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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ken, Huam Ming Behjati, Mehran Rafsanjani, Ahmad Sahban Aslam, Saad Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system designed to predict ozone concentration in real-time. The system employs an Arduino Nano 33 BLE Sense microcontroller equipped with an MQ7 sensor for carbon monoxide (CO) detection and built-in sensors for temperature and pressure measurements. The data, sourced from a Kaggle dataset on air quality parameters from India, underwent thorough cleaning and preprocessing. Model training and evaluation were performed using Edge Impulse, considering various combinations of input parameters (CO, temperature, and pressure). The optimal model, incorporating all three variables, achieved a mean squared error (MSE) of 0.03 and an R-squared value of 0.95, indicating high predictive accuracy. The regression model was deployed on the microcontroller via the Arduino IDE, showcasing robust real-time performance. Sensitivity analysis identified CO levels as the most critical predictor of ozone concentration, followed by pressure and temperature. The system’s low-cost and low-power design makes it suitable for widespread implementation, particularly in resource-constrained settings. This TinyML approach provides precise real-time predictions of ozone levels, enabling prompt responses to pollution events and enhancing public health protection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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Impact of Environmental Changes on Optimized Robotics Collective Motion for Multi-objective Coverage Tasks 13th

Impact of Environmental Changes on Optimized Robotics Coll...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Ghanem, Reda Ali, Ismail M. Kasmarik, Kathryn Garratt, Matthew School of Systems and Computing University of New South Wales Canberra Australia School of Engineering and Technology University of New South Wales Canberra Australia

ISBN: (纸本)9789819635054

Coverage tasks have garnered significant interest in recent years due to their applications in fields such as environmental monitoring, search and rescue, and robotic exploration. Swarm robotics has emerged as a promising approach to tackle these challenges, leveraging collective behaviors to enhance efficiency. However, the effectiveness of swarm algorithms often hinges on the careful tuning of parameters, which can be influenced by varying environmental features. This study investigates the impact of these features on the performance of optimized collective motion in swarm robotics for solving multi-objective coverage problems. Through a comprehensive sensitivity analysis, we evaluate how changes in parameters-such as arena size, number of robots, obstacle density, and obstacle structure-affect swarm performance in solving coverage problems. The analysis focuses on key performance metrics across multiple objectives: coverage percentage, coverage time, robot connectivity, and collision rates. Our findings provide critical insights into the adaptability of swarm algorithms, highlighting the importance of environmental context in optimizing swarm efficacy across competing objectives. These results contribute to the development of more robust and adaptive swarm-based solutions for multi-objective coverage tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM 3

Hybrid Approach for Electricity Price Forecasting using Alex...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sambana, Bosubabu Devi, Kotamsetty Geethika Reddy, Bandi Rajeswara Hussain, Galeti Mohammad Siddartha, Gownivalla School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively. © 2025 IEEE.

关键词： Prediction models

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Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML 2nd

Edge Intelligence for Wildlife Conservation: Real-Time Hornb...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Hing, Kong Ka Behjati, Mehran Saleh, Vala Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Electrical Engineering Shiraz University of Technology Shiraz Iran Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real-time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno-canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel-Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real-world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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Generation of Routes Using Genetic Algorithms to Optimize the Domestic Solid Waste Collection 8th

Generation of Routes Using Genetic Algorithms to Optimize th...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Vega-Huerta, Hugo Marcos, Jordy Mayhuay Acosta, Marx Campos Monrroy, Xavier Villanueva Maquen-Niño, Gisella Luisa Elena Puelles-Bulnes, María Gil-Calvo, Rubén School of Systems Engineering Universidad Nacional Mayor de San Marcos Lima Peru School of Computing and Informatics Engineering Universidad Nacional Pedro Ruiz Gallo Lambayeque Peru

ISBN: (纸本)9789819738588

The district of San Juan de Lurigancho (SJL) in Lima—Peru, is one of the districts crowed in the capital and has one of the highest amounts solid domestic wastes. To fix this problem, a model was developed to map the distance and calculate the best route to collect the domes-tic solid waste in the whole San Carlos neighborhood in SJL district. Our model was developed using genetic algorithms in JavaScript with Node JS to calculate the shortest path. For the data visualization was used the React JS for the frontend and Google Maps API for the display of an interactive map. The results obtained allowed were able to find an optimal route that joins two opposite points within the map of the San Carlos neighborhood, which is made up of 5 nodes and measures 514 m away. The model developed could generate populations of 10 to 100 individuals with the map nodes through selection, crossing and mutation processes, obtaining an optimal route that facilitates the domestic waste collection processes with the shortest distance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collection Genetic algorithm Minimum route Pathfinding Solid waste

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Dual Distillation: A Lightweight and Effective Intrusion Detection Model for IoT Security 15

Dual Distillation: A Lightweight and Effective Intrusion Det...

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15th Global Information Infrastructure and Networking Symposium, GIIS 2025

作者： Pei, Chonghao Mhamdi, Lotfi University of Leeds School of Electronic and Electrical Engineering United Kingdom Abdullah Al Salem University College of Computing & Systems Kuwait

ISBN: (纸本)9798331520113

With the exponential growth of IoT devices, ensuring efficient and accurate network anomaly detection in resource-constrained environments has become a pressing challenge. A lightweight dual distillation approach is proposed, combining data distillation and model distillation. Data distillation is achieved through KMeans clustering and refinement, while model distillation leverages a random forest (RF) teacher model to guide a simplified multi-layer perceptron (MLP) student model. The approach is validated on CICIDS2017 and TON-IoT datasets, where dataset sizes are reduced to 20% and 40%, respectively. Experimental results demonstrate that the proposed method enhances MLP accuracy on CICIDS2017 from 93.17% to 99.66% and on TON-IoT from 89.00% to 93.14%. Moreover, the method significantly reduces model parameters compared to the RF teacher model while maintaining competitive performance, making it highly suitable for resource-constrained IoT environments. © 2025 IEEE.

