Complexity Reduction Scheme Research Articles

Joint PAPR reduction and channel estimation in low-complexity VLC systems

In recent times, there has been a surge in interest surrounding visible light communication (VLC) as a compelling adjunct to radio frequency (RF) technology. This enthusiasm stems from deployments showcasing its efficacy in high-speed data transmission, characterized by low complexity, robust security, and high reliability. Among the variants of orthogonal frequency division multiplexing (OFDM), direct current-biased optical OFDM (DCO-OFDM) stands out in the realm of VLC systems, owing to its ability to furnish rapid data provisioning. However, a significant drawback of selective mapping (SLM) based DCO-OFDM transceivers lies in its computational complexity and practical implementation challenges. To address this limitation, this paper presents a modified SLM technique termed selective phase modulation (SPM) aimed at mitigating the peak-to-average power ratio (PAPR) in the transmitter section while simplifying the data decoding process at the receiver end. The SPM method utilizes a phase modulation and demodulation process based on clusters and assisted by pilot signals, streamlining the transceiver architecture. The SPM methodology offers reduced computational complexity at the transmitter by selectively applying phase sequences to data, circumventing the necessity for side information (SI) transmission and SI estimation at the receiver with a known pilot symbol inserted in the data. This dual-functionality mechanism not only achieves complexity reduction but also facilitates channel estimation at the receiver for proper data recovery. Based on complexity analysis and simulation outcomes, the computational complexity reduction strategy outperforms the conventional selective mapping (CSLM) technique, VLC-based pilot-assisted PAPR reduction systems, and embedded coded modulation (ECM). This allows for smooth incorporation of this technology into future VLC systems.

Revisiting reachability-driven explicit MPC for embedded control

The real-time implementation of the explicit MPC suffers from the evaluation of the, potentially large, lookup table. The paper revisits the original approach and presents an efficient reachability-sets-driven-based explicit MPC method addressing this issue by splitting the look-up table into the set of the “relevant” subsets. Simultaneously, effective binary encoding is introduced to minimize the runtimes and the memory footprint. Further acceleration is achieved by introducing the “smart” order of the considered critical regions. Then, the significant real-time complexity reduction is ensured by online pruning and traversing the sorted lookup table associated with the optimal control law evaluation. Technically, the number of critical regions to be explored is reduced and the order is redefined to accelerate the point location problem and minimize the computational effort. While the optimality of the control law is still preserved, the cost that we need to pay for the accelerated point location problem lies in an additional offline computation effort and a minor increase in memory requirements of the underlying controller. The benefits of the proposed method are demonstrated using an extensive case study. The complexity reduction strategy was investigated on two fast-dynamic benchmark systems and the computational burden was analyzed by implementing the designed controllers on an embedded hardware.

Adversarial Modeling Regularization for Modeling and Linearization of Power Amplifiers

Overfitting problem is very common in the coefficient identification process of digital predistortion (DPD) technique. Especially in some related DPD topics, the overfitting problem seriously limits the linearization performance. In this article, to improve the robustness of coefficient identification, we present the adversarial modeling regularization (AMR) technique. By regarding the linear-in-coefficient power amplifier (PA) behavioral models as the prior knowledge of the PA itself, the proposed regularization constrains the PA model’s coefficients by evaluating the fitting performance of other adversarial models. In addition, we present two complexity reduction strategies for the AMR. The AMR is extended and validated under four related tasks that are prone to overfitting: the few-sample learning, the piecewise DPD technique, the aliasing-sampling technique, and the band-limited technique. The experiment results show that the proposed technique can dramatically improve the forward behavioral modeling and linearization performance compared with the other state-of-the-art algorithms in all tasks.

