP method decreases the complexity of the patch segmentation from 25.75 s to 0.10 s per frame, and the entire process becomes 8.76 times faster than the conventional one. Consequently, this proposed DPP method yields similar peak signal-to-noise ratio (PSNR) outcomes to those of the conventional method at reduced times (4.7-5.5 times) at the cost of bitrate overhead. The objective and subjective results show that the proposed DPP method can be considered when low-complexity requirements are required in lightweight device environments.

This paper presents an innovative formation control method using the leader–follower strategy for a marine vehicle system, addressing the computing power disparity between the leader Unmanned Surface Vehicle (USV) and the follower Autonomous Underwater Helicopters (AUHs). Accounting for model uncertainties and external disturbances affecting the leader USV, we develop a control law for trajectory tracking, which ensures precise parameterized path tracking. A finite-time distributed observer based on the consensus principle is designed for the follower AUHs. The observer estimates the leader's position within communication constraints, ensuring timely and accurate information sharing. To achieve effective tracking and formation of the follower AUHs relative to the leader USV, we propose a formation control method with low complexity and predefined performance constraints. This method introduces a novel prescribed performance function and a Nussbaum function to manage tracking errors and address unknown propeller control directions. Finally, we validate the system's stability using Lyapunov stability theory and demonstrate the method's effectiveness through detailed MATLAB simulations. The results show that our approach significantly improves tracking accuracy and formation stability under various conditions. [ABSTRACT FROM AUTHOR]

This paper proposes an ultrasound imaging algorithm based on sub-beamformer and multi-apodization with cross-correlation (SUB-MAX), aiming to achieve high resolution close to the minimum variance (MV) beamforming with low complexity and to enhance image contrast while maintaining background quality. The output of two (N /2)-element DAS beamformers with asymmetric phase centers is subtracted, resulting in a large drop in the main-lobe amplitude, while the sidelobe maintains a relatively high amplitude level. Inspired by this characteristic, the coefficients with opposite trends compared with the subtracted output are obtained and fused with the normalized cross-correlation (NCC) weighting matrix acquired by using multi-pair received apodization, the proposed SUB-MAX obtains a new weighting matrix to weight the output of the DAS beamformer. For ats_wire point targets, the average full-width at half-maximum (FWHM) of SUB-MAX compared with DAS, DMAS, CF, and MAX decreases by 52.7%, 43.5%, 33.3%, and 52.7%, respectively. For geabr_0 cysts, the average contrast ratio (CR) of SUB-MAX compared with DAS, MV, DMAS, and CF increases by 57.7%, 86.8%, 2.5%, and 14.4%, respectively. Experiments on rat_tumor dataset also indicate that SUB-MAX has a superior comprehensive imaging performance. The experimental results indicate that the superior comprehensive imaging performance of the proposed SUB-MAX is expected to be suitable for real-time imaging systems due to its non-reliance on covariance matrix inversion. [ABSTRACT FROM AUTHOR]

We propose a robust super-resolution algorithm for vital sign radar in a low signal to noise ratio (SNR) environment. Conventional approaches, such as fast Fourier transform and super-resolution based algorithms, suffered to provide reliable results due to the limited data length and high noise level. To overcome these limitations, our proposed algorithm utilizes a low-complexity least mean square (LMS) filter and relaxation (RELAX) techniques to achieve robust performance in low SNR environments. To evaluate the effectiveness of our algorithm, we conducted both simulation and experimental studies. Our results show that the proposed method significantly outperforms conventional methods, with Monte-Carlo simulations of respiration and heartbeat achieving an RMSE approximately 7 and 120 times lower than that of the conventional method, respectively. Overall, our algorithm provides a promising solution for robust vital sign detection in challenging low SNR environments.

This letter proposes an online energy management framework for smart buildings. Two low-complexity estimators for thermal comfort and electrical load are investigated and integrated into an optimization framework. The proposed framework optimizes operations of a battery system and multiple heating, ventilation, and air conditioning (HVAC) systems to minimize energy consumption and power peak while maximizing occupants’ thermal comfort. The effectiveness of the proposed framework is demonstrated using data measured in actual campus buildings in terms of system costs, thermal comfort, and computational complexity. The results show that the proposed framework can reduce electricity costs by 5.7% compared to the baseline. [ABSTRACT FROM AUTHOR]

