Reduced Implementation Complexity Research Articles

Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

Musculoskeletal Disorder (MSD) Health Data Collection, Personalized Management and Exchange Using Fast Healthcare Interoperability Resources (FHIR).

With the proliferation and growing complexity of healthcare systems emerges the challenge of implementing scalable and interoperable solutions to seamlessly integrate heterogenous data from sources such as wearables, electronic health records, and patient reports that can provide a comprehensive and personalized view of the patient's health. Lack of standardization hinders the coordination between systems and stakeholders, impacting continuity of care and patient outcomes. Common musculoskeletal conditions affect people of all ages and can have a significant impact on quality of life. With physical activity and rehabilitation, these conditions can be mitigated, promoting recovery and preventing recurrence. Proper management of patient data allows for clinical decision support, facilitating personalized interventions and a patient-centered approach. Fast Healthcare Interoperability Resources (FHIR) is a widely adopted standard that defines healthcare concepts with the objective of easing information exchange and enabling interoperability throughout the healthcare sector, reducing implementation complexity without losing information integrity. This article explores the literature that reviews the contemporary role of FHIR, approaching its functioning, benefits, and challenges, and presents a methodology for structuring several types of health and wellbeing data, that can be routinely collected as observations and then encapsulated in FHIR resources, to ensure interoperability across systems. These were developed considering health industry standard guidelines, technological specifications, and using the experience gained from the implementation in various study cases, within European health-related research projects, to assess its effectiveness in the exchange of patient data in existing healthcare systems towards improving musculoskeletal disorders (MSDs).

Low-complexity MLSE for coherent optical fiber transmission systems with symbol correlation.

This study demonstrates the effectiveness of a simplified maximum likelihood sequence estimation (MLSE) approach in enhancing the performance of coherent optical fiber transmission systems, particularly under conditions of linear and nonlinear symbol correlation. To counter the high complexity of traditional MLSE in high-speed systems, we introduce what we believe to be a novel simplification method. This approach replaces the conventional channel impulse response (CIR) convolution with a look-up table (LUT) and simplifies the Euclidean distance (ED) calculations. Additionally, we provide a detailed analysis of algorithmic operations, complementary metal oxide semiconductor (CMOS) transistor count, and power consumption across various technology nodes. The proposed simplified MLSE scheme has the potential to reduce implementation complexity to approximately 5% of the conventional approach. The proposed low-complexity approach was verified with both simulations and experiments. The results show that the performance loss is less than 0.3 dB in Q-factors for both linear and nonlinear bandwidth-constrained transmission scenarios.

Enhancing data tables user experience via automated adaptations

User experience is a crucial aspect of software quality, and while various guidelines and heuristics have been proposed to enhance it, tables have not received as much attention as other elements. Additionally, many developers are unaware of the guidelines they should follow in this regard. The objective of this article is to enhance the user experience on websites by streamlining the development process of usable tables. To achieve this, we propose a set of heuristics integrated into a tool called BetterTable that enables automatic application of usability improvements while parsing the HTML document. The complexity involved in developing a table with applied usability guidelines is evaluated using different implementations: the proposed approach, DataTables, and AntDesign. Furthermore, tests are conducted to validate the effectiveness of the proposed heuristics through user interactions with 13 table usage scenarios, measuring time, mouse movement, and clicks. Each user is randomly presented with 13 scenarios that either meet or do not meet a specific guideline. The results indicate that our proposal reduces implementation complexity by 43.83% and 24.69% compared to DataTables and AntDesign, respectively. Moreover, user testing reveals that 6 out of the 13 guidelines show improvements in at least 2 of the 3 calculated metrics. Guidelines fulfilled show 40.37% less average completion time and 20.39% less mouse movement than the baseline. Based on the conducted studies, we observed that compliant tables, on average, exhibit reduced user time and mouse movements compared to non-compliant tables. However, no significant differences are found in terms of clicks.

너비 우선 탐색 기반 비재귀 트리 탐색 알고리즘의 효율적 구현과 성능 비교

The objective of this study is to present an efficient tree traversal algorithm in terms of execution time and implementation complexity. Trees, defined as acyclic connected graphs, are widely used in various fields of computer science. Unlike traditional recursive methods or depth first search implementations, this research proposes an implementation based on breadth first search. The algorithm improves execution time using a non-recursive approach and reduces implementation complexity by using only one-dimensional arrays and loops. Additionally, the algorithm has been optimized through a preprocessing phase. To evaluate the performance of the proposed algorithm compared to existing ones, trees with random structures were generated and their execution times were measured. The results demonstrated that the proposed algorithm exhibits efficient performance.

