Micromachines

Research

23 pages, 6890 KiB

Open AccessArticle

K8sSim: A Simulation Tool for Kubernetes Schedulers and Its Applications in Scheduling Algorithm Optimization

by Shilin Wen, Rui Han, Ke Qiu, Xiaoxin Ma, Zeqing Li, Hongjie Deng and Chi Harold Liu

Micromachines 2023, 14(3), 651; https://doi.org/10.3390/mi14030651 - 13 Mar 2023

Cited by 2 | Viewed by 3810

In recent years, Kubernetes (K8s) has become a dominant resource management and scheduling system in the cloud. In practical scenarios, short-running cloud workloads are usually scheduled through different scheduling algorithms provided by Kubernetes. For example, artificial intelligence (AI) workloads are scheduled through different [...] Read more.

In recent years, Kubernetes (K8s) has become a dominant resource management and scheduling system in the cloud. In practical scenarios, short-running cloud workloads are usually scheduled through different scheduling algorithms provided by Kubernetes. For example, artificial intelligence (AI) workloads are scheduled through different Volcano scheduling algorithms, such as

G A N G_M R P

,

G A N G_L R P

, and

G A N G_B R A

. One key challenge is that the selection of scheduling algorithms has considerable impacts on job performance results. However, it takes a prohibitively long time to select the optimal algorithm because applying one algorithm in one single job may take a few minutes to complete. This poses the urgent requirement of a simulator that can quickly evaluate the performance impacts of different algorithms, while also considering scheduling-related factors, such as cluster resources, job structures and scheduler configurations. In this paper, we design and implement a Kubernetes simulator called K8sSim, which incorporates typical Kubernetes and Volcano scheduling algorithms for both generic and AI workloads, and provides an accurate simulation of their scheduling process in real clusters. We use real cluster traces from Alibaba to evaluate the effectiveness of K8sSim, and the evaluation results show that (i) compared to the real cluster, K8sSim can accurately evaluate the performance of different scheduling algorithms with similar

C l o s e R a t e

(a novel metric we define to intuitively show the simulation accuracy), and (ii) it can also quickly obtain the scheduling results of different scheduling algorithms by accelerating the scheduling time by an average of 38.56×. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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23 pages, 6468 KiB

Open AccessArticle

Miniature Noninvasive Sensor Based on Impedance-Change Detection in Branches for Measuring Branch Ice Content in Overwintering Woody Plants

by Hao Tian, Chao Gao, Tao Xie and Chongchong Yu

Micromachines 2023, 14(2), 440; https://doi.org/10.3390/mi14020440 - 12 Feb 2023

Cited by 1 | Viewed by 1794

Abstract

Advancements in detection instruments have enabled the real-time acquisition of water information during plant growth; however, the real-time monitoring of freeze–thaw information during plant overwintering remains a challenge. Based on the relationship between the change in the water–ice ratio and branch impedance during [...] Read more.

Advancements in detection instruments have enabled the real-time acquisition of water information during plant growth; however, the real-time monitoring of freeze–thaw information during plant overwintering remains a challenge. Based on the relationship between the change in the water–ice ratio and branch impedance during freezing, a miniature noninvasive branch volume ice content (BVIC) sensor was developed for monitoring real-time changes in volumetric ice content and the ice freeze-thaw rate of woody plant branches during the overwintering period. The results of the performance analysis of the impedance measurement circuit show that the circuit has a lateral sensitivity range, measurement range, resolution, measurement accuracy, and power consumption of 0–35 mm, 0–100%, 0.05%, ±1.76%, and 0.25 W, respectively. The dynamic response time was 0.296 s. The maximum allowable error by the output voltage fluctuation, owing to the ambient temperature and humidity, was only ±0.635%, which meets the actual use requirements. The calibration curve fit coefficients were >0.98, indicating a significant correlation. The ice content of plant branches under cold stress was measured for indoor and field environments, and the sensors could effectively monitor changes in the branch ice content in plants exposed to cold stress. Additionally, they can differentiate between plants with different cold resistances, indicating the reliability of the BVIC sensor. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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17 pages, 16999 KiB

Open AccessArticle

Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System

by Jehong An, Kwonwook Son, Kwanghyun Jung, Sangyoo Kim, Yoonchul Lee, Sangbin Song and Jaeyoung Joo

Micromachines 2023, 14(2), 342; https://doi.org/10.3390/mi14020342 - 29 Jan 2023

Cited by 3 | Viewed by 1915

Abstract

This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to conduct the research. A [...] Read more.

