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Investigating the automatic classification of algae using fusion of spectral and morphological characteristics of algae via deep residual learning

arXiv

引用

arXiv 2018年

作者： Deglint, Jason L. Jin, Chao Wong, Alexander Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada Waterloo Artificial Intelligence Institute WaterlooON Canada School of Environmental Science and Engineering Sun Yat-sen University Guangzhou510275 China

Under the impact of global climate changes and human activities, harmful algae blooms (HABs) in surface waters have become a growing concern due to negative im-pacts on water related industries, such as tourism, fishing and safe water supply. Many jurisdictions have introduced specific water quality regulations to protect public health and safety. Currently, drinking water quality guidelines related to cyanobacteria are based on maximum acceptable concentrations of toxins or elevated levels of cyanobacte-ria cells in water supplies. Therefore, reliable and cost effective methods of quantifying the type and concentration of threshold levels of algae cells has become critical for ensuring successful water management. In this work, we present SAMSON, an in-novative system to automatically classify multiple types of algae from different phyla groups by combining standard morphological features with their multi-wavelength sig-nals. Two phyla with focused investigation in this study are the Cyanophyta phylum (blue-green algae), and the Chlorophyta phylum (green algae). To accomplish this, we use a custom-designed microscopy imaging system which is configured to image water samples at two uorescent wavelengths and seven absorption wavelengths using discrete-wavelength high-powered light emitting diodes (LEDs). Powered by computer vision and machine learning, we investigate the possibility and effectiveness of auto-matic classification using a deep residual convolutional neural network. More specifically, a classification accuracy of 96% was achieved in an experiment conducted with six different algae types. This high level of accuracy was achieved using a deep resid-ual convolutional neural network that learns the optimal combination of spectral and morphological features. These findings elude to the possibility of leveraging a unique fingerprint of algae cell (i.e. spectral wavelengths and morphological features) to auto-matically distinguish different algae types. Our wo

关键词： Water supply

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Field emission efficiency of a carbon nanotube array under parasitic nonlinearities

Field emission efficiency of a carbon nanotube array under p...

引用

ASME 2010 International Mechanical engineering Congress and Exposition, IMECE 2010

作者： Anand, Sandeep V. Roy Mahapatra, D. Sinha, Niraj Yeow, J.T.W. Melnik, R.V.N. Department of Aerospace Engineering Indian Institute of Science Bangalore 560012 India Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02139 United States Department of Systems Design Engineering University of Waterloo Waterloo ON N2L 3G1 Canada M2NeT Lab Wilfrid Laurier University Waterloo ON N2L3C5 Canada BCAM Bizkaia Technology Park 48160 Derio Spain

ISBN: (纸本)9780791844472

Carbon Nanotubes (CNTs) grown on substrates are potential electron sources in field emission applications. Several studies have reported the use of CNTs in field emission devices, including field emission displays, X-ray tube, electron microscopes, cathode-ray lamps, etc. Also, in recent years, conventional cold field emission cathodes have been realized in micro-fabricated arrays for medical X-ray imaging. CNTbased field emission cathode devices have potential applications in a variety of industrial and medical applications, including cancer treatment. Field emission performance of a single isolated CNT is found to be remarkable, but the situation becomes complex when an array of CNTs is used. At the same time, use of arrays of CNTs is practical and economical. Indeed, such arrays on cathode substrates can be grown easily and their collective dynamics can be utilized in a statistical sense such that the average emission intensity is high enough and the collective dynamics lead to longer emission life. The authors in their previous publications had proposed a novel approach to obtain stabilized field emission current from a stacked CNT array of pointed height distribution. A mesoscopic modeling technique was employed, which took into account electro-mechanical forces in the CNTs, as well as transport of conduction electron coupled with electron-phonon induced heat generation from the CNT tips. The reported analysis of pointed arrangements of the array showed that the current density distribution was greatly localized in the middle of the array, the scatter due to electrodynamic force field was minimized, and the temperature transients were much smaller compared to those in an array with random height distribution. In the present paper we develop a method to compute the emission efficiency of the CNT array in terms of the amount of electrons hitting the anode surface using trajectory calculations. Effects of secondary electron emission and parasitic capacitive nonlin

关键词： Carbon nanotubes

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BRINT: A BINARY ROTATION INVARIANT AND NOISE TOLERANT TEXTURE DESCRIPTOR

BRINT: A BINARY ROTATION INVARIANT AND NOISE TOLERANT TEXTUR...