Joint Active and Passive Beamforming Design for IRS-Aided Radar-Communication

In this paper, we study an intelligent reflecting surface (IRS)-aided radar-communication (Radcom) system, where the IRS is leveraged to help Radcom base station (BS) transmit the joint of communication signals and radar signals for serving communication users and tracking targets simultaneously. The objective of this paper is to minimize the total transmit power at the Radcom BS by jointly optimizing the active beamformers, including communication beamformers and radar beamformers, at the Radcom BS and the phase shifts at the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) required by communication users, the minimum SINR required by the radar, and the cross-correlation pattern design. In particular, we consider two cases, namely, case I and case II, based on the presence or absence of the radar cross-correlation design and the interference introduced by the IRS on the Radcom BS. For case I where the cross-correlation design and the interference are not considered, we prove that the dedicated radar signals are not needed, which significantly reduces implementation complexity and simplifies algorithm design. Then, a penalty-based algorithm is proposed to solve the resulting non-convex optimization problem. Whereas for case II considering the cross-correlation design and the interference, we unveil that the dedicated radar signals are needed in general to enhance the system performance. Since the resulting optimization problem is more challenging to solve as compared with the case I, the semidefinite relaxation (SDR) based alternating optimization (AO) algorithm is proposed. Particularly, instead of relying on the Gaussian randomization technique to obtain an approximate solution by reconstructing rank-one solution, the tightness is achieved by our proposed reconstruction strategy. Simulation results demonstrate the effectiveness of proposed algorithms and also show the superiority of the proposed scheme over various benchmark schemes.

Non-residual unrestricted pruned ultra-faster line detection for edge devices

Line detection with deep learning is a popular visual task that focuses mostly on lane detection. It requires quicker inference speed and lower consumption, especially for high-speed edge device applications. Based on the UFAST, we propose the Non-Residual Unrestricted Pruned Ultra-faster (NRUPU) line detection via a novel model compression method including non-interference structural reconstruction (NISR), shallow channel priority reservation (SCPR) pruning and non-residual equivalent transformation (NRET). NISR is a structure reconstruction scheme allocating residual branches into each layer to solve the cross-layer channel interference in ResNet-18. SCPR pruning directly uses the factors of BN layers to build channel importance evaluation for backbone and designs channel selection method for head based on data distribution consistency, reducing the parameters of each layer independently. Then NRET losslessly converts the multi-branch model to a single-branch one containing only convolution, linear, and relu, which reduces implementation complexity on edge devices. These designs follow the theoretical foundations: the data distribution transformation trend and effect of gradient back-propagation on model learning ability. Compared with previous pruning methods, our method optimizes not only the parameters of the model but also the structure of the model. We train NRUPU in RTX2080Ti and deploy tests on edge devices NVIDIA Jetson Xavier NX (NJXN) and Atlas 200 DK (A2DK). Extensive experiments are conducted on the dataset TuSimple, CULane and our belt dataset with 11,894 data. Results show that NRUPU achieves over 96% speed increase and over 66% parameter reduction on all datasets within 0.7% accuracy loss. The FPS can reach 749, 665 and 783 on RTX2080Ti, 133, 117 and 143 on NJNX, 178, 161 and 183 on A2DK respectively. The code is released at https://anonymous.4open.science/r/NRUPU-29B0.

Progressive pattern orthogonal interleaver set for interleave division multiple access based, non orthogonal multiple access schemes: Beyond 5G perspective

Abstract This communication suggests an orthogonal interleaver set for interleave division multiple access (IDMA) based non orthogonal multiple access (NOMA) schemes from beyond 5G viewpoint to support enormous increase in user count. The method generates an orthogonal interleaver set by providing two mother interleavers as seed to generate other users’ interleaving patterns progressively. The key feature of the proposed scheme is that it reduces implementation complexity and memory requirement at the base station, while implementing iterative multiuser detection (MUD), which most of the interlaever designs suggested in literature do not consider. It provides additional security to the user data due to progressively changing mother interleavers’ pattern along with the conventional purpose of providing unique identity for individual users in the system. The proposed orthogonal interleaver set is tested through simulations under multiple IDMA system configurations. It has been observed that it preserves the bit error rate (BER) performance of the IDMA scheme along with the optimal implementation complexity and minimal information exchange requirement between base station and mobile station to share the interleaver design.