This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to conduct the research. A deep learning-based dehaze model was learned by collecting real marine environment and open haze image data sets. By applying this learned model to the original hazy images, we obtained clear dehaze images. Comparing those two images, the concentration level of sea fog was derived into the PSNR and SSIM values. The brightness of the marine lantern was controlled through serial communication with the derived PSNR and SSIM values in a realized sea fog environment. As a result, it was possible to autonomously control the brightness of the marine lantern according to the concentration of sea fog, unlike the current marine lanterns, which adjust their brightness manually. This novel-developed lantern can efficiently utilize power consumption while enhancing its visibility. This method can be used for other fog concentration estimation systems at the embedded board level, so that applicable for local weather expectations, UAM navigation, and autonomous driving for marine ships. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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10 pages, 2490 KiB

Open AccessArticle

Internet of Things (IoT) for Soil Moisture Tensiometer Automation

by Ahmed Ali Abdelmoneim, Roula Khadra, Bilal Derardja and Giovanna Dragonetti

Micromachines 2023, 14(2), 263; https://doi.org/10.3390/mi14020263 - 19 Jan 2023

Cited by 5 | Viewed by 3223

Abstract

Monitoring of water retention behavior in soils is an essential process to schedule irrigation. To this end, soil moisture tensiometers usually equipped with mechanical manometers provide an easy and cost-effective monitoring of tension in unsaturated soils. Yet, periodic manual monitoring of many devices [...] Read more.

Monitoring of water retention behavior in soils is an essential process to schedule irrigation. To this end, soil moisture tensiometers usually equipped with mechanical manometers provide an easy and cost-effective monitoring of tension in unsaturated soils. Yet, periodic manual monitoring of many devices is a tedious task hindering the full exploitation of soil moisture tensiometers. This research develops and lab validates a low cost IoT soil moisture tensiometer. The IoT-prototype is capable of measuring tension up to −80 Kpa with R² = 0.99 as compared to the same tensiometer equipped with a mechanical manometer. It uses an ESP32 MCU, BMP180 barometric sensor and an SD card module to upload the measured points to a cloud service platform and establishes an online soil water potential curve. Moreover, it stores the reading on a micro-SD card as txt file. Being relatively cheap (76 USD) the prototype allows for more extensive measurements and, thus, for several potential applications such as soil water matric potential mapping, precision irrigation, and smart irrigation scheduling. In terms of energy, the prototype is totally autonomous, using a 2400 mAh Li-ion battery and a solar panel for charging, knowing that it uses deep sleep feature and sends three data points to the cloud each 6 h. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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18 pages, 7726 KiB

Open AccessArticle

Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images

by Jiaqiang Yang, Danyang Qin, Huapeng Tang, Haoze Bie, Gengxin Zhang and Lin Ma

Micromachines 2023, 14(2), 242; https://doi.org/10.3390/mi14020242 - 18 Jan 2023

Cited by 3 | Viewed by 2801

Abstract

With the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas [...] Read more.

With the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas with complex electromagnetic environments. However, in practical applications, position estimation using visual images is easily influenced by the user’s photo pose. In this paper, we propose a multiple-sensor-assisted visual localization method in which the method constructs a machine learning classifier using multiple smart sensors for pedestrian pose estimation, which improves the retrieval efficiency and localization accuracy. The method mainly combines the advantages of visual image location estimation and pedestrian pose estimation based on multiple smart sensors and considers the effect of pedestrian photographing poses on location estimation. The built-in sensors of smartphones are used as the source of pedestrian pose estimation data, which constitutes a feasible location estimation method based on visual information. Experimental results show that the method proposed in this paper has good localization accuracy and robustness. In addition, the experimental scene in this paper is a common indoor scene and the experimental device is a common smartphone. Therefore, we believe that the proposed method in this paper has the potential to be widely used in future indoor navigation applications in complex scenarios (e.g., mall navigation). Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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17 pages, 14622 KiB

Open AccessArticle

Implementation of an Embedded System into the Internet of Robotic Things

by Jakub Krejčí, Marek Babiuch, Ján Babjak, Jiří Suder and Rostislav Wierbica

Micromachines 2023, 14(1), 113; https://doi.org/10.3390/mi14010113 - 30 Dec 2022

Cited by 5 | Viewed by 3147

Abstract

The article describes the use of embedded systems in the Industrial Internet of Things and its benefits for industrial robots. For this purpose, the article presents a case study, which deals with an embedded system using an advanced microcontroller designed to be placed [...] Read more.

