Digital Algorithm Research Articles

Improved digital holographic reconstruction algorithm by zero-order term elimination based on Riesz-transform

Improved digital holographic reconstruction algorithm by zero-order term elimination based on Riesz-transform

Application of Compensation Algorithms to Control the Speed and Course of a Four-Wheeled Mobile Robot

This article presents a tuned control algorithm for the speed and course of a four-wheeled automobile-type robot as a single nonlinear object, developed by the analytical approach of compensation for the object’s dynamics and additive effects. The method is based on assessment of external effects and as a result new, advanced feedback features may appear in the control system. This approach ensures automatic movement of the object with accuracy up to a given reference filter, which is important for stable and accurate control under various conditions. In the process of the synthesis control algorithm, an inverse mathematical model of the robot was built, and reference filters were developed for a closed-loop control system through external effect channels, providing the possibility of physical implementation of the control algorithm and compensation of external effects through feedback. This combined approach allows us to take into account various effects on the robot and ensure its stable control. The developed algorithm provides control of the robot both when moving forward and backward, which expands the capabilities of maneuvering and planning motion trajectories and is especially important for robots working in confined spaces or requiring precise movement into various directions. The efficiency of the algorithm is demonstrated using a computer simulation of a closed-loop control system under various external effects. It is planned to further develop a digital algorithm for implementation on an onboard microcontroller, in order to use the new algorithm in the overall motion control system of a four-wheeled mobile robot.

Scattering wave packets of hadrons in gauge theories: Preparation on a quantum computer

Quantum simulation holds promise of enabling a complete description of high-energy scattering processes rooted in gauge theories of the Standard Model. A first step in such simulations is preparation of interacting hadronic wave packets. To create the wave packets, one typically resorts to adiabatic evolution to bridge between wave packets in the free theory and those in the interacting theory, rendering the simulation resource intensive. In this work, we construct a wave-packet creation operator directly in the interacting theory to circumvent adiabatic evolution, taking advantage of resource-efficient schemes for ground-state preparation, such as variational quantum eigensolvers. By means of an ansatz for bound mesonic excitations in confining gauge theories, which is subsequently optimized using classical or quantum methods, we show that interacting mesonic wave packets can be created efficiently and accurately using digital quantum algorithms that we develop. Specifically, we obtain high-fidelity mesonic wave packets in the Z2 and U(1) lattice gauge theories coupled to fermionic matter in 1+1 dimensions. Our method is applicable to both perturbative and non-perturbative regimes of couplings. The wave-packet creation circuit for the case of the Z2 lattice gauge theory is built and implemented on the Quantinuum H1-1 trapped-ion quantum computer using 13 qubits and up to 308 entangling gates. The fidelities agree well with classical benchmark calculations after employing a simple symmetry-based noise-mitigation technique. This work serves as a step toward quantum computing scattering processes in quantum chromodynamics.

Minimum-Time Control of a Linear System With Input Saturation: A Practical Approach

Abstract A simple algorithm is presented to find near-minimum-time control inputs for a controllable continuous-time linear time-invariant system with any number of states and inputs. The well-known digital deadbeat control algorithm is modified to satisfy input constraints, and the design issue is the selection of sampling period to minimize the time required to reach the desired final state. During each sampling period, the input required to place all the poles at the origin of z-plane (deadbeat control) via state feedback is computed. If the infinity norm of the deadbeat input exceeds the input constraint, the input is found by placing the poles as close to the origin as possible. A sufficient condition for the system stability is developed. Numerical examples illustrate that this algorithm can lead to true time-optimal control or near time-optimal control.

