Journal articles: 'Experimental Jetset'

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Krawiec, Marcela. "Effect of Storage Duration and Temperature on Sets Loss and Bolting of Onion." Vegetable Crops Research Bulletin 66, no. 1 (January 1, 2007): 47–58. http://dx.doi.org/10.2478/v10032-007-0007-7.

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Effect of Storage Duration and Temperature on Sets Loss and Bolting of OnionOnion sets of two cultivars - Rawska and Jetset F1were calibrated into three grades depending on the diameter of onion (11-15, 16-20 and 21-25 mm). From October to March, sets were kept in the following conditions: I - 24 weeks at 0-1°C, II - 15 weeks at 0-1°C, then 9 weeks at 18-20°C, III - 11 weeks at 0-1°C, then 13 weeks at 18-20°C, IV - 24 weeks at 18-20°C. The storage loss caused by complete drying up, sprouting into leaves and occurrence of disease symptoms were determined. The sets left over after evaluation of storage loss were planted in the field in order to determine bolting of onion. Cold storage (0-1°C) for 24 weeks reduced loss but stimulated bolting. In the case of Jetset F1, warm storage (18-20°C) for the last 9 weeks of the 24 weeks' experimental storage period practically eliminated bolting. The sets of Rawska required longer exposures of 18-20°C at the end of storage for suppressing of inflorescence development than Jetset F1. The smaller onion sets were kept the shorter the duration of warm storage required to reduce bolting. The longer onion sets were stored at 18-20°C the greater storage loss were noted.

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Suder, Jakub, Kacper Podbucki, and Tomasz Marciniak. "Power Requirements Evaluation of Embedded Devices for Real-Time Video Line Detection." Energies 16, no. 18 (September 18, 2023): 6677. http://dx.doi.org/10.3390/en16186677.

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In this paper, the comparison of the power requirements during real-time processing of video sequences in embedded systems was investigated. During the experimental tests, four modules were tested: Raspberry Pi 4B, NVIDIA Jetson Nano, NVIDIA Jetson Xavier AGX, and NVIDIA Jetson Orin AGX. The processing speed and energy consumption have been checked, depending on input frame size resolution and the particular power mode. Two vision algorithms for detecting lines located in airport areas were tested. The results show that the power modes of the NVIDIA Jetson modules have sufficient computing resources to effectively detect lines based on the camera image, such as Jetson Xavier in mode MAXN or Jetson Orin in mode MAXN, with a resolution of 1920 × 1080 pixels and a power consumption of about 19 W for 24 FPS for both algorithms tested.

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Riho, Hiramoto, Shirahama Yoshikuni, and Toyoda Kuniaki. "1122 EXPERIMENTAL STUDY ON VELOCITY FIELD OF PARALLEL CIRCULAR JETS." Proceedings of the International Conference on Jets, Wakes and Separated Flows (ICJWSF) 2013.4 (2013): _1122–1_—_1122–4_. http://dx.doi.org/10.1299/jsmeicjwsf.2013.4._1122-1_.

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Bao, Xiujuan, Lianhui Li, Weiqiang Ou, and Lu Zhou. "Robot intelligent grasping experimental platform combining Jetson NANO and machine vision." Journal of Physics: Conference Series 2303, no. 1 (July 1, 2022): 012053. http://dx.doi.org/10.1088/1742-6596/2303/1/012053.

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Abstract According to the requirements of the intelligent manufacturing experimental teaching project, this experimental platform develops a robot intelligent grasping experimental platform based on Jetson NANO and vision. The platform adopts the Jetson NANO controller based on Google open source machine learning framework TensorFlow + Keras, cooperates with the computer vision library OpenCV to develop the machine vision algorithm, and realizes the grasping and handling of materials based on a 6-DOF articulated robot, it can support open project-based experimental teaching. The experimental platform integrates the knowledge of robot control, machine vision, mechatronics and other aspects of courses, which can train students’ ability to solve complex engineering problems and stimulate students’ creative thinking. And the experimental platform has good openness. In the experimental teaching, it only needs to specify the objectives to be achieved without restricting the specific implementation methods. Students can independently design and test the algorithm program of each link in the visual positioning system through software, and deeply experience the relevant theoretical knowledge and practical methods through hands-on experiments.

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Ayoub, Naeem, and Peter Schneider-Kamp. "Real-Time On-Board Deep Learning Fault Detection for Autonomous UAV Inspections." Electronics 10, no. 9 (May 5, 2021): 1091. http://dx.doi.org/10.3390/electronics10091091.

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Inspection of high-voltage power lines using unmanned aerial vehicles is an emerging technological alternative to traditional methods. In the Drones4Energy project, we work toward building an autonomous vision-based beyond-visual-line-of-sight (BVLOS) power line inspection system. In this paper, we present a deep learning-based autonomous vision system to detect faults in power line components. We trained a YOLOv4-tiny architecture-based deep neural network, as it showed prominent results for detecting components with high accuracy. For running such deep learning models in a real-time environment, different single-board devices such as the Raspberry Pi 4, Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier were used for the experimental evaluation. Our experimental results demonstrated that the proposed approach can be effective and efficient for fully automatic real-time on-board visual power line inspection.

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Narasimha Reddy, S., and M. Venkata Ratnam. "An Experimental Analysis of Lean Binary Mixture Segregation in a Continuous Liquid Fluidized Bed." International Journal of Chemical Engineering 2023 (November 9, 2023): 1–17. http://dx.doi.org/10.1155/2023/7756174.

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This study examined the phenomenon of particle segregation in lean-phase binary mixtures, with a specific focus on the effect of particle size variations while flowing over a continuous liquid fluidized bed (LFB). The experimental configuration included a cylindrical column with a 72 mm internal diameter and 3 m vertical height. The binary mixture considered for this investigation was made up of solid materials that were rich in flotsam and jetsam. The study encompassed various factors, including liquid velocity, solid feed rate, and feed composition, in order to examine the separations containing flotsam and jetsam. A segregation index was calculated for each of the various combinations. On the other hand, the fluidization of the blend consisting of two solid components displayed notable differences in its behavior when compared to the reported effects of particle separation in any of the mixtures. Empirical correlations have been employed to establish relationships between variables, particularly with respect to solid entrainment and top and bottom product purity levels.

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Yose, Edward, Victor Victor, and Nico Surantha. "Portable smart attendance system on Jetson Nano." Bulletin of Electrical Engineering and Informatics 13, no. 2 (April 1, 2024): 1050–59. http://dx.doi.org/10.11591/eei.v13i2.6061.