The article describes the use of embedded systems in the Industrial Internet of Things and its benefits for industrial robots. For this purpose, the article presents a case study, which deals with an embedded system using an advanced microcontroller designed to be placed directly on the robot. The proposed system is being used to collect information about industrial robot parameters that impact its behavior and its long-term condition. The device measures the robot’s surroundings parameters and its vibrations while working. Besides that, it also has an enormous potential to collect other parameters such as air pollution or humidity. The collected data are stored on the cloud platform and processed and analysed. The embedded system proposed in this article is conceived to be small and mobile, as it is a wireless system that can be easily applied to any industrial robot. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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23 pages, 52668 KiB

Open AccessArticle

An Automatic Detection Method for Cutting Path of Chips in Wafer

by Yuezong Wang, Haoran Jia, Pengxuan Jia and Kexin Chen

Micromachines 2023, 14(1), 59; https://doi.org/10.3390/mi14010059 - 26 Dec 2022

Cited by 2 | Viewed by 1953

Abstract

Microscopic imaging is easily affected by the strength of illumination, and the chip surface qualities of different wafers are different. Therefore, wafer images have defects such as uneven brightness distribution, obvious differences in chip region characteristics, etc., which affect the positioning accuracy of [...] Read more.

Microscopic imaging is easily affected by the strength of illumination, and the chip surface qualities of different wafers are different. Therefore, wafer images have defects such as uneven brightness distribution, obvious differences in chip region characteristics, etc., which affect the positioning accuracy of the wafer cutting path. For this reason, this thesis proposes an automatic chip-cutting path-planning method in the wafer image of the Glass Passivation Parts (GPPs) process without a mark. First, the wafer image is calibrated for brightness. Then, the template matching algorithm is used to determine the chip region and the center of gravity position of the chip region. We find the position of the geometric feature (interlayer) in the chip region, and the interlayer is used as an auxiliary location to determine the final cutting path. The experiment shows that the image quality can be improved, and chip region features can be highlighted when preprocessing the image with brightness calibration. The results show that the average deviation of the gravity coordinates of the chip region in the x direction is 2.82 pixels. We proceeded by finding the interlayer in the chip region, marking it with discrete points, and using the improved Random Sample Consensus (RANSAC) algorithm to remove the abnormal discrete points and fit the remaining discrete points. The average fitting error is 0.8 pixels, which is better than the least squares method (LSM). The cutting path location algorithm proposed in this paper can adapt to environmental brightness changes and different qualities of chips, accurately and quickly determine the cutting path, and improve the chip cutting yield. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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12 pages, 4190 KiB

Open AccessArticle

Implementation of the Haptic Tele-Weight Device Using a 10 MHz Smart Torch VLC Link

by Aqeel Farooq and Xiping Wu

Micromachines 2022, 13(11), 2031; https://doi.org/10.3390/mi13112031 - 20 Nov 2022

Viewed by 2049

Abstract

Considering the prerequisite need for a protected e-commerce platform, absence of haptic interaction in head-mounted displays (HMD), and exploitation of faster communication technology, this research work aims to present an amended version of the tele-weight device that utilizes the 6G visible light communication [...] Read more.

Considering the prerequisite need for a protected e-commerce platform, absence of haptic interaction in head-mounted displays (HMD), and exploitation of faster communication technology, this research work aims to present an amended version of the tele-weight device that utilizes the 6G visible light communication (VLC) technology, is faster in performance, and deals with a heavier article. The enhanced version of the device is to be called the ‘VLC tele-weight device’ and the aim for the VLC tele-weight device is to get it affixed over the headset which will allow the user to have the weight-based sensation of the product ordered on the virtual store. The proposed device sending end and receiving end part performs communication over the VLC link. Furthermore, Arduino Nano is used as the microcontroller (MCU) in the project. Sending end circuitry measures the weight using the load cell and HX711 amplifier combination and transmits it via the connected LED. The pre-equalizer circuit is connected between the LED and sending end part to improve the bandwidth. On the receiver side, the post-equalizer circuit improves the shape of the received pulse. The received weight value is then displayed using the motor-gear combination. The sending end device is to be sited at the virtual store, while the receiving end is planned to be positioned over the VR headset. The performance of the device was measured by performing repeated trials and the percentage error was found to be between 0.5–3%. Merging the field of embedded systems, internet of things (IoT), VLC, signal processing, virtual reality (VR), e-commerce, and haptic sensing, the idea proposed in this research work can help introduce the haptic interaction, and sensational realization-based innovation in immersive visualization (IV) and graphical user interface (GUI) domain. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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21 pages, 5352 KiB

