Complexity Of Algorithm Research Articles

Robot inverse kinematics solution for center selection battle royale optimization algorithm

In order to solve the problems of high time complexity and insufficient global exploration ability of the battle royale optimization algorithm, this paper proposes an improved battle royale optimization algorithm based on chaos mapping, center selection and elite adaptive strategy. Through benchmark function testing, compared to particle swarm optimization algorithm, whale optimization algorithm, and the battle royale optimization algorithm, the improved algorithm significantly reduces time complexity and notably enhances convergence precision, speed, and stability. The improved algorithm is applied to the inverse kinematics problem of robots. The accuracy and The stability of the improved algorithm is better than those of the traditional battle royale optimization algorithm, it demonstrates superior solving precision and stability, proving its practicality and potential for development in solving robot inverse kinematics problems.

Security primitives for memoryless IoT devices based on Physical Unclonable Functions and True Random Number Generators

The article describes various security primitives for significantly resource-constrained devices, such as sensors or sensor networks, IoT devices, wearables, etc. — i.e., devices without programmable memory. It is dedicated to parts which cannot handle complex algorithms of modern secure cryptography, cannot be equipped with programmable memories, or their circuits or data in permanent memories can be easily reverse-engineered. Instead, all security techniques (e.g., identification, authentication, and encryption) are based on modern hardware cryptography, mainly: physical unclonable functions (PUFs) and true random number generators (TRNGs). The paper addresses numerous issues from untraceable identification to mutual authentication to one-time pad encryption. The communication security is considered to be a trade-off between the device’s resources (processing ability, energy consumption, implementation size, response time), preparation complicity (initialization time, size of a server data storage) and the security capabilities and protection levels. Primitives can be included into the communication protocol based on particular needs and available hardware resources.

New Lightweight Cryptosystem for IoT in Smart City Environments

Internet of Things (IoT) devices, user interfaces (UI), software, as well as communication networks are all deployed within Smart Cities topology. The security approach designed for Internet of Things IoT should be able to prevent and detect both internal and external attacks. The problem in IoT network that not every linked node or device has an adequate amount of processing power. This means that data encryption and other related activities will be impossible and means that the security of any kind must be lightweight. A trustworthy security solution that stops illegal access to private data on the network is necessary for maintaining the privacy of information on the Internet of Things. Cryptographic processes need to be quicker and more compact without sacrificing security. The aim of this study is to reduce the execution time and power consumption of encryption processes without compromise the complexity of the encryption algorithm. This research presents a new lightweight cryptographic technique to protect various multimedia and real-time traffics across IoT network, by using two S-box in SubByte of encryption process, without affecting its performance. In this study, different audio samples will be used to test the new algorithm efficiency. Comparing the suggested method to the most advanced standard algorithm, it can reduce the cryptography process's execution time as well as energy consumption while maintaining the required security level. The outcomes demonstrate good performance in terms of power usage and delay. The new technique consumed a roughly 0.2 µJ for encryption process while the typical AES algorithm consumed 0.29 µJ, this mean the new algorithm achieved (33% power savings), while maintaining a good complexity level (security) within the process of encryption according to the results in tables I, II, and the comparison in table III. The novelty of this work can be showed by using dual XOR S-box technique which increased the complexity of SubByte process making it more secure without overload the processing performance, in addition to the reduction in encryption rounds which contribute to enhance the performance without compromise the security. Making it more suited for the Internet of Things (IoT) used in smart city environments.

Real-time reactive task allocation and planning of large heterogeneous multi-robot systems with temporal logic specifications

Existing methods for the task allocation and planning (TAP) of multi-robot systems with temporal logic specifications mainly rely on optimization-based approaches or graph search techniques applied to the product automaton. However, these methods suffer from high computational cost and scale poorly with the number of robots and the complexity of temporal logic tasks, thus limiting the applicability in real-time implementation, especially for large multi-robot systems. To address these challenges, this work develops a novel TAP framework that can solve reactive temporal logic planning problems for large-scale heterogeneous multi-robot systems (HMRS) in real time. Specifically, we develop a planning decision tree (PDT) to represent the task progression and task allocation specialized for HMRS with temporal logic specifications. Based on the PDT, we develop two key search algorithms—the planning decision tree search (PDTS) and the interactive planning decision tree search (IPDTS)—where PDTS generates an offline plan which will be modified online by PDTS and IPDTS jointly to enable fast reactive planning if environmental changes or temporary tasks occur. Such a design can generate satisfying plan for HMRS with multiple orders of magnitude more robots than those that existing methods can manipulate. Rigorous analysis shows that the PDT-based planning is feasible (i.e., the generated plan is applicable) and complete (i.e., a feasible plan, if exits, is guaranteed to be found). The algorithm complexity further indicates that the solution time is only linearly proportional to the robot numbers and types. Simulation and experiment results demonstrate that reactive plan can be generated for large HMRS in real-time, which outperforms the state-of-the-art methods.