A Complex Exponential Structure for Low-Complexity Variable Fractional Delay FIR Filters

Variable fractional delay (VFD) filters generally require significantly greater computational resources for implementation than static filters. The motivation of this work is therefore to develop ways for implementation complexity reduction. The Farrow structure is adopted by most of the variable fractional delay (VFD) filters due to their effectiveness. This structure essentially assumes an algebraic polynomial approximation to the continuously varying impulse response. In this paper, we dispense with the polynomial function and instead propose the use of complex exponential functions for approximation. This new approach leads to a Farrow-like implementation structure with complex coefficient subfilters and a shape parameter. The complex exponential (CE) VFD filter is analyzed, and various types of symmetry properties are derived. Accordingly, a simplified implementation structure is obtained. The complexity analysis shows that the design and implementation complexities can be reduced to something comparable to the classical real algebraic polynomial (AP) VFD filters with the same number of subfilters and filter order. However, the CE filters can achieve a better approximation accuracy to the desired fractional delay characteristics, compared to the AP filters. Moreover, this superiority becomes significant, when only a few subfilters are used, or when the filter orders are high. Therefore, the reduction in complexity can be achieved for the same design specification. The design algorithm is developed using a weighted least-squares formulation. Using a matrix-based approach, the closed-form solution is derived firstly for a fixed shape parameter. This is then followed by optimization of the shape parameter using the Golden Cut method. Comprehensive design examples will be presented that compare the CE filters with the AP filters.

A method for reducing implementation complexity in linear parameter-varying controllers

Gain-scheduling is a popular control technique to deal with nonlinear and time varying systems. The linear parameter-varying (LPV) system approach offers systematic tools to design gain-scheduled controllers. However, the implementation of these controllers might demand complex mathematical operations to be performed in real-time. This limits the hardware and the applications in which LPV controllers can be used. In this article, we analyze these limitations and propose a design methodology to reduce the implementation complexity of gain-scheduled LPV controllers. The methodology is illustrated with a nonlinear bicycle model used in electric vehicle control.

Decoding Quadratic Residue Codes Using Deep Neural Networks

In this paper, a low-complexity decoder based on a neural network is proposed to decode binary quadratic residue (QR) codes. The proposed decoder is based on the neural min-sum algorithm and the modified random redundant decoder (mRRD) algorithm. This new method has the same asymptotic time complexity as the min-sum algorithm, which is much lower than the difference on syndromes (DS) algorithm. Simulation results show that the proposed algorithm achieves a gain of more than 0.4 dB when compared to the DS algorithm. Furthermore, a simplified approach based on trapping sets is applied to reduce the complexity of the mRRD. This simplification leads to a slight loss in error performance and a reduction in implementation complexity.

Distributed deep learning training using silicon photonic switched architectures

The scaling trends of deep learning models and distributed training workloads are challenging network capacities in today’s datacenters and high-performance computing (HPC) systems. We propose a system architecture that leverages silicon photonic (SiP) switch-enabled server regrouping using bandwidth steering to tackle the challenges and accelerate distributed deep learning training. In addition, our proposed system architecture utilizes a highly integrated operating system-based SiP switch control scheme to reduce implementation complexity. To demonstrate the feasibility of our proposal, we built an experimental testbed with a SiP switch-enabled reconfigurable fat tree topology and evaluated the network performance of distributed ring all-reduce and parameter server workloads. The experimental results show up to 3.6× improvements over the static non-reconfigurable fat tree. Our large-scale simulation results show that server regrouping can deliver up to 2.3× flow throughput improvement for a 2× tapered fat tree and a further 11% improvement when higher-layer bandwidth steering is employed. The collective results show the potential of integrating SiP switches into datacenters and HPC systems to accelerate distributed deep learning training.

Adaptive Codebook-Based Channel Estimation in OFDM-Aided Hybrid Beamforming mmWave Systems

In order to reduce the hardware complexity and cost of mmWave transceivers, hybrid beamforming techniques have been developed, which rely on the channel state information (CSI) available to the receiver and/or transmitter. In mmWave channel estimation, the compressed sensing (CS)-based algorithms like orthogonal matching pursuit (OMP) have been widely studied to take the advantages of the sparse characteristics of mmWave channels. Specifically, the OMP-assisted adaptive codebook channel estimation has the merit of reduced implementation complexity, but it performs undesirably in low signal to noise ratio (SNR) scenarios. To circumvent this problem, in this paper, we develop an improved adaptive codebook channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) mmWave systems, which enhances the estimation performance by exploiting the multi-carrier signals for joint decision making. Our studies show that the proposed channel estimation is capable of significantly improving the estimation accuracy at low SNR, while enjoying a low complexity for implementation.