Digital watermarking algorithm for QR code based on hybrid PSOGWO optimization

Abstract With the rapid development of digital media, QR codes have become widely used as an important medium for information transmission. However, this also presents serious security challenges, such as frequent image infringement and inevitable compression and scaling issues during data transfer. These challenges directly threaten the effectiveness and integrity of image information, and effective protection measures must be taken. Under this background, digital watermarking technology comes into being as an innovative method to enhance security. This paper presents an innovative Hybrid Particle Swarm Optimization and Gray Wolf optimization (HPG) algorithm, which combines the advantages of PSO and overcomes the limitations of traditional GWO algorithm. The algorithm is further improved by using Bernoulli chaotic mapping and nonlinear convergence factor. The experiments focus on the global and local search balance of the algorithm and its ability to resist repeated scaling attacks. The results show that the nonlinear convergence factor of HPG algorithm slows down the iteration speed at the initial stage, strengthens the global search, and avoids falling into the local optimal. In the later stage, the iteration is accelerated to improve the local search speed and convergence efficiency, thus optimizing the performance of the algorithm and ensuring the safety and quality of image information. In addition, the algorithm has better performance in balancing imperceptibility and robustness of digital watermarking, and its peak signal-to-noise ratio (PSNR) is higher. Even with many scaling iterations, the normalized correlation (NC) value is still close to 1. Compared with the existing typical algorithms, HPG algorithm is more robust to repeated scaling attacks.

A CDSS based Integrated Pathway for Influenza Vaccination Across Primary and Secondary Care

Abstract Background In Italy, influenza vaccination coverage in people ≥ 65 years is unsatisfactory (56.7%), underlining the need to improve current vaccination strategies. The objectives of this study have been: to define a novel pathway to increase influenza vaccination adherence among ≥ 65 years population in the Lazio region; to provide a predictive estimate of the epidemiological and economic pathway’s potential impact. Methods A multidisciplinary working group (WG) featuring cross-sectoral expertise was created and WG periodic meetings were held to define the patient journey and its flow-chart representation. The pathway’s potential impact was assessed through epidemiological and economic indicators and scenario analyses. Results An integrated pathway across primary and secondary care was defined, based on the active patient in-Hospital recruitment and vaccination and enhanced by a Clinical Decision Support System relying on a digital algorithm to identify eligible patients. Assuming an increase of influenza vaccination coverage from the current rate of 60% (scenario 1) to 65% (scenario 2) in ≥ 65 years population in the Lazio region thanks to the pathway implementation, an increase of 8% in avoided influenza cases, influenza- or pneumonia-related hospitalizations and influenza-related outpatient visits was estimated with a relative increase in savings for hospitalizations and outpatients visits of up 2,367,310 euros. Setting the vaccination coverage at 70% (scenario 3), an increase of 16% in avoided influenza cases, hospitalizations and outpatient visits was estimated with a relative increase in savings for hospitalizations and outpatients’ visits of up to 4,833,259 euros. Conclusions Alongside offering a predictive estimate of the relevant pathway’s potential impact, both epidemiological and economic, this project, with its robust methodology, may serve as a scalable and transferable model for enhancing vaccination coverage at national and international level. Key messages • The proposed pathway, offering the option of receiving flu vaccination within the Hospital, supports the paradigm shift towards primary prevention pathways in secondary care settings. • Another relevant aspect of the integrated pathway is the adoption of an Artificial Intelligence tool to identify suitable patients and improve their recruitment and adherence to vaccination.

Clinical Utility of a Digital Dermoscopy Image-Based Artificial Intelligence Device in the Diagnosis and Management of Skin Cancer by Dermatologists.

Patients with skin lesions suspicious for skin cancer or atypical melanocytic nevi of uncertain malignant potential often present to dermatologists, who may have variable dermoscopy triage clinical experience. To evaluate the clinical utility of a digital dermoscopy image-based artificial intelligence algorithm (DDI-AI device) on the diagnosis and management of skin cancers by dermatologists. Thirty-six United States board-certified dermatologists evaluated 50 clinical images and 50 digital dermoscopy images of the same skin lesions (25 malignant and 25 benign), first without and then with knowledge of the DDI-AI device output. Participants indicated whether they thought the lesion was likely benign (unremarkable) or malignant (suspicious). The management sensitivity of dermatologists using the DDI-AI device was 91.1%, compared to 84.3% with DDI, and 70.0% with clinical images. The management specificity was 71.0%, compared to 68.4% and 64.9%, respectively. The diagnostic sensitivity of dermatologists using the DDI-AI device was 86.1%, compared to 78.8% with DDI, and 63.4% with clinical images. Diagnostic specificity using the DDI-AI device increased to 80.7%, compared to 75.9% and 73.6%, respectively. The use of the DDI-AI device may quickly, safely, and effectively improve dermoscopy performance, skin cancer diagnosis, and management when used by dermatologists, independent of training and experience.

