Low Complexity Research Articles

Recombinant real-Björck sequences assisted frequency-domain MIMO scheme for multi-impairment compensation in self-homodyne MDM system

Self-homodyne mode division multiplexing (SH-MDM) is considered a promising candidate for achieving high capacity and low complexity in next-generation optical interconnects. However, practical implementation remains challenging with channel impairments caused by transmission mechanisms, particularly mode coupling (MC) and differential mode group delay (DMGD), as well as receiver hardware impairments (RHI) originating from imperfect coherent receivers, including IQ skew, IQ amplitude imbalance, and IQ phase imbalance. To address these challenges, this paper, for the first time, proposes a recombinant real-Björck sequences assisted frequency-domain multiple input multiple output (RRB-FD-MIMO) scheme to achieve the joint equalization of MC, DMGD and RHI . The enhanced RRB sequences with constant amplitude, ideal periodicity, and excellent autocorrelation properties are employed to achieve high-precision joint impairment matrix estimation of mode impairments and RHI. Simultaneous compensation of these effects is accomplished through frequency-domain equalization. Experimental validation using a 28 GBaud SH-MDM-16QAM signal transmission over strongly coupled few-mode fiber indicates that, by setting the MIMO with a fixed 21-symbol channel memory length, the scheme can effectively compensate for DMGD within the range of –705 to 603 ps, IQ skew of ±300 ps, IQ amplitude imbalance from –19 to 18 dB, and IQ phase imbalance from –70∘ to 73∘, by considering a Q-factor penalty within 1dB. For comparative study, a reference complex-Zadoff–Chu sequence assisted FD-MIMO (CZC-FD-MIMO) scheme shows significant sensitivity to various IQ impairments, with rapid performance deterioration occurring even under small amount of IQ skew, IQ amplitude imbalance, or IQ phase imbalance. The results confirmed the robustness and superiority of the RRB-FD-MIMO scheme and its potential as an efficient digital signal processing (DSP) solution for high-speed, low-cost optical interconnects in future networks.

Design and implementation of a module for teaching and research on SCRs for power electronics.

The conversion of energy is a fundamental aspect for adequate use of energy resources; thus, counting on appropriate devices and methodologies in education and research is essential. This document presents an experimental module for teaching and research on silicon controlled rectifiers (SCRs) in power systems, where different experiments designed to cover a diverse range of complexities are also proposed. In low complexity, the experiments focus on implementing phase control rectifiers and three-phase AC-AC voltage controllers, with particular attention to passive loads. On the other hand, in the field of high complexity, the experiments delve into the application of full-wave phase control three-phase rectifiers in direct current transmission systems, as well as the use of AC-AC voltage controllers in static reactive power compensators. The results show that the desired behavior is achieved according to the theory for the different experiments proposed.

Nursing's Role in Advancing Care for Rare Genetic Diseases.

Nursing's Role in Advancing Care for Rare Genetic Diseases.

Improving alcohol-related care in small-medium primary care practices: An evaluation of an adaptation of the SPARC trial intervention for small-medium sized practices.

Improving alcohol-related care in small-medium primary care practices: An evaluation of an adaptation of the SPARC trial intervention for small-medium sized practices.

Addressing challenges and barriers to rural Veteran participation in clinical research within the Veterans Affairs healthcare system.

Addressing challenges and barriers to rural Veteran participation in clinical research within the Veterans Affairs healthcare system.

Microplastic type and concentration affect prokaryotic community structure and species coexistence in deep-sea cold seep sediments.

Microplastic type and concentration affect prokaryotic community structure and species coexistence in deep-sea cold seep sediments.

Deep Q-Network Enabled Low Complexity Beam Alignment for mmWave Massive MIMO System

Deep Q-Network Enabled Low Complexity Beam Alignment for mmWave Massive MIMO System

A low complexity NOMA-aided DFrFT-OFDM-IM system for 5G and beyond communication

A low complexity NOMA-aided DFrFT-OFDM-IM system for 5G and beyond communication

Versatility of vimentin assemblies: From filaments to biomolecular condensates and back.

Versatility of vimentin assemblies: From filaments to biomolecular condensates and back.

