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On Crowdsourcing-Design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms 48

On Crowdsourcing-Design with Comparison Category Rating for ...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Suarez, Angelica S. Z. Laroche, Clement Clemmensen, Line H. Das, Sneha Technical University of Denmark Dept. of Applied Mathematics and Computer Science Denmark GN Audio Research Ballerup Denmark

ISBN: (纸本)9781728163277

Speech enhancement techniques improve the quality or the intelligibility of an audio signal by removing unwanted noise. It is used as preprocessing in numerous applications such as speech recognition, hearing aids, broadcasting and telephony. The evaluation of such algorithms often relies on reference-based objective metrics that are shown to correlate poorly with human perception. In order to evaluate audio quality as perceived by human observers it is thus fundamental to resort to subjective quality assessment and in doing so we identify subgroups of users where the subjective assessments correlate better to objective metrics. In this paper, a user evaluation based on crowdsourcing (subjective) and the Comparison Category Rating (CCR) method is compared against the DNS-MOS, ViSQOL and 3QUEST (objective) metrics. The overall quality scores of three speech enhancement algorithms from real time communications (RTC) are used in the comparison using the P.808 toolkit. Results indicate that while the CCR scale allows participants to identify differences between processed and unprocessed audio samples, two groups of preferences emerge: some users rate positively by focusing on noise suppression processing, while others rate negatively by focusing mainly on speech quality. We further present results on the parameters, size considerations and speaker variations that are critical and should be considered when designing the CCR-based crowdsourcing evaluation1. © 2023 IEEE.

关键词： Speech enhancement

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Sensorless Unmanned Bicycle Robot

Sensorless Unmanned Bicycle Robot

引用

2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Efraim, Hanoch Basri, Ronen Weizmann Institute of Science Dept. of Computer Science and Applied Mathematics Rehovot Israel

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this work, we present a new approach for designing an autonomous bicycle robot. It is well known that bicycles can be laterally stable at certain velocities. This means that if the steering bar remains free to rotate, a bicycle can be designed so that it naturally steers in a way that corrects for deviations, keeping the bicycle upright. However, to the best of our knowledge this desirable property is not taken advantage of in previous works, since connecting a standard actuator to the steering bar, which is required in order to guide the bicycle to a desired direction, does not allow the steerer to be free to rotate. In this work we propose a unique actuation mechanism that allows guidance while keeping the steerer free to rotate, thus preserving the natural stability of the bicycle. Since the bicycle is mechanically stable there is no need for a low level controller, and thus, no need for sensors and sensor fusion algorithms. We demonstrate the concept with experiments performed on a 3D printed bicycle powered by a small electric motor and guided remotely. We provide all the CAD files and software in hope that it will be used by additional researchers in the field, and advance the frontiers of autonomous systems. © 2022 IEEE.

关键词： Bicycles

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Physics-Informed Neural Networks: A Step Towards Data-Driven Optimization of Additive Manufacturing 29

Physics-Informed Neural Networks: A Step Towards Data-Driven...

引用

29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Depaoli, Fabio Felicioni, Stefano Ponzio, Francesco Aliberti, Alessandro MacIi, Enrico Bondioli, Federica Padovano, Elisa Di Cataldo, Santa Dept. of Control and Computer Engineering Politecnico di Torino Italy Dept. of Applied Science and Technology Politecnico di Torino Italy Interuniv. Dept. of Reg. and Urban Studies and Planning Politecnico di Torino Italy

ISBN: (纸本)9798350361230

Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach. © 2024 IEEE.

关键词： Smart manufacturing

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Exploring Mixed-Grid Environments for Drone-Based Last-Mile Logistics Optimization 20

Exploring Mixed-Grid Environments for Drone-Based Last-Mile ...

引用

20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Sorbelli, Francesco Betti Coro, Federico Pinotti, Cristina M. Shende, Anil University of Perugia Dept. of Computer Science and Mathematics Italy University of Padua Dept. of Mathematics Italy Roanoke College Dept. of Math. CS & Physics United States

ISBN: (纸本)9798350324334

In recent years, drone-based delivery systems have gained significant attention for last-mile logistics in various domains. This study focuses on optimizing drone deliveries in mixed-grid environments, which combine urban and rural areas. The objective is to minimize the sum of distances between the delivery locations and the drone's depot. By selecting the optimal depot placement, we aim to improve factors such as expected delivery time, energy consumption, and environmental impacts. To address this optimization problem, we conducted a comprehensive comparison of several state-of-the-art algorithms specifically designed for computing the drone placement in mixed-grid environments. Our evaluation utilized synthetic and quasi-real data and two distinct scenarios were considered: the full-grid scenario, and the partial-grid scenario. Through empirical analyses, we demonstrate the effectiveness of the proposed algorithms and provide valuable insights into their trade-offs in terms of performance and time complexity. © 2023 IEEE.