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The masked face recognition-based attendance management system is an important biometric-based attendance tracking solution, especially in light of the COVID-19 pandemic. Despite the use of various methods and techniques for face detection and recognition, there currently needs to be a system that can accurately recognize individuals while they are wearing a mask. This system has been designed to overcome the challenges of widespread mask use, impacting the effectiveness of traditional face recognition-based attendance systems. The proposed system uses an innovative method that recognizes individuals even while wearing a mask without the need for removal. With its high compatibility and real-time operation, it can be easily integrated into schools and workplaces through an embedded system like the Jetson Nano or conventional computers executing attendance applications. This innovative approach and its compatibility make it a desirable solution for organizations looking to improve their attendance-tracking process. The Experimental results indicates using maximum resources possible the execution time needed on Jetson Nano is 15 to 22 seconds and 14 to 18 seconds respectively and the average frame capture if there are at least one face detected on Jetson Nano is 3-4 frames.

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Lin, Chun-Yuan, Jin Ye, Che-Lun Hung, Chung-Hung Wang, Min Su, and Jianjun Tan. "Constructing a Bioinformatics Platform with Web and Mobile Services Based on NVIDIA Jetson TK1." International Journal of Grid and High Performance Computing 7, no. 4 (October 2015): 57–73. http://dx.doi.org/10.4018/ijghpc.2015100105.

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Current high-end graphics processing units (abbreviate to GPUs), such as NVIDIA Tesla, Fermi, Kepler series cards which contain up to thousand cores per-chip, are widely used in the high performance computing fields. These GPU cards (called desktop GPUs) should be installed in personal computers/servers with desktop CPUs; moreover, the cost and power consumption of constructing a high performance computing platform with these desktop CPUs and GPUs are high. NVIDIA releases Tegra K1, called Jetson TK1, which contains 4 ARM Cortex-A15 CPUs and 192 CUDA cores (Kepler GPU) and is an embedded board with low cost, low power consumption and high applicability advantages for embedded applications. NVIDIA Jetson TK1 becomes a new research direction. Hence, in this paper, a bioinformatics platform was constructed based on NVIDIA Jetson TK1. ClustalWtk and MCCtk tools for sequence alignment and compound comparison were designed on this platform, respectively. Moreover, the web and mobile services for these two tools with user friendly interfaces also were provided. The experimental results showed that the cost-performance ratio by NVIDIA Jetson TK1 is higher than that by Intel XEON E5-2650 CPU and NVIDIA Tesla K20m GPU card.

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Kei, Wada, and Ishii Tatsuya. "1127 ACOUSTIC ABSORPTION OF PERFORATED PLATES WITH FINE JETS: EXPERIMENTAL RESULTS AND ANALYTICAL MODELS." Proceedings of the International Conference on Jets, Wakes and Separated Flows (ICJWSF) 2013.4 (2013): _1127–1_—_1127–6_. http://dx.doi.org/10.1299/jsmeicjwsf.2013.4._1127-1_.

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10

Sa, Rongyuan, and Minoru Takahashi. "ICONE19-43422 EXPERIMENTAL STUDY ON THERMAL INTERACTION OF ETHANOL JETS IN HIGH TEMPERATURE FLUORINERT." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_175.

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11

Xiang, Rui Le, Tian Feng Zhou, Zhi Qiang Liang, and Xi Bin Wang. "Experimental Research on Flexible Polishing by Compound Diamond Powder." Advanced Materials Research 1136 (January 2016): 484–89. http://dx.doi.org/10.4028/www.scientific.net/amr.1136.484.

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Surface polishing is an important technique in the precision/ultra-precision process, especially in the optical industry. This paper introduces a new polishing method named Flexible Polishing by Compound Diamond Powder (FPCDP), in which the polishing abrasive grains are made of a kind of compound particles with flexible base and abrasive diamond powder. In FPCDP process, the compound abrasive grains are jetted onto the workpiece surface with an accelerated speed at an incident angle, and the micro irregularities on the workpiece surface are removed during the scratching, rolling and ploughing by the compound abrasive grains. The mechanism of polishing process is analyzed and the machining condition is optimized by experiments, which shows that FPCDP is an efficient method to improve the glass surface roughness.

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Xie, Man, Lianguo Wang, Miao Ma, and Pengfei Zhang. "Performance Evaluation Method for Intelligent Computing Components for Space Applications." Sensors 24, no. 1 (December 27, 2023): 145. http://dx.doi.org/10.3390/s24010145.

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The computational performance requirements of space payloads are constantly increasing, and the redevelopment of space-grade processors requires a significant amount of time and is costly. This study investigates performance evaluation benchmarks for processors designed for various application scenarios. It also constructs benchmark modules and typical space application benchmarks specifically tailored for the space domain. Furthermore, the study systematically evaluates and analyzes the performance of NVIDIA Jetson AGX Xavier platform and Loongson platforms to identify processors that are suitable for space missions. The experimental results of the evaluation demonstrate that Jetson AGX Xavier performs exceptionally well and consumes less power during dense computations. The Loongson platform can achieve 80% of Xavier’s performance in certain parallel optimized computations, surpassing Xavier’s performance at the expense of higher power consumption.

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Hyde, T. H., and A. C. Chambers. "An experimental investigation of mixed-mode creep crack growth in Jethete M152 at 550°C." Materials at High Temperatures 9, no. 3 (August 1991): 127–38. http://dx.doi.org/10.1080/09603409.1991.11689650.

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14

Xu, Jianyu, Siwei Cheng, Libin Su, and Yonggang Guo. "Research and development of plateau portable landslide detection equipment based on Jetson Nano." Transactions on Computer Science and Intelligent Systems Research 4 (June 20, 2024): 162–71. http://dx.doi.org/10.62051/eqrtb452.

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In response to the problems that landslides occur in remote locations, are difficult to monitor and landslide target detection is not easily deployed at the embedded end, a landslide detection device based on the Jetson nano edge device is developed. The technology detects information of landslide feature objects through YOLOV5m technology, and is able to output landslide area information as well as geographic location information while accurately detecting landslide targets. A dataset of 25,000 landslide images is used to build the data set, and the training and validation sets are divided 9:1. A deep learning network is used to extract landslide features and build a landslide target detection model. After that, the train.py file is placed on the cloud server for training, and the best.pt file is migrated to Jetson Nano and tested on the embedded platform. The experimental results show that the average running time of single frame of YOLOV5m model in the embedded device is 100ms, and the detection accuracy can be maintained above 80%, which can achieve accurate detection and information acquisition of landslide target on Jetson Nano device, and lay the foundation for the development of edge device module for landslide detection later.

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Caba, Julián, María Díaz, Jesús Barba, Raúl Guerra, and Jose A. de la Torre and Sebastián López. "FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations." Remote Sensing 12, no. 22 (November 13, 2020): 3741. http://dx.doi.org/10.3390/rs12223741.

