Processing Algorithms Research Articles

Influence of Front-End Electronics on Metrological Performance of QCM Systems.

Quartz Crystal Microbalances (QCMs) are versatile sensors employed in various fields, from environmental monitoring to biomedical applications, owing mainly to their very high sensitivity. However, the assessment of their metrological performance, including the impact of conditioning circuits, digital processing algorithms, and working conditions, is a complex and novel area of study. The purpose of this work is to investigate and understand the measurement errors associated with different QCM measurement techniques, specifically focusing on the influence of conditioning electronic circuits. Through a tailored and novel experimental setup, two measurement architectures-a Quartz Crystal Microbalance with dissipation monitoring (QCM-D) system and an oscillator-based QCM-R system-were compared under the same mechanical load conditions. Through rigorous experimentation and signal processing techniques, the study elucidated the complexities of accurately assessing QCM parameters, especially in liquid environments and under large mechanical loads. The comparison between the two different techniques allows for highlighting the critical aspects of the measurement techniques. The experimental results were discussed and interpreted based on models allowing for a deep understanding of the measurement problems encountered with QCM-based measurement systems. The performance of the different techniques was derived, showing that while the QCM-D technique exhibited higher accuracy, the QCM-R technique offered greater precision with a simpler design. This research advances our understanding of QCM-based measurements, providing insights for designing robust measurement systems adaptable to diverse conditions, thus enhancing their effectiveness in various applications.

Parallel GEMM-based convolutions for deep learning on multicore ARM and RISC-V architectures

We present high performance, multi-threaded implementations of three GEMM-based convolution algorithms for multicore processors with ARM and RISC-V architectures. The codes are integrated into CONVLIB, a library that has the following unique features: (1) scripts to automatically generate a key component of GEMM, known as the micro-kernel, which is typically written in assembly language; (2) a modified analytical model to automatically tune the algorithms to the underlying cache architecture; (3) the ability to select four hyper-parameters: micro-kernel, cache parameters, parallel loop, and GEMM algorithm dynamically between calls to the library, without recompiling it; and (4) a driver to identify the best hyper-parameters. In addition, we provide a detailed performance evaluation of the convolution algorithms, on five ARM and RISC-V processors, and we publicly release the codes.

A novel sparse recovery space‐time adaptive processing algorithm using the log‐sum penalty to approximate the ℓ0 − norm penalty

AbstractApplying the sparse recovery (SR) technique to airborne radar space‐time adaptive processing (STAP) can greatly reduce the number of required training samples, which is advantageous in detecting targets in non‐homogeneous and non‐stationary clutter environments. However, the poor performance, the slow convergence speed or the high computational complexity of the traditional SR STAP algorithms limit their practical application. To tackle this problem, a novel efficient SR STAP algorithm is proposed. The newly proposed SR STAP algorithm utilises the log‐sum penalty to approximate the penalty, which exhibits improved convergence performance and clutter suppression performance compared to the traditional SR STAP algorithms. Besides, the proposed algorithm can ensure the convergence in each iteration by offering a closed‐form analytic solution. Furthermore, the result of the mathematical derivation demonstrates the essential equivalence between our method and the iterative reweighted method. By utilising this equivalence, two additional methods are proposed that incorporate the knowledge of the clutter Capon spectrum and the clutter spectrum of the iterative adaptive approach (IAA) as the components of weighted values, resulting in further performance improvement of the proposed algorithm. Finally, simulation results with both simulated data and Mountain‐Top data demonstrate the high effectiveness and performance of the proposed methods.

Rapid measurement method for key dimensions of train wheelset based on improved image processing algorithm

The key dimensions of train wheelsets change with the increase of running time. To ensure the safe operation of the train, the key dimensions should meet the relevant technical requirements. Maintenance of wheelsets and measurement of key dimensions are important part of the daily maintenance work. A rapid measurement method based on image processing algorithms for key dimensions of hand-held wheelsets integrating multiple parameter measurements is studied. The general framework of the system is examined and the roles of the modules are described. The optical center for the initial position of the continuous line spot is searched. The continuous line spot center extraction algorithm during tread measurement is optimized. A three-dimensional model is reconstructed using point cloud data, then key dimensions are measured and calculated from the model. In order to verify the feasibility of the method, traditional measurement methods are used for comparison tests. The results show that the method is effective in the daily maintenance of trains.

