A study on improved methods in Micro-electromechanical systems technology

Khatokar, J Aditya; Vinay, N; Bale, Ajay Sudhir; Nayana, M.A.; Harini, R.; Reddy, V. Suhaas; Soundarya, N.; Satheesha, T Y; Huddar, Shivashankar

doi:10.1016/j.matpr.2020.10.993

Cited by 12 publications

(2 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These micro-size devices are employed for sensing and controlling, resulting in an electrical response typically on the macro scale. MEMS is recognized as one of the most promising technologies in the industry and for research purposes [587][588][589][590]. In addition to micromachining technology, recent commercial MEMS sensors have made far-reaching changes in the industry and in consumer products using silicon-based microelectronics.…”

Section: Microelectromechanical Systems (Mems)mentioning

confidence: 99%

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

et al. 2023

View full text Add to dashboard Cite

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

show abstract

Section: Microelectromechanical Systems (Mems)mentioning

confidence: 99%

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Advancements in microelectromechanical systems (MEMS) ( 24 ), now common in modern smartphones, have led to the utilization of MEMS sensors in smartphone-based HR monitoring systems ( 25 , 26 ). Mechanocardiography (MCG), which includes gyrocardiography (GCG), seismocardiography (SCG), and ballistocardiography (BCG), measures cardiac mechanics induced by heartbeats ( 27 ).…”

Section: Introductionmentioning

confidence: 99%

Detection of heart rate using smartphone gyroscope data: a scoping review

Wu,

Elgendi,

Fletcher

et al. 2023

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

Heart rate (HR) is closely related to heart rhythm patterns, and its irregularity can imply serious health problems. Therefore, HR is used in the diagnosis of many health conditions. Traditionally, HR has been measured through an electrocardiograph (ECG), which is subject to several practical limitations when applied in everyday settings. In recent years, the emergence of smartphones and microelectromechanical systems has allowed innovative solutions for conveniently measuring HR, such as smartphone ECG, smartphone photoplethysmography (PPG), and seismocardiography (SCG). However, these measurements generally rely on external sensor hardware or are highly susceptible to inaccuracies due to the presence of significant levels of motion artifact. Data from gyrocardiography (GCG), however, while largely overlooked for this application, has the potential to overcome the limitations of other forms of measurements. For this scoping review, we performed a literature search on HR measurement using smartphone gyroscope data. In this review, from among the 114 articles that we identified, we include seven relevant articles from the last decade (December 2012 to January 2023) for further analysis of their respective methods for data collection, signal pre-processing, and HR estimation. The seven selected articles’ sample sizes varied from 11 to 435 participants. Two articles used a sample size of less than 40, and three articles used a sample size of 300 or more. We provide elaborations about the algorithms used in the studies and discuss the advantages and disadvantages of these methods. Across the articles, we noticed an inconsistency in the algorithms used and a lack of established standardization for performance evaluation for HR estimation using smartphone GCG data. Among the seven articles included, five did not perform any performance evaluation, while the other two used different reference signals (HR and PPG respectively) and metrics for accuracy evaluation. We conclude the review with a discussion of challenges and future directions for the application of GCG technology.

show abstract

Lossless Phase‐Change Material Enabled Wideband High‐Efficiency Spatial Light Phase Modulation at Near‐Infrared

Li,

Zeng

et al. 2024

Laser & Photonics Reviews

View full text Add to dashboard Cite

High‐efficiency spatial light phase modulation with wide operating bandwidth is highly significant yet challenging. Dynamic metasurfaces leveraging active materials with tunable optical response provide a promising solution. Current work is generally confronted with restricted operation bandwidth and diminished modulation efficiency, constrained by the limited tunable range and inherent absorption of active materials particular at optical frequency. Recently, the emergence of lossless phase‐change material Sb2Se3 has garnered widespread attention. Its unique characteristics, including near‐zero absorption at near‐infrared and a substantial refractive index contrast ≈0.93 during phase transition, enable the possibility of high‐performance spatial light modulation. Pioneering studies have validated the capability of lossless phase‐change metasurfaces for wavefront control, but are typically restricted to limited efficiency. Here, a hybrid phase‐change metasurface utilizing over‐coupled resonances supported by Sb2Se3 nanoholes is proposed. For the first time in optical frequency, high‐efficiency 4‐level phase modulation covering over π range is experimentally demonstrated with a sizable operating bandwidth of 42 nm and a minimum reflectance of exceeding 0.5. Leveraging optically driven localized phase‐transition technique, dynamic beam deflection is further demonstrated. The work validates the tremendous potential of phase‐change metasurfaces in achieving advanced spatial light control, signifying significant progress for the development and application of phase‐change photonic devices.

show abstract

A study on improved methods in Micro-electromechanical systems technology

Cited by 12 publications

References 88 publications

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Detection of heart rate using smartphone gyroscope data: a scoping review

Lossless Phase‐Change Material Enabled Wideband High‐Efficiency Spatial Light Phase Modulation at Near‐Infrared

Contact Info

Product

Resources

About