Digital Twin-assisted anomaly detection for industrial scenarios

Industry 5.0 is the current industrial paradigm that inherits the technological diversity of its predecessor, Industry 4.0, but includes three priority goals: (i) resilience, (ii) sustainability and (iii) human-centeredness. Through these three goals, Industry 5.0 pursues a more far-reaching digital transformation in industrial ecosystems with high protection guarantees. However, the deployment of innovative information technologies for this new digital transformation also requires considering their implicit vulnerabilities and threats in order to avoid any negative impacts on the three Industry 5.0 goals, and to prioritize cybersecurity aspects so as to ensure acceptable protection levels. This paper, therefore, proposes a detection framework composed of a Digital Twin (DT) and machine learning algorithms for online protection, supporting the resilience that Industry 5.0 seeks. To validate the approach, this work includes several practical studies on a real industrial control testbed to demonstrate the feasibility and accuracy of the framework, taking into account a set of malicious perturbations in several critical sections of the system. The results highlight the effectiveness of the DT in complementing the anomaly detection processes, especially for advanced and stealthy threats.

Missed encounters: what may be relevant for an AI is not for a human being

Abstract The World Wide Web has been a fundamental part of our daily lives for years. Its algorithmic framework ensnares our online journeys in an “endless recurrence” of the “same” by creating multiple filter bubbles. Digital algorithms establish a precise “order of discourse,” leaving little to no room for deviation. Functioning as a colossal machinic apparatus, the web embodies the culmination of Artificial Intelligence (AI), transforming every piece of posted content into a database that profiles our online behavior and activities. This article explores whether approaches that describe everyday human communication, such as the theories of relevance developed by Alfred Schutz, or by Dan Sperber and Deirdre Wilson, can be applied to the realm of digital algorithmic grammars governing the web, and to the semiospheres that animate it. The conclusions raise some doubts: a gap exists between algorithmic language and its historical counterparts, characterized by a disparity between logical-mathematical grammar and other linguistic-historical-natural ones. While these two types of languages coexist within the digital landscape, their relevance differs; what may be “relevant” in an algebraic context may not necessarily translate to our live conversational exchanges, and vice versa. Although merged in the digital sphere, these languages operate distinctly, and proficiency in one does not guarantee adeptness in interpreting the other accurately.

Digital background calibration algorithm for pipelined ADC based on time-delay neural network with genetic algorithm feature selection

This paper presents a novel background calibration method for pipelined analog-to-digital converters (ADCs) using a time-delay neural network (TDNN), which is optimized through genetic algorithm (GA) techniques. The proposed technique leverages TDNN to create enhanced feature sets, significantly improving the calibration of nonlinear errors exhibiting memory effects. It harnesses the GA's global optimization capabilities for feature selection, effectively reducing the feature dimension and consequently alleviating the NN's computational burden. A parallel pipeline architecture is devised for the calibration circuit, with its implementation realized on FPGA to facilitate forward inference processing. The inference circuit is synthesized using TSMC's 90 nm CMOS process, achieving a power consumption of 40.11 mW and an area of 0.45 mm2. Simulations based on MATLAB for a 14-bit Pipelined ADC demonstrate that the proposed calibration method significantly improves the SFDR from 59.77 dB to 165.52 dB, and ENOB from 8.79 bits to 19.23 bits, surpassing the target ADC's specifications. Moreover, the dimensionality of features is effectively reduced by up to 34 % without compromising the calibration performance.