Finger Knuckle Print classification: Leveraging vision Mamba for low complexity and high accuracy

Finger Knuckle Print classification: Leveraging vision Mamba for low complexity and high accuracy

Hybrid lightweight temporal-frequency analysis network for multi-channel speech enhancement

Speech signals captured by microphone arrays are often contaminated by noise and spatial reverberation, highlighting the importance of multi-channel speech enhancement (MCSE) in microphone array signal processing. In recent years, deep learning has led to significant advancements in MCSE tasks. Several models have been developed to effectively mitigate noise and reverberation, enhancing the quality and intelligibility of speech. However, they rely heavily on multi-head self-attention mechanisms to capture long-term features, resulting in high model complexity, which hinders their practical deployment. Lightweight models, which utilize numerous convolution layers for feature extraction, substantially reduce model complexity but tend to focus too much on local features, often achieving suboptimal performance. To address these limitations, we propose the Hybrid Lightweight Time-Frequency Analysis Network (HLTFA), which achieves an optimal trade-off between computational efficiency and feature extraction capabilities. The Lightweight Attentive Fourier Module (LAFM) is proposed as HLTFA’s encoder. It employs frequency-domain Fourier modules and time-domain lightweight attention modules to efficiently extract axial global features. Additionally, we propose a plug-and-play lightweight component, the High-Low Energy Module (HLEM), which captures spectral information from high and low energy regions, complementing the axial feature representation. Experimental results demonstrate that HLTFA outperforms the latest models while maintaining lower complexity.

Systematic Integration of Computer Technology and Financial Management to Reduce Costs and Increase Operational Efficiency: A Systematic Review

In the era of exponential data expansion and digital technology, companies that want to boost efficiency and get a competitive advantage must combine computer science and financial management. Using The integration of technologies such as cloud computing, artificial intelligence, the Internet of Things, and big data analytics primarily serves to improve the ability to make well-informed judgements. reduce running costs, and enhance financial result accuracy. According to the assessment studies, this integration yields several advantages including lower complexity, better financial prediction accuracy, and higher productivity. While there are several benefits, management of disorganized data, cybersecurity, and regulatory compliance constitute among the several drawbacks. Studies suggest that funding staff training, improving cybersecurity standards, and including innovative technologies like blockchain and 3D printing could help to raise operational effectiveness. Combining computer science with financial management offers a great chance to simplify and enhance financial practices. However, to assure the success of the digital procedure, regulatory and technological impediments must be overcome.

DEFIF-Net: A lightweight dual-encoding feature interaction fusion network for medical image segmentation

Medical image segmentation plays a crucial role in computer-aided diagnosis. By segmenting pathological tissues in medical images, doctors can observe anatomical structures more clearly, thereby achieving more accurate disease diagnoses. However, existing medical image segmentation networks have issues such as insufficient capability to extract features from target areas, as well as high number of parameters and increased computational complexity. To address these issues, a lightweight Dual-Encoding Feature Interaction Fusion network (DEFIF-Net) is proposed in this paper for medical image segmentation. Firstly, in the encoding stage of DEFIF-Net, a global dependency fusion branch is introduced as an additional encoder to capture distant feature dependencies, whereby the neighboring and distant feature dependencies are effectively integrated by the newly designed feature interaction fusion convolution. Secondly, between the encoder and decoder, channel feature reconstruction modules (CFRMs) are used to enhance the feature representation of important channels. Additionally, a novel multi-branch ghost module (MBGM) is used in the bottleneck layer of the network to enhance its efficiency in capturing and retaining different types of feature information. Lastly, a novel residual feature enhancement (RFE) decoder is utilized to emphasize boundary features, thereby increasing the network’s sensitivity to lesion boundaries. The segmentation performance of the proposed DEFIF-Net network is evaluated in two different medical image segmentation tasks. The obtained experimental results demonstrate that, compared to state-of-the-art networks, DEFIF-Net exhibits superior segmentation performance on all three datasets used, while also having a lower parameter count and computational complexity.