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IEEE International Conference on Image Processing

作者： Li Liu Bing Yang Paul Fieguth Zheng Yang Yingmei Wei Information System Engineering Key Lab School of Information System and Management National University of Defense Technology Department of Systems Design Engineering University of Waterloo

ISBN: (纸本)9781479923427

Local Binary Pattern (LBP) and its variants are effective and popular descriptors for texture classification. Most LBP like descriptors have disadvantages including sensitiveness to noise and inability to capture long distance texture information. In this paper we propose a simple, efficient, yet robust multi-resolution descriptor to texture classification - Binary Rotation Invariant and Noise Tolerant (BRINT). The proposed descriptor is very fast to build, very compact while remaining robust to illumination variations, rotation changes and noise. We develop a novel and simple strategy - averaging before binarization - to compute a local binary descriptor based on the conventional LBP approach. Points are sampled in a circular neigh-borhood, but keeping the number of bins in a single-scale LBP histogram constant and small by averaging over several contiguous pixels in the circle. There is no need for pre-training, no texton dictionary, and no tuning of parameters to deal with different datasets. Experiments on the Outex test suite demonstrate that the proposed approach is very robust to noise and significantly outperforms the state-of-the-art in terms of classifying noise corrupted textures.

关键词： Texture descriptors Rotation invariance Local binary pattern (LBP) Noise robust Feature extraction Texture analysis Noise robustness texture descriptor Noise texture analysis Feature extraction Descriptors LBP gene LBP Binary textures averaging binarization

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Coaxial Circular Test Structure Applicable to both Ohmic and Schottky Characteristics for ZnO/Si Heterojunctions Assessment

Coaxial Circular Test Structure Applicable to both Ohmic and...

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IEEE International Conference on Microelectronic Test Structures

作者： Norihiro Miyazawa Naoto Usami Haibin Wang Takaya Kubo Hiroshi Segawa Yoshio Mita Akio Higo Department of Electrical Engineering and Information Systems The University of Tokyo Research Center for Advanced Science and Technology The University of Tokyo Systems Design Lab School of Engineering The University of Tokyo

ISBN: (数字)9781728140087

ISBN: (纸本)9781728140094

Characterizing junction is essential in new devices research. We are investigating a hybrid infrared-sensitive opto-electronic device on silicon-based large scale integrated circuits, among others, PbS colloidal quantum dots/ZnO/Si hybrid IR detector. To assess such new materials we propose a coaxial circular test structure. The structure is composed of common-central inner (circular) and outer (doughnut) electrodes. In addition to traditional Circular TLM (CTLM) test structure, which was used for ohmic contact, the junction type (ohmic or Schottky) can be identified and quantitatively be analyzed by measuring various gaps and surface areas of the proposed structure (Coaxial CTLM, CCTLM). The measurement using test structure on our n-type ZnO / n-type Si heterojunction sample revealed that the junction was Schottky type, which was opposite to our expectation.

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Text Detection & Recognition in the Wild for Robot Localization

arXiv

引用

arXiv 2022年

作者： Raisi, Zobeir Zelek, John Vision and Image Processing Lab Systems Design Engineering Department University of Waterloo WaterlooONN2L 3G1 Canada

Signage is everywhere and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection & recognition in the wild is challenging due such factors as pose, irregular text, illumination and occlusion. We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. This model is more suitable for VPR. Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions in different challenging places. To evaluate our proposed architecture’s performance for VPR, we conducted several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The initial experimental results show that the proposed method outperforms the SOTA methods in terms of precision and recall when tested on this benchmark. © 2022, CC BY-NC-SA.

关键词： Robot applications

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MPC-based Battery Thermal Management Controller for Plug-in Hybrid Electric Vehicles

MPC-based Battery Thermal Management Controller for Plug-in ...

引用

American Control Conference

作者： Yasaman Masoudi Nasser L. Azad the Faculty of Engineering Smart Hybrid and Electric Vehicle Lab Systems Design Engineering Department University of Waterloo Waterloo ON N2L3G1 Canada

ISBN: (纸本)9781509045839

This paper proposes a model predictive control (MPC) scheme for the battery thermal management system (BTMS) of a given plug-in hybrid electric vehicle, namely the Toyota Plug-in Prius. As temperature plays a key-role in battery life and performance, BTMS design has become a critical problem in all of the battery-based technologies. Although BTMS control design in its basic form can be well represented by a reference tracking problem, there exists only little research in the literature addressing this important control problem. Due to the importance of a prediction component in thermal systems, the idea in this paper is to design a concrete BTMS controller using the nonlinear model predictive control (NMPC) theory and examine its applicability to fill this gap. The promising simulation results indicate the prosperity of the proposed BTMS control methodology and thus pave the way for use of the model-based thermal management techniques in a wide range of upcoming battery-based devices.

关键词： proposed BTMS control Thermal management Hybrid electric vehicles Battery Life Batteries Standing Orders nonlinear model predictive control living standards Controllers

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PbS Colloidal Quantum Dots/ZnO/Si Hybrid Photodiode with Various Reverse Bias Voltages

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IEEJ Transactions on Sensors and Micromachines 2022年第1期142卷 8-12页

作者： Miyazawa, Norihiro Wang, Haibin Usami, Naoto Kubo, Takaya Segawa, Hiroshi Mita, Yoshio Higo, Akio Department of Electrical Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan 3-1-1 Yoshinodai Chuo-ku Sagamihara252-5210 Japan Systems Design Lab School of Engineering The University of Tokyo 2-11-16 Yayoi Bunkyo-ku Tokyo113-0032 Japan

We have realized an infrared (IR) photodiode with silicon for the future beyond-VLSI photonics. Colloidal quantum dot (CQD) integration with Si is a simple process. We previously reported a PbS CQD/ZnO/Si hybrid IR photodiode. The device had an absorption peak at 1230 nm. To improve the quantum efficiency, we investigated the dose levels of Si substrates and improved the external quantum efficiency (EQE) by using a structural approach, such as using a higher doping level of Si. We achieved a 1230 nm absorption peak with an ITO/Au/PbS-EDT/PbS-I CQDs/ZnO/Si hybrid IR photodiode with an EQE close to 7%, at a reverse bias of −0.5 V. © 2022 The Institute of Electrical Engineers of Japan.