The Output Signal-to-Noise Ratio and Speech Perception in Noise: The Effect of Multichannel and Free-Channel Hearing Aids

Background and Aim: The output Signal-to-Noise Ratio (SNR) is one of the essential factors in hearing aid benefits. There is limited evidence regarding SNR improvement by the Channel-Free (CFHA) and Multi-Channel Hearing Aid (MCHA) and the speech understanding in noise through them. This study aimed to investigate the extent to which output SNR was modified by CFHA and MCHA processing and the variation in aided speech recognition abilities with a change in output SNR. Methods: Thirty-six participants aged 50–65 years were included. A chosen CFHA and MCHA were used to obtain the output SNR and sentence recognition in noise in four different processing algorithms (linear, linear+noise reduction, WDRC, WDRC+noise reduction). Hagerman’s phase inversion technique was used to measure the attenuation of noise and, in turn, to obtain the output SNR of the hearing aid. Results: In all hearing aid processing algorithms among those with normal hearing and people with hearing loss, the output of CFHA revealed higher attenuation values than that of MCHA. There was a significant effect of the hearing aids and processing algorithms in both normal and individuals with hearing impairment on the mean SNR. Further, multiple linear regression analysis results showed that whether the hearing is channel-free or multi- channel significantly predicted speech recognition scores, while output SNR and processing algorithms did not. Conclusion: The signal processing algorithms in CFHA had greater noise attenuation values, better output SNR, and speech recognition scores, showing an advantage over the modern MCHA among individuals with hearing impairment. Keywords: Channel-free hearing aids; multi-channel hearing aids; sensorineural hearing loss; speech recognition

OptiComm-GPT: a GPT-based versatile research assistant for optical fiber communication systems

With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide-and-conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy-to-start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT’s abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well-crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user’s intent, accurately extract system parameters from the user’s request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements.

Integrating RGB-thermal image sensors for non-contact automatic respiration rate monitoring

Respiration rate (RR) holds significance as a human health indicator. Presently, the conventional RR monitoring system requires direct physical contact, which may cause discomfort and pain. Therefore, this paper proposes a non-contact RR monitoring system integrating RGB and thermal imaging through RGB-thermal image alignment. The proposed method employs an advanced image processing algorithm for automatic region of interest (ROI) selection. The experimental results demonstrated a close correlation and a lower error rate between measured thermal, measured RGB, and reference data. In summary, the proposed non-contact system emerges as a promising alternative to conventional contact-based approaches without the associated discomfort and pain.

Innovations in Risk Measurement and Management for Strategic Financing Decisions

This study aims to explore the integration of innovative quantitative models, behavioral finance insights, and advanced analytics techniques in contemporary risk management practices to bolster organizations' resilience and informed decision-making within dynamic financial landscapes. Through a systematic literature review spanning disciplines such as finance, psychology, and technology, this research synthesizes diverse insights to comprehensively understand the evolving nature of risk management strategies. The findings elucidate that incorporating advanced quantitative models like Value at Risk (VaR) and Conditional Value at Risk (CVaR) empowers organizations to meticulously assess and quantify financial risks, thereby enhancing risk management accuracy and granularity. Furthermore, the assimilation of behavioral finance insights illuminates the psychological determinants shaping risk perception and decision-making behaviors, leading to the development of more robust risk management frameworks that accommodate both rational and irrational factors. Moreover, leveraging advanced analytics techniques, notably natural language processing (NLP) algorithms, enables proactive risk management by analyzing textual data and market sentiments with heightened precision. This study underscores the imperative of perpetual innovation and adaptability in risk management practices to adeptly navigate the intricacies of modern financial landscapes. By harnessing emerging technologies, behavioral insights, and interdisciplinary methodologies, organizations can fortify their risk management capabilities, mitigate threats, and seize opportunities in today's globally interconnected economy. Addressing the challenges associated with implementing innovative risk management practices necessitates proactive leadership, resource allocation, and organizational culture transformation. Ultimately, this research contributes to advancing the understanding and application of innovative risk measurement and management approaches, fostering sustainable growth and success for organizations.