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Remote-sensing platforms, such as Unmanned Aerial Vehicles, are characterized by limited power budget and low-bandwidth downlinks. Therefore, handling hyperspectral data in this context can jeopardize the operational time of the system. FPGAs have been traditionally regarded as the most power-efficient computing platforms. However, there is little experimental evidence to support this claim, which is especially critical since the actual behavior of the solutions based on reconfigurable technology is highly dependent on the type of application. In this work, a highly optimized implementation of an FPGA accelerator of the novel HyperLCA algorithm has been developed and thoughtfully analyzed in terms of performance and power efficiency. In this regard, a modification of the aforementioned lossy compression solution has also been proposed to be efficiently executed into FPGA devices using fixed-point arithmetic. Single and multi-core versions of the reconfigurable computing platforms are compared with three GPU-based implementations of the algorithm on as many NVIDIA computing boards: Jetson Nano, Jetson TX2 and Jetson Xavier NX. Results show that the single-core version of our FPGA-based solution fulfils the real-time requirements of a real-life hyperspectral application using a mid-range Xilinx Zynq-7000 SoC chip (XC7Z020-CLG484). Performance levels of the custom hardware accelerator are above the figures obtained by the Jetson Nano and TX2 boards, and power efficiency is higher for smaller sizes of the image block to be processed. To close the performance gap between our proposal and the Jetson Xavier NX, a multi-core version is proposed. The results demonstrate that a solution based on the use of various instances of the FPGA hardware compressor core achieves similar levels of performance than the state-of-the-art GPU, with better efficiency in terms of processed frames by watt.

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Abdul Hassan, Noor Faleh, Ali A. Abed, and Turki Y. Abdalla. "Face mask detection using deep learning on NVIDIA Jetson Nano." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5427. http://dx.doi.org/10.11591/ijece.v12i5.pp5427-5434.

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<span>In December 2019, the coronavirus pandemic started. Coronavirus desease-19 (COVID-19) is transmitted directly from contaminated surfaces via direct touch. To combat the virus, a multitude of equipment is needed. Masks are a vital element of personal protection in crowded places. As a result, determining if a person is wearing a face mask is critical to assimilating to contemporary society. To accomplish the objective, the model presented in this paper used deep learning libraries and OpenCV. This approach was chosen for safety concerns due to its high resource efficiency during deployment. The classifier was built using the MobileNetV2 structure, which was designed to be lightweight and capable of being utilized in embedded devices such as the NVIDIA Jetson Nano to do real-time mask recognition. The stages of model construction were collecting, pre-processing, splitting data, creating the model, training the model, and applying the model. This system utilized image processing techniques and deep learning to process a live video feed. When someone is not wearing a mask, the output eventually produces an alarm sound through a built-in buzzer. Experimental results and testing were used to verify the suggested system's performance. Including both training and testing, the achieved recognition rate was 99%.</span>

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Liu, Lei, Eric B. Blancaflor, and Mideth Abisado. "A LIGHTWEIGHT MULTI-PERSON POSE ESTIMATION SCHEME BASED ON JETSON NANO." Applied Computer Science 19, no. 1 (March 31, 2023): 1–14. http://dx.doi.org/10.35784/acs-2023-01.

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As the basic technology of human action recognition, pose estimation is attracting more and more researchers' attention, while edge application scenarios pose a higher challenge. This paper proposes a lightweight multi-person pose estimation scheme to meet the needs of real-time human action recognition on the edge end. This scheme uses AlphaPose to extract human skeleton nodes, and adds ResNet and Dense Upsampling Revolution to improve its accuracy. Meanwhile, we use YOLO to enhance AlphaPose’s support for multi-person pose estimation, and optimize the proposed model with TensorRT. In addition, this paper sets Jetson Nano as the Edge AI deployment device of the proposed model and successfully realizes the model migration to the edge end. The experimental results show that the speed of the optimized object detection model can reach 20 FPS, and the optimized multi-person pose estimation model can reach 10 FPS. With the image resolution of 320×240, the model’s accuracy is 73.2%, which can meet the real-time requirements. In short, our scheme can provide a basis for lightweight multi-person action recognition scheme on the edge end.

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Radhouane, Amina, Nejla Mahjoub Said, Hatem Mhiri, George Lepalec, and Philippe Bournot. "OS8-1-5 Experimental and numerical study of twin inclined circular jets emerging into a cool crossflow." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2007.6 (2007): _OS8–1–5–1—_OS8–1–5–6. http://dx.doi.org/10.1299/jsmeatem.2007.6._os8-1-5-1.

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Valera-Rodriguez, Francisco-Jose, Pilar Manzanares-Lopez, and Maria-Dolores Cano. "Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL." Applied Sciences 14, no. 10 (May 10, 2024): 4047. http://dx.doi.org/10.3390/app14104047.

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In the context of the increasing integration of Internet of Things technologies and the growing importance of data lakes, the need for robust cybersecurity measures to protect privacy without compromising data utility becomes key. Aiming to address the privacy–security challenge in such digital ecosystems, this study explores the application of Fully Homomorphic Encryption (FHE) using the Microsoft SEAL library. FHE allows for operations on encrypted data, offering a promising opportunity for maintaining data confidentiality during processing. Our research employs systematic experimental tests on datasets to evaluate the performance of homomorphic encryption in terms of CPU usage and execution time, executed across traditional PC configurations and a NVIDIA Jetson Nano device to assess the scalability and practicality of FHE in edge computing. The results reveal a performance disparity between computing environments, with the PC showing stable performance and the Jetson Nano revealing the limitations of edge devices in handling encryption tasks due to computational and memory constraints.

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Barkela, Veronika, and Miriam Leuchter. "Effectiveness of Automated Formative Feedback in an Online Tutorial for Promoting Summarizing." Journal of Educational Technology Development and Exchange 17, no. 1 (2024): 67–95. http://dx.doi.org/10.18785/jetde.1701.04.

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We conducted a study with the aim to investigate the effectiveness of automated formative feedback in improving students’ ability to summarize. One-hundred and thirty-eight undergraduate students in an elementary education program were asked to summarize six scientific texts, with the experimental group (N=87) receiving automated formative feedback in a computer-based learning environment (FALB). FALB provides automated feedback about content coverage, copying words avoidance, redundancy avoidance, relevance, and length. Comparing the experimental group to a control group (N=51), results implied that summarizing skills could be fostered when interacting with FALB. In particular, the automated formative feedback promoted the adherence to the predefined length and the avoidance of copying words while maintaining a high content coverage, fostering cognitive processes essential for constructing a mental model of a text. In addition, students in the experimental group were able to maintain high quality summaries in their final session when not scaffolded. In conclusion, FALB supports the alignment of internal standards with external standards and provides an incentive to revise and engage with texts.

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Fujita, K., T. Ito, and N. Kohno. "Experimental study on the vibration of circular cylinders subjected to cross-flow jetted from a narrow gap." Journal of Fluids and Structures 4, no. 1 (January 1990): 99–124. http://dx.doi.org/10.1016/0889-9746(90)90057-c.

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Zhi, Liang Ze, Jun Feng Wang, Feng Gu, and Zong Hai Zhang. "Experimental Investigation on the Effect of PM₁₀ Removal by Charged Fine Mist." Advanced Materials Research 518-523 (May 2012): 3015–19. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.3015.