The hyperspectral image provides rich spectral information content, which facilitates multiple applications. With the rapid advancement of the spatial and spectral resolution of optical instruments, the image data size has increased by many folds. For that, it requires a compression algorithm having low coding complexity, low coding memory demand and high coding efficiency. In recent years, many coding algorithms are proposed. The wavelet transform-based set-partitioned hyperspectral compression algorithms have superior coding performance. These algorithms employ linked lists or state tables to track the significant/insignificant of the partitioned sets/coefficients. The proposed algorithm uses the pyramid hierarchy property of wavelet transform. The markers are used to track the significance/insignificance of the pyramid level. A single pyramid level has many sets. An insignificant pyramid level having multiple sets is represented as a single bit in proposed compression algorithm, while a single insignificant set in 3D Set Partition Embedded bloCK (3D-SPECK) and 3D-Listless SPECK (3D-LSK) is represented as a single bit. Through this, the requirement of the bits in the proposed algorithm is less than other wavelet transform compression algorithms at the high bit planes. The simulation result shows that the proposed compression algorithm has high coding efficiency with very less coding complexity and moderate coding memory requirement. The reduced coding complexity improves the performance of the image sensor and lowers the power consumption. Thus, the proposed compression algorithm has great potential in low-resource onboard hyperspectral imaging systems. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technological support for the simultaneous and frequent access by a large number of users to radio resources. For 5G cellular systems, this exhaust is not enough to provide physical layer services. An appropriate Peak-to-average power ratio (PAPR) minimization principle, which maximizes data capacity and channel utility, has been used to address this issue. In this paper, mainly focus on minimize the high PAPR of the candidate sequence of the OFDM sub-block using modified enhancement asymmetric arithmetic coding scheme (M-EAAC). According to this, circular shifting of the candidate sequence is established in the spatial circular shifting and temporal interleaving (SCS-TI) form to generated different set of conjugated phases which is multiplied with candidate sequence. Then, the transmitting antenna is identified the best lowest PAPR of the candidate sequence is chosen for entire OFDM data transmission. The simulation results conveys that the proposed SCS-TI method provide acceptable improvement in the PAPR reduction as compared with conventional selective mapping (SLM) and pseudo-random SLM (PR-SLM). Moreover, the complexity evaluation which ensure the proposed method provides better improvement at three important stages includes inverse fast Fourier transform (IFFT) operation, optimization process, and PAPR calculation at each candidate sequence. [ABSTRACT FROM AUTHOR]

In order to address the fragility issues associated with the current prescribed performance control (PPC) strategy and ensure both transient and steady-state performance of the tracking error, a non-fragility prescribed performance control scheme is proposed. A non-fragile prescribed performance control method for robotic systems with model uncertainties and unknown disturbances is developed. This method not only addresses the inherent vulnerability defects of the existing prescribed performance control but also effectively reduces the computational complexity of the controller. Firstly, addressing the fragility issues of existing PPC, a new non-fragile prescribed performance control strategy is proposed. To address the fragile issue with the current PPC, the shift function is employed to handle the tracking error. Based on the non-fragile PPC mentioned above, a new prescribed performance controller is designed without the requirement for approximation or estimation. This effectively reduces the complexity of controller design. At last, the feasibility of achieving non-fragile prescribed performance is verified through stability analysis, and the superiority of the designed controller is confirmed through simulation comparisons. The results show that the designed controller effectively resolves the control singularity issue arising from the inherent limitations of the PPC. [ABSTRACT FROM AUTHOR]

We propose a robust super-resolution algorithm for vital sign radar in a low signal-to-noise ratio (SNR) environment. Conventional approaches, such as fast Fourier transform and super-resolution based algorithms, suffered to provide reliable results due to the limited data length and high noise level. To overcome these limitations, our proposed algorithm utilizes a low-complexity least mean square (LMS) filter and relaxation (RELAX) techniques to achieve robust performance in low SNR environments. To evaluate the effectiveness of our algorithm, we conducted both simulation and experimental studies. Our results show that the proposed method significantly outperforms conventional methods, with Monte-Carlo simulations of respiration and heartbeat achieving an RMSE approximately 7 and 120 times lower than that of the conventional method, respectively. Overall, our algorithm provides a promising solution for robust vital sign detection in challenging low SNR environments [ABSTRACT FROM AUTHOR]

Terahertz (THz) communication is becoming a key technology for future 6G wireless networks because of its ultra-wide band. However, the implementation of THz communication systems confronts formidable challenges, notably beam splitting effects and high computational complexity associated with them. Our primary objective is to design a hybrid precoder that minimizes the Euclidean distance from the fully digital precoder. The analog precoding part adopts the delay-phase alternating minimization (DP-AltMin) algorithm, which divides the analog precoder into phase shifters and time delayers. This effectively addresses the beam splitting effects within THz communication by incorporating time delays. The traditional digital precoding solution, however, needs matrix inversion in THz massive multiple-input multiple-output (MIMO) communication systems, resulting in significant computational complexity and complicating the design of the analog precoder. To address this issue, we exploit the characteristics of THz massive MIMO communication systems and construct the digital precoder as a product of scale factors and semi-unitary matrices. We utilize Schatten norm and Hölder's inequality to create semi-unitary matrices after initializing the scale factors depending on the power allocation. Finally, the analog precoder and digital precoder are alternately optimized to obtain the ultimate hybrid precoding scheme. Extensive numerical simulations have demonstrated that our proposed algorithm outperforms existing methods in mitigating the beam splitting issue, improving system performance, and exhibiting lower complexity. Furthermore, our approach exhibits a more favorable alignment with practical application requirements, underlying its practicality and efficiency. [ABSTRACT FROM AUTHOR]