Tuning similarity-based fuzzy logic programs

We have recently designed a symbolic extension of FASILL (acronym of “Fuzzy Aggregators and Similarity Into a Logic Language”), where some truth degrees, similarity annotations and fuzzy connectives can be left unknown, so that the user can easily see the impact of their possible values at execution time. By extending our previous results in the development of tuning techniques not dealing yet with similarity relations, in this work we automatically tune FASILL programs by appropriately substituting the symbolic constants appearing on their rules and similarity relations with the concrete values that best satisfy the user's preferences. Firstly, we have formally proved two theoretical results with different levels of generality/practicability for tuning programs in a safe and effective way. Regarding efficiency, we have drastically reduced the exponential complexity of the tuning algorithms by splitting the initial set of symbolic constants in disjoint sets and using thresholding techniques. These effects have been evidenced by several experiments and benchmarks developed with the online tool we provide to verify in practice the high performance of the improved system.

Quantum positive matrix-positive matrix multiplication algorithm

Abstract In quantum computing,
matrix multiplication occurs as a subroutine in more complex algorithms. 
We improve the quantum linear system algorithm 
[Harrow et al. 2009 Phys. Rev. Lett. 103 150502] 
to perform sparse matrix-matrix multiplication and propose a subroutine for parallel vector addition. 
Based on them, a quantum algorithm is exhibited for N×N positive matrix-positive matrix multiplication.
For some small ratio of the maximum element to the minimum element of the positive matrices (i.e.,O(poly logN)), 
this algorithm achieves exponential acceleration.

Iteration Complexity of Variational Quantum Algorithms

Iteration Complexity of Variational Quantum Algorithms

Development of an Optimization Software for Bioremediation of Hydrocarbon-Contaminated Soils mechanisms

A sophisticated software solution designed to enhance bioremediation processes in hydrocarbon-contaminated environments. This Advanced Bioremediation Optimization Software combines complex algorithms, real-time sensor data integration, and a user-friendly interface to deliver customized solutions for environmental restoration projects. The software utilizes predictive modeling to forecast remediation outcomes, optimizes treatment strategies based on ongoing data analysis, evaluates microbial communities through metagenomic sequencing data, and generates evidence-based recommendations to improve bioremediation efficiency. This tool represents a significant advancement in environmental restoration technology, offering practitioners a means to enhance the efficacy and cost-effectiveness of bioremediation projects. It also provides detailed economic projections to support informed decision-making by stakeholders, making it a valuable asset in the field of environmental remediation.

Dynamic frequent subgraph mining algorithms over evolving graphs: a survey

Frequent subgraph mining (FSM) is an essential and challenging graph mining task used in several applications of the modern data science. Some of the FSM algorithms have the objective of finding all frequent subgraphs whereas some of the algorithms focus on discovering frequent subgraphs approximately. On the other hand, modern applications employ evolving graphs where the increments are small graphs or stream of nodes and edges. In such cases, FSM task becomes more challenging due to growing data size and complexity of the base algorithms. Recently we see frequent subgraph mining algorithms designed for dynamic graph data. However, there is no comparative review of the dynamic subgraph mining algorithms focusing on the discovery of frequent subgraphs over evolving graph data. This article focuses on the characteristics of dynamic frequent subgraph mining algorithms over evolving graphs. We first introduce and compare dynamic frequent subgraph mining algorithms; trying to highlight their attributes as increment type, graph type, graph representation, internal data structure, algorithmic approach, programming approach, base algorithm and output type. Secondly, we introduce and compare the approximate frequent subgraph mining algorithms for dynamic graphs with additional attributes as their sampling strategy, data in the sample, statistical guarantees on the sample and their main objective. Finally, we highlight research opportunities in this specific domain from our perspective. Overall, we aim to introduce the research area of frequent subgraph mining over evolving graphs with the hope that this can serve as a reference and inspiration for the researchers of the field.

