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Micromachines
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Artificial Intelligence Integration with Micro-Nano Systems
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Artificial Intelligence Integration with Micro-Nano Systems

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 31151

Special Issue Editor

Prof. Dr. Juvenal Rodriguez-Resendiz

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of technology has changed dramatically in recent decades, and its development has been exponential. A clear pattern in recent years is the miniaturization of systems, which have become nano- or microsystems. These systems have been implemented in multiple areas of industry and research to the point that specialized fields are required for their study. The control, estimation, and optimization of these systems employing artificial intelligence provide a new horizon in knowledge to achieve substantial improvements and multiple applications for this type of system through fuzzy techniques, neural networks, and metaheuristic algorithms, among other intelligent artificial methods.

This Special Issue aims to highlight the results of research and developments in the use of artificial intelligence applied to micro-nanosystems.

We invite contributions to this Special Issue on topics including but not limited to the following:

Neural networks in micro-nanosystems:

  • Machine learning
  • Deep learning

Optimization or Intelligent control in micro-nanosystems by artificial intelligence:

  • Metaheuristic algorithms
  • Fuzzy or neural technics
  • Mixed techniques

Embedded intelligent artificial for micro-nanosystems:

  • FPGA
  • DSP
  • Microcontroller

Applications with micro-nanosystems and artificial intelligence:

  • Robotics
  • Retrofitting
  • Mechatronics
  • Educative mechatronic systems
  • Manufacturing systems 

Dr. Juvenal Rodriguez-Resendiz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (9 papers)