Simulation of performance skills teaching system based on machine vision and sensor audio signal processing

In the traditional network media education system, there is little content about skill performance, and the malleability is poor. With the combination of Internet technology, online teaching videos of performance skills began to be promoted on a large scale, and with the help of the Internet, the learning of online performance skills has been updated. The aim of this study is to develop a performance skills teaching system based on machine vision and sensor audio signal processing technology to provide a simulated performance environment and personalized instruction. In this study, multiple sensors were used to capture various movement data during the performance of students. Through the use of sensor technology, the system is able to capture information about students' posture, finger position and force in real time, and analyze posture and movement in combination with machine vision technology. The system can also process and analyze the audio signals in the students' performance to evaluate the accuracy and fluency of the playing technique. The application of sensor technology enables the system to accurately capture students' movements and provide timely feedback. The use of machine vision technology allows the system to accurately analyze the student's posture and movements to provide more targeted guidance. The application of audio signal processing algorithm enables the system to objectively evaluate the students' playing skills and help them improve the accuracy and fluency in the playing process.

A gathering storm: offensive and defensive accelerationism in an online far-right community

ABSTRACT Increasingly fringe and violent far-right ideologies have become a significant threat to national security in Western countries in the twenty-first century. Accelerationism is growing in far-right narratives while populism and ethnocentrism garnered public attention. This study presents an empirical study of Stormfront, the largest and oldest online white nationalist community in the world, to identify accelerationist themes in the ideological narratives used to discuss white nationalist philosophy over time. The study applied a Natural Language Processing (NLP) algorithm and a supervised learning AI model to a corpus of more than 66 m words collected from 367,000 posts across a 20-year period. This analysis identified two kinds of accelerationist sentiment in the dataset, an offensive form, which portrays violence as both an inevitable and desirable mechanism to pursue political change, and a defensive form, which portrays violence as a reactionary and protective means to prevent undesirable social changes. The study then used a thematic analysis on a subset of the data to support its findings that both offensive and defensive accelerationism are embedded in ideological narratives on Stormfront. This is an important consideration for further research on ways that accelerationism may be influencing popular white nationalist ideology, particularly fringe and violent narratives.

Statement of Retraction: Image processing algorithm for mechanical properties testing of high temperature materials based on time‐frequency analysis

Statement of Retraction: Image processing algorithm for mechanical properties testing of high temperature materials based on time‐frequency analysis

Process Planning for Large Container Ship Propeller Shaft Machining Based on an Improved Ant Colony Algorithm

To accommodate the production and manufacture of complex and customized marine components and to avoid the empirical nature of process planning, machining operations can be automatically sequenced and optimized using ant colony algorithms. However, traditional ant colony algorithms exhibit issues in the context of machining process planning. In this study, an improved ant colony algorithm is proposed to address these challenges. The introduction of a tiered distribution of initial pheromones mitigates the blindness of initial searches. By incorporating the number of iterations into the expectation heuristic function and introducing a ‘reward–penalty system’ for pheromones, the contradictions between convergence speed and the tendency to fall into local optima are avoided. Applying the improved ant colony algorithm to the process planning of large container ship propeller shaft machining, this study constructs a ‘distance’ model for each machining unit and develops a process constraint table. The results show significant improvements in initial search capabilities and convergence speed with the improved ant colony algorithm while also resolving the contradiction between convergence speed and optimal solutions. This verifies the feasibility and effectiveness of the improved ant colony algorithm in intelligent process planning for ships.

Association of Lipoprotein (a) and Standard Modifiable Cardiovascular Risk Factors With Incident Myocardial Infarction: The Mass General Brigham Lp(a) Registry.