Linked Data Interfaces: A Survey

In the era of big data, linked data interfaces play a critical role in enabling access to and management of large-scale, heterogeneous datasets. This survey investigates forty-seven interfaces developed by the semantic web community in the context of the Web of Linked Data, displaying information about general topics and digital library contents. The interfaces are classified based on their interaction paradigm, the type of information they display, and the complexity reduction strategies they employ. The main purpose to be addressed is the possibility of categorizing a great number of available tools so that comparison among them becomes feasible and valuable. The analysis reveals that most interfaces use a hybrid interaction paradigm combining browsing, searching, and displaying information in lists or tables. Complexity reduction strategies, such as faceted search and summary visualization, are also identified. Emerging trends in linked data interface focus on user-centric design and advancements in semantic annotation methods, leveraging machine learning techniques for data enrichment and retrieval. Additionally, an interactive platform is provided to explore and compare data on the analyzed tools. Overall, there is no one-size-fits-all solution for developing linked data interfaces and tailoring the interaction paradigm and complexity reduction strategies to specific user needs is essential.

Resilience, complexity and digital transformation: three case studies in the valves industry

PurposeThis paper shows how the interplay between organisational resilience and environmental complexity justifies the existence of differentiated yet successful approaches to digital transformation.Design/methodology/approachA multi-case method is applied to test our research hypotheses by contrasting the digital transformation of three Italian companies in the valves industry.FindingsDifferent combinations of technological and organisational tools, hence diversified digital transformations, can be successful, provided that they are supported by a coherent set of resilience factors and allow for the implementation of strategic approaches aligned with the resilience capacity of the firm.Practical implicationsAwareness that resilience capacity shapes digital transformation and the strategies available to engage with external complexity should focus managers to invest in the alignment and the reinforcement of the factors underlying organisational resilience.Originality/valueMost literature so far focused on the antecedents to digital transformation. In contrast, this paper focuses on the transformation process and highlights how the resilience capacity of the firm affects the unfolding of digital transformation and the emergence of diversified yet successful paths. In addition, in contrast with a dichotomous approach to external complexity this paper shows that digital transformation involves a mix of complexity reduction and complexity absorption strategies.

Engineering resource allocation in artificially minimized cells: Is genome reduction the best strategy?

The elimination of the expression of cellular functions that are not needed in a certain well-defined artificial environment, such as those used in industrial production facilities, has been the goal of many cellular minimization projects. The generation of a minimal cell with reduced burden and less host-function interactions has been pursued as a tool to improve microbial production strains. In this work, we analysed two cellular complexity reduction strategies: genome and proteome reduction. With the aid of an absolute proteomics data set and a genome-scale model of metabolism and protein expression (ME-model), we quantitatively assessed the difference of reducing genome to the correspondence of reducing proteome. We compare the approaches in terms of energy consumption, defined in ATP equivalents. We aim to show what is the best strategy for improving resource allocation in minimized cells. Our results show that genome reduction by length is not proportional to reducing resource use. When we normalize calculated energy savings, we show that strains with the larger calculated proteome reduction show the largest resource use reduction. Furthermore, we propose that reducing highly expressed proteins should be the target as the translation of a gene uses most of the energy. The strategies proposed here should guide cell design when the aim of a project is to reduce the maximum amount or cellular resources.

Decoding Reed-Muller Codes With Successive Codeword Permutations

A novel recursive list decoding (RLD) algorithm for Reed-Muller (RM) codes based on successive permutations (SP) of the codeword is presented. A low-complexity SP scheme applied to a subset of the symmetry group of RM codes is first proposed to carefully select a good codeword permutation on the fly. Then, the proposed SP technique is integrated into an improved RLD algorithm that initializes different decoding paths with random codeword permutations, which are sampled from the full symmetry group of RM codes. Finally, efficient latency and complexity reduction schemes are introduced that virtually preserve the error-correction performance of the proposed decoder. Simulation results demonstrate that at the target frame error rate of 10−3 for the RM code of length 256 with 163 information bits, the proposed decoder reduces 6% of the computational complexity and 22% of the decoding latency of the state-of-the-art semi-parallel simplified successive-cancellation decoder with fast Hadamard transform (SSC-FHT) that uses 96 permutations from the full symmetry group of RM codes, while relatively maintaining the error-correction performance and memory consumption of the semi-parallel permuted SSC-FHT decoder.