Real-Time Embedded Control of Vehicle Dynamics Using ESP32: A Discrete Nonlinear Approach

This article explores the application of the Espressif ESP32 System-On-Chip (SoC) for managing vehicle dynamics through real-time digital proportional–integral (PI-like) control. We present the development of advanced driving assistance algorithms for Active Front Steering (AFS) and Rear Torque Vectoring (RTV) on this cost-effective, commercially available embedded system. Using digital PI-like control algorithms designed for AFS and RTV, the primary ESP32 board receives and processes steering signals, executing a discrete-time control model of the vehicle dynamic to enable dynamic adjustments to steering and torque. To enhance simulation realism, a secondary ESP32 is employed to generate the steering signal, effectively mimicking a steer-by-wire system via its analog output ports. This configuration facilitates the simulation and evaluation of control algorithms in a realistic test environment, ensuring enhanced vehicle dynamic stability and maneuverability under various conditions. Additionally, simulations are conducted using MATLAB 2023a and CarSim 2017.1 to compare the efficacy and benefits of the implementation. Our objective is to establish a platform for evaluating discrete controllers capable of real-time vehicle operation. This methodology accelerates and reduces the cost of improving vehicle system stability and responsiveness, enabling the immediate verification and fine-tuning of control parameters as needed.

Pathology Report Interpretation and Disease Diagnosis Using Fuzzy Logic Embedded in an Artificial Intelligence Framework: A New Paradigm for Digital Technologies.

Introduction Accurate diagnosis is essential for effective treatment in healthcare settings. Digital technologies have revolutionized medical diagnostics, particularly in pathology laboratory report analyses, by enhancing speed, quality, and precision. Diagnostic pathology has advanced significantly with digital imaging, AI algorithms, and computer-assisted techniques. Traditional machine learning methods require extensive training data, struggle with decision-making flexibility, and lack interpretability when handling new data. Methods This paper introduces a novel approach to address these challenges: a fuzzy logic-based system for interpreting pathology laboratory reports and diagnosing diseases, while considering the restrictions of engineered interpretations so that the degree of reliability of the output of the system is at par with human perception. Results The analysis highlights the impressive accuracy of the fuzzy logic-based diagnosis system, which is closely aligned with professional diagnoses. Notably, it correctly identified "normal" with an 83% probability, showcasing its potential for early disease detection. Performance evaluations indicated that the precision, recall, and F-measure significantly improved as the number of probable diagnoses considered increased. Furthermore, the system exhibited enhanced predictive accuracy for disease recurrence when incorporating multiple probable diagnoses, underscoring its robust clinical effectiveness. Conclusion A fuzzy logic-based system within an AI framework for interpreting pathology laboratory reports and diagnosing 17 diseases across various demographics is presented. The performance of the algorithm, assessed using specialized classification metrics, showed stable and satisfactory results. The analysis revealed a direct correlation between the number of probable diagnoses considered and improved diagnostic accuracy with enhancements in precision, recall, and F-measure. Broadening the diagnostic scope optimizes immediate patient care and long-term treatment planning, potentially enhancing patient outcomes and healthcare quality.

Concurrent Image Differentiation and Integration Processings Enabled By Polarization‐Multiplexed Metasurface

AbstractOptical computing and image processing performed by sensor front‐end metasurfaces is receiving increasing interest because of advantages such as significant reduction of latency time, energy consumption, and system complexity. Despite the rapid progress, concurrent processing, the most important feature of electronic computing, has not yet been well implemented in optical computing. Here, a metasurface‐based optical image processor that can perform optical differentiation and integration tasks simultaneously is proposed. This optical front‐end processor integrates two coherent transfer functions corresponding to differential and integral convolution kernels into a built‐in metasurface by polarization encoding, allowing concurrent processing of multiple all‐optical computational tasks. The simultaneous differentiation and integration operations on images for edge enhancement and denoising are demonstrated at multiple visible wavelengths. This concurrent processing architecture paves a promising pathway toward multifunctional and higher‐speed image processing for machine vision and biomedical imaging and shows the potential to expedite and potentially supplant certain digital neural network algorithms.