Reduced-complexity decoding implementation of QC-LDPC codes with modified shuffling

Layered decoding (LD) facilitates a partially parallel architecture for performing belief propagation (BP) algorithm for decoding low-density parity-check (LDPC) codes. Such a schedule for LDPC codes has, in general, reduced implementation complexity compared to a fully parallel architecture and higher convergence rate compared to both serial and parallel architectures, regardless of the codeword length or code-rate. In this paper, we introduce a modified shuffling method which shuffles the rows of the parity-check matrix (PCM) of a quasi-cyclic LDPC (QC-LDPC) code, yielding a PCM in which each layer can be produced by the circulation of its above layer one symbol to the right. The proposed shuffling scheme additionally guarantees the columns of a layer of the shuffled PCM to be either zero weight or single weight. This condition has a key role in further decreasing LD complexity. We show that due to these two properties, the number of occupied look-up tables (LUTs) on a field programmable gate array (FPGA) reduces by about 93% and consumed on-chip power by nearly 80%, while the bit error rate (BER) performance is maintained. The only drawback of the shuffling is the degradation of decoding throughput, which is negligible for low values of E_b/N_0 until the BER of 1e−6.

Frequency-Domain Signal Processing for Spectrally-Enhanced CP-OFDM Waveforms in 5G New Radio

Orthogonal frequency-division multiplexing (OFDM) has been selected as the basis for the fifth-generation new radio (5G NR) waveform developments. However, effective signal processing tools are needed for enhancing the OFDM spectrum in various advanced transmission scenarios. In earlier work, we have shown that fast-convolution (FC) processing is a very flexible and efficient tool for filtered-OFDM signal generation and receiver-side subband filtering, e.g., for the mixed-numerology scenarios of the 5G NR. FC filtering approximates linear convolution through effective fast Fourier transform (FFT)-based circular convolutions using partly overlapping processing blocks. However, with the continuous overlap-and-save and overlap-and-add processing models with fixed block-size and fixed overlap, the FC-processing blocks cannot be aligned with all OFDM symbols of a transmission frame. Furthermore, 5G NR numerology does not allow to use transform lengths shorter than 128 because this would lead to non-integer cyclic prefix (CP) lengths. In this article, we present new FC-processing schemes which solve or avoid the mentioned limitations. These schemes are based on dynamically adjusting the overlap periods and extrapolating the CP samples, which make it possible to align the FC blocks with each OFDM symbol, even in case of variable CP lengths. This reduces complexity and latency, e.g., in mini-slot transmissions and, as an example, allows to use 16-point transforms in case of a 12-subcarrier-wide subband allocation, greatly reducing the implementation complexity. On the receiver side, the proposed scheme makes it possible to effectively combine cascaded inverse and forward FFT units in FC-filtered OFDM processing. Transform decomposition is used to simplify these computations, leading to significantly reduced implementation complexity in various transmission scenarios. A very extensive set of numerical results is also provided, in terms of the radio-link performance and associated processing complexity.

Reconfigurable Rounding Based Approximate Multiplier for Energy Efficient Multimedia Applications

The approximate design has emerged as a revolutionary design paradigm to obtain energy efficient digital signal processing cores while exhibiting acceptable accuracy. In different signal processing architectures, multiplier is the prime arithmetic unit and significantly influences the performance of these cores. Therefore, four novel energy efficient rounding based approximate (RBA) multiplier architectures are proposed in this paper. These multipliers first approximate input operands to the nearest power of two values and then achieve multiplication using few adders and shifters. The proposed RBA multipliers significantly reduce implementation complexity and provide higher energy efficiency. Further, a novel reconfigurable rounding based approximate (RRBA) multiplier is proposed to achieve desired performance-quality tradeoff. Further, the performance of proposed RBA and RRBA multipliers is evaluated and analysed over the existing approximate multiplier architectures. The proposed 8-bit RBA0 requires 59.8% (54.7%) reduced area (delay) compared to the existing approximate multiplier. Finally, the efficacy of the proposed multipliers is demonstrated in the application by implementing Gaussian filters embedded with existing and proposed approximate multipliers. The Gaussian filter designed using RBA0 provides 32.5% reduced energy consumption over the filter with existing multiplier.