The four-level active neutral point clamped (ANPC) inverter has become increasingly widely used in the renewable energy industry since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter. The model predictive current control (MPCC) is a promising control method for multi-level inverters. However, the conventional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications. A low-complexity MPCC without weighting factors for a four-level ANPC inverter is proposed in this paper. The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance. The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing. The efficacy of the proposed MPCC is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we proposed two simple refinement segmentation algorithms that can provide options to improve the computational complexity of the Video-based Point Cloud Compression (V-PCC) encoder. The patch image generation process in the encoding process is the most time-consuming and computationally intensive, accounting for about 70% of the encoder’s self-running time in TMC2 v13.0. Since the real-time encoding of V-PCC is within the requirement of industry, it is highly necessary to research methods that can achieve good compression performance with low computational complexity. The grid-based refinement segmentation is one of the most computationally intensive processes in V-PCC. We found that the computational complexity can be reduced by further reducing the refinement segmentation process. Therefore, we propose to change the grid-based refinement segmentation loop process, thereby reducing the computational complexity by reducing some computational processes when the projection plane index of the neighboring grid point does not change. In the experiment, the compression performance of some sequences is improved by 0.1% to 0.9%, and the refinement segmentation time used is 79.21% and 79.53% of the anchor.

To apply the interference alignment (IA) technology to practical communication networks effectively, the low complexity IA design problem still needs to be solved well. In this paper, we study the low complexity closed-form IA design method, for the multiple-cell multiple-user multiple-input multiple-output (MIMO) cellular interference network. To eliminate the complex interference conveniently, the multiple-cell multiple-user cellular network model is transformed into several relatively simple network models. Based on the model transformation of the cellular network, the IA precoding is divided into two parts. Then the two-stage IA design scheme is proposed, which includes the outer IA design and inter one. The former is to eliminate the inter-cell interference, while the latter is to eliminate the inter-user interference. Simulation results show the higher system sum-rate performance and low computational complexity of the proposed IA design scheme. [ABSTRACT FROM AUTHOR]

Signal detection turns out to be a critical challenge in massive MIMO (m-MIMO) system due to the deployment of large number of antennas at the base station. Although, the minimum mean square error (MMSE) is one of the popular signal detection method, but, it requires matrix inversion with cubic complexity. In order to reduce computational complexity, several suboptimal signal detection methods were proposed such as Gauss-Seidel, successive over relaxation, Jacobi, Richardson methods. Although, these methods provide low complexity but their performance are limited to MMSE method. In this paper, we have proposed two signal detection techniques namely QR decompositions (QRD) and ordered QRD (OQRD). Finally, the performances of proposed signal detection methods are compared with various conventional methods in terms of symbol error rate (SER) and computational complexity. The simulation results validate that the proposed methods outperform the MMSE method with substantially lower computational complexity. [ABSTRACT FROM AUTHOR]

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We propose a circuit optimization algorithm that facilitates the implementation of various applications on noise intermediate-scale quantum (NISQ) devices. The algorithm is hardware-independent and reduces the overall circuit cost of Hamiltonian simulation, particularly by minimizing the number of CNOT gates. Our approach employs a sub-circuit synthesis scheme for intermediate representation and proposes the practical template matching algorithm (TM) for gate elimination to minimize CNOT counts. This algorithm demonstrates low complexity and enhances the circuit performance of Hamiltonian simulations. In our simulations, we benchmarked different algorithms across various Hamiltonian models to quantify and compare the benefits of our approach. Compared with advanced generic compilers and specific quantum compilers, the results obtained from simulating our algorithm show an average reduction of 1.5 × (up to 2.56 × ) in CNOT counts, and 1.4 × (up to 3.1 × ) in circuit depth. This improvement further advances the practical application of Hamiltonian simulation in the NISQ era. [ABSTRACT FROM AUTHOR]

