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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Nelson D. Goosman Basavaraj R. Amogi Lav R. Khot School of Electrical Engineering and Computer Science Washington State University Pullman Washington USA Department of Biological Systems Engineering Washington State University Prosser Washington USA

Heat stress to maturing apple fruit is a key concern to tree fruit growers in the Pacific Northwest region of the United States and around the globe. Localized weather-based fruit surface temperature (FST) prediction, a key indicator of fruit stress, can help in planning better mitigation strategies and ultimately reduce crop losses. Therefore, this study evaluated localized weather (solar radiation, temperature, relative humidity, dew point, and wind speed) and fruit size data driven multiple linear regression (MLR) and Long Short-Term Memory (LSTM) models for predicting apple FST. The models were trained on either the localized in-orchard or open field weather station data collected in the 2022 field season and validated against the actual FST of ‘Honeycrisp’ apple. The MLR model was able to predict FST with an average root mean square error (RMSE) of 2. $1^{\circ}\mathrm{C}$ using the in-orchard weather and fruit size dataset as inputs. The LSTM model prediction average RMSE for the same dataset was 2. $3^{\circ}\mathrm{C}$. Using open field weather data, the RMSE was 2. $69^{\circ}\mathrm{C}$ and 2. $25^{\circ}\mathrm{C}$ for the LSTM and MLR models, respectively. Additionally, both in-orchard and open-field trained models outperformed the existing energy balance FST prediction approach on the same dataset. Overall, these findings can be helpful to growers for real-time and reliable FST monitoring using localized as well as publicly available weather data inputs.

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Prediction of Fruit Texture Features Using Deep Learning Techniques

Prediction of Fruit Texture Features Using Deep Learning Tec...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： M. Sangeetha S.Rajes Kannan Sampath Boopathi J Ramya Mohammad Ishrat G. Sabarinathan Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer science and Engineering Chennai institute of Technology Chennai Tamil Nadu India Department of Mechanical Engineering Muthayammal Engineering College Rasipuram Tamil Nadu India Department of Computer Science and Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

In this research, a prototype has been created to automatically collect the photos of fruits using a smartphone camera and a thermal camera, generating two distinct datasets: thermal and RGB. Fruit classification and bruise detection have both been accomplished using image processing, machine learning, and deep learning. To assess quality and shelf-life, a second set of RGB and thermal picture datasets was created. The suggested study enhances fruit categorization and flaw detection systems by utilizing multiple Grey level co-occurrence matrix (GLCM)-based texture characteristics. Using color-texture characteristics, pictures are classified using RF and KNN algorithms. RGB features were classified with 97.24 percent accuracy by K-Nearest Neighbor (KNN) and Random Forest (RF) classifiers, while GLCM-based features were classified with 96.40 percent accuracy by the RF classifier. Using thermal pictures, the KNN and RF algorithms' prediction accuracy was 81.70 percent and 84.12 percent, respectively. KNN and RF algorithms have kappa (percentage) values of 79.97 and 81.86, respectively. The random forest (RF) technique is hence the most appropriate for classifying fruit using a thermal picture dataset.

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Wide-Band and Wide-Angle Scanning Phased Array Antenna for Mobile Communication System

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 203-212页

作者： Yang, Guangwei Zhang, Yiming Zhang, Shuai Department of Electronic Systems Antennas Propagation and Millimeter-Wave Systems Section Aalborg University Aalborg9220 Denmark School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

A wide-band phased array antenna with wide-angle scanning capability for mobile communication system is proposed in this article. An air cavity is properly embedded into the substrate under each array element. This method is very simple and can efficiently enhance wide-angle scanning performance by improving the wide-angle scanning impedance matching (WAIM) and realizing the beam-width of elements in the array. Besides, the operating bandwidth is also extended with the proposed approach. The wide-angle scanning capability is analyzed and verified by both the linear array antennas (on large ground planes in detail) and the planar array. Two 1\times 8 linear arrays and one 8\times 8 planar array are demonstrated, which achieve the beam scanning of around ±60° with a realized gain reduction under 3.5 dB in the wide operating bandwidth (37.4%). Furthermore, the beam in the E-plane can scan over ±70° with a realized gain reduction under less than 3 dB. Two linear array prototypes with wide-angle scanning capacity in two planes are fabricated and characterized, yielding good performance within overall operational bandwidth. The measured results align very well with the simulated. The proposed wideband phased array with large scanning coverage is a promising candidate for 5G mobile communications. © 2020 IEEE.