关键词： Lead compounds

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REGION-LEVEL labELS IN ICE CHARTS CAN PRODUCE PIXEL-LEVEL SEGMENTATION FOR SEA ICE TYPES

arXiv

引用

arXiv 2024年

作者： Patel, Muhammed Chen, Xinwei Xu, Linlin Chen, Yuhao Scott, K. Andrea Clausi, David A. Vision and Image Processing Lab Systems Design Engineering University of Waterloo Canada School of Marine Science and Engineering South China University of Technology China Department of Mechanical and Mechatronics Engineering University of Waterloo Canada

Fully supervised deep learning approaches have demonstrated impressive accuracy in sea ice classification, but their dependence on high-resolution labels presents a significant challenge due to the difficulty of obtaining such data. In response, our weakly supervised learning method provides a compelling alternative by utilizing lower-resolution regional labels from expert-annotated ice charts. This approach achieves exceptional pixel-level classification performance by introducing regional loss representations during training to measure the disparity between predicted and ice chart-derived sea ice type distributions. Leveraging the AI4Arctic Sea Ice Challenge Dataset, our method outperforms the fully supervised U-Net benchmark, the top solution of the AutoIce challenge, in both mapping resolution and class-wise accuracy, marking a significant advancement in automated operational sea ice mapping. © 2024, CC BY.

关键词： Sea ice

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COMPLETE AND SCAlabLE MULTI-ROBOT PLANNING IN TUNNEL ENVIRONMENTS

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IFAC Proceedings Volumes 2006年第20期39卷 26-31页

作者： Mike Peasgood John McPhee Christopher Clark Lab for Intelligent and Autonomous Robotics Department of Mechanical Engineering University of Waterloo Waterloo Ontario Canada N2L 3G1 Motion Research Group Department of Systems Design Engineering University of Waterloo Waterloo Ontario Canada N2L 3G1

This paper addresses the challenging problem of finding collision-free trajectories for many robots moving to individual goals within a common environment. Most popular algorithms for multi-robot planning manage the complexity of the problem by planning trajectories for robots sequentially; such decoupled methods may fail to find a solution even if one exists. In contrast, this paper describes a multi-phase approach to the planning problem that guarantees a solution by creating and maintaining obstacle-free paths through the environment as required for each robot to reach its goal. Using a topological graph and spanning tree representation of a tunnel or corridor environment, the multi-phase planner is capable of planning trajectories for up to r = L – 1 robots, where the spanning tree includes L leaves. Monte Carlo simulations in a large environment with varying number of robots demonstrate that the algorithm can solve planning problems requiring complex coordination of many robots that cannot be solved with a decoupled approach, and is scalable with complexity linear in the number of robots.

关键词： mobile robots efficient algorithms path planning trajectory planning

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Distributed nonlinear model predictive control and metric learning for heterogeneous vehicle platooning with cut-in/cut-out maneuvers

arXiv

引用

arXiv 2020年

作者： Basiri, Mohammad Hossein Ghojogh, Benyamin Azad, Nasser L. Fischmeister, Sebastian Karray, Fakhri Crowley, Mark The Department of Systems Design Engineering University of Waterloo ON Canada The Department of Electrical and Computer Engineering University of Waterloo ON Canada The Smart Hybrid and Electric Vehicles Systems Lab The Real-Time Embedded Software Lab The Machine Learning Lab The Centre for Pattern Analysis and Machine Intelligence

Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC) is widely used in literature for platoon control to achieve certain objectives, such as safely reducing the distance among consecutive vehicles while following the leader vehicle. In this paper, we propose a Distributed Nonlinear MPC (DNMPC), based upon an existing approach, to control a heterogeneous dynamic platoon with unidirectional topologies, handling possible cut-in/cut-out maneuvers. The introduced method addresses a collision-free driving experience while tracking the desired speed profile and maintaining a safe desired gap among the vehicles. The time of convergence in the dynamic platooning is derived based on the time of cut-in and/or cut-out maneuvers. In addition, we analyze the improvement level of driving comfort, fuel economy, and absolute and relative convergence of the method by using distributed metric learning and distributed optimization with Alternating Direction Method of Multipliers (ADMM). Simulation results on a dynamic platoon with cut-in and cut-out maneuvers and with different unidirectional topologies show the effectiveness of the introduced method. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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