Biogeographic pattern and potential distribution of Glossophaga valens Miller, 1913 (Chiroptera: Phyllostomidae)

Glossophaga valens is a generalist nectar-feeding bat known from the western slopes of Ecuador and Peru. Recently elevated to species level, key information about its geographic boundaries and environmental preferences remains limited. We developed a niche-based species distribution model to describe its biogeographic pattern, delimit its distribution, and evaluate its potential range. We compiled 107 occurrence records from scientific literature and museum collections, covering the complete known distribution of the species. After curating these data, 83 high-quality and spatially independent records were combined with five bioclimatic variables to build a model using the Maxent algorithm in the Wallace software. The best-performing model was selected from 50 candidates based on lower complexity criteria. Our results indicate that G. valens exhibits a nearly continuous distribution with high to very high suitability along the Pacific coast of Ecuador and Peru, while isolated conditions occur in northeastern Peru. Our findings update the marginal localities for the species and highlight its strong preference for arid to semi-arid conditions in western lowlands below 1500 m. The model refines the species’ range, placing its northernmost boundary in Esmeraldas, Ecuador, and suggesting its absence from Colombia. It also suggests that its southern limit could extend from southern Arequipa to northern Chile, pending future surveys and specimen revisions. Additionally, we report new easternmost records in northeastern Peru, aligning with the Marañón Valley. Key geographic features such as Seasonally Dry Tropical Forests, the Andes, and the Huancabamba Depression, alongside ecological traits like generalist feeding habits and adaptability, play a crucial role in shaping its distribution. Finally, we discussed how these geographic features have influenced the distribution of G. valens and other western Andean bats, and remark conservation implications in their threatened habitats.

Optimized design of digital comparator using QCA

Abstract This study introduces a single-bit comparator circuit designed using QCA. The proposed design surpasses existing designs comprehensively in every aspect. This study introduces an innovative approach to enhancing the robustness of a QCA-based single-bit comparator through a newly developed design. This approach contributes to its low complexity and potentially reduced energy consumption. In the comparison with the literature, the proposed design comprises of only 19 cells and best clock latency is observed with a value of 0.25 clock cycles. Fault tolerance analysis is performed on the comparator to identify potential vulnerabilities and optimize the fabrication process. To evaluate the performance of this comparator, software tools such as QCA Designer-E and QCA Pro are used to estimate its overall energy dissipation. The detailed findings are presented within the broader context of the paper.

Optimized Tandem Affinity Purification Strategy Enables High-Yield Isolation and Functional Characterization of Native COMPASS in Saccharomyces cerevisiae

Histone 3 lysine 4 methylation (H3K4me) is an evolutionarily conserved epigenetic marker associated with transcriptional activation, playing a crucial role in growth and development. In yeast, all forms of H3K4 methylation are catalyzed by the COMPASS complex. However, purifying endogenous COMPASS remains challenging due to its low abundance, compositional complexity, and structural instability, resulting in low yield, poor purity, and heterogeneity in isolated complexes. These technical limitations have impeded the structural elucidation of the intact COMPASS complex and contributed to inconsistencies in reported in vitro enzymatic activity, thereby limiting a comprehensive understanding of its functions. Here, we present an optimized tandem affinity purification strategy that enables the high-yield isolation of native COMPASS from Saccharomyces cerevisiae with >99% purity and intact subunit composition, as validated by biochemical analyses. Using recombinant nucleosomes as substrates, we systematically characterized its catalytic properties and found that endogenously purified COMPASS exhibited strict dependence on H2B ubiquitination for catalyzing H3K4 methylation. This work establishes an efficient purification strategy for future structural and functional studies of COMPASS and provides critical insights into its catalytic properties.