Intra Complexity Control Algorithm for VVC

Versatile Video Coding (VVC) has advantages over High Efficiency Video Coding (HEVC); it can save nearly half of the bit rate and significantly improve the compression efficiency, but VVC’s coding complexity is extremely high. Therefore, VVC encoders are difficult to implement in video devices with different computing capabilities and power constraints. In this paper, we apply texture information and propose a VVC intra complexity control algorithm. The algorithm assigns a different encoding time to each CU based on the corresponding texture entropy. Besides, the complexity reduction strategy at the CU level is designed to balance the complexity control while taking rate-distortion performance into consideration. Experiments in our paper show that the coding complexity can be accurately controlled from 90% to 70% with a slight loss of RD performance.

A new complexity reduction scheme in selective mapping‐based visible light communication direct current‐biased optical orthogonal frequency division multiplexing systems

Visible light communication (VLC) has emerged as a good accompaniment to radio-frequency (RF) technologies by deploying multicarrier schemes such as orthogonal frequency division multiplexing (OFDM). However, the coherent summation of carriers in the OFDM system leads to a high peak-to-average-power ratio (PAPR), causing non-linear clipping distortion at the transmitting light-emitting diode. This intricacy becomes a potential barrier for intensity modulation and limits the VLC systems' bandwidth. In the literature, non-distorting PAPR lowering approaches, such as the selective mapping (SLM) approach, have been confirmed as the most effective strategy for reducing ineludible high PAPR in optical OFDM systems among all other available techniques. Besides its astounding performance, the computational complexity also becomes a major complication in SLM due to the generation of multiple candidates. This paper proposes a computational complexity minimisation approach using inherit system properties in the SLM-based PAPR suppression method for VLC systems, where the phase sequence vectors are considered periodic. The alternative direct current-biased optical orthogonal frequency division multiplexing candidates for the single frequency-domain data block are generated with the periodic phase rotation vector. The evaluation of the complexity analysis and the simulation results shows that the mitigation of computational complexity surpasses the standard SLM technique.

Symmetrized persistency of Bell correlations for Dicke states and GHZ-based mixtures

Quantum correlations, in particular those, which enable to violate a Bell inequality, open a way to advantage in certain communication tasks. However, the main difficulty in harnessing quantumness is its fragility to, e.g, noise or loss of particles. We study the persistency of Bell correlations of GHZ based mixtures and Dicke states. For the former, we consider quantum communication complexity reduction (QCCR) scheme, and propose new Bell inequalities (BIs), which can be used in that scheme for higher persistency in the limit of large number of particles N. In case of Dicke states, we show that persistency can reach 0.482N, significantly more than reported in previous studies.

Social Goals in the Theory of the Firm: A Systems Theory View

Drawing on Luhmannian social systems theory, this article revisits the single- versus multiple-objective debate on the theory of the firm. Firms are conceptualized as complexity reducing systems structurally coupled with potentially risky environments, and profit maximization is considered as a complexity reduction strategy for making sense of these environments. Whereas single-objective approaches reflect cases when environmental risks do not materialize into corporate sustainability problems, multiple-objective approaches address these problems by increasing the corporation’s environmental responsiveness beyond the profit maximization function. Our systems-theoretic framework therefore identifies the common ground between the two approaches and draws attention to the circumstances under which they can claim validity.

Complexity reduction of test zonal search for fast motion estimation in uni-prediction of High Efficiency Video Coding

The complexity of motion estimation in the High Efficiency Video Coding (HEVC) standard is very high as it uses a number of prediction block sizes. The test zonal search (TZS) mechanism is used as motion estimation algorithm in the fast search mode of HM encoder, the reference software for HEVC. In this paper, we present schemes for reducing the complexity of the TZS algorithm for uni-prediction in HEVC. The proposed mechanisms help in reducing complexity of grid, raster and refinement search stages of TZS. The performance of the proposed mechanisms is tested independently and also combined in the fast search mode of HM-16.18. The motion estimation time and the number of search points in the combined algorithm are reduced by 68.14% and 77.10% with a BD-rate of 0.30% and a BD-PSNR of $$-0.007$$ dB in comparison with the original fast search mode in the low-delay P main profile. The proposed complexity reduction schemes are compared with those adopted in other recently proposed fast motion estimation algorithms and the performance of the proposed scheme is found to be superior in terms of the reduction in motion estimation time.

