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IEEE Conference on Wireless Communications and Networking

作者： Caijuan Chen Chaofeng Zhang Gaolei Li Yuchen Liu The Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science NC State University North Carolina US

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints.

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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Human-centered GeoAI foundation models: where GeoAI meets human dynamics

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Urban Informatics 2025年第1期4卷 1-15页

作者： Ye, Xinyue Du, Jiaxin Li, Xinyu Shaw, Shih-Lung Fu, Yanjie Dong, Xishuang Zhang, Zhe Wu, Ling Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Computer Science and Engineering Texas A&M University College Station USA College of Computing Grand Valley State University Allendale USA Department of Geography & Sustainability University of Tennessee Knoxville USA School of Computing and AI Arizona State University Tempe USA Department of Electrical & Computer Engineering Prairie View A&M University Prairie View USA Department of Geography Texas A&M University College Station USA Department of Justice Studies Prairie View A&M University Prairie View USA

This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts.

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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International Electrical Engineering Congress (iEECON)

作者： Prayoot Akkaraekthalin Tanee Somwong Nonchanutt Chudpooti Nuttawut Hirun Suwaluck. Meesomklin Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation.

关键词： Temperature sensors Temperature measurement Smart agriculture System performance Green products Sensor systems Real-time systems Sensors Internet of Things Monitoring

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From Detection to Traversal: a Probabilistic Framework for UAS-Assisted Landmine Mapping and Circumvention

From Detection to Traversal: a Probabilistic Framework for U...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： J. Josiah Steckenrider Dongbin Kim Pratheek Manjunath Jack Simpson Charles Rosbrook Micah Weiss Department of Civil and Mechanical Engineering United States Military Academy West Point NY USA College of Engineering Technology and Architecture University of Hartford West Hartford CT USA Department of Electrical Engineering and Computer Science United States Military Academy West Point NY USA

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This research presents a robust probabilistic framework for minefield localization, mapping, and avoidance, addressing a technological gap in the field of aerial countermine intelligence, while bypassing the well-established techniques of landmine detection. Our approach propagates the pose uncertainty matrix delivered by a drone's flight controller's Kalman filter to probabilistically estimate the location of detected mines. This probability map then seeds an artificial potential field path generator which creates a safe path for ground traversal by producing waypoints through the minefield. The system's performance is evaluated in simulations and validated through flight trials, demonstrating its potential to improve the efficiency and safety of UAV-assisted minefield navigation and threat avoidance.

关键词： Location awareness Landmine detection Uncertainty System performance Atmospheric modeling Probabilistic logic Aircraft navigation Generators Safety Kalman filters

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A new parallel scheduling system for multiple radio wireless mesh network

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中国高等学校学术文摘·计算机科学 2009年第4期3卷 550-559页

作者： Yun HU Shoubao YANG Qi ZHANG Dapeng WANG Qinwei SHEN Department of Computer Science University of Science and Technology of ChinaHefei 230026China Department of Computer Science University of Science and Technology of ChinaHefei 230026China Department of Computer Engineering Anhui University of ArchitectureHefei 230022China

To enhance the capacity of wireless mesh networks,a key technique is widely investigated which is the usage of multi-radio and multi-channel *** this paper,a new parallel scheduling system is proposed which exploits MAC diversities by transmitting packets on the radios *** with conventional packet transmission which follows 'one flow one radio',the new system uses radio diversity to transmit the packets on different radios *** kernel components of this system are selection module and schedule module.A localized selecting algorithm is implemented in the selection model to choose the right radios based on the quality of wireless links; two distributed packet-scheduling algorithms are optional with the schedule ***,a routing metric adapting this system is *** have carried out a comprehensive performance evaluation of this system using *** results show that it can successfully harness diversity of multi-radio and multi-channel to provide considerable improvements over a baseline multi-channel system in several situations.

关键词： wireless mesh network diversity parallel scheduling algorithm

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Balancing accuracy and interpretability: AI-driven predictive modeling of construction schedule performance in India

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Asian Journal of Civil Engineering 2025年 1-16页

作者： Maurya, Sudhanshu Lakkimsetty, Nageswara Rao Manjunath, T. C. Shukla, Anoop Kumar Sethy, Barada Prasad Behera, Rashmi Rekha Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Nagpur India Department of Chemical and Petroleum Engineering School of Engineering American University of Ras Al Khaimah (AURAK) Ras al Khaimah United Arab Emirates Ras al Khaimah UAE Department of Computer Science & Engineering Cyber Security & Blockchain Technology Rajarajeswari College of Engineering Bengaluru India Manipal School of Architecture and Planning Manipal Academy of Higher Education Manipal India NIST University Berhampur India Centurion University of Technology and Management Gajapati India