Lipoprotein (a) [Lp(a)] is a robust predictor of coronary heart disease outcomes, with targeted therapies currently under investigation. We aimed to evaluate the association of high Lp(a) with standard modifiable risk factors (SMuRFs) for incident first acute myocardial infarction (AMI). This retrospective study used the Mass General Brigham Lp(a) Registry, which included patients aged ≥18 years with an Lp(a) measurement between 2000 and 2019. Exclusion criteria were severe kidney dysfunction, malignant neoplasm, and prior known atherosclerotic cardiovascular disease. Diabetes, dyslipidemia, hypertension, and smoking were considered SMuRFs. High Lp(a) was defined as >90th percentile, and low Lp(a) was defined as <50th percentile. The primary outcome was fatal or nonfatal AMI. A combination of natural language processing algorithms, International Classification of Diseases (ICD) codes, and laboratory data was used to identify the outcome and covariates. A total of 6238 patients met the eligibility criteria. The median age was 54 (interquartile range, 43-65) years, and 45% were women. Overall, 23.7% had no SMuRFs, and 17.8% had ≥3 SMuRFs. Over a median follow-up of 8.8 (interquartile range, 4.2-12.8) years, the incidence of AMI increased gradually, with higher number of SMuRFs among patients with high (log-rank P=0.031) and low Lp(a) (log-rank P<0.001). Across all SMuRF subgroups, the incidence of AMI was significantly higher for patients with high Lp(a) versus low Lp(a). The risk of high Lp(a) was similar to having 2 SMuRFs. Following adjustment for confounders and number of SMuRFs, high Lp(a) remained significantly associated with the primary outcome (hazard ratio, 2.9 [95% CI, 2.0-4.3]; P<0.001). Among patients with no prior atherosclerotic cardiovascular disease, high Lp(a) is associated with significantly higher risk for first AMI regardless of the number of SMuRFs.

A fuel consumption-based method for developing local-specific CO2 emission rate database using open-source big data

Emission data collection has always been a significant burden and challenge for Chinese counties to develop a CO2 emission inventory. This paper proposed a fuel consumption-based method to develop a local-specific CO2 emission rate database for Chinese counties using only open-source big data. Localized vehicle fuel consumption data is obtained through natural language processing (NLP) algorithm and large language model (LLM). The emission rates derived by our proposed method are consistent with field test results in literature. Besides, the CO2 emission estimation results using local-specific traffic activity data indicate that our method could effectively improve the accuracy of vehicle emission assessment. Compared with conventional method, the novel approach proposed in this paper can provide a pathway for convenient, universal, and cost-saving assessment for local scale CO2 emission rates. With this method, it is possible to formulate a local-specific CO2 emission database in various Chinese counties using only open-access big data.

Self-calibration method for LaBr3 coupled with SiPM detector based on internal radiation of 138La

Gamma detectors consisting of lanthanum bromide (LaBr3) scintillator and silicon photomultiplier (SiPM) have extensive applications in various scientific fields. Accurate gamma energy measurement depends on accurate calibration of these detectors. While some studies have attempted detector self-calibration based on the natural background of LaBr3, these methods are limited to specific LaBr3 crystals and lack generality. To address this limitation, this paper proposes a self-calibration method that solely utilizes the natural radioactivity of 138La. By solving for the maximum similarity between the test background from measurement and the standard spectrum obtained from the Geant4 simulation, the detector is effectively calibrated. Furthermore, the application of a series of energy spectrum processing algorithms simplifies the solution complexity of the maximum similarity problem. Experimental results from four different crystals at varying temperatures demonstrate calibration errors of less than 0.8% at 662 keV, highlighting the method's broad applicability and accuracy. Besides, the influence of the background measurement time and the natural environmental background on the self-calibration method are discussed in detail, further illustrating the value of the application.

Optimization of the concentration of ozone generated by DBD using PSO algorithm for water treatment process

The water treatment process with ozone is influenced by various operating parameters and environmental factors that can impact its efficiency. In this study, experiments were conducted using a Venturi pumping frame to investigate the effects of three controllable variables: oxygen flow height, applied voltage level, and water flow rate. The tests aimed to develop a mathematical model that accurately represents the relationship between these input variables and the resulting ozone concentration in the treated water. The experimental data was analyzed using the MODDE 5.0 software, a specialized application for statistical modeling and design of experiments. By fitting the data to appropriate model equations, a mathematical model was obtained that quantifies the influence of each variable and their interactions on the ozone concentration response. To optimize the process performance, a particle swarm optimization (PSO) algorithm was employed to extract the best-fit parameters for the mathematical model. PSO is a computational technique inspired by the social behavior of bird flocks or fish schools, utilizing a population of candidate solutions that evolve iteratively to converge on the global optimum solution. In this case, PSO searched for the model parameter values that minimized the error between predicted and experimentally measured ozone concentrations, rapidly converging to an accurate solution. The optimized mathematical model enables predicting the ozone concentration under any combination of oxygen flow height, voltage, and water flow rate within the experimental range. This predictive capability facilitates identifying the optimum operating conditions to maximize ozone concentration, thereby enhancing the efficiency of the water treatment process. The model serves as a valuable tool for process control, monitoring, and optimization, ensuring consistent treatment quality while minimizing resource consumption and operational costs.