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In order to study the effect of the dust（PM10） removal in the outdoor environment by the charged fine mist, the adsorption characteristics of the charged fine mist on the aerosol particles was considered. This paper reports an experimental investigation of the phenomena which occur when charged fine mist was jetted into the aerosol laden space. The results presents evidence to show that the charged fine mist is an efficient and convenient method of gathering the particulate matter, especially for sub-micron size solid particles. The more narrow the particle size distribution is, the better the effect of the removal of fine solid particles becomes. This study showed that the charged fine mist can greatly reduced particulate matter in the outdoor environment, which could improve the quality of environment significantly.

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Aboneh, Tagel, Abebe Rorissa, Ramasamy Srinivasagan, and Ashenafi Gemechu. "Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure." Technologies 9, no. 3 (July 2, 2021): 47. http://dx.doi.org/10.3390/technologies9030047.

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Diseases have adverse effects on crop production and yield loss. Various diseases such as leaf rust, stem rust, and strip rust can affect yield quality and quantity for a studied area. In addition, manual wheat disease identification and interpretation is time-consuming and cumbersome. Currently, decisions related to plants mainly rely on the level of expertise in the domain. To resolve these challenges and to identify wheat disease as early as possible, we implemented different deep learning models such as Inceptionv3, Resnet50, and VGG16/19. This research was conducted in collaboration with Bishoftu Agricultural Research Institute, Ethiopia. Our main objective was to automate plant-disease identification using advanced deep learning approaches and image data. For the experiment, RGB image data were collected from the Bishoftu area. From the experimental results, the VGG19 model classified wheat disease with 99.38% accuracy.

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Yu, Zhaotao, Liang Zhang, and Jongwon Kim. "The Performance Analysis of PSO-ResNet for the Fault Diagnosis of Vibration Signals Based on the Pipeline Robot." Sensors 23, no. 9 (April 26, 2023): 4289. http://dx.doi.org/10.3390/s23094289.

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In the context of pipeline robots, the timely detection of faults is crucial in preventing safety incidents. In order to ensure the reliability and safety of the entire application process, robots’ fault diagnosis techniques play a vital role. However, traditional diagnostic methods for motor drive end-bearing faults in pipeline robots are often ineffective when the operating conditions are variable. An efficient solution for fault diagnosis is the application of deep learning algorithms. This paper proposes a rolling bearing fault diagnosis method (PSO-ResNet) that combines a Particle Swarm Optimization algorithm (PSO) with a residual network. A number of vibration signal sensors are placed at different locations in the pipeline robot to obtain vibration signals from different parts. The input to the PSO-ResNet algorithm is a two-bit image obtained by continuous wavelet transform of the vibration signal. The accuracy of this fault diagnosis method is compared with different types of fault diagnosis algorithms, and the experimental analysis shows that PSO-ResNet has higher accuracy. The algorithm was also deployed on an Nvidia Jetson Nano and a Raspberry Pi 4B. Through comparative experimental analysis, the proposed fault diagnosis algorithm was chosen to be deployed on the Nvidia Jetson Nano and used as the core fault diagnosis control unit of the pipeline robot for practical scenarios. However, the PSO-ResNet model needs further improvement in terms of accuracy, which is the focus of future research work.

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Shanmuganatan, S. P., M. Madhusudan, Sherina Josephine, S. Samsak, N. Thejaswi Yoganarasimha, and P. Sewanth Gowda. "Experimental Investigation and Optimization of process parameter in Binder Jet 3D Printing." Journal of Physics: Conference Series 2748, no. 1 (April 1, 2024): 012006. http://dx.doi.org/10.1088/1742-6596/2748/1/012006.

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Abstract Prototyping is frequently performed using Additive Manufacturing (AM), which has practical usages in the aerospace, defence, medical, and automotive industries. AM is a viable alternative to the established powder metallurgy method that guarantees a good and enhanced surface polish. The primary purpose of the current study is to optimize the printing process parameters related to binder jet printing technology. In the additive manufacturing technique known as ‘binder jetting,’ a commercial printer selectively applies a liquid binding agent to a thin coating of powder particles. The parameters governing AM process includes roller speed, layer thickness, infill density and bed temperature. Taguchi analysis based on L16 orthogonal array is adapted in the study to optimize the various process parameters. The output responses namely ultimate tensile strength and toughness properties were evaluated to optimize the process. Tensile testing is carried out as per ASTM standard. The study also involves the observation of the binder jet process, associative science of densification after sintering, evolution of microstructural characteristics of the binder jetted part and application of the optimized process parameters of selected material.

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Wang, Yao, and Peizhi Yu. "A Fast Intrusion Detection Method for High-Speed Railway Clearance Based on Low-Cost Embedded GPUs." Sensors 21, no. 21 (November 1, 2021): 7279. http://dx.doi.org/10.3390/s21217279.

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The efficiency and the effectiveness of railway intrusion detection are crucial to the safety of railway transportation. Most current methods of railway intrusion detection or obstacle detection are inappropriate for large-scale applications due to their high cost or limited coverage. In this study, we present a fast and low-cost solution to intrusion detection of high-speed railways. As the solution to heavy computational burdens in the current convolutional-neural-network-based detection methods, the proposed method is mainly a novel neural network based on the SSD framework, which includes a feature extractor using an improved MobileNet and a lightweight and efficient feature fusion module. In addition, aiming to improve the detection accuracy of small objects, the feature map weights are introduced through convolution operation to fuse features at different scales. TensorRT is employed to optimize and deploy the proposed network in the low-cost embedded GPU platform, NVIDIA Jetson TX2, to enhance the efficiency. The experimental results show that the proposed methods achieved 89% mAP on the railway intrusion detection dataset, and the average processing time for a single frame was 38.6 ms on the Jetson TX2 module, which satisfies the need of real-time processing.

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Dang, Tuan Minh, Hai Ngo Minh, Hung Trung Nguyen, and Dong Nhu Hoang. "An Efficient Face Recognition System Based on Edge Processing Using GPUs." JST: Smart Systems and Devices 34, no. 1 (January 15, 2024): 1–8. http://dx.doi.org/10.51316/jst.171.ssad.2024.34.1.1.

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In this work, an efficient and accurate face recognition system based on edge processing using GPUs was completely developed. A complete pipeline that contains a sequence of processing steps, including pre-processing, face feature extraction, and matching, is proposed. For processing steps, lightweight deep neural models were developed and optimized so that they could be computationally accelerated on an embedded hardware of Nvidia’s Jetson Nano. Besides the core processing pipeline, a database, as well as a user application server were also developed to fully meet the requirements of readily commercialized applications. The experimental evaluation results show that our system has a very high accuracy based on the BLUFR benchmark, with a precision of 98.642%. Also, the system is very computationally efficient, as the computing time to recognize an ID in a dataset of 1171IDs with 10141 images on the Jetson Nano is only 165ms. For the critical case, the system can process 4 camera streams and simultaneously recognize a maximum of 40 IDs within a computing time of 458ms for each ID. With its high-speed and accuracy characteristics, the developed system has a high potential for practical applications.