关键词： Beamforming

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Pancreatic Cancer Diagnosis using Swarm Optimization Combined with Machine Learning Techniques

Pancreatic Cancer Diagnosis using Swarm Optimization Combine...

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Smart Technologies, Communication and Robotics (STCR)

作者： N Bharanidharan Pratham Aggarwal Aryan Batra Deep Shah Sannasi Chakravarthy S R Harikumar Rajaguru School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India

When compared to different types of malignant tumors, pancreatic malignancy is the 12 th widespread tumor disease among humans globally. Pancreatic cancer can be identified at the earlier stages using microarray gene analysis. The objective of this work is to classify the gene samples as normal and tumoral through the usage of Particle Swarm Optimization technique along with the supervised machine learning classifiers. A variance-based feature selection is employed to select the most appropriate features and the selected features are transformed using particle swarm optimization to improve the classification performance. Four different machine learning supervised algorithms are realized as classification techniques. When these four vanilla classifiers are considered, the random forest algorithm performs relatively well with balanced accuracy score of 87.5%. This score is improved to 94.4% through the usage of the proposed technique. In addition, the proposed technique enhances the prediction of the other two classifiers as well.

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An Intriguing Relation Between the Power Consumption and Number of Antenna Elements in Multi-Beam Phased Arrays

An Intriguing Relation Between the Power Consumption and Num...

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European Conference on Antennas and Propagation, EuCAP

作者： Natasha Birari Yanki Aslan Alexander Yarovoy Department of Microelectronics Faculty of Electrical Engineering Mathematics and Computer Science Microwave Sensing Signals and Systems Group Delft University of Technology Delft The Netherlands

An improved system-level power consumption model (PCM) for 5G base station multi-beam phased-array transmit architectures is developed. Using this model, it is shown that an optimum number of antenna elements of the array exists with respect to the total power consumption. The proposed model is benchmarked against a recent study which is shown to underestimate the total power consumed in analog and digital antenna systems by 37% and 126% respectively.

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN computer science 2023年第4期4卷 363页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers

arXiv

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arXiv 2023年

作者： Zaidi, Syed Aun Muhammad Latif, Siddique Qadir, Junaid USQ Australia Distributed Sensing Systems Group Data61 CSIRO Australia Computer Science and Engineering Department College of Engineering Qatar University Doha Qatar

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this paper, we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language SER. Our model utilises pretrained models for multimodal feature extraction and is equipped with a dual attention mechanism including graph attention and co-attention to capture complex dependencies across different modalities and achieve improved cross-language SER results using minimal target language data. In addition, our model also exploits a transformer encoder layer for high-level feature representation to improve emotion classification accuracy. In this way, MDAT performs refinement of feature representation at various stages and provides emotional salient features to the classification layer. This novel approach also ensures the preservation of modality-specific emotional information while enhancing cross-modality and cross-language interactions. We assess our model's performance on four publicly available SER datasets and establish its superior effectiveness compared to recent approaches and baseline models. Copyright © 2023, The Authors. All rights reserved.