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Research

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15 pages, 4649 KiB  
Article
Real-Time Detection of Drones Using Channel and Layer Pruning, Based on the YOLOv3-SPP3 Deep Learning Algorithm
by Xuetao Zhang, Kuangang Fan, Haonan Hou and Chuankai Liu
Micromachines 2022, 13(12), 2199; https://doi.org/10.3390/mi13122199 - 11 Dec 2022
Cited by 8 | Viewed by 1824
Abstract
Achieving a real-time and accurate detection of drones in natural environments is essential for the interception of drones intruding into high-security areas. However, a rapid and accurate detection of drones is difficult because of their small size and fast speed. In this paper [...] Read more.
Achieving a real-time and accurate detection of drones in natural environments is essential for the interception of drones intruding into high-security areas. However, a rapid and accurate detection of drones is difficult because of their small size and fast speed. In this paper a drone detection method as proposed by pruning the convolutional channel and residual structures of YOLOv3-SPP3. First, the k-means algorithm was used to cluster label the boxes. Second, the channel and shortcut layer pruning algorithm was used to prune the model. Third, the model was fine tuned to achieve a real-time detection of drones. The experimental results obtained by using the Ubuntu server under the Python 3.6 environment show that the YOLOv3-SPP3 algorithm is better than YOLOV3, Tiny-YOLOv3, CenterNet, SSD300, and faster R-CNN. There is significant compression in the size, the maximum compression factor is 20.1 times, the maximum detection speed is increased by 10.2 times, the maximum map value is increased by 15.2%, and the maximum precision is increased by 16.54%. The proposed algorithm achieves the mAP score of 95.15% and the detection speed of 112 f/s, which can meet the requirements of the real-time detection of UAVs. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Micro-Nano Systems)
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15 pages, 402 KiB  
Article
SCA-Safe Implementation of Modified SaMAL2R Algorithm in FPGA
by José de Jesús Morales Romero, Mario Alfredo Reyes Barranca, David Tinoco Varela, Luis Martin Flores Nava and Emilio Rafael Espinosa Garcia
Micromachines 2022, 13(11), 1872; https://doi.org/10.3390/mi13111872 - 30 Oct 2022
Cited by 1 | Viewed by 1496
Abstract
Cryptographic algorithms (RSA, DSA, and ECC) use modular exponentiation as part of the principal operation. However, Non-profiled Side Channel Attacks such as Simple Power Analysis and Differential Power Analysis compromise cryptographic algorithms that use such operation. In this work, we present a modification [...] Read more.
Cryptographic algorithms (RSA, DSA, and ECC) use modular exponentiation as part of the principal operation. However, Non-profiled Side Channel Attacks such as Simple Power Analysis and Differential Power Analysis compromise cryptographic algorithms that use such operation. In this work, we present a modification of a modular exponentiation algorithm implemented in programmable devices, such as the Field Programmable Gate Array, for which we use Virtex-6 and Artix-7 evaluation boards. It is shown that this proposal is not vulnerable to the attacks mentioned previously. Further, a comparison was made with other related works, which use the same family of FPGAs. These comparisons show that this proposal not only defeats physical attack but also reduces the number of resources. For instance, the present work reduces the Look-Up Tables by 3550 and the number of Flip-Flops was decreased by 62,583 compared with other works. Besides, the number of memory blocks used is zero in the present work, in contrast with others that use a large number of blocks. Finally, the clock cycles (latency) are compared in different programmable devices to perform operations. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Micro-Nano Systems)
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12 pages, 2527 KiB  
Article
Deep Learning Based Feature Selection Algorithm for Small Targets Based on mRMR
by Zhigang Ren, Guoquan Ren and Dinhai Wu
Micromachines 2022, 13(10), 1765; https://doi.org/10.3390/mi13101765 - 18 Oct 2022
Cited by 13 | Viewed by 1873
Abstract
Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature [...] Read more.
Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature recognition accuracy. The YOLOv5 neural network is used in this paper to achieve preliminary feature extraction, and the minimum redundancy maximum relevance algorithm is used for the 512 candidate features extracted in the fully connected layer to perform de-redundancy processing on the features with high correlation, reducing the dimension of the feature set and making small target feature recognition a reality. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can be improved. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can significantly improve the recognition accuracy. The experimental results demonstrate that using the minimum redundancy maximum relevance algorithm can effectively reduce the feature dimension and identify small target features. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Micro-Nano Systems)
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20 pages, 14843 KiB  
Article
Design and Implementation of a Prosthesis System Controlled by Electromyographic Signals Means, Characterized with Artificial Neural Networks
by David Tinoco-Varela, Jose Amado Ferrer-Varela, Raúl Dalí Cruz-Morales and Erick Axel Padilla-García
Micromachines 2022, 13(10), 1681; https://doi.org/10.3390/mi13101681 - 6 Oct 2022