关键词： Drones

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Optimal Local Parameters For Hermite Type Cubic Splines 17

Optimal Local Parameters For Hermite Type Cubic Splines

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17th International Conference on Engineering of Modern Electric Systems, EMES 2023

作者： Bica, Alexandru Mihai Laslo, Eugen Crainic, Cristian G. University of Oradea Dept. of Math. and Computer Science Oradea Romania University of Oradea Dept. of Forestry and Forest Eng. Oradea Romania

ISBN: (纸本)9798350310634

The local derivatives of Hermite type cubic splines are optimally determined on each subinterval of a given mesh through a set of five classical interpolation procedures in order to minimize a certain objective function. We construct an iterative method that selects on each subinterval, the adequate algorithm for providing the local derivatives which minimize the objective function such that the values of the local derivatives computed at the previous subinterval are preserved in order to keep the smoothness of the spline. The admissible set of classical interpolation procedures consists of the natural cubic spline, the Catmull-Rom cubic spline, the Akima's cubic spline, and the cubic splines with minimal derivative oscillation and those with minimal deviation by the data polygon. A numerical experiment is presented in order to illustrate the performances of the algorithm. © 2023 IEEE.

关键词： Interpolation

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Evaluation and Comparison of Facial Recognition Technology in Drawn Portraits 13

Evaluation and Comparison of Facial Recognition Technology i...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Chang, Cheng Fanjiang, Yong-Yi Hung, Chi-Huang Chen, Jung-Hua Phung, Kiem-Trinh Trieu, Van-Duc Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan Lee-Ming Institute of Technology Dept. Electronical Engineering New Taipei City Taiwan

ISBN: (纸本)9798350355079

Numerous studies have proposed various methods and applications for facial recognition. In camera-equipped environments, these methods can easily identify real faces. However, recognizing faces on printed or Portrait Drawing becomes more complex. In other words, real faces are three-dimensional, and while algorithms can sample feature values, identifying portrait drawing face on paper is relatively more challenging. This study evaluates and compares the effectiveness of various facial recognition techniques on paper-based images, including Haar-cascade, MTCNN, and Dlib 68 feature points. © 2024 IEEE.

关键词： Face recognition

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Forward Difference Properties of the (n, k)-Star Graph and Some Other Interconnection Networks 29

Forward Difference Properties of the (n, k)-Star Graph and S...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Cheng, Eddie Gibbons, Ethan Qiu, Ke Shen, Zhizhang Oakland University Dept. of Math and Stats RochesterMI48309 United States Brock University Dept. of Computer Science St. Catharines Canada Plymouth State University Dept. of Computer Science and Technology PlymouthNH03264 United States

ISBN: (纸本)9798350330717

An important invariant of an interconnection network is its surface area, the number of vertices at distance i from a node. Although much work has been done to obtain formulas for the surface areas for many interconnection networks, most of the formulas are not in the so-called closed form except for a very few trivial graphs. It is known that for an interconnection network, if its surface area satisfies the so-called forward difference property, then for any specific distance i, its surface area of radius i in closed form (a polynomial of degree i) can be obtained, provided that we have i + 1 initial values of the surface area of radius i. This property is known to hold for the hypercube and the star graph. We show in this paper that the property also holds for the (n, k)-star graph, 1 ≤ k ≤ n - 1, a family of interconnection networks that also include the star graph when k = n - 1. We then show that the technique we use for the result is general that can also be used to prove the property for some other networks. © 2023 IEEE.

关键词： forward difference interconnection network surface area

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An Efficient Approach of Drowsiness Detection Based on ResNet50 and Xception Architectures 3

An Efficient Approach of Drowsiness Detection Based on ResNe...