K-means 알고리즘을 기반으로 한 야오족 자수 색상 추출 및 가상 캐릭터 의상디자인 응용

To promote the digital preservation and transmission of traditional cultural elements, this study focuses on Yao embroidery as a case study, combining digital technologies and algorithms to extract and analyze its color characteristics. These extracted color features were applied to the design of virtual character clothing. First, a bilateral filtering algorithm was used to preprocess the images of Yao embroidery, reducing noise while preserving intricate details. Subsequently, Euclidean distance was calculated in the Lab color space, and the OSTU adaptive binarization method was employed to separate the image from its background. Additionally, K-means clustering was applied multiple times to the color data, and the optimal result was achieved when the number of clusters was set to eight. Through this process, eight major colors of Yao embroidery were extracted. Based on these extracted colors, 3D point cloud analysis was conducted to study the three-dimensional spatial distribution of color points, and a color combination model based on hue, brightness, and saturation was constructed. The results revealed that the color composition of Yao embroidery predominantly consists of high-saturation colors, with strong color contrasts and distinct ethnic characteristics. Ultimately, this study demonstrated the applicability of integrating the color model and design patterns of Yao embroidery into virtual character clothing design, showcasing the potential for incorporating traditional embroidery into digital design. This contributes to the preservation and promotion of intangible cultural heritage and serves as a valuable reference for further research on the color systems of traditional ethnic costumes.

Artificial intelligence as a mode of ordering. Automated-decision making in primary care

ABSTRACT This paper examines the process of organizing primary care from the increasing production of digital data and algorithms, leading to decision-making being delegated to automated processes. Based on a case study of primary care in Catalonia (Spain), we develop an interpretative framework to analyze the effects and controversies of the introduction of AI in primary care, stressing the need to understand these technologies as historically situated trends, as part of a growing automation process of ordering frontline clinical care. To foster a nuanced discussion on the mode of ordering of automation, we replace the notion of AI with Automated Decision-Making (ADM) and use the conceptual distinction between striated and smooth spaces, as developed by Deleuze & Guattari. With the analysis we identify four dynamics that order and organize health, illness, and public health systems: (1) The capacity of Electronic Health Records and associated software to shape the daily routines of primary care; (2) The dual role of healthcare professionals, who serve as both data users and data collectors; (3) The system of values and priorities configured by the network of classification systems in use and; (4) The emerging tensions during the progressive automation processes. These dynamics configure a mode of ordering characterized by diminishing the importance of experiential knowledge, the reduction of patient negotiation capacity, the professionals’ discomfort with being constantly monitored and controlled, as well as the gradual neglect of factors related to socio-economic determinants of health.

An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories.

Background: Microscopic examination of peripheral blood is a standard practice in clinical medicine. Although manual examination is considered the gold standard, it presents several disadvantages, such as interobserver variability, being quite time-consuming, and requiring well-trained professionals. New automatic digital algorithms have been developed to eliminate the disadvantages of manual examination and improve the workload of clinical laboratories. Objectives: Regular analysis of peripheral blood cells and careful interpretation of their results are critical for protecting individual health and early diagnosis of diseases. Because many diseases can occur due to this, this study aims to detect white blood cells automatically. Methods: A hybrid model has been developed for this purpose. In the developed model, feature extraction has been performed with MobileNetV2 and EfficientNetb0 architectures. In the next step, the neighborhood component analysis (NCA) method eliminated unnecessary features in the feature maps so that the model could work faster. Then, different features of the same image were combined, and the extracted features were combined to increase the model's performance. Results: The optimized feature map was classified into different classifiers in the last step. The proposed model obtained a competitive accuracy value of 95.6%. Conclusions: The results obtained in the proposed model show that the proposed model can be used in the detection of white blood cells.