In recent years, the Discrete Tchebichef Transform (DTT) has gained popularity as a signal processing tool for image and video compression due to its efficient coding and decorrelation properties. However, in the context of real-time applications and embedded systems, it is critical to develop approximate algorithms with reduced complexity and energy consumption. While three DTT approximations have been proposed to date, there is still room for further improvements. To address this gap, we propose two new low-complexity DTT approximations that employ a modified deviation metric, resulting in better compression efficiency and reduced complexity. We validate our proposed methods by implementing them on the Xilinx Virtex-6 XC6VSX475T-1FF1759-2 Field Programmable Gate Array (FPGA) through rapid prototyping. Our proposed transformations exhibit superior performance in terms of hardware resources and energy consumption, particularly for 1D 8 inputs. Furthermore, compared to the state-of-the-art DTT approximations in image compression, our proposed transformations demonstrate a quality gain of up to 2 dB. Overall, our proposed approximations provide a promising trade-off between image quality, hardware resources, and energy consumption, making them ideal for real-time applications and embedded systems. [ABSTRACT FROM AUTHOR]

Deep neural networks have achieved outstanding performance in computer vision tasks. Convolutional Neural Networks (CNNs) typically operate in the spatial domain with raw images, but in practice, images are usually stored and transmitted in their compressed representation where JPEG is one of the most widely used encoder. Also, these networks are computationally intensive and slow. This paper proposes performing the learning and inference processes in the compressed domain in order to reduce the computational complexity and improve the speed of popular CNNs. For this purpose, a novel graph-based frequency channel selection method is proposed to identify and select the most important frequency channels. The computational complexity is reduced by retaining the important frequency components and discarding the insignificant ones as well as eliminating the unnecessary layers of the network. Experimental results show that the modified ResNet-50 operating in the compressed domain is up to 70% faster than the spatial-based traditional ResNet-50 while resulting in similar classification accuracy. Moreover, this paper proposes a preprocessing step with partial encoding to improve the resilience to distortions caused by low-quality encoded images. Finally, we show that training a network with highly compressed data can achieve a good classification accuracy with up to 93% reduction in the storage requirements of the training data. [ABSTRACT FROM AUTHOR]

In this article, the elimination of ambiguity of a joined coprime array has been examined, with a focus on such of its properties as large aperture size and complete degree of freedom (DOF). The existing methods suffer from a high degree of computation complexity due to the loss constant characteristic and high peak searching. Therefore, in this paper, a DOA estimation method for a jointed coprime array, characterized by a low degree of computational complexity, is proposed. The variance of the diagonal eigenvalues of the estimated covariance matrix is designed to enhance the accuracy of the covariance matrix of the joined coprime array. Then, the Capon beamforming methods is employed for peak searching. The simulation shows that the proposed method accomplishes accurate estimation with shorter computation times and fewer operations compared to other DOA estimation methods. [ABSTRACT FROM AUTHOR]

A hybrid energy-efficient, area-efficient, low-complexity switching scheme in SAR ADC for biosensor applications is proposed. This scheme is a combination of the monotonic technique, the MSB capacitor-splitting technique, and a new switching method. The MSB capacitor-splitting technique, as well as the reference voltage Vaq allow for more options for reference voltage conversion, resulting in higher area savings and higher energy efficiency. In a capacitor array, the circuit performs unilateral switching during all comparisons except for the second and last two comparisons, reducing the difficulty in designing the drive circuit. The proposed switching scheme saves 98.4% of the switching energy and reduces the number of unit capacitors by 87.5% compared to a conventional scheme. Furthermore, the SAR ADC employs low-noise and low-power dynamic comparators utilizing multi-clock control, low-sampling error-sampling switches based on the bootstrap technique, and dynamic SAR logic. The simulation results demonstrated that the proposed SAR ADC achieves 61.51 dB SNDR, 79.21 dB SFDR and consumes 0.278 μW of power in a 180 nm process with a 1 V power supply, a full swing input signal frequency of 23.33 kHz, and a sampling rate of 100 kS/s. [ABSTRACT FROM AUTHOR]

The wind farm layout determines the power generation ability, and its optimization remains challenging in terms of algorithm complexity and performance. Several greedy-derived and evolutionary-derived algorithms have been proposed to solve or alleviate the layout planning problems. However, greedy algorithms are efficient in finding the optimal layout but their exploration ability is poor. In contrast, evolutionary algorithms own better global search ability but require huge computational effort to find the optimal layout. Considering that all wind turbines are deployed in the same flat area (i.e. the coordinates of each wind turbine are of dimension 2, and each dimension of all turbines has the same search range), a low-complexity grey wolf optimization technique using a 2-D encoding mechanism (GWOEM) is proposed in this work. which is low-complexity and accelerates search efficiency. The proposed GWOEM effectively combines the advantages of the greedy algorithms and the evolutionary algorithms, and improves the search efficiency while maintaining accuracy. To validate the performance of the proposed GWOEM, a comparative analysis is performed based on two wind scenarios. Simulation results show that the proposed GWOEM is competitive with other state-of-the-art techniques. Compared with TDA, ADE, ADE-GRNN, and DEEM, the average power output in the two wind scenarios considered herein increases by 44.41%, 34.45%, 34.97%, and 37.06%, respectively. In terms of execution time, GWOEM is shorter than TDA, ADE, and DEEM except for ADEGRNN which increases by 12.18%.