Fast depth intra coding based on spatial correlation and rate distortion cost in 3D-HEVC

3D-HEVC video coding standard adopts many sophisticated algorithms to improve the coding performance, which results in heavy calculation complexity. To reduce the computation burden, a complexity reduction scheme for depth map based on spatial correlation and the rate–distortion (RD) cost is proposed. Firstly, A maximum depth layer decision (MDLD) algorithm is proposed to predict the maximum depth layer (MDL) of each coding tree unit (CTU) accurately. Using the frequency distribution histogram theory in the statistics, the frequency distribution characteristics (FDC) of the RDcost J0 of the CTU level in depth layer 0 is investigated under different MDL. The initial threshold for different MDL is designed based on the FDC of the J0 under allowable false rate (FR). An exponential relationship between the threshold and QP is discovered. Secondly, A fast depth intra mode decision (FDIMD) algorithm is proposed to skip unnecessary prediction modes. Considering the spatial correlation, the FDC of the RDcost JORM of the optimal rough mode (ORM) in rough mode decision (RMD) is analyzed under different optimal prediction mode. Based on the FDC of the JORM, the mode-skipping threshold THst is designed to skip angular modes and depth modeling modes (DMMs). An exponential model is built to represent the relationship between the THst and QP. Finally, a fast wedgelet pattern decision method based on K-Means is developed to reduce the complexity of DMM1. Experimental results show that the proposed algorithm achieves an average reduction of 58.9% on intra coding time, with negligible degradation of coding performance. The proposed algorithm performs better in terms of complexity and performance than other approaches.

A Complexity Reduction Scheme for Depth Coding in 3D-HEVC

3D-high efficiency video coding (3D-HEVC) is the next-generation compression standard for multiview system applications, which has recently been approved by MPEG and VCEG as an extension of HEVC. To improve the compression efficiency of depth map, several compression tools have been developed for a better representation depth edges. These supplementary coding tools together with existing prediction modes can achieve high compression efficiency, but require a very high complexity that restricts the encoders from ongoing application. In this paper, we introduce a fast scheme to reduce complexity of depth coding in inter and intramode prediction procedure. A simulation analysis is performed to study intra and intermode distribution correlations in the depth compression information. Based on that correlation, we exploit two complexity reduction strategies, including early SKIP and adaptive intra prediction selection. Experimental results demonstrate that our scheme can achieve a complexity reduction up to 63.0%, without any noticeable loss of compression efficiency.

Energy and Rate-Aware Design for HEVC Motion Estimation Based on Pareto Efficiency

This paper presents a high-throughput energy and rate-aware hardware design for the Motion Estimation (ME) according to the High Efficiency Video Coding (HEVC) standard. The hardware design implements a modified Test Zone Search (TZS) algorithm to perform Integer Motion Estimation (IME) as well as the Fractional Motion Estimation (FME) defined by the HEVC standard. Based on evaluations with the HEVC Reference Software, a complexity-reduction strategy was adopted in the developed architecture that mainly consists of supporting only the 8x8, 16x16, 32x32, and 64x64 Prediction Unit (PU) sizes rather than using the 24 possible PU sizes. The architecture allows an external control unit selects a subset of these four PU sizes according to the energy and rate targets for a specific application. The possible operation points were determined based on Pareto Efficiency. The architecture was described in VHDL, and the synthesis results for ASIC 45nm Nangate standard cells show that the developed architecture can process at least 53 frames per second (fps) considering Ultra-High Definition (UHD) 4320p videos. When an average-case of processing is considered, the architecture is able to process 112fps at UHD 4320p resolution.