Open AccessArticle

Research on Intelligent Robot Point Cloud Grasping in Internet of Things

by Zhongyu Wang, Shaobo Li, Qiang Bai, Qisong Song, Xingxing Zhang and Ruiqiang Pu

Micromachines 2022, 13(11), 1999; https://doi.org/10.3390/mi13111999 - 17 Nov 2022

Cited by 3 | Viewed by 1915

Abstract

The development of Internet of Things (IoT) technology has enabled intelligent robots to have more sensing and decision-making capabilities, broadening the application areas of robots. Grasping operation is one of the basic tasks of intelligent robots, and vision-based robot grasping technology can enable [...] Read more.

The development of Internet of Things (IoT) technology has enabled intelligent robots to have more sensing and decision-making capabilities, broadening the application areas of robots. Grasping operation is one of the basic tasks of intelligent robots, and vision-based robot grasping technology can enable robots to perform dexterous grasping. Compared with 2D images, 3D point clouds based on objects can generate more reasonable and stable grasping poses. In this paper, we propose a new algorithm structure based on the PointNet network to process object point cloud information. First, we use the T-Net network to align the point cloud to ensure its rotation invariance; then we use a multilayer perceptron to extract point cloud characteristics and use the symmetric function to get global features, while adding the point cloud characteristics attention mechanism to make the network more focused on the object local point cloud. Finally, a grasp quality evaluation network is proposed to evaluate the quality of the generated candidate grasp positions, and the grasp with the highest score is obtained. A grasping dataset is generated based on the YCB dataset to train the proposed network, which achieves excellent classification accuracy. The actual grasping experiments are carried out using the Baxter robot and compared with the existing methods; the proposed method achieves good grasping effect. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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18 pages, 8981 KiB

Open AccessArticle

Deep Learning for Clothing Style Recognition Using YOLOv5

by Yeong-Hwa Chang and Ya-Ying Zhang

Micromachines 2022, 13(10), 1678; https://doi.org/10.3390/mi13101678 - 5 Oct 2022

Cited by 22 | Viewed by 9062

Abstract

With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices [...] Read more.

With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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22 pages, 7074 KiB

Open AccessArticle

A Siamese Vision Transformer for Bearings Fault Diagnosis

by Qiuchen He, Shaobo Li, Qiang Bai, Ansi Zhang, Jing Yang and Mingming Shen

Micromachines 2022, 13(10), 1656; https://doi.org/10.3390/mi13101656 - 30 Sep 2022

Cited by 12 | Viewed by 2927

Abstract

Fault diagnosis methods based on deep learning have progressed greatly in recent years. However, the limited training data and complex work conditions still restrict the application of these intelligent methods. This paper proposes an intelligent bearing fault diagnosis method, i.e., Siamese Vision Transformer, [...] Read more.

Fault diagnosis methods based on deep learning have progressed greatly in recent years. However, the limited training data and complex work conditions still restrict the application of these intelligent methods. This paper proposes an intelligent bearing fault diagnosis method, i.e., Siamese Vision Transformer, suiting limited training data and complex work conditions. The Siamese Vision Transformer, combining Siamese network and Vision Transformer, is designed to efficiently extract the feature vectors of input samples in high-level space and complete the classification of the fault. In addition, a new loss function combining the Kullback-Liebler divergence both directions is proposed to improve the performance of the proposed model. Furthermore, a new training strategy termed random mask is designed to enhance input data diversity. A comparative test is conducted on the Case Western Reserve University bearing dataset and Paderborn dataset and our method achieves reasonably high accuracy with limited data and satisfactory generation capability for cross-domain tasks. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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13 pages, 4282 KiB

Open AccessArticle

Tactile Perception Object Recognition Based on an Improved Support Vector Machine

by Xingxing Zhang, Shaobo Li, Jing Yang, Yang Wang, Zichen Huang and Jinhu Zhang

Micromachines 2022, 13(9), 1538; https://doi.org/10.3390/mi13091538 - 17 Sep 2022

Viewed by 2039

Abstract

Tactile perception is an irreplaceable source of information for humans to explore the surrounding environment and has advantages over sight and hearing in processing the material properties and detailed shapes of objects. However, with the increasing uncertainty and complexity of tactile perception features, [...] Read more.