SkipNet: an adaptive neural network equalization algorithm for future passive optical networking

In this paper, we propose an original adaptive neural network equalizer (NNE) algorithm named SkipNet, which is suitable for rapid training on a packet-by-packet basis for burst-mode non-linear equalization in upstream PON transmission. SkipNet uses the simple LMS algorithm and avoids complex neural network training algorithms such as backpropagation and mini-batch training. We demonstrate SkipNet on captured continuous mode 100 Gbit/s PAM4 signals using an SOA preamplifier to achieve the challenging 29 dB PON optical loss budget. The adaptive SkipNet equalizer is shown to overcome combinations of severe SOA patterning effects and fiber dispersion impairments to achieve >29dB dynamic range back-to-back and >22.9dB dynamic range for up to 81.6 ps/nm accumulated dispersion. It can adapt in as little as 250 training symbols to each impairment scenario, which is equivalent to existing FFE/DFE solutions, while matching the non-linear performance of previously proposed static NNE solutions. To the best of our knowledge, SkipNet is the first ever adaptive NNE framework that can realistically be trained and adapted on a packet-by-packet basis and within strict PON packet preamble lengths.

Synchronization of M-sequences based on fast Нadamard transform

Options have been developed for constructing circulant matrices of any M-sequence (MS) based on automorphic multiplicative groups of the extended Galois field, constructed using an irreducible primitive polynomial, on the basis of which the original MS is formed. The result of this approach is the identification of new methods for transforming MS circulant matrices to a matrix of Walsh functions, ordered by the powers of the antiderivative element of the field. It is shown for the first time that, depending on the initial conditions of the transformation, a set of any number of any cyclic shifts of the MP, shifted relative to each other by one symbol, can be transformed to any rows of the ordered matrix of Walsh functions, following one another. This circumstance makes it possible to simplify the MS synchronization algorithm for a known range of its cyclic shifts, especially in the case of large periods of its repetition, and also to reduce the computational complexity of the processing algorithm when working in a truncated basis of Walsh–Hadamard functions.

Comparing Machine Learning Techniques for Detecting Chronic Kidney Disease in Early Stage

CKD is a gradual disease that affects millions of people throughout the United States and results in high morbidity and mortality rates. Chronic Kidney Disease is an ailment that culminates in a gradual loss of kidney function over t,ime. Early detection is essential since timely interventions may prevent the progression of CKD, improve outcomes and survival for patients with CKD, and reduce healthcare costs. In the recent decade, machine learning models have emerged as a game-changing tool in medical diagnostics, leveraging big data and complex algorithms to find patterns almost invisible to clinicians and physicians. This study deployed and evaluated various machine learning approaches for the early detection of CKD, focusing on their comparative performance, strengths, and weaknesses. Machine learning transforms medical diagnosis by leveraging big data and sophisticated algorithms to find patterns that might otherwise elude healthcare professionals. The dataset used for this research will be the CKD dataset, which was contributed to by the Cleveland Clinic in 2021. The dataset can be accessed publicly through the University of California, Irvine's UCI Machine Learning Repository. In this project, the analyst compared and contrasted the performance of Logistic Regression, Decision Trees, and Random Forests. Experimentation results demonstrated that logistic regression had the best performance, yielding a perfect F1 score and accuracy, closely followed by random forest. This result showed that the Logistic model ideally classified all the instances in the test set. Consolidating machine learning algorithms into the early detection of Chronic Kidney Disease (CKD) holds substantial promise for transforming clinical practice. Healthcare professionals can enhance diagnostic accuracy and facilitate timely interventions by leveraging proposed algorithms such as logistic regression.

When two chains meet: Optimizing the design of blockchain-enabled supply chain networks

Applications of the blockchain technology are rapidly emerging across sectors. By redefining information flow, blockchain technology promises to instill trust among supply chain members, improve transaction efficiency, thus reshaping the structure and configuration of supply chains. The practical exploratory use of blockchain has shown immense potential in supply chain management. In order to explore the new changes brought by blockchain technology to supply chain network design issues, this study focuses on the supply chain network design problem in the context of blockchain technology. Specifically, we consider a supply chain network comprised of a number of distribution centers (DCs) and retailers. The operation of this physical network is supported by blockchain-based information infrastructure. As the interrelated relationship between these two networks, it is necessary to optimize them simultaneously to improve the profitability and information exchange level. We propose an objective function based on previous studies to maximize the profits of the entire supply chain network, with decision variables including DC locations, retailer assignment, inventory replenishment policy, and blockchain adoption level. At the same time, to characterize the interrelation between the two networks, the demand and certain cost parameters of the model become endogenous depending on the blockchain adoption level, which significantly increases the complexity of the solution algorithm. We reformulate the model and use the polymatroid cutting plane approach to address this issue. We conducted calculations and numerical analysis. The computational results show that the proposed method can solve practically sized problems, and integrating blockchain design reduces the system-wide cost. We discuss managerial insights based on a range of numerical studies and solve a case study using publicly available instance data.