关键词： Network intrusion

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Sobol Sequence Optimization for Hardware-Efficient Vector Symbolic Architectures

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 937-950页

作者： Aygun, Sercan Najafi, Hassan University of Louisiana at Lafayette School of Computing and Informatics LafayetteLA70503 United States Case Western Reserve University Electrical Computer and Systems Engineering Department ClevelandOH44106 United States

Hyperdimensional computing (HDC) is an emerging computing paradigm with significant promise for efficient and robust learning. In HDC, objects are encoded with high-dimensional vector symbolic sequences called hypervectors. The quality of hypervectors, defined by their distribution and independence, directly impacts the performance of HDC systems. Despite a large body of work on the processing parts of HDC systems, little to no attention has been paid to data encoding and the quality of hypervectors. Most prior studies have generated hypervectors using inherent random functions, such as MATLAB’s or Python’s random function. This work introduces an optimization technique for generating hypervectors by employing quasi-random sequences. These sequences have recently demonstrated their effectiveness in achieving accurate and low-discrepancy data encoding in stochastic computing systems. The study outlines the optimization steps for utilizing Sobol sequences to produce high-quality hypervectors in HDC systems. An optimization algorithm is proposed to select the most suitable Sobol sequences via indexes for generating minimally correlated hypervectors, particularly in applications related to symbol-oriented architectures. The performance of the proposed technique is evaluated in comparison to two traditional approaches of generating hypervectors based on linear-feedback shift registers and MATLAB random functions. The evaluation is conducted for three applications: 1) language;2) headline;and 3) medical image classification. Our experimental results demonstrate accuracy improvements of up to 10.79%, depending on the vector size. Additionally, the proposed encoding hardware exhibits reduced energy consumption and a superior area-delay product. © 2024 IEEE.

关键词： Stochastic systems

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Identifying key nodes for enhancing community stability in bipartite networks

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World Wide Web 2025年第3期28卷 1-26页

作者： Chen, Jinyi Choudhury, Farhana Xin, Junchang Wang, Zhiqiong School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computing and Information Systems The University of Melbourne Melbourne3053 Australia College of Medicine and Biological Information Engineering Northeastern University Shenyang110819 China

Bipartite networks are widely applied to model relationships between two distinct types of entities in various real-world applications. The departure of key nodes can trigger a cascading effect, potentially leading to the collapse of the entire community. Identifying such key nodes to enhance community stability is crucial, a topic well-explored in unipartite networks but largely unexplored in bipartite networks. In this paper, we aim to identify key nodes whose removal results in the smallest (α,β)-core in bipartite networks, and protecting these nodes from being removed can greatly enhance community stability. Formally, given a bipartite graph G with degree constraints α and β, and budgets b1 and b2, our goal is to identify b1 upper and b2 lower vertices (collapsers) whose removal maximizes the number of non-collapsed vertices (followers) cascading out of the (α,β)-core. We prove the problem is NP-hard and propose a greedy algorithm that identifies the best collapser in each of the b1+b2 iterations. Several well-designed pruning strategies are employed to reduce the pool of candidate collapsers and expedite follower computation. Theoretical analysis and extensive empirical evaluations on 12 real-world datasets demonstrate the efficiency and effectiveness of our proposed algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： NP-hard

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OFDM-structure based waveform designs for integrated sensing and communication

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Science China(Information Sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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Colorful Vertex Recoloring of Bipartite Graphs 42

Colorful Vertex Recoloring of Bipartite Graphs

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42nd International Symposium on Theoretical Aspects of Computer Science, STACS 2025

作者： Patt-Shamir, Boaz Rosén, Adi Umboh, Seeun William School of Electrical Engineering Tel Aviv University Israel CNRS Université Paris Cité France School of Computing and Information Systems The University of Melbourne Australia Melbourne Australia

ISBN: (纸本)9783959773652

We consider the problem of vertex recoloring: we are given n vertices with their initial coloring, and edges arrive in an online fashion. The algorithm is required to maintain a valid coloring by means of vertex recoloring, where recoloring a vertex incurs a cost. The problem abstracts a scenario of job placement in machines (possibly in the cloud), where vertices represent jobs, colors represent machines, and edges represent "anti affinity" (disengagement) constraints. Online coloring in this setting is a hard problem, and only a few cases were analyzed. One family of instances which is fairly well-understood is bipartite graphs, i.e., instances in which two colors are sufficient to satisfy all constraints. In this case it is known that the competitive ratio of vertex recoloring is Θ(log n). In this paper we propose a generalization of the problem, which allows using additional colors (possibly at a higher cost), to improve overall performance. Concretely, we analyze the simple case of bipartite graphs of bounded largest bond (a bond of a connected graph is an edge-cut that partitions the graph into two connected components). From the upper bound perspective, we propose two algorithms. One algorithm exhibits a trade-off for the uniform-cost case: given Ω(log β) ≤ c ≤ O(log n) colors, the algorithm guarantees that its cost is at most O(logcn ) times the optimal offline cost for two colors, where n is the number of vertices and β is the size of the largest bond of the graph. The other algorithm is designed for the case where the additional colors come at a higher cost, D > 1: given ∆ additional colors, where ∆ is the maximum degree in the graph, the algorithm guarantees a competitive ratio of O(log D). From the lower bounds viewpoint, we show that if the cost of the extra colors is D > 1, no algorithm (even randomized) can achieve a competitive ratio of o(log D). We also show that in the case of general bipartite graphs (i.e., of unbounded bond size), any determinist

关键词： Graph algorithms

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