关键词： Emotion Recognition

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Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning

arXiv

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arXiv 2025年

作者： Wu, Xinghao Niu, Jianwei Liu, Xuefeng Zhu, Guogang Zhang, Jiayuan Tang, Shaojie School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Management Science and Systems School of Management Center for AI Business Innovation University at Buffalo BuffaloNY United States

Federated Prototype Learning (FedPL) has emerged as an effective strategy for handling data heterogeneity in Federated Learning (FL). In FedPL, clients collaboratively construct a set of global feature centers (prototypes), and let local features align with these prototypes to mitigate the effects of data heterogeneity. The performance of FedPL highly depends on the quality of prototypes. Existing methods assume that larger inter-class distances among prototypes yield better performance, and thus design different methods to increase these distances. However, we observe that while these methods increase prototype distances to enhance class discrimination, they inevitably disrupt essential semantic relationships among classes, which are crucial for model generalization. This raises an important question: how to construct prototypes that inherently preserve semantic relationships among classes? Directly learning these relationships from limited and heterogeneous client data can be problematic in FL. Recently, the success of pretrained language models (PLMs) demonstrates their ability to capture semantic relationships from vast textual corpora. Motivated by this, we propose FedTSP, a novel method that leverages PLMs to construct semantically enriched prototypes from the textual modality, enabling more effective collaboration in heterogeneous data settings. We first use a large language model (LLM) to generate fine-grained textual descriptions for each class, which are then processed by a PLM on the server to form textual prototypes. To address the modality gap between client image models and the PLM, we introduce trainable prompts, allowing prototypes to adapt better to client tasks. Extensive experiments demonstrate that FedTSP mitigates data heterogeneity while significantly accelerating convergence. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs

Cross-Modality Time-Variant Relation Learning for Generating...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jingyi Wang Jinfa Huang Can Zhang Zhidong Deng Department of Computer Science Tsinghua University Beijing China School of Electronic and Computer Engineering Peking University China Department of Computer Science Beijing National Research Center for Information Science and Technology (BNRist) THUAI State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

Dynamic scene graphs generated from video clips could help enhance the semantic visual understanding in a wide range of challenging tasks such as environmental perception, autonomous navigation, and task planning of self-driving vehicles and mobile robots. In the process of temporal and spatial modeling during dynamic scene graph generation, it is particularly intractable to learn time-variant relations in dynamic scene graphs among frames. In this paper, we propose a Time-variant Relation-aware TRansformer (TR 2 ), which aims to model the temporal change of relations in dynamic scene graphs. Explicitly, we leverage the difference of text embeddings of prompted sentences about relation labels as the supervision signal for relations. In this way, cross-modality feature guidance is realized for the learning of time-variant relations. Implicitly, we design a relation feature fusion module with a transformer and an additional message token that describes the difference between adjacent frames. Extensive experiments on the Action Genome dataset prove that our TR2 can effectively model the time-variant relations. TR2 significantly outperforms previous state-of-the-art methods under two different settings by 2.1 % and 2.6% respectively.

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Sustainable Smart Road: A Unified Cost Estimation Model for Renewable-Powered Smart Roads

Sustainable Smart Road: A Unified Cost Estimation Model for ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Cristian Giovanni Colombo Michela Longo Leonardo Nocera Ryosuke Ota Dario Zaninelli Department of Energy Politecnico di Milano Milan Italy Politecnico di Milano Milan Italy Department of Electrical Engineering and Computer Science Faculty of Systems Design Tokyo Metropolitan University Tokyo Japan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

With new stringent policies affecting the transport and generation sectors, new technologies have emerged to drive the electric and technological transition. One of these is the smart road, a highly sensorised system to enable the integration of new vehicles and safety technologies. In this paper, the implementation of a smart road is simulated, first from an energy point of view and then from an economic point of view, by identifying the power demand of components and their cost. Next, the energetic core of the smart road is implemented. The energy core consists of a PV plant capable of powering the infrastructure. In this paper a simulation to test the proposed methodology is implemented. In the case study, a 280.05 kW smart road is powered by an optimally dimensioned PV plant. The production guaranteed by the 283.8 kWp plant will be 389.49 MW $h$ /year and requires $5723\mathrm{m}^{2}$ . Furthermore, the 234.57 $\mathrm{k}\unicode{x20AC}$ investment for the plant will represent less than 10% of the total investment, highlighting how renewable integration can be an economic sustainable purpose. Finally, another method to return the investment as quickly as possible will be proposed.

关键词： Renewable energy sources Costs Power demand Roads Net zero Estimation Production Safety Solar radiation Investment

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