Investigating the Impact of Quantum Computing on Algorithmic Complexity

This paper investigated the transformation that quantum computing brought into algorithmic complexity in the theoretical setting of computer science. This involved the appearance of new paradigm changes brought forth by quantum technologies, imposing on a glance at the performance of quantum algorithms in respect to classical models of computation regarding their effectiveness. Our contributions encompassed key quantum algorithms, specifically Shor's and Grover's algorithms, underlining the performance metrics of those algorithms. The mathematical modeling was supported by simulations using python libraries like Qiskit, which helped analyze the complexities of both algorithm types. From our simulations, it emerged that for smaller sizes of input, classical algorithms executed faster and illustrated their established efficiency. In contrast, quantum computing-algorithms like Grover's and Shor's - performed so much better for larger input sizes, showing potential advantages that may change the face of computational limitations. While promising much, it seemed from our findings that quantum computing would not show a quantum advantage for all computational tasks, because classical algorithms still remained robust and effective for many scenarios. This nuanced understanding underlines how complementary both the classical and quantum approaches are. Therefore, the insights gained through this work provide the basis for investigating where boundaries of computational capabilities evolve and what practical implications are of the integration of quantum technologies into existing systems.

Phase Error Correction in Sparse Linear MIMO Radar Based on the Equivalent Phase Center Principle

Multiple-input multiple-output (MIMO) technology is widely used in the field of radar imaging. Array sparse optimization reduces the hardware cost of MIMO radar, while virtual aperture and the equivalent phase center (EPC) principle simplify the radar signal model and reduce the computation and complexity of imaging algorithms. However, the application of sparse array structure and the EPC principle produces a non-negligible phase error, which affects the imaging quality. This paper simplifies the MIMO radar signal model based on the phase center approximation, analyzes the phase error generated by this method, and proposes an improved phase error correction method to solve the problem that the target cannot be well-focused at non-reference distance during imaging. In addition, this paper designs a sparse linear MIMO array with a periodic structure, which reduces the number of transmitting and receiving units, system complexity, and hardware costs. The proposed phase correction method was combined with the wavenumber domain algorithm to simulate and experiment on the designed antenna array, and good experimental results were obtained to verify the effectiveness of the proposed method.

Integrating Building Information Modelling and Artificial Intelligence in Construction Projects: A Review of Challenges and Mitigation Strategies

Artificial intelligence (AI), including machine learning and decision support systems, can deploy complex algorithms to learn sufficiently from the large corpus of building information modelling (BIM) data. An integrated BIM-AI system can leverage the insights to make smart and informed decisions. Hence, the integration of BIM-AI offers vast opportunities to extend the possibilities of innovations in the design and construction of projects. However, this synergy suffers unprecedented challenges. This study conducted a systematic literature review of the challenges and constraints to BIM-AI integration in the construction industry and categorise them into different taxonomies. It used 64 articles, retrieved from the Scopus database using the PRISMA protocol, that were published between 2015 and July 2024. The findings revealed thirty-nine (39) challenges clustered into six taxonomies: technical, knowledge, data, organisational, managerial, and financial. The mean index score analysis revealed financial (µ = 30.50) challenges are the most significant, followed by organisational (µ = 23.86), and technical (µ = 22.29) challenges. Using Pareto analysis, the study highlighted the twenty (20) most important BIM-AI integration challenges. The study further developed strategic mitigation maps containing strategies and targeted interventions to address the identified challenges to the BIM-AI integration. The findings provide insights into the competing issues stifling BIM-AI integration in construction and provide targeted interventions to improve synergy.

Current application of ChatGPT in undergraduate nuclear medicine education: Taking Chongqing Medical University as an example

Background Nuclear Medicine(NM), as an inherently interdisciplinary field, integrates diverse scientific principles and advanced imaging techniques. The advent of ChatGPT, a large language model, opens new avenues for medical educational innovation. With its advanced natural language processing abilities and complex algorithms, ChatGPT holds the potential to substantially enrich medical education, particularly in NM. Objective To investigate the current application of ChatGPT in undergraduate Nuclear Medicine Education(NME). Methods Employing a mixed-methods sequential explanatory design, the research investigates the current status of NME, the use of ChatGPT and the attitude towards ChatGPT among teachers and students in the Second Clinical College of Chongqing Medical University. Results The investigation yields several salient findings: (1) Students and educators in NM face numerous challenges in the learning process; (2) ChatGPT is found to possess significant applicability and potential benefits in NME; (3) There is a pronounced inclination among respondents to adopt ChatGPT, with a keen interest in its diverse applications within the educational sphere. Conclusion ChatGPT has been utilized to address the difficulties faced by undergraduates at Chongqing Medical University in NME, and has been applied in various aspects to assist learning. The findings of this survey may offer some insights into how ChatGPT can be integrated into practical medical education.