Construction schedule delays are a persistent challenge in India’s infrastructure sector, leading to significant cost overruns, stakeholder dissatisfaction, and inefficient resource utilization. In this context, the integration of Artificial Intelligence (AI) into project forecasting offers a promising solution. This study presents an AI-driven predictive modeling framework aimed at estimating construction schedule adherence, with a dual focus on predictive accuracy and model interpretability. Three machine learning models—multiple linear regression (MLR), artificial neural networks (ANN), and gradient boosting machine (GBM)—were developed and evaluated using real-world data from 150 construction projects across India. The models were assessed based on standard performance metrics (MAE, RMSE, R²), computational efficiency, and transparency. While MLR offered high interpretability, it lacked accuracy in handling nonlinear interactions. ANN showed high accuracy but limited explainability. GBM outperformed both, achieving the best predictive performance (R² = 0.94) and a favorable trade-off between accuracy and interpretability. Feature importance and sensitivity analyses identified equipment utilization, material availability, and labor productivity as key influencers. Case study validation further reinforced the practical value of GBM in diverse project settings. The findings advocate for the strategic adoption of interpretable AI tools like GBM in construction management to enhance scheduling precision and decision-making efficiency.

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Design of Miniaturized Metamaterial Reflector with Doubly Interdigital Structure for 5G Use Cases

Design of Miniaturized Metamaterial Reflector with Doubly In...

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International Electrical Engineering Congress (iEECON)

作者： Tanee Somwong Pongsathorn Chomtong Jirayut Khamsuthat Nonchanutt Chudpooti Prayoot Akkaraekthalin Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents the design of a miniaturized metamaterial reflector composing of an open-loop transmission line unit cell structure. The transmission line ends are connected by using a double interdigital technique, resulting in a large capacitive value compared to the conventional methods. When combined with the open-loop line employing the trisection step-impedance structure with adjusting of the capacitive value, its second and third resonant frequencies will be controllable. As a result, this approach can reduce the unit cell size from a typical value of λ/2 to an approximate value of λ/16, therefore overall size of the reflector is significantly decreased. The designed unit cell is measured to be 1.05 cm × 1.0 cm. We implemented the proposed reflector on an FR-4 PCB with a relative permittivity of about 4.4, a substrate height (h) of 1.6 mm, and a loss tangent of about 0.02. The operating frequency range targets of 5G use cases were set to be at 1.8 GHz, 2.6 GHz and 3.5 GHz, respectively. When used with a dipole antenna, the bandwidths of 390 MHz, 604 MHz, and 1.1 GHz and the antenna gains of about 8.23 dB, 8.14 dB, and 8.19 dB were obtained at the operating frequencies, respectively.

关键词： Wireless communication 5G mobile communication Dipole antennas Simulation Resonant frequency Reflector antennas Transmission line measurements Metamaterials Gain Substrates

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Constrained Optimization with Decision-Dependent Distributions

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IEEE Transactions on Automatic Control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Automatic tuning of sparse matrix-vector multiplication on multicore clusters

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science China(Information sciences) 2015年第9期58卷 17-30页

作者： LI ShiGang HU ChangJun ZHANG JunChao ZHANG YunQuan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences School of Computer and Communication Engineering University of Science and Technology Beijing Department of Computer Science University of Illinois at Urbana-Champaign

To have good performance and scalability, parallel applications should be sophisticatedly optimized to exploit intra-node parallelism and reduce inter-node communication on multicore clusters. This paper investigates the automatic tuning of the sparse matrix-vector(Sp MV) multiplication kernel implemented in a partitioned global address space language, which supports a hybrid thread- and process-based communication layer for multicore systems. One-sided communication is used for inter-node data exchange, while intra-node communication uses a mix of process shared memory and multithreading. We develop performance models to facilitate selecting the best configuration of threads and processes hybridization as well as the best communication pattern for Sp MV. As a result, our tuned Sp MV in the hybrid runtime environment consumes less memory and reduces inter-node communication volume, without damaging the data locality. Experiments are conducted on 12 real sparse matrices. On 16-node Xeon and 8-node Opteron clusters, our tuned Sp MV kernel gets on average 1.4X and 1.5X improvement in performance over the well-optimized process-based message-passing implementation, respectively.

关键词： Sp MV PGAS hybridization model-driven multicore clusters

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Coupled Matrix Factorization for Question Similarity

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Chinese Journal of Electronics 2016年第4期25卷 665-671页

作者： WEI Chuyuan LI Fangfang ZHAN Qiang FAN Xiongzhong School of Computer Science and Technology Beijing Institute of Technology Department of Computer Beijing University of Civil Engineering and Architecture Advanced Analytics Institute University of TechnologySydneyAustralia

Community question answering(CQA)has provided an increasingly popular service where users ask and answer questions and access historical questionanswer *** a fundamental task in CQA,question similarity measure is to compute the similarity between the queried question and the historical questions which have been solved by other *** mine and use the most important semantic features as the semantic representation of questions,and try to incorporate the couplings of semantic features into vector space *** propose Coupled question similarity(CQS) model,and compute the similarity in matrix factorization *** conducted on real CQA data sets demonstrate that with the incorporation of such couplings,the performance of sentence similarity is improved compared to a variety of baseline methods significantly.

关键词： Sentence similarity Community question answering(CQA) Matrix factorization(MF) Coupled question similarity(CQS)

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