Mapping causal mutations by exome sequencing in a wheat TILLING population: a tall mutant case study

Forward genetic screens of induced mutant plant populations are powerful tools to identify genes underlying phenotypes of interest. Using traditional techniques, mapping causative mutations from forward screens is a lengthy, multi-step process, requiring the identification of a broad genetic region followed by candidate gene sequencing to characterize the causal variant. Mapping by whole genome sequencing accelerates the identification of causal mutations by simultaneously defining a mapping region and providing information on the induced genetic variants. In wheat, although the availability of a high-quality draft genome assembly facilitates mapping and mutation calling, whole genome resequencing remains prohibitively expensive due to its large genome. In the current study, we used exome sequencing as a complexity reduction strategy to detect mutations associated with a target phenotype. In a segregating wheat EMS population, we identified a clear peak region on chromosome arm 4BS associated with increased plant height. Although none of the significant SNPs seemed causative for the mutant phenotype, they were sufficient to identify a linked ~ 1.9 Mb deletion encompassing nine genes. These genes included Rht-B1, which is known to have a strong effect on plant height and is a strong candidate for the observed phenotype. We performed simulation experiments to determine the impacts of sequencing depth and bulk size and discuss the importance of considering each factor when designing mapping-by-sequencing experiments in wheat. This approach can accelerate the identification of candidate causal point mutations or linked deletions underlying important phenotypes.

Anonymous subject identification and privacy information management in video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework.

Nonlinear self-interference cancellation in MIMO full-duplex transceivers under crosstalk

This paper presents a novel digital self-interference canceller for an inband multiple-input-multiple-output (MIMO) full-duplex radio. The signal model utilized by the canceller is capable of modeling the in-phase quadrature (IQ) imbalance, the nonlinearity of the transmitter power amplifier, and the crosstalk between the transmitters, thereby being the most comprehensive signal model presented thus far within the full-duplex literature. Furthermore, it is also shown to be valid for various different radio frequency (RF) cancellation solutions. In addition to this, a novel complexity reduction scheme for the digital canceller is also presented. It is based on the widely known principal component analysis, which is used to generate a transformation matrix for controlling the number of parameters in the canceller. Extensive waveform simulations are then carried out, and the obtained results confirm the high performance of the proposed digital canceller under various circuit imperfections. The complexity reduction scheme is also shown to be capable of removing up to 65% of the parameters in the digital canceller, thereby significantly reducing its computational requirements.

Online-Learning-Based Complexity Reduction Scheme for 3D-HEVC

3-D High Efficiency Video Coding (HEVC) is a new emerging video compression standard for multiview video applications. This standard utilizes advanced interview prediction characteristics in addition to the prediction features of the HEVC standard for efficient encoding of multiview video content. While using combined features improves the compression performance by utilizing the correlation between the views captured from slightly different angles of the same scene, they also increase coding complexity. The focus of this paper is on developing an efficient complexity reduction scheme for 3D-HEVC, with the intention to facilitate the adoption of this upcoming standard, especially for real-time applications. In this regard, first, we introduce two ways to decrease the complexity of the inter-/ intra-mode search process of the to-be-encoded blocks in the dependent texture views ( ${\mathrm {DV}}_{t}\text{s}$ ) of 3D-HEVC. Then, we propose a hybrid complexity reduction scheme that utilizes the two-mode prediction approaches, motion information of the base texture view (BV t ), and the rate distortion cost of the already encoded blocks in the BV t and DV t . The performance of our proposed scheme is tested for the case with two views (i.e., base view + dependent view). The evaluations confirm that our proposed hybrid complexity reduction scheme reduces the 3D-HEVC codec complexity by 67.70% on average for the DV t compared with the unmodified 3D-HEVC encoder, while maintaining the overall video quality. Compared with the state-of-the-art method, it reduces complexity by 25.74% on average.

An Encoder Complexity Reduction Scheme for Quality/Fidelity Scalable HEVC

Due to the compression performance of the latest coding standard known as high efficiency video coding (HEVC), there has been a significant interest in developing a scalable version to address industry’s demand for cost effective universal access of digital media. One of the issues with the scalable extension of HEVC (known as SHVC) is its computational complexity. The focus of this study is on developing complexity reduction schemes for SHVC encoder with quality/fidelity scalability. In this paper, we propose a complexity reduction scheme for quality scalable extension of HEVC with two enhancement layers. The proposed scheme potentially can make coding/transmission of several different-quality versions of the same video content in one bit-stream an attractive proposition for the content delivery industry, allowing for cost effective digital media delivery to a variety of playback display devices. Performance evaluations confirm that the proposed complexity reduction scheme reduces total SHVC’s encoding time by up to 51.95% while maintaining the overall quality of the coded streams.