引用

3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： El-Nabi, Samy Abd El-Rabaie, El-Sayed M. Emam, Ahmed Ramadan, Khalil F. Faculty of Computer Science and Engineering Dept. of Artificial Intelligence Engineering South Sinai46511 Egypt Menoufia University Faculty of Electronic Engineering Dept. of Electronics and Electrical Communications Menouf32952 Egypt Menoufia University Dept. of Computer Science and Math Faculty of Science Menoufia Egypt

ISBN: (纸本)9798350323511

This paper delves into a critical aspect of road safety by investigating a method to detect drowsiness in images. The accurate utilization of this technology by fatigued drivers holds the potential to prevent numerous fatal accidents, as the proposed models exhibit swift responsiveness in identifying instances of driver drowsiness. Within the realm of eye closure detection, two drowsiness models, namely ResNet50 and Xception, are employed to portray various eye state classifications. Nonetheless, the scarcity of readily available and reliable eye datasets poses a formidable challenge. Leveraging the power of ResNet50, deep facial features are extracted, resulting in an impressive accuracy of 98.06%. Similarly, the utilization of Xception yields a commendable accuracy of 95.69% when evaluated with the MRL dataset. These experimental outcomes firmly establish the higher accuracy and cost-effectiveness of eye closure estimation, thereby underscoring the efficacy of the proposed framework for drowsiness recognition. © 2023 IEEE.

关键词： Cost effectiveness

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Machine Learning Models for Foreign Worker Prediction in Thailand: A Study of Linear Regression and Decision Tree

Machine Learning Models for Foreign Worker Prediction in Tha...

引用

2024 International Conference on Measurement, Communication, and Virtual Reality, MCVR 2024

作者： Jomsri, P. Prangchumpol, D. Eamthanakul, B. Poonsilp, K. Pudprommarat, C. Dept. of Information Technology Suan Sunandha Rajabhat University Dusit Bangkok10300 Thailand Dept. of Computer Science Suan Sunandha Rajabhat University Dusit Bangkok10300 Thailand Dept. of Applied Science Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok Thailand

ISBN: (纸本)9781510691773

This study proposed to predict the immigration patterns of foreign workers in Thailand using machine learning techniques. The primary focus is to analyze how factors such as the year, month, area, and legal immigration type (MOU) influence the number of foreign workers entering the country. MOU represents the Memorandum of Understanding, a legal framework for foreign migration. By utilizing two distinct machine learning models—Linear Regression and Decision Tree—this research seeks to identify the most effective method for forecasting immigration trends. This study analyzes foreign worker immigration in Thailand using data collected from 2560 to 2561 B.E. The data used in this study includes key variables that are indicative of immigration movements, providing a comprehensive view of the factors influencing foreign migration. The findings reveal that the Decision Tree model outperforms Linear Regression in terms of accuracy at 0.99. This analysis is crucial for policymakers and stakeholders as it provides insights into the patterns of foreign worker immigration, enabling better planning and management of foreign worker resources. Understanding these patterns is essential for developing effective immigration policies and ensuring that foreign worker market needs are met in a regulated and orderly manner. The comparative analysis demonstrates the importance of selecting appropriate machine learning models for accurate predictions in the domain of foreign immigration. © 2025 SPIE.

关键词： Linear regression

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SITS-BERT ++: Enhancing Satellite Image Time Series Analysis with Novel Architectural and Methodological Innovations 8

SITS-BERT ++: Enhancing Satellite Image Time Series Analysis...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Shravan, Gade Sai Sai Kulvanth, S. Ashrith Raj, S. Vidyasager, G.R. Bhaskaran, V. Rajamani, Kumar T Srisailam, Swaroop Institute of Higher Learning Dept. of Math and Computer Science Sri Sathya Sai Anantapur India CropIn Cropin Ai Labs Bengaluru India

ISBN: (纸本)9798331531539

In this study, we introduce SITS-BERT++, a novel adaptation of the SITS-BERT model tailored for the analysis of annual satellite observation time series data. Our approach integrates the Swish activation function and a sophisticated time warping data augmentation technique to significantly en-hance model performance. Additionally, we propose a multi-faceted architectural overhaul that includes dual activation functions-Swish for the overall model and GeL U for specific prediction and classification tasks-resulting in a substantial improvement in accuracy from 90.61 % to 93.07%. These inno-vations demonstrate the efficacy of SITS-BERT ++ in advancing the state-of-the-art in satellite time series analysis, surpassing the SITS analysis models. © 2024 IEEE.

关键词： Crop Type Classification Pre-training Satellite Image Time Series(SITS) Self-Supervised Learning Sentinel-2

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