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Han, R. D., Jun Yan Liu, Y. Zhang, and L. Zhang. "Experimental Study on Green Cutting with Water Vapor as Coolant and Lubricant." Key Engineering Materials 315-316 (July 2006): 45–50. http://dx.doi.org/10.4028/www.scientific.net/kem.315-316.45.

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Green cutting has become focus of attention in ecological and environmental protection. Water vapor is cheap, pollution-free and eco-friendly. Therefore water vapor is a good and economical coolant and lubricant. Water vapor generator and vapor feeding system are developed to generate and feed water vapor. The lubricating method of water vapor is that the water vapor jet flow is directly jetted on the cutting zone and it cancels the fluid phase penetrating the capillaries in cutting zone. So it increases the time reserve of penetration and improves the property of penetration and lubricating effect. In order to find the influenced disciplinarians on lubricating effect with nozzle diameter, the parameters of water vapor jet flow and cooling distance (the distance between nozzle and cutting zone), experiments are carried out which hard alloy YT15 (P10 type in ISO) tool is used in cutting C45 steel. Experimental results show that the cutting force becomes lowered and chip thickness becomes thinned with the nozzle diameter decreasing. With the saturated vapor pressure increasing and the cooling distance shortening, the cutting force is lowed and the chip thickness is thinned too. Therefore the application of water vapor as coolant and lubricant can realize the green cutting in industry.

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Jabłoński, Bartłomiej, Dariusz Makowski, Piotr Perek, Patryk Nowak vel Nowakowski, Aleix Puig Sitjes, Marcin Jakubowski, Yu Gao, and Axel Winter. "Evaluation of NVIDIA Xavier NX Platform for Real-Time Image Processing for Plasma Diagnostics." Energies 15, no. 6 (March 12, 2022): 2088. http://dx.doi.org/10.3390/en15062088.

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Machine protection is a core task of real-time image diagnostics aiming for steady-state operation in nuclear fusion devices. The paper evaluates the applicability of the newest low-power NVIDIA Jetson Xavier NX platform for image plasma diagnostics. This embedded NVIDIA Tegra System-on-a-Chip (SoC) integrates a Graphics Processing Unit (GPU) and Central Processing Unit (CPU) on a single chip. The hardware differences and features compared to the previous NVIDIA Jetson TX2 are signified. Implemented algorithms detect thermal events in real-time, utilising the high parallelism provided by the embedded General-Purpose computing on Graphics Processing Units (GPGPU). The performance and accuracy are evaluated on the experimental data from the Wendelstein 7-X (W7-X) stellarator. Strike-line and reflection events are primarily investigated, yet benchmarks for overload hotspots, surface layers and visualisation algorithms are also included. Their detection might allow for automating real-time risk evaluation incorporated in the divertor protection system in W7-X. For the first time, the paper demonstrates the feasibility of complex real-time image processing in nuclear fusion applications on low-power embedded devices. Moreover, GPU-accelerated reference processing pipelines yielding higher accuracy compared to the literature results are proposed, and remarkable performance improvement resulting from the upgrade to the Xavier NX platform is attained.

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Cheng, Wangfeng, Xuanyao Wang, and Bangguo Mao. "Research on Lane Line Detection Algorithm Based on Instance Segmentation." Sensors 23, no. 2 (January 10, 2023): 789. http://dx.doi.org/10.3390/s23020789.

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Aiming at the current lane line detection algorithm in complex traffic scenes, such as lane lines being blocked by shadows, blurred roads, and road sparseness, which lead to low lane line detection accuracy and poor real-time detection speed, this paper proposes a lane line detection algorithm based on instance segmentation. Firstly, the improved lightweight network RepVgg-A0 is used to encode road images, which expands the receptive field of the network; secondly, a multi-size asymmetric shuffling convolution model is proposed for the characteristics of sparse and slender lane lines, which enhances the ability to extract lane line features; an adaptive upsampling model is further proposed as a decoder, which upsamples the feature map to the original resolution for pixel-level classification and detection, and adds the lane line prediction branch to output the confidence of the lane line; and finally, the instance segmentation-based lane line detection algorithm is successfully deployed on the embedded platform Jetson Nano, and half-precision acceleration is performed using NVDIA’s TensorRT framework. The experimental results show that the Acc value of the lane line detection algorithm based on instance segmentation is 96.7%, and the FPS is 77.5 fps/s. The detection speed deployed on the embedded platform Jetson Nano reaches 27 fps/s.

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Liu, Mingyang, Min Chen, Zhigang Wu, Bin Zhong, and Wangfen Deng. "Implementation of Intelligent Indoor Service Robot Based on ROS and Deep Learning." Machines 12, no. 4 (April 11, 2024): 256. http://dx.doi.org/10.3390/machines12040256.

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When faced with challenges such as adapting to dynamic environments and handling ambiguous identification, indoor service robots encounter manifold difficulties. This paper aims to address this issue by proposing the design of a service robot equipped with precise small-object recognition, autonomous path planning, and obstacle-avoidance capabilities. We conducted in-depth research on the suitability of three SLAM algorithms (GMapping, Hector-SLAM, and Cartographer) in indoor environments and explored their performance disparities. Upon this foundation, we have elected to utilize the STM32F407VET6 and Nvidia Jetson Nano B01 as our processing controllers. For the program design on the STM32 side, we are employing the FreeRTOS operating system, while for the Jetson Nano side, we are employing ROS (Robot Operating System) for program design. The robot employs a differential drive chassis, enabling successful autonomous path planning and obstacle-avoidance maneuvers. Within indoor environments, we utilized the YOLOv3 algorithm for target detection, achieving precise target identification. Through a series of simulations and real-world experiments, we validated the performance and feasibility of the robot, including mapping, navigation, and target detection functionalities. Experimental results demonstrate the robot’s outstanding performance and accuracy in indoor environments, offering users efficient service and presenting new avenues and methodologies for the development of indoor service robots.

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Staar, Benjamin, Suleyman Bayrak, Dominik Paulkowski, and Michael Freitag. "A U-Net Based Approach for Automating Tribological Experiments." Sensors 20, no. 22 (November 23, 2020): 6703. http://dx.doi.org/10.3390/s20226703.

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Tribological experiments (i.e., characterizing the friction and wear behavior of materials) are crucial for determining their potential areas of application. Automating such tests could hence help speed up the development of novel materials and coatings. Here, we utilize convolutional neural networks (CNNs) to automate a common experimental setup whereby an endoscopic camera was used to measure the contact area between a rubber sample and a spherical counterpart. Instead of manually determining the contact area, our approach utilizes a U-Net-like CNN architecture to automate this task, creating a much more efficient and versatile experimental setup. Using a 5× random permutation cross validation as well as additional sanity checks, we show that we approached human-level performance. To ensure a flexible and mobile setup, we implemented the method on an NVIDIA Jetson AGX Xavier development kit where we achieved ~18 frames per second by employing mixed-precision training.