Abstract In intelligent transportation systems, real-time video streaming via vehicle networks has been seen as a vital difficulty. The goal of this paper is to decrease the computational complexity of the versatile video coding (VVC) encoder for VANETs. In this paper, a low-complexity enhancement VVC encoder is designed for vehicular communication. First, a fast coding unit (CU) partitioning scheme based on CU texture features is proposed in VVC, which aims to decide the final type of CU partition by calculating CU texture complexity and gray-level co-occurrence matrix (GLCM). Second, to reduce the number of candidate prediction mode types in advance, a fast chroma intra-prediction mode optimization technique based on CU texture complexity aims to combine intra-prediction mode features. Moreover, the simulation outcomes demonstrate that the overall approach may substantially reduce encoding time, while the loss of coding efficiency is reasonably low. The encoding time can be reduced by up to 53.29% when compared to the VVC reference model, although the average BD rate is only raised by 1.26%. The suggested VVC encoder is also hardware-friendly and has a minimal level of complexity for video encoders used in connected vehicle applications.

Distributed Denial of Service (DDoS) attacks disrupt service availability, leading to significant financial setbacks for individuals and businesses. This paper introduces Eye-Net, a deep learning-based system optimized for DDoS attack detection that combines feature selection, balancing methods, Multilayer Perceptron (MLP), and quantization-aware training (QAT) techniques. An Analysis of Variance (ANOVA) algorithm is initially applied to the dataset to identify the most distinctive features. Subsequently, the Synthetic Minority Oversampling Technique (SMOTE) balances the dataset by augmenting samples for under-represented classes. Two distinct MLP models are developed: one for the binary classification of flow packets as regular or DDoS traffic and another for identifying six specific DDoS attack types. We store MLP model weights at 8-bit precision by incorporating the quantization-aware training technique. This adjustment slashes memory use by a factor of four and reduces computational cost similarly, making Eye-Net suitable for Internet of Things (IoT) devices. Both models are rigorously trained and assessed using the CICDDoS2019 dataset. Test results reveal that Eye-Net excels, surpassing contemporary DDoS detection techniques in accuracy, recall, precision, and F1 Score. The multiclass model achieves an impressive accuracy of 96.47% with an error rate of 8.78%, while the binary model showcases an outstanding 99.99% accuracy, maintaining a negligible error rate of 0.02%.

In this paper, we investigate dynamic resource scheduling (i.e., joint user, subchannel, and power scheduling) for downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems over time-varying fading channels. Specifically, we address the weighted average sum rate maximization problem with quality-of-service (QoS) constraints. In particular, to facilitate fast resource scheduling, we focus on developing a very low-complexity algorithm. To this end, by leveraging Lagrangian duality and the stochastic optimization theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby the original problem is decomposed into a series of subproblems, one for each time slot. Accordingly, resource scheduling works in an online manner by solving one subproblem per time slot, making it more applicable to practical systems. Then, we further develop a heuristic joint subchannel assignment and power allocation (Joint-SAPA) algorithm with very low computational complexity, called Joint-SAPA-LCC, that solves each subproblem. Finally, through simulation, we show that our Joint-SAPA-LCC algorithm provides good performance comparable to the existing Joint-SAPA algorithms despite requiring much lower computational complexity. We also demonstrate that our opportunistic MC-NOMA scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works well while satisfying given QoS requirements.

A low-power SAR ADC with capacitor-splitting energy-efficient switching scheme is proposed for wearable biosensor applications. Based on capacitor-splitting, additional reference voltage Vcm, and common-mode techniques, the proposed switching scheme achieves 93.76% less switching energy compared to the conventional scheme with common-mode voltage shift in one LSB. With the switching scheme, the proposed SAR ADC can lower the dependency on the accuracy of Vcm and the complexity of digital control logic and DAC driver circuits. Furthermore, the SAR ADC employs low-noise and low-power dynamic comparators utilizing multi-clock control, low sampling error sampling switches based on the bootstrap technique, and dynamic SAR logic. The simulation results demonstrate that the ADC achieves a 61.77 dB SNDR and a 78.06 dB SFDR and consumes 4.45 μW of power in a 180 nm process with a 1 V power supply, a full-swing input signal frequency of 93.33 kHz, and a sampling rate of 200 kS/s. [ABSTRACT FROM AUTHOR]