Heat Stress Metrics, Trends, and Extremes in the Southeastern United States

Abstract Humid heat and associated heat stress have increased in frequency, intensity, and duration across the globe, particularly at lower latitudes. One of the more robust metrics for heat stress impacts on the human body is wet-bulb globe temperature (WBGT), because it incorporates temperature, humidity, wind speed, and solar radiation. WBGT can typically only be measured using nonstandard instrumentation (e.g., black globe thermometers). However, estimation formulas have been developed to calculate WBGT using standard surface meteorological variables. This study evaluates several WBGT estimation formulas for the southeastern United States using North Carolina Environment and Climate Observing Network (ECONet) and U.S. Military measurement campaign data as verification. The estimation algorithm with the smallest mean absolute error was subsequently chosen to evaluate summer WBGT trends and extremes at 39 ASOS stations with long continuous (1950–2023) data records. Trend results showed that summer WBGT has increased throughout much of the southeastern United States, with larger increases at night than during the day. Although there were some surprisingly large WBGT trends at higher elevation locations far from coastlines, the greatest increases were predominantly located in the Florida Peninsula and Louisiana. Increases in the intensity and frequency of extreme (90th percentile) WBGTs were particularly stark in large coastal urban centers (e.g., New Orleans, Tampa, and Miami). Some locations like New Orleans and Tampa have experienced more than two additional extreme heat stress days and nights per decade since 1950, with an exponential escalation in the number of extreme summer nights during the most recent decade. Significance Statement Humid heat and associated heat stress pose threats to health in the moist subtropical climate of the southeastern United States. Wet-bulb globe temperature (WBGT) is a robust metric for heat stress but must be estimated using complex algorithms. We first evaluated the accuracy of three WBGT algorithms in the southeastern United States, using measured verification data. Subsequently, we used the most accurate algorithm to investigate WBGT trends and extremes since 1950 in 39 cities. Results showed that summer heat stress has increased throughout the region, especially at night. Increases in the intensity and frequency of extreme heat stress were most prevalent at urban coastal locations in Florida and Louisiana, emphasizing the impacts of increased urbanization and evaporation on heat stress.

Two-phase matheuristic for assignment and truck loading problems

We introduce a novel two-phase matheuristic for assignment and truck loading. The scope of our approach involves scenarios with an extensive amount of items requiring assignment to a heterogeneous fleet of trucks and subsequent transportation. A distinguishing feature of our matheuristic lies in the fact that the complex underlying problem is divided into subproblems which later are merged into a global solution. The proposed matheuristic tackles the assignment problem in its first phase, followed by a complex truck loading algorithm to validate the assignment solution in the second phase. This enables the identification of optimal solutions and adaptability to diverse use cases. This approach is motivated by the ROADEF/EURO challenge 2022 and is awarded with the scientific prize of the challenge. We demonstrate superior performance on selected instances, showcasing the potential for significant cost reduction in Renault’s supply chains.

A Big Data Intelligent Evaluation System for Sports Knowledge

The rapid evolution of computer technology has significantly impacted the field of medicine, particularly in the utilization of information and image evidence. In the realm of sports medicine, this technological advancement plays a crucial role in ensuring sports safety, especially in the context of injury recovery following sports-related activities. The necessity to interpret and utilize a vast amount of sports medical data effectively has emerged as a pivotal research avenue. This paper delves into the challenges associated with extracting, studying, and the accuracy training of complex algorithms essential for analyzing critical sporting medical data.Central to this discussion is introducing an Optimized Convolutional Neural Network (OCNN) model, which is based on deep learning principles. This model is designed to enhance the detection and risk assessment of diseases related to sport medicine. It incorporates a novel Self-Adjustment Resizing algorithm (SAR), augmented by a self-coding method of convolution (SCM). The proposed OCNN model comprises two convolutional layers, two pooling layers, a fully connected layer, and a SoftMax structure. This architecture is tailored for the classification and analysis of sport-related medical data.