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Mantau, Aprinaldi, Irawan Widayat, Jenq-Shiou Leu, and Mario Köppen. "A Human-Detection Method Based on YOLOv5 and Transfer Learning Using Thermal Image Data from UAV Perspective for Surveillance System." Drones 6, no. 10 (October 4, 2022): 290. http://dx.doi.org/10.3390/drones6100290.

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At this time, many illegal activities are being been carried out, such as illegal mining, hunting, logging, and forest burning. These things can have a substantial negative impact on the environment. These illegal activities are increasingly rampant because of the limited number of mofficers and the high cost required to monitor them. One possible solution is to create a surveillance system that utilizes artificial intelligence to monitor the area. Unmanned aerial vehicles (UAV) and NVIDIA Jetson modules (general-purpose GPUs) can be inexpensive and efficient because they use few resources. The problem from the object-detection field utilizing the drone’s perspective is that the objects are relatively small compared to the observation space, and there are also illumination and environmental challenges. In this study, we will demonstrate the use of the state-of-the-art object-detection method you only look once (YOLO) v5 using a dataset of visual images taken from a UAV (RGB-image), along with thermal infrared information (TIR), to find poachers. There are seven scenario training methods that we have employed in this research with RGB and thermal infrared data to find the best model that we will deploy on the Jetson Nano module later. The experimental result shows that a new model with pre-trained model transfer learning from the MS COCO dataset can improve YOLOv5 to detect the human–object in the RGBT image dataset.

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Zhuo, Sichao, Xiaoming Zhang, Ziyi Chen, Wei Wei, Fang Wang, Quanlong Li, and Yufan Guan. "DAMP-YOLO: A Lightweight Network Based on Deformable Features and Aggregation for Meter Reading Recognition." Applied Sciences 13, no. 20 (October 20, 2023): 11493. http://dx.doi.org/10.3390/app132011493.

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With the development of Industry 4.0, although some smart meters have appeared on the market, traditional mechanical meters are still widely used due to their long-standing presence and the difficulty of modifying or replacing them in large quantities. Most meter readings are still manually taken on-site, and some are even taken in high-risk locations such as hazardous chemical storage. However, existing methods often fail to provide real-time detections or result in misreadings due to the complex nature of natural environments. Thus, we propose a lightweight network called DAMP-YOLO. It combines the deformable CSP bottleneck (DCB) module, aggregated triplet attention (ATA) mechanism, meter data augmentation (MDA), and network pruning (NP) with the YOLOv8 model. In the meter reading recognition dataset, the model parameters decreased by 30.64% while mAP50:95 rose from 87.92% to 88.82%, with a short inference time of 129.6 ms for the Jetson TX1 intelligent car. In the VOC dataset, our model demonstrated improved performance, with mAP50:95 increasing from 41.03% to 45.64%. The experimental results show that the proposed model is competitive for general object detection tasks and possesses exceptional feature extraction capabilities. Additionally, we have devised and implemented a pipeline on the Jetson TX1 intelligent vehicle, facilitating real-time meter reading recognition in situations where manual interventions are inconvenient and hazardous, thereby confirming its feasibility for practical applications.

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Grizzle, Andrew C., Amy Elliott, Kate L. Klein, and Pawan Tyagi. "Surface Finishing and Coating Parameters Impact on Additively Manufactured Binder-Jetted Steel–Bronze Composites." Materials 17, no. 3 (January 26, 2024): 598. http://dx.doi.org/10.3390/ma17030598.

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In this paper, electroless nickel plating is explored for the protection of binder-jetting-based additively manufactured (AM) composite materials. Electroless nickel plating was attempted on binder-jetted composites composed of stainless steel and bronze, resulting in differences in the physicochemical properties. We investigated the impact of surface finishing, plating solution chemistry, and plating parameters to attain a wide range of surface morphologies and roughness levels. We employed the Keyence microscope to quantitatively evaluate dramatically different surface properties before and after the coating of AM composites. Scanning electron microscopy revealed a wide range of microstructural properties in relation to each combination of surface finishing and coating parameters. We studied chempolishing, plasma cleaning, and organic cleaning as the surface preparation methods prior to coating. We found that surface preparation dictated the surface roughness. Taguchi statistical analysis was performed to investigate the relative strength of experimental factors and interconnectedness among process parameters to attain optimum coating qualities. The quantitative impacts of phosphorous level, temperature, surface preparation, and time factor on the roughness of the nickel-plated surface were 17.95%, 8.2%, 50.02%, and 13.21%, respectively. On the other hand, the quantitative impacts of phosphorous level, temperature, surface preparation, and time factor on the thickness of nickel plating were 35.12%, 41.40%, 3.87%, and 18.24%, respectively. The optimum combination of the factors’ level projected the lowest roughness of Ra at 7.76 µm. The optimum combination of the factors’ level projected the maximum achievable thickness of ~149 µm. This paper provides insights into coating process for overcoming the sensitivity of AM composites in hazardous application spaces via robust coating.

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FUJITA, Katsuhisa, Tomohiro ITO, and Norio KOHNO. "Experimental study on the vibration of circular cylinders subjected to cross-flow. (Case of cross-flow jetted from a narrow gap)." JSME international journal 30, no. 268 (1987): 1622–30. http://dx.doi.org/10.1299/jsme1987.30.1622.

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Chang, Rong, Bangyuan Li, Junpeng Dang, Chuanxu Yang, Anning Pan, and Yang Yang. "Real-Time Intelligent Detection System for Illegal Wearing of On-Site Power Construction Worker Based on Edge-YOLO and Low-Cost Edge Devices." Applied Sciences 13, no. 14 (July 18, 2023): 8287. http://dx.doi.org/10.3390/app13148287.

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Ensuring personal safety and preventing accidents are critical aspects of power construction safety supervision. However, current monitoring methods are inefficient and unreliable as most of them rely on manual monitoring and transmission, which results in slow detection and delayed warnings regarding violations. To overcome these challenges, we propose an intelligent detection system that can accurately identify instances of illegal wearing of power construction workers in real-time. Firstly, we integrated the squeeze-and-excitation (SE) module into our convolutional neural network to enhance detection accuracy. This module effectively prioritizes informative features while suppressing less relevant ones, resulting in improved overall performance. Secondly, we present an embedded real-time detection system that utilizes Jetson Xavier NX and Edge-YOLO. This system promptly detects and alerts power construction workers of instances of illegal wearing behavior. To ensure a lightweight implementation, we design appropriate detection heads based on target size and distribution, reducing model parameters while enhancing detection speed and minimizing accuracy loss. Additionally, we employed data augmentation to enhance the system’s robustness. Our experimental results demonstrate that our improved Edge-YOLO model achieves high detection precision and recall rates of 0.964 and 0.966, respectively, with a frame rate of 35.36 frames per second when deployed on Jetson Xavier NX. Therefore, Edge-YOLO proves to be an ideal choice for intelligent real-time detection systems, providing superior accuracy and speed performance compared to the original YOLOv5s model and other models in the YOLO series for safety monitoring at construction sites.