In recent years, due to the rise of the Internet of Things (IoT), various sensors have come to be in great demand for IoT devices. Analog-to-digital converters (ADCs) act as an important part of receivers in sensors. To improve the uptime of IoT devices, a bridged-switch energy-efficient switching scheme for successive approximation register (SAR) ADCs with a low-complexity capacitor drive circuit is proposed. The technique of top-plate sampling and closed-loop charge recycling is used in the proposed switching scheme so that neither the first nor the second comparison consumes switching energy. The third comparison uses bridge switches to connect the subarray to the main array, effectively reducing switching's energy consumption. Only the least significant bit (LSB) is dependent on the accuracy of Vcm; thus, the last comparison consumes little switching energy. The proposed switching scheme achieves an average switching energy value of 47.5 CV2ref, which is 96.52% lower than that of the conventional capacitor switching scheme and reduces the area by 75%. The other major circuit modules employed are bootstrapped switches, a fully dynamic comparator, and dynamic SAR logic. The proposed ADC was simulated under the conditions of 180 nm CMOS process and 1 MS/s, resulting in a 9.8-bit effective number of bits (ENOB), a signal-to-noise and distortion ratio (SNDR) of 60.76 dB, a spurious-free dynamic range (SFDR) of 69.85 dB, a power consumption of 14.7 μW, and a figure of merit (FoM) of 16.55 fJ/conv.-step. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems, real-time video streaming via vehicle networks has been seen as a vital difficulty. The goal of this paper is to decrease the computational complexity of the versatile video coding (VVC) encoder for VANETs. In this paper, a low-complexity enhancement VVC encoder is designed for vehicular communication. First, a fast coding unit (CU) partitioning scheme based on CU texture features is proposed in VVC, which aims to decide the final type of CU partition by calculating CU texture complexity and gray-level co-occurrence matrix (GLCM). Second, to reduce the number of candidate prediction mode types in advance, a fast chroma intra-prediction mode optimization technique based on CU texture complexity aims to combine intra-prediction mode features. Moreover, the simulation outcomes demonstrate that the overall approach may substantially reduce encoding time, while the loss of coding efficiency is reasonably low. The encoding time can be reduced by up to 53.29% when compared to the VVC reference model, although the average BD rate is only raised by 1.26%. The suggested VVC encoder is also hardware-friendly and has a minimal level of complexity for video encoders used in connected vehicle applications. [ABSTRACT FROM AUTHOR]

From the analysis of chirp spread spectrum (CSS) signals in radar and communication applications, we can distinguish the cases of estimating only data or time-delay propagation or Doppler frequency when all the other parameters are known or set to zero. The case of estimating all three parameters at the same time has not been treated in the literature of radar or communications. We consider in this paper the use of a CSS signal for a LEO-PNT signal, which perfectly fits with the case of estimating the three parameters mentioned above. Specifically, the main goal of this paper is to explain the design of a CSS signal for PNT. This will include the design of the ranging component and the data component. The performance evaluation of both components will also be analyzed and linked. It is important to note that the focus of this paper will be the data component, and we link it with the pilot component through sensitivity performance. The results given in this paper show the capabilities of CSS for PNT. Previous works have shown accuracies on the level of meters with achievable data rates shown in this paper in the order of tens of kbps. [ABSTRACT FROM AUTHOR]

In this article, the elimination of ambiguity of a joined coprime array has been examined, with a focus on such of its properties as large aperture size and complete degree of freedom (DOF). The existing methods suffer from a high degree of computation complexity due to the loss constant characteristic and high peak searching. Therefore, in this paper, a DOA estimation method for a jointed coprime array, characterized by a low degree of computational complexity, is proposed. The variance of the diagonal eigenvalues of the estimated covariance matrix is designed to enhance the accuracy of the covariance matrix of the joined coprime array. Then, the Capon beamforming methods is employed for peak searching. The simulation shows that the proposed method accomplishes accurate estimation with shorter computation times and fewer operations compared to other DOA estimation methods.