Cited by 3 | Viewed by 2367
Abstract
Around the world many people loss a body member for many reasons, where advances of technology may be useful to help these people to improve the quality of their lives. Then, designing a technologically advanced prosthesis with natural movements is worthy for scientific, [...] Read more.
Around the world many people loss a body member for many reasons, where advances of technology may be useful to help these people to improve the quality of their lives. Then, designing a technologically advanced prosthesis with natural movements is worthy for scientific, commercial, and social reasons. Thus, research of manufacturing, designing, and signal processing may lead up to a low-cost affordable prosthesis. This manuscript presents a low-cost design proposal for an electromyographic electronic system, which is characterized by a neural network based process. Moreover, a hand-type prosthesis is presented and controlled by using the processed electromyographic signals for a required particular use. For this purpose, the user performs several movements by using the healthy-hand to get some electromyographic signals. After that, the obtained signals are processed in a neural network based controller. Once an usable behavior is obtained, an exact replica of controlled motions are adapted for the other hand by using the designed prosthesis. The characterization process of bioelectrical signals was performed by training twenty characteristics obtained from the original raw signal in contrast with other papers in which seven characteristics have been tested on average. The proposed model reached a 95.2% computer test accuracy and 93% accuracy in a real environment experiment. The platform was tested via online and offline, where the best response was obtained in the online execution time. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Micro-Nano Systems)
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20 pages, 2150 KiB  
Article
Towards the Estimation of Body Weight in Sheep Using Metaheuristic Algorithms from Biometric Parameters in Microsystems
by Enrique Camacho-Pérez, Alfonso Juventino Chay-Canul, Juan Manuel Garcia-Guendulain and Omar Rodríguez-Abreo
Micromachines 2022, 13(8), 1325; https://doi.org/10.3390/mi13081325 - 16 Aug 2022
Viewed by 2386
Abstract
The Body Weight (BW) of sheep is an important indicator for producers. Genetic management, nutrition, and health activities can benefit from weight monitoring. This article presents a polynomial model with an adjustable degree for estimating the weight of sheep from the biometric parameters [...] Read more.
The Body Weight (BW) of sheep is an important indicator for producers. Genetic management, nutrition, and health activities can benefit from weight monitoring. This article presents a polynomial model with an adjustable degree for estimating the weight of sheep from the biometric parameters of the animal. Computer vision tools were used to measure these parameters, obtaining a margin of error of less than 5%. A polynomial model is proposed after the parameters were obtained, where a coefficient and an unknown exponent go with each biometric variable. Two metaheuristic algorithms determine the values of these constants. The first is the most extended algorithm, the Genetic Algorithm (GA). Subsequently, the Cuckoo Search Algorithm (CSA) has a similar performance to the GA, which indicates that the value obtained by the GA is not a local optimum due to the poor parameter selection in the GA. The results show a Root-Mean-Squared Error (RMSE) of 7.68% for the GA and an RMSE of 7.55% for the CSA, proving the feasibility of the mathematical model for estimating the weight from biometric parameters. The proposed mathematical model, as well as the estimation of the biometric parameters can be easily adapted to an embedded microsystem. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Micro-Nano Systems)
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15 pages, 7626 KiB  
Article
Dual U-Net-Based Conditional Generative Adversarial Network for Blood Vessel Segmentation with Reduced Cerebral MR Training Volumes
by Oliver J. Quintana-Quintana, Alejandro De León-Cuevas, Arturo González-Gutiérrez, Efrén Gorrostieta-Hurtado and Saúl Tovar-Arriaga
Micromachines 2022, 13(6), 823; https://doi.org/10.3390/mi13060823 - 25 May 2022
Cited by 5 | Viewed by 3285
Abstract
Segmenting vessels in brain images is a critical step for many medical interventions and diagnoses of illnesses. Recent advances in artificial intelligence provide better models, achieving a human-like level of expertise in many tasks. In this paper, we present a new approach to [...] Read more.
Segmenting vessels in brain images is a critical step for many medical interventions and diagnoses of illnesses. Recent advances in artificial intelligence provide better models, achieving a human-like level of expertise in many tasks. In this paper, we present a new approach to segment Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) images, relying on fewer training samples than state-of-the-art methods. We propose a conditional generative adversarial network with an adapted generator based on a concatenated U-Net with a residual U-Net architecture (UUr-cGAN) to carry out blood vessel segmentation in TOF-MRA images, relying on data augmentation to diminish the drawback of having few volumes at disposal for training the model, while preventing overfitting by using regularization techniques. The proposed model achieves 89.52% precision and 87.23% in Dice score on average from the cross-validated experiment for brain blood vessel segmentation tasks, which is similar to other state-of-the-art methods while using considerably fewer training samples. UUr-cGAN extracts important features from small datasets while preventing overfitting compared to other CNN-based methods and still achieve a relatively good performance in image segmentation tasks such as brain blood vessels from TOF-MRA. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Micro-Nano Systems)