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Zhao, Zuopeng, Zhongxin Zhang, Xinzheng Xu, Yi Xu, Hualin Yan, and Lan Zhang. "A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices." Computational Intelligence and Neuroscience 2020 (December 15, 2020): 1–12. http://dx.doi.org/10.1155/2020/6616584.

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It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network—Lightweight Microscopic Detection Network (LMS-DN). The network can be implemented on embedded devices such as NVIDIA Jetson TX2. The experimental results show that LMS-DN only needs fewer parameters and calculation costs to obtain higher identification accuracy and stronger anti-interference than other popular object detection models.

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Zhang, Yan, Hongfei Wang, Ruixuan Xu, Xinyu Yang, Yichen Wang, and Yunling Liu. "High-Precision Seedling Detection Model Based on Multi-Activation Layer and Depth-Separable Convolution Using Images Acquired by Drones." Drones 6, no. 6 (June 20, 2022): 152. http://dx.doi.org/10.3390/drones6060152.

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Crop seedling detection is an important task in the seedling stage of crops in fine agriculture. In this paper, we propose a high-precision lightweight object detection network model based on a multi-activation layer and depth-separable convolution module to detect crop seedlings, aiming to improve the accuracy of traditional artificial intelligence methods. Due to the insufficient dataset, various image enhancement methods are used in this paper. The dataset in this paper was collected from Shahe Town, Laizhou City, Yantai City, Shandong Province, China. Experimental results on this dataset show that the proposed method can effectively improve the seedling detection accuracy, with the F1 score and mAP reaching 0.95 and 0.89, respectively, which are the best values among the compared models. In order to verify the generalization performance of the model, we also conducted a validation on the maize seedling dataset, and experimental results verified the generalization performance of the model. In order to apply the proposed method to real agricultural scenarios, we encapsulated the proposed model in a Jetson logic board and built a smart hardware that can quickly detect seedlings.

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Липко, Юрий, Yuriy Lipko, Александр Пашинин, Aleksandr Pashinin, Равиль Рахматулин, Ravil Rakhmatulin, Виталий Хахинов, and Vitaliy Khakhinov. "Geomagnetic effects caused by rocket exhaust jets." Solar-Terrestrial Physics 2, no. 3 (October 27, 2016): 43–55. http://dx.doi.org/10.12737/22284.

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In the space experiment Radar–Progress, we have made 33 series of measurements of geomagnetic variations during ignitions of engines of Progress cargo spacecraft in low Earth orbit. We used magneto-measuring complexes, installed at observatories of the Institute of Solar-Terrestrial Physics of Siberian Branch of the Russian Academy of Sciences, and magnetotelluric equipment of a mobile complex. We assumed that engine running can cause geomagnetic disturbances in field tubes crossed by the spacecraft. When analyzing experimental data, we took into account the following space weather factors: solar wind parameters, total daily mid-latitude geomagnetic activity index Kр, geomagnetic auroral electrojet index AE, global geomagnetic activity.

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Jabłoński, Bartłomiej, Dariusz Makowski, and Piotr Perek. "Implementation of Thermal Event Image Processing Algorithms on NVIDIA Tegra Jetson TX2 Embedded System-on-a-Chip." Energies 14, no. 15 (July 22, 2021): 4416. http://dx.doi.org/10.3390/en14154416.

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Advances in Infrared (IR) cameras, as well as hardware computational capabilities, contributed towards qualifying vision systems as reliable plasma diagnostics for nuclear fusion experiments. Robust autonomous machine protection and plasma control during operation require real-time processing that might be facilitated by Graphics Processing Units (GPUs). One of the current aims of image plasma diagnostics involves thermal events detection and analysis with thermal imaging. The paper investigates the suitability of the NVIDIA Jetson TX2 Tegra-based embedded platform for real-time thermal events detection. Development of real-time processing algorithms on an embedded System-on-a-Chip (SoC) requires additional effort due to the constrained resources, yet low-power consumption enables embedded GPUs to be applied in MicroTCA.4 computing architecture that is prevalent in nuclear fusion projects. For this purpose, the authors have proposed, developed and optimised GPU-accelerated algorithms with the use of available software tools for NVIDIA Tegra systems. Furthermore, the implemented algorithms are evaluated and benchmarked on Wendelstein 7-X (W7-X) stellarator experimental data against the corresponding alternative Central Processing Unit (CPU) implementations. Considerable improvement is observed for the accelerated algorithms that enable real-time detection on the embedded SoC platform, yet some encountered limitations when developing parallel image processing routines are described and signified.

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FUJITA, Katsuhisa, Tomohiro ITO, and Norio KOHNO. "Experimental study on the vibration of circular cylinders subjected to cross flow. (1st report Case of cross flow jetted from a narrow gap)." Transactions of the Japan Society of Mechanical Engineers Series C 52, no. 484 (1986): 3130–36. http://dx.doi.org/10.1299/kikaic.52.3130.

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Li, Rui, and Zi Ming Kou. "High-Pressure Water Spray Research Based on Neural Network." Applied Mechanics and Materials 29-32 (August 2010): 138–42. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.138.

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The spray cleaning method is important and universal in many industrial processes and other occasion. Because the size of the waterdrop is one of key factors for cleaning, this paper not only studied the relationship between the size of waterdrop and other influencing factors, but also researched the forecasted method for the size of waterdrop. In lab, by measuring the size of the waterdrop, jetted by one kind of nozzle, data were acquired and were used to train the Back Propagation Neural Network ( BPNN ). Through comparing those diameters, between measured in lab and calculated by BPNN after trained. It was acquired that the maximum errors was smaller than 1.62%, between the computed results and the factual measured ones. The experimental results showed that BPNN is an effective tool to predict the variation of the non-linear waterdrop diameter.

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Cheng, Qianqing, Hongjun Wang, Bin Zhu, Yingchun Shi, and Bo Xie. "A Real-Time UAV Target Detection Algorithm Based on Edge Computing." Drones 7, no. 2 (January 30, 2023): 95. http://dx.doi.org/10.3390/drones7020095.