Enhancing maintenance efficiency in energy assets through AI: a case study of maintAI

Energy assets demand periodic regular maintenance to ensure safe, reliable and efficient operations. The dynamic nature of operating conditions and evolving best practices necessitate frequent optimisation of maintenance programs. Traditionally, these programs were labour-intensive and costly to deploy, often yielding unclear outcomes due to subjective decision-making. The advent of artificial intelligence (AI) presents an opportunity to challenge traditional approaches. This paper presents a case study where asset knowledge, data, and AI capabilities are leveraged to streamline maintenance optimisation using our ‘maintAI’ approach. The program addresses maintenance strategy, backlog, spares, and predictive maintenance optimisation with a focus on value creation, data-driven decisions, and consistent recommendations. A systematic methodology employs AI to sift through and eliminate non-value-adding tasks, enabling prioritisation of work and enhancing reliability and productivity throughout the production facility lifecycle. AI, including Natural Language Processing and Generative AI algorithms, enhances the speed and accuracy of failure mode classification from operational maintenance data. Reliability modelling techniques provide insights into equipment reliability. Recommendations undergo expert review before integration into a Computerised Maintenance Management System. Implementation of this data-driven approach demonstrates rapid deployment and sustained efficiency, yielding substantial gains in production uptime, cost reduction, and safety. The user-centric design ensures agility and ease of configuration. A recent project, which took only 6 weeks to deliver, led to a ~28% reduction in maintenance backlog, freeing capacity for critical focus areas. The maintAI approach proves a meaningful change for energy producers, offering a new solution in maintenance optimisation for enhanced reliability and productivity.

Vehicle Accident Alerting System

This journal presents a detailed exploration of the design, development, and implementation of a vehicle accident alerting system aimed at significantly enhancing road safety and improving emergency response to accidents. The system employs a multi-faceted approach, integrating advanced sensor technology, real-time data processing algorithms, and efficient communication protocols to detect accidents promptly and notify emergency services and relevant stakeholders. The core components of the system include sensors strategically installed within vehicles to monitor motion, orientation, and environmental conditions. These sensors provide raw data, which is processed in real-time using sophisticated algorithms to detect patterns indicative of potential accidents. The system incorporates confirmation mechanisms to reduce false positives and accurately assess the severity of detected events. Key Words: Accident detection, alert system, GPS, GSM, Accelerometer, Android application.

Peripheral preprocessing in Drosophila facilitates odor classification

The mammalian brain implements sophisticated sensory processing algorithms along multilayered ("deep") neural networks. Strategies that insects use to meet similar computational demands, while relying on smaller nervous systems with shallow architectures, remain elusive. Using Drosophila as a model, we uncover the algorithmic role of odor preprocessing by a shallow network of compartmentalized olfactory receptor neurons. Each compartment operates as a ratiometric unit for specific odor-mixtures. This computation arises from a simple mechanism: electrical coupling between two differently sized neurons. We demonstrate that downstream synaptic connectivity is shaped to optimally leverage amplification of a hedonic value signal in the periphery. Furthermore, peripheral preprocessing is shown to markedly improve novel odor classification in a higher brain center. Together, our work highlights a far-reaching functional role of the sensory periphery for downstream processing. By elucidating the implementation of powerful computations by a shallow network, we provide insights into general principles of efficient sensory processing algorithms.

A micro-credential approach for life-long learning in the urban renewable energy sector

The present paper describes the development of a novel framework for the creation of specialised Micro-Credentials supporting the training and re-training of students and professionals on the integration of renewable energy and sustainable fuels in urban settings. The framework is built upon a set of stackable micro-credentials, co-designed with the stakeholders input and it uses advanced natural language processing (NLP) and machine learning algorithms to assess the impact on the job market at regional level. The use of Micro-Credentials are considered to be the optimal methodology for enhancing the knowledge of specific topics especially for life-long learning for professionals. A co-design approach of such an innovative educational framework with relevant stakeholders fosters a tailored educational path for green upskilling. Finally, the framework is validated processing and analysing a large dataset of job descriptions, extracting the skills required by the market, organising the skills in categories and clusters. All this process lead to the creation of a basket of 74 Micro-Credentials oriented to students and professional with both a technical and non-technical backgrounds.