Tactile perception is an irreplaceable source of information for humans to explore the surrounding environment and has advantages over sight and hearing in processing the material properties and detailed shapes of objects. However, with the increasing uncertainty and complexity of tactile perception features, it is often difficult to collect highly available pure tactile datasets for research in the field of tactile perception. Here, we have proposed a method for object recognition on a purely tactile dataset and provide the original tactile dataset. First, we improved the differential evolution (DE) algorithm and then used the DE algorithm to optimize the important parameter of the Gaussian kernel function of the support vector machine (SVM) to improve the accuracy of pure tactile target recognition. The experimental comparison results show that our method has a better target recognition effect than the classical machine learning algorithm. We hope to further improve the generalizability of this method and provide an important reference for research in the field of tactile perception and recognition. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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15 pages, 796 KiB

Open AccessArticle

Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels

by Amr Marey, Mohamed Marey and Hala Mostafa

Micromachines 2022, 13(9), 1533; https://doi.org/10.3390/mi13091533 - 17 Sep 2022

Cited by 4 | Viewed by 1841

Abstract

Modulation recognition (MR) has become an essential topic in today’s wireless communications systems. Recently, convolutional neural networks (CNNs) have been employed as a potent tool for MR because of their ability to minimize the feature’s susceptibility to its surroundings and reduce the need [...] Read more.

Modulation recognition (MR) has become an essential topic in today’s wireless communications systems. Recently, convolutional neural networks (CNNs) have been employed as a potent tool for MR because of their ability to minimize the feature’s susceptibility to its surroundings and reduce the need for human feature extraction and evaluation. In particular, these investigations rely on the unrealistic assumption that the channel coefficient is typically one. This motivates us to overcome the previous constraint by providing a novel MR suited to fading wireless channels. This paper proposes a novel MR algorithm that is capable of recognizing a broad variety of modulation types, including M-ary QAM and M-ary PSK, without enforcing any restrictions on the modulation size, M. The analysis has shown that each modulation choice has a distinct two-dimensional in-phase quadrature histogram. This property is beneficially utilized to design a convolutional neural-network-based MR algorithm. When compared to the existing techniques, Monte Carlo simulations demonstrated the success of the proposed design. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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19 pages, 4492 KiB

Open AccessArticle

Smart Soil Water Sensor with Soil Impedance Detected via Edge Electromagnetic Field Induction

by Hao Tian, Chao Gao, Xin Zhang, Chongchong Yu and Tao Xie

Micromachines 2022, 13(9), 1427; https://doi.org/10.3390/mi13091427 - 29 Aug 2022

Cited by 6 | Viewed by 1872

Abstract

To address the problems in the calibration of soil water content sensors, in this study, we designed a low-cost edge electromagnetic field induction (EEMFI) sensor for soil water content measurement and proposed a normalized calibration method to eliminate the errors caused by the [...] Read more.

To address the problems in the calibration of soil water content sensors, in this study, we designed a low-cost edge electromagnetic field induction (EEMFI) sensor for soil water content measurement and proposed a normalized calibration method to eliminate the errors caused by the measurement sensor’s characteristics and improve the probe’s consistency, replaceability, and calibration efficiency. The model calibration curve-fitting coefficients of the EEMFI sensors were above 0.98, which indicated a significant correlation. The experimental results of the static and dynamic characteristics showed that the measurement range of the sensor varied from 0% to 100% saturation, measurement accuracy was within ±2%, the maximum value of the extreme difference of the stability test was 1.09%, the resolution was 0.05%, the delay time was 3.9 s, and the effective measurement diameter of the EEMFI sensor probe was 10 cm. The linear fit coefficient of determination of the results was greater than 0.99, and the maximum absolute error of the measurement results with the drying method was less than ±2%, which meets the requirements of soil water content measurement in agriculture and forestry fields. The field experiment results further showed that the EEMFI sensor can accurately respond to changes in soil water content, indicating that the EEMFI sensor is reliable. Full article

(This article belongs to the Special Issue Embedded System for Smart Sensors/Actuators and IoT Applications)

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