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Small UAV target detection plays an important role in maintaining the security of cities and citizens. UAV targets have the characteristics of low-altitude flights, slow speeds, and miniaturization. Taking these characteristics into account, we present a real-time UAV target detection algorithm called Fast-YOLOv4 based on edge computing. By adopting Fast-YOLOv4 in the edge computing platform NVIDIA Jetson Nano, intelligent analysis can be performed on the video to realize the fast detection of UAV targets. However, the current iteration of the edge-embedded detection algorithm has low accuracy and poor real-time performance. To solve these problems, this paper introduces the lightweight networks MobileNetV3, Multiscale-PANet, and soft-merge to improve YOLOv4, thus obtaining the Fast-YOLOv4 model. The backbone of the model uses depth-wise separable convolution and an inverse residual structure to simplify the network’s structure and to improve its detection speed. The neck of the model adds a scale fusion branch to improve the feature extraction ability and strengthen small-scale target detection. Then, the predicted boxes filtering algorithm uses the soft-merge function to replace the traditionally used NMS (non-maximum suppression). Soft-merge can improve the model’s detection accuracy by fusing the information of predicted boxes. Finally, the experimental results show that the mAP (mean average precision) and FPS (frames per second) of Fast-YOLOv4 reach 90.62% and 54 f/s, respectively, in the workstation. In the NVIDIA Jetson Nano platform, the FPS of Fast-YOLOv4 is 2.5 times that of YOLOv4. This improved model performance meets the requirements for real-time detection and thus has theoretical significance and application value.

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Li, Chunchao, Yuanxi Peng, Mingrui Su, and Tian Jiang. "GPU Parallel Implementation for Real-Time Feature Extraction of Hyperspectral Images." Applied Sciences 10, no. 19 (September 24, 2020): 6680. http://dx.doi.org/10.3390/app10196680.

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As the application of real-time requirements gradually increases or real-time processing and responding become the bottleneck of the task, parallel computing in hyperspectral image applications has also become a significant research focus. In this article, a flexible and efficient method is utilized in the noise adaptive principal component (NAPC) algorithm for feature extraction of hyperspectral images. From noise estimation to feature extraction, we deploy a complete CPU-GPU collaborative computing solution. Through the computer experiments on three traditional hyperspectral datasets, our proposed improved NAPC (INAPC) has stable superiority and provides a significant speedup compared with the OpenCV and PyTorch implementation. What’s more, we creatively establish a complete set of uncrewed aerial vehicle (UAV) photoelectric platform, including UAV, hyperspectral camera, NVIDIA Jetson Xavier, etc. Flight experimental results show, considering hyperspectral image data acquisition and transmission time, the proposed algorithm meets the feature extraction of real-time processing.

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Milana, Edoardo, Oscar Hernán Ramírez-Agudelo, and Jacob Estevam Schmiedt. "Autonomous Reading of Gauges in Unstructured Environments." Sensors 22, no. 17 (September 3, 2022): 6681. http://dx.doi.org/10.3390/s22176681.

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This paper introduces GAUREAD, an end-to-end computer vision system that is able to autonomously read analogic gauges with circular shapes and linear scales in unstructured environments. Existing gauge reading software still relies on some manual entry, like the gauge location and the gauge scale, or they are able to work just with a frontal view. On the contrary, GAUREAD comprises all the necessary steps to make the measurement unconstrained from previous information, including gauge detection from scene, perspective rectification and scale reconstruction. Our algorithm achieves a speed of 800 milliseconds per reading on the NVIDIA Jetson Nano 4 GB. Experimental tests show that GAUREAD can provide a measurement with an error within 3% for perspective angles below 20° and within 9% up to 50°. The system is foreseen to be implemented on mobile robotics to automatise not only safety routines, but also critical security operations.

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Vu, Khanh Hung, Duc Phuc Nguyen, Duc Dung Nguyen, and Hoang-Anh Pham. "Investigation into Perceptual-Aware Optimization for Single-Image Super-Resolution in Embedded Systems." Electronics 12, no. 11 (June 5, 2023): 2544. http://dx.doi.org/10.3390/electronics12112544.

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Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight Generative Adversarial Network (GAN)-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.

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Chen, Weizhuo, Lijie Zhang, and Fangrui Luo. "An attitude estimation algorithm for the telescopic arm of the boarding bridge based on YOLOv5 and EPnP." Journal of Physics: Conference Series 2492, no. 1 (May 1, 2023): 012022. http://dx.doi.org/10.1088/1742-6596/2492/1/012022.

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Abstract Aiming at the attitude estimation of the telescopic arm of the boarding bridge in the process of docking with the offshore wind turbine, an attitude estimation algorithm for the telescopic arm of the boarding bridge based on YOLOv5 and EPnP is proposed in this paper. YOLOv5 algorithm is used to detect four marks in the offshore wind turbine logo image, and then the EPnP algorithm is used to solve attitude angles of the telescopic arm relative to the landing port of the offshore wind turbine according to the 2D pixel coordinates of the center point of each mark. The experimental results show that the attitude estimation error of the proposed algorithm is less than 0.4° and the data update frequency of the algorithm implemented in NVIDIA Jetson AGX Xavier is 25Hz, which meets the real-time and accuracy requirements for the attitude detection of the boarding bridge telescopic arm.

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Hoang, Toan, Phong Nguyen, Noi Truong, Young Lee, and Kang Park. "Deep RetinaNet-Based Detection and Classification of Road Markings by Visible Light Camera Sensors." Sensors 19, no. 2 (January 11, 2019): 281. http://dx.doi.org/10.3390/s19020281.

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Detection and classification of road markings are a prerequisite for operating autonomous vehicles. Although most studies have focused on the detection of road lane markings, the detection and classification of other road markings, such as arrows and bike markings, have not received much attention. Therefore, we propose a detection and classification method for various types of arrow markings and bike markings on the road in various complex environments using a one-stage deep convolutional neural network (CNN), called RetinaNet. We tested the proposed method in complex road scenarios with three open datasets captured by visible light camera sensors, namely the Malaga urban dataset, the Cambridge dataset, and the Daimler dataset on both a desktop computer and an NVIDIA Jetson TX2 embedded system. Experimental results obtained using the three open databases showed that the proposed RetinaNet-based method outperformed other methods for detection and classification of road markings in terms of both accuracy and processing time.

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Chen, Yen-Lin, Ming-Feng Chang, Chao-Wei Yu, Xiu-Zhi Chen, and Wen-Yew Liang. "Learning-Directed Dynamic Voltage and Frequency Scaling Scheme with Adjustable Performance for Single-Core and Multi-Core Embedded and Mobile Systems." Sensors 18, no. 9 (September 12, 2018): 3068. http://dx.doi.org/10.3390/s18093068.

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Dynamic voltage and frequency scaling (DVFS) is a well-known method for saving energy consumption. Several DVFS studies have applied learning-based methods to implement the DVFS prediction model instead of complicated mathematical models. This paper proposes a lightweight learning-directed DVFS method that involves using counter propagation networks to sense and classify the task behavior and predict the best voltage/frequency setting for the system. An intelligent adjustment mechanism for performance is also provided to users under various performance requirements. The comparative experimental results of the proposed algorithms and other competitive techniques are evaluated on the NVIDIA JETSON Tegra K1 multicore platform and Intel PXA270 embedded platforms. The results demonstrate that the learning-directed DVFS method can accurately predict the suitable central processing unit (CPU) frequency, given the runtime statistical information of a running program, and achieve an energy savings rate up to 42%. Through this method, users can easily achieve effective energy consumption and performance by specifying the factors of performance loss.

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Journal articles on the topic 'Experimental Jetset'

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