This paper proposes a simple and efficient modification to the state-of-the-art zero memory set partitioned embedded block (ZM-SPECK) image coding algorithm to reduce its computational complexity without any significant increase in memory. It has been observed that comparing every element of blocks/sets with the current threshold in every bit-plane is one of the time-consuming process in the ZM-SPECK algorithm. The main contribution of this paper is to avoid this computationally complex process by using the magnitude of the largest coefficient in each subband, which is searched and stored while computing the DWT, prior to the coding. Moreover, the peak-signal-to-noise-ratio (PSNR) of the proposed technique is exactly the same as that obtained by ZM-SPECK. The simulation results show that the proposed method can reduce the complexity of ZM-SPECK by approximately 29% making it suitable for resource-constrained sensor nodes in wireless multimedia sensor networks (WMSNs), Internet of things (IoT), body area networks etc. [ABSTRACT FROM AUTHOR]

We study graph sampling methods that seek to reconstruct original graph signals from sampled signals. Greedy approach that previous methods typically adopt computes the cost function (e.g., reconstruction error) for all remaining nodes at each step to find the node that minimizes the cost function. Noting that this computation causes a huge cost, we propose a low-complexity sampling process which computes the cost function for the nodes in a limited search range which is constructed by collecting the nodes with low values of the cost function since those are likely to be selected at the next step. Further, to achieve a complexity reduction for the proposed method, the search range is updated in the fixed interval which is experimentally determined in order to maintain reconstruction performance. We investigate the performance of the proposed algorithm in comparison with the previous methods for various graphs, demonstrating about 1.5 times faster execution time without degradation of reconstruction performance. [ABSTRACT FROM AUTHOR]

This paper presents a highly configurable and low-complexity CNN accelerator based on the MobileNetV3 model. To the best of authors' knowledge, this is the first design of CNN accelerator based on the MobileNetV3 model. A highly efficient processing flow and memory-access scheme are proposed in this paper so that the throughput is greatly enhanced for the structural features in MobileNetV3 model. Furthermore, the proposed processing flow enhances the efficiency for the utilization of hardware components to reduce the complexity. Based on the proposed processing flow, this paper presents a highly configurable architecture to support various operation modes in MobileNetV3 model. The designed architecture is synthesized and layout with TSMC 90 nm technology. The evaluations for the performance and area complexity are conducted based on the post-layout estimations. It is shown in this paper that the performance of 197.7 FPS is achieved with the hardware complexity of 5392 KGEs for the MobileNetV3-Large. Compared to the state-of-the-art accelerator based on MobileNet, the FPS of the proposed design is improved by 3.4 × and the complexity is reduced by 18%. [ABSTRACT FROM AUTHOR]

The High-Efficiency Video Coding (HEVC) standard has high compression efficiency. This efficiency is achieved at the expense of increasing the computational complexity. The HEVC encoder has the hierarchical search for optimal Coding Unit (CU) partitioning. It is based on rate-distortion optimization. Various solutions are proposed to reduce the encoding time. But, the machine learning-based methods have more effective in reducing the encoding time. Yet, deep learning tools have a relatively high computational load. So, in this paper a new low complexity convolutional neural network has been designed. It is called Convolutional Neural Network-based CTU Partitioner (CNNCP). It reduces the computational complexity of the HEVC encoding. The CNNCP takes the CTU luminance component and the quantization parameter (QP) as inputs, and provides the CU depth matrix in output at once. The CNNCP does not follow the hierarchical approach. Thus, it has a fixed computation structure that facilitates the use of parallel processing tools. The CNNCP has a simple structure with a least number of parameters, and thus, it has the least computational complexity. It has been trained and tested with a large database for all QP values. The results show that it reduced the encoding time by more than 90%, and makes it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

In this article, we focus on addressing the problem of DOD, DOA, and Doppler Frequency estimation with co-prime MIMO radar. Traditional spatial-temporal smoothing methods suffer from high operational complexity and poor algorithm timeliness when overcoming the inability of a single snapshot virtual signal to be eigenvalue decomposed due to the rank loss problem. This drawback is particularly evident in the process of joint multi-dimensional parameter estimation. We first perform co-prime sampling on the received signal based on the co-prime array and then vectorize the covariance matrix to obtain a single snapshot virtual signal. After removing the redundant information from the individual snapshot signals, we obtain equivalent multi-sampled snapshot signals by means of three iterative Toeplitz matrix reconstructions. Finally, the improved multi-dimensional estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm is applied to obtain the directions of departure (DODs), directions of arrival (DOAs) and Doppler frequency of the targets. Through various simulations, it is confirmed that the algorithm in this paper has the advantages of low complexity, good timeliness and high accuracy of parameter estimation.

In an integrated sensing and communication (ISAC) system, improving the accuracy of delay and Doppler shift sensing parameter estimation is a critical task that sustains the performance of the communication system. To tackle this task, we introduce a two-stage estimation algorithm named matched filter-Fibonacci (MF-F), which employs the orthogonal time frequency space (OTFS) waveform characteristics in the delay-Doppler (DD) domain. In the first step (MF), the sensing parameters are approximated on an integer grid using the relationship between the input and output signals of the DD domain ISAC model. This approximation is executed on an integer grid using the cyclic shift property of the matrix. In the following step (F), we implement a two-dimensional (2D) searching technique based on the Fibonacci sequence, called the Fibonacci search method, to achieve sensing parameter estimation with fractional accuracy. This method decreases the number of comparisons needed and enhances the search process speed. Finally, the proposed method in this study was tested using numerical simulations and hardware experiments. The results demonstrate that the MF-F method can precisely estimate the speed and distance with millimeter-level accuracy and has the robustness and low complexity in numerical simulation. Moreover, the hardware experiment’s estimated Doppler and delay parameters can reach the centimeter and meter levels.