Development of Digital Image Processing Algorithms via FPGA Implementation

Real-time image processing is one of the fundamental elements in achieving IR 4.0. The rapid development of digital image processing techniques has enabled various applications in fields such as healthcare, transportation, and manufacturing. People are seeking higher-performance image processing as traditional image processing is no longer fulfilling the demands. FPGA-based digital image processing has become one of the choices for the public due to its parallel pipelining, which enables shorter processing time and better performance. Several digital image processing algorithms have been developed in this project, which are gray level transformation, brightness manipulation, contrast adjustment, thresholding, and inversion. They are the most popular algorithms used in digital image processing. Microsoft Paint is used to convert the format of the color input image to bitmap format, followed by MATLAB to convert it into a hexadecimal file to be read and written in FPGA. Platforms such as ModelSim Altera and Intel Quartus II are used to write Verilog HDL for digital image processing algorithms. As a result, five hexadecimal files are obtained from the simulation. The output hexadecimal files are further processed in MATLAB to generate respective images.

Prevention and Detection of Bid Rigging in Public Procurement in India: Role of Artificial Intelligence

India’s National Crime Record Bureau’s report, ‘Crime in India’, stipulates that the conviction rate of economic crimes is only 29.4% which is way lesser than that of criminal conviction rate of 57%. Punitive measures under the Competition Act, 2002 are relatively less explored in the Indian context. This article is an attempt to examine the punitive measures in the Act, the evidence used for conviction and to see how the same deter the violators. An attempt is made to find out how the apex court in Rajasthan Cylinders case ( Rajasthan Cylinders and Containers Ltd v. UOI and Anr., 2018 SCC Online SC 1718.) might have diluted the already weak conviction rate of India. Artificial intelligence is used in many jurisdictions for cartel identification. The evidentiary standards in Indian jurisdiction in regard to cartel identification are almost equivalent to ‘beyond reasonable doubt’ instead of ‘preponderance of probability’. Need arises that the evidentiary jurisprudence of ‘reverse onus or reverse burden’, whereby shifting the burden of proof on the accused, should be used liberally in instances of anticompetitive practices. Did the apex court fail? If digital algorithms are there to guide and highlight the red flags in clandestine cartelization, how will the jurisdictions use them to counter the anti-competitive practices?

Research on improved YOLOV7-SSWD digital meter reading recognition algorithms.

Meter reading recognition is an important link for robots to complete inspection tasks. To solve the problems of low detection accuracy and inaccurate localization of current meter reading recognition algorithms, the YOLOV7-SSWD (YOLOV7-SiLU-SimAM-Wise-IoU-DyHeads) model is proposed, a novel detection model based on the multi-head attention mechanism, which is improved on the YOLOV7-Tiny model. First, the Wise-IoU loss function is used to solve the problem of sample quality imbalance and improve the model's detection accuracy. Second, a new convolutional block is constructed using the SiLU activation function and applied to the YOLOV7-Tiny model to enhance the model's generalization ability. The dynamic detection header is then built as the header of YOLOV7-Tiny, which realizes the fusion of multi-scale feature information and improves the target recognition performance. Finally, we introduce SimAM to improve the feature extraction capability of the network. In this paper, the importance of each component is fully verified by ablation experiments and comparative analysis. The experiments showed that the mAP and F1-scores of the YOLOV7-SSWD model reached 89.8% and 0.84. Compared with the original network, the mAP increased by 8.1% and the F1-scores increased by 0.1. The YOLOV7-SSWD algorithm has better localization and recognition accuracy and provides a reference for deploying inspection robots to perform automatic inspections.

Improved Foucault method with double-shoot shadow grams in optical shop testing.

The lack of rapid, effective, and quantitative testing methods in the initial stages of optical processing has been identified as a significant challenge. In response, an innovative approach has been developed to enhance the Foucault knife-edge test method, integrating digital image processing and spatial geometry algorithms. This has resulted in the creation of a convenient and rapid optical testing method, which has been successfully implemented in practice. The method requires the collection of only two knife-edge shadow grams, which are then subjected to calculation. This enables the theoretical results of the knife-edge shadow gram at the focal point, and the reference results of the wavefront surface processing error of the mirror to be measured can be obtained. The method can be well applied to the testing work of optical workshops and provides a guiding solution with better performance for optical testing than the traditional knife-edge testing method.