An FPGA-Based LDPC Decoder With Ultra-Long Codes for Continuous-Variable Quantum Key Distribution

In this paper, we propose a good decoding performance, low-complexity, and high-speed decoder architecture for ultra-long quasi-cyclic LDPC codes by using the layered sum-product decoding scheme. To reduce implementation complexity and hardware resource consumption, the messages in the iteration process are uniformly quantified and the function Ψ(x) is approximated with second-order functions. The decoder architecture improves the decoding throughput by using partial parallel and pipeline structures. A modified construction method of parity check matrices was applied to prevent read&write conflicts and achieve high-speed pipeline structure. The simulation results show that our decoder architecture has good performance at signal-to-noise ratios (SNRs) as low as -0.6 dB. We have implemented our decoder architecture on a Virtex-7 XC7VX690T field programmable gate array (FPGA) device. The implementation results show that the FPGA-based LDPC decoder can achieve throughputs of 108.64 Mb/s and 70.32 Mb/s at SNR of 1.0 dB when the code length is 262,144 and 349,952, respectively. The decoder can find useful applications in those scenarios that require very low SNRs and high throughputs, such as the information reconciliation of continuous-variable quantum key distribution.

Compact low‐noise power amplifier design and implementation for millimetre wave frequencies

In this paper, we have designed and realized a two-stage low-noise power amplifier (LNPA) with resistive feedback network targeting for Ka-band compact RF front-end applications. Featuring the characteristics of both low noise and high power at the same time, the LNPA is expected to be a possible one-chip replacement of power and low noise amplifiers integrated in a conventional transceiver/receiver (T/R) module. Such configuration features size compactness while reduces implementation complexity which is of crucial importance for integration in antenna arrays with large number of antenna elements. Implemented in 0.15-μm GaAs pseudomorphic high electron mobility transistor (pHEMT) technology, the LNPA, operating at 36–40 GHz, exhibits a peak gain of 15.96 dB, a minimum noise figure of 2.88 dB, a power consumption of 152 mW and a measured 1-dB compression output power of 14.92 dBm at 38 GHz, respectively. The LNPA also featured a very good linearity performance with a measured output third-order interception point (IP3) of 22.22 dBm at 38 GHz.

A practical octree liquid simulator with adaptive surface resolution

We propose a new adaptive liquid simulation framework that achieves highly detailed behavior with reduced implementation complexity. Prior work has shown that spatially adaptive grids are efficient for simulating large-scale liquid scenarios, but in order to enable adaptivity along the liquid surface these methods require either expensive boundary-conforming (re-)meshing or elaborate treatments for second order accurate interface conditions. This complexity greatly increases the difficulty of implementation and maintainability, potentially making it infeasible for practitioners. We therefore present new algorithms for adaptive simulation that are comparatively easy to implement yet efficiently yield high quality results. First, we develop a novel staggered octree Poisson discretization for free surfaces that is second order in pressure and gives smooth surface motions even across octree T-junctions, without a power/Voronoi diagram construction. We augment this discretization with an adaptivity-compatible surface tension force that likewise supports T-junctions. Second, we propose a moving least squares strategy for level set and velocity interpolation that requires minimal knowledge of the local tree structure while blending near-seamlessly with standard trilinear interpolation in uniform regions. Finally, to maximally exploit the flexibility of our new surface-adaptive solver, we propose several novel extensions to sizing function design that enhance its effectiveness and flexibility. We perform a range of rigorous numerical experiments to evaluate the reliability and limitations of our method, as well as demonstrating it on several complex high-resolution liquid animation scenarios.

Predistortion With Low Feedback Sampling Rate for Wideband Nonlinear Satellite Downlinks

In the satellite channel, the traveling wave tube amplifier (TWTA) and output multiplexing (OMUX) filter introduce the distortions to degrade the signal quality. Traditional signal predistortion methods often compensate for the distortions separately by sampling TWTA output and OMUX filter output, which leads to high sampling cost and system complexity. In this letter, a low sampling rate predistortion method based on deep neural network (DNN) is proposed for satellite downlink. This method introduces a novel DNN-based signal recovery module in the feedback path to recover the TWTA output through the OMUX filter output, and then the distortions of the TWTA and OMUX filter are compensated respectively. To reduce implementation complexity, the DNN onboard is trained offline on the ground with historical data. The experimental results show that the proposed method can achieve the same compensation effect as traditional method while requiring 28% sampling rate.