Virtex-7 FPGA Realization of AES-256 with Enhanced SubBytes and Key Schedule Performance

With the rising need for secure and efficient cryptographic solutions, especially in embedded and real-time systems, hardware-level enhancements have become increasingly important. This study presents a refined version of the AES-256 encryption algorithm, specifically adapted for resource-constrained environments where performance and energy efficiency are essential. By transitioning from the traditional 128-bit block structure to a 32-bit block framework, the design reduces computational demands while preserving the strong security features of AES-256. This modification supports a more modular and compact implementation, making it ideal for applications with limited hardware capabilities. Key optimizations include reusing S-box components and simplifying key table operations, which significantly lower logical complexity. Additionally, the use of pipelining improves data processing speeds while keeping latency under control. These design choices make the approach especially suitable for secure Internet of Things (IoT) devices, mobile gadgets, and compact communication modules. When implemented on the Xilinx Virtex-7 FPGA, the architecture demonstrates notable reductions in power usage and hardware resource consumption. This includes substantial savings in slice registers, LUTs, and LUT-FF pairs, along with a measurable increase in data throughput. the optimized AES-256 model offers a scalable and energy-conscious solution for modern cryptographic hardware implementations.

Dynamic Reporting Nodes Selection Method for Network Awareness Based on Active–Passive Integrated Network Telemetry in LEO Satellite Networks

Satellite network awareness is a key technology for obtaining the status of onboard resources and supporting efficient network management. However, the dynamic characteristics of the network topology results in dynamic transmission delays of awareness data, while the instability of satellite-ground links poses challenges in selecting awareness reporting nodes. Therefore, a dynamic reporting nodes selection method for network awareness based on active-passive integrated network telemetry (DRNSM-APINT) is proposed. Specifically, an APINT model is built which can dynamically select the optimal reporting node and reduce awareness time through the bidirectional aggregation of telemetry packets along the service path. To address the requirements of various services, flexible division strategy through virtual boundaries and the integration of APINT models are combined to achieve customized selection of multiple reporting nodes on each path. Additionally, a lightweight Q-learning algorithm is proposed to dynamically solve the above issue with lower computational complexity. The simulation results demonstrate that the DRNSM-APINT method can decrease the awareness time by 34.5% and improve the overall awareness performance by 17.1%, thereby significantly enhancing the timeliness performance and dynamic characteristics of network awareness.

Robust and Fast Monitoring Method of Micro-Milling Tool Wear Using Image Processing

In micro milling machining, tool wear directly affects workpiece quality and accuracy, making effective tool wear monitoring a key factor in ensuring product integrity. The use of machine vision-based methods can provide an intuitive and efficient representation of tool wear conditions. However, micro milling tools have non-flat flanks, thin coatings can peel off, and spindle orientation is uncertain during downtime. These factors result in low pixel values, uneven illumination, and arbitrary tool position. To address this, we propose an image-based tool wear monitoring method. It combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation and accurately extract wear areas. Experimental results demonstrate that the proposed algorithm exhibits high robustness to such images, effectively addressing the effects of illumination and spindle orientation. Additionally, the algorithm has low complexity, fast execution time, and significantly reduces the detection time in situ.

An Improved Power Optimizer Architecture for Photovoltaic (PV) String Under Partial Shading Conditions

In this paper, a better power optimizer architecture has been presented for PV strings, using a buck converter for each PV module to address partial shading conditions. The buck converter, though rarely used, is a natural converter for partial shading effects, as it converts the lower current of the shaded module to a higher output current. Usually, the advanced architecture activates the isolated converters (complex) of only shaded modules to draw extra current from the inverter’s DC-link node to maintain the string current (Istring). On the other hand, the conventional architecture activates converters (basic) of all modules regardless of their shading status. The proposed architecture contains a unique design with a new schematic layout, where it activates the buck converters of only shaded modules without drawing extra current from the DC-link. Thus, it combines the benefits of both architectures—selective converter operation, basic topology, high efficiency, low voltage stress, and low control complexity—while eliminating their drawbacks. The designing philosophy, control mechanism, and fundamental operation of the proposed architecture have been comprehensively explained and validated through simulation experiments. Three levels of shading are used to test the proposed architecture for string containing three PV modules: (1) a single module moderate (15%) shading level, (2) a single module strong (50%) shading level, and (3) a double module extreme (75%) and moderate (25%) shading levels. The results show a successful operation of the proposed architecture as it maintains a common Istring for an inverter, where all the shaded modules remain active. The architecture exhibits an average efficiency over 97% under normal conditions. A comparative analysis of architectures has been presented to indicate the enhanced features of the proposed architecture.