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20 pages, 1737 KiB  
Article
Self-Tuning Control Using an Online-Trained Neural Network to Position a Linear Actuator
by Rodrigo Hernandez-Alvarado, Omar Rodriguez-Abreo, Juan Manuel Garcia-Guendulain and Teresa Hernandez-Diaz
Micromachines 2022, 13(5), 696; https://doi.org/10.3390/mi13050696 - 29 Apr 2022
Cited by 5 | Viewed by 3085
Abstract
Linear actuators are widely used in all kinds of industrial applications due to being devices that convert the rotation motion of motors into linear or straight traction/thrust motion. These actuators are ideal for all types of applications where inclination, lifting, traction, or thrust [...] Read more.
Linear actuators are widely used in all kinds of industrial applications due to being devices that convert the rotation motion of motors into linear or straight traction/thrust motion. These actuators are ideal for all types of applications where inclination, lifting, traction, or thrust is required under heavy loads, such as wheelchairs, medical beds, and lifting tables. Due to the remarkable ability to exert forces and good precision, they are used classic control systems and controls of high-order. Still, they present difficulties in changing their dynamics and are designed for a range of disturbances. Therefore, in this paper, we present the study of an electric linear actuator. We analyze the positioning in real-time and attack the sudden changes of loads and limitation range by the control. It uses a general-purpose control with self-tuning gains, which can deal with the essential uncertainties of the actuator and suppress disturbances, as they can change their weights to interact with changing systems. The neural network combined with PID control compensates the simplicity of this type of control with artificial intelligence, making it robust to drastic changes in its parameters. Unlike other similar works, this research proposes an online training network with an advantage over typical neural self-adjustment systems. All of this can also be dispensed with the engine model for its operation. The results obtained show a decrease of 42% in the root mean square error (RMSE) during trajectory tracking and saving in energy consumption by 25%. The results were obtained both in simulation and in real tests. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Micro-Nano Systems)
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13 pages, 9426 KiB  
Article
Distortion Calculation Method Based on Image Processing for Automobile Lateral Mirrors
by Carlos Paredes-Orta, Luis M. Valentin-Coronado, Arturo Díaz-Ponce, Juvenal Rodríguez-Reséndiz and Jorge Domingo Mendiola-Santibañez
Micromachines 2022, 13(3), 401; https://doi.org/10.3390/mi13030401 - 28 Feb 2022
Cited by 3 | Viewed by 3099
Abstract
The automobile lateral-view mirrors are the most important visual support for driver safety; therefore, it is important they have robust quality control. Typically, the distortion of a lateral-view mirror is measured using the JIS-D-5705 standard; however, this methodology requires an expert person to [...] Read more.
The automobile lateral-view mirrors are the most important visual support for driver safety; therefore, it is important they have robust quality control. Typically, the distortion of a lateral-view mirror is measured using the JIS-D-5705 standard; however, this methodology requires an expert person to perform the measurements and calculations manually, which can induce measurement errors. In this work, a semi-automatic distortion calculation method based on image processing is presented. Distortion calculations of five commercial mirrors from different manufacturers were performed, and a comparative study was carried out between the JIS-D-5705 standard and the proposed method. Experimental results performed according to the JIS-D-5705 standard showed that all mirrors have a distortion lower than 5%, indicating that all meet the standard. On the other hand, the proposed method was able to detect that one of the mirrors presented an important distortion, which was not detected by the methodology proposed in the standard; therefore, that mirror should not meet the standard. Then, it was possible to conclude that the proposed distortion calculation method, based on image processing, has higher robustness and precision than the standard. In addition, an appropriate and effective behavior against changes in scale, resolution, and, unlike the standard, against changes in image rotation was also shown. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Micro-Nano Systems)
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19 pages, 3156 KiB  
Review
Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review
by César G. Villegas-Mier, Juvenal Rodriguez-Resendiz, José M. Álvarez-Alvarado, Hugo Rodriguez-Resendiz, Ana Marcela Herrera-Navarro and Omar Rodríguez-Abreo
Micromachines 2021, 12(10), 1260; https://doi.org/10.3390/mi12101260 - 17 Oct 2021
Cited by 108 | Viewed by 9803
Abstract
The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction [...] Read more.
The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research. Full article
(This article belongs to the Special Issue Artificial Intelligence Integration with Micro-Nano Systems)
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