Incorporating low-resolution finite-alphabet equalizers into all-digital base station architectures is a promising approach to enhance energy efficiency and cost-effectiveness in massive multi-user multiple-input multiple-output uplink systems. These equalizers represent the spatial equalization matrix using low-resolution coefficients. However, designing these coefficients to achieve the desired bit error rate (BER) performance is a computationally challenging task that is classified as NP-hard. A previous study used Riemannian manifold optimization (RMO) to design a low-resolution spatial equalization matrix to address this challenge. Although the RMO-assisted equalizer provides excellent BER performance, it has high computational complexity and runtime. In this study, we propose a computationally efficient algorithm based on the projected gradient descent (PGD) framework to simultaneously reduce the computational time and complexity of the equalization algorithm while maintaining the same BER performance as the RMO-assisted equalizer. Our simulation results demonstrate that the proposed PGD-assisted equalizer achieves almost the same performance as the RMO-assisted equalizer with significantly lower computational complexity. Notably, the proposed PGD-based algorithm has a faster convergence rate than RMO, running approximately 151.75 times faster.

Environmental Sound Classification (ESC) is an important field in a broad range of applications, such as smart cities, audio surveillance, and health care. Recently, Convolutional Neural Networks (CNNs) have taken the lead from traditional approaches and have produced promising results. However, the achieved improvements are often accompanied by increasing depth, complexity, and size of the network, which prevents their usage in many practical applications. In this work, our goal is to empower a small-size low-complexity CNN model to achieve superior performance. To this end, we concentrate on the importance of global pooling technique, which is less investigated in ESC. In most previous works, models utilize global average pooling layer which does not consider regional saliency, and thus weakens the salient time-frequency regions contributions to the classification, and also to the training of convolutional kernels. We propose a novel global pooling method, called Sparse Salient Region Pooling (SSRP), which computes the channel descriptors using a sparse subset of features, and guides the model to effectively learn from the more salient time-frequency regions. Experimental results demonstrate that the proposed model with only 700K parameters yields accuracies of 86.7% on ESC-50 and 94.8% on ESC-10, which are comparable to that of the state-of-the-art methods. Compared to the baseline model, our model achieves absolute improvement of 21.8% in accuracy on ESC-50, with 98% smaller model size. Our visual analyses show that SSRP intensifies the responses of low-energy regions such that they contribute even more than high-energy regions to the classification of specific sound classes.

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This paper studied the fixed-time prescribed performance torsional vibration suppression control for the transmission system with actuator faults. Firstly, a novel electromechanical coupling torsional vibration model was established on the basis of the Lagrangian equation with the dissipation term and Kirchhoff's law, which considered the nonlinear damping, nonlinear stiffness and the mechanical-electrical coupling relationship. Subsequently, an asymmetric fixed-time predetermined performance function was introduced to ensure that the state errors can converge in a given region in the fixed time, and a torsional vibration suppression control strategy with actuator faults was designed. A stability analysis indicated that the designed torsional vibration system was stable, and the performances of the transformed states were preserved. Finally, the transmission system of the rolling mill was selected as a research object for simulation testing, and the results illustrate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

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Low complexity sequences (LCRs) are well known within coding as well as non-coding sequences. A low complexity region within a protein must be encoded by the underlying DNA sequence. Here, we examine the relationship between the entropy of the protein sequence and that of the DNA sequence which encodes it. We show that they are poorly correlated whether starting with a low complexity region within the protein and comparing it to the corresponding sequence in the DNA or by finding a low complexity region within coding DNA and comparing it to the corresponding sequence in the protein. We show this is the case within the proteomes of five model organisms: Homo sapiens , Saccharomyces cerevisiae , Drosophila melanogaster , Caenorhabditis elegans , and Arabidopsis thaliana. We also report a significant bias against mononucleic codons in LCR encoding sequences. By comparison with simulated proteomes, we show that highly repetitive LCRs may be explained by neutral, slippage-based evolution, but compositionally biased LCRs with cryptic repeats are not. We demonstrate that other biological biases and forces must be acting to create and maintain these LCRs. Uncovering these forces will improve our understanding of protein LCR evolution. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)