Consumer Electronics Applications Research Articles

Integration of High‐Tc Superconductors with High‐Q‐Factor Oxide Mechanical Resonators

AbstractMicro‐mechanical resonators are building blocks of a variety of applications in basic science and consumer electronics. This device technology is mainly based on well‐established and reproducible silicon‐based fabrication processes with outstanding performances in term of mechanical Q‐factor and sensitivity to external perturbations. Broadening the functionalities of micro‐electro‐mechanical systems (MEMS) by the integration of functional materials is a key step for both applied and fundamental science. However, combining functional materials with silicon‐based devices is challenging. An alternative approach is directly fabricating MEMS based on compounds inherently showing non‐trivial functional properties, such as transition metal oxides. Here, a full‐oxide approach is reported, where a high‐ superconductor YBa2Cu3O7 (YBCO) is integrated with high Q‐factor micro‐bridge resonators made of single‐crystal LaAlO3 (LAO) thin films. LAO resonators are tensile strained, with a stress of about 350 MPa, show a Q‐factor above 200k, and have low roughness. YBCO overlayers are grown ex situ by pulsed laser deposition and YBCO/LAO bridges show zero resistance below 78 K and mechanical properties similar to those of bare LAO resonators. These results open new possibilities toward the development of advanced transducers, such as bolometers or magnetic field detectors, as well as experiments in solid state physics, material science, and quantum opto‐mechanics.

Formulation analysis and follow-up study of multi-turn configuration for performance enhancement of MEMS piezoresistive pressure sensor utilized for low-pressure measurement applications

PurposeThe purpose of this research is to study the performance of piezoresistive pressure sensors using polysilicon as the piezoresistive material, which is typically used to measure pressure in high-temperature environments.Design/methodology/approachThe performance of this sensor is enhanced by studying the influence of multi-turn configuration at which the piezoresistors are arranged. Different configurations are studied and compared by laying down their analytical solution.FindingsThe validation of analytical results is accomplished through finite element analysis using the software COMSOL Multiphysics. The best configuration, which uses a partial triple-turn configuration, was able to achieve a sensitivity of 116.00 mV/V/MPa over a simulated pressure range of 0 to 500 KPa.Originality/valueThe literature shows the study of single-turn and double-turn meander-shaped configuration of micro-electromechanical systems piezoresistive pressure sensor but multi-turn meander-shaped configuration using a square silicon diaphragm has not been reported. Its study has reflected promising results than its counterparts based on key performance parameters such as sensitivity and linearity and are more effective to be used for automotive, aviation, biomedical and consumer electronics applications.

Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach

Abstract Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user’s perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution.

Federated Constrastive Learning and Visual Transformers for Personal Recommendation

This paper introduces a novel solution for personal recommendation in consumer electronic applications. It addresses, on the one hand, the data confidentiality during the training, by exploring federated learning and trusted authority mechanisms. On the other hand, it deals with data quantity, and quality by exploring both transformers and consumer clustering. The process starts by clustering the consumers into similar clusters using contrastive learning and k-means algorithm. The local model of each consumer is trained on the local data. The local models of the consumers with the clustering information are then sent to the server, where integrity verification is performed by a trusted authority. Instead of traditional federated learning solutions, two kinds of aggregation are performed. The first one is the aggregation of all models of the consumers to derive the global model. The second one is the aggregation of the models of each cluster to derive a local model of similar consumers. Both models are sent to the consumers, where each consumer decides which appropriate model might be used for personal recommendation. Robust experiments have been carried out to demonstrate the applicability of the method using MovieLens-1M, and Amazon-book. The results reveal the superiority of the proposed method compared to the baseline methods, where it reaches an average accuracy of 0.27, against the other methods that do not exceed 0.25.

Binary and ternary leading-systematic LCD codes from special functions

Linear complementary dual codes (LCD codes for short) are an important subclass of linear codes which have nice applications in communication systems, cryptography, consumer electronics and information protection. In the literature, it has been proved that an [n,k,d] Euclidean LCD code over Fq with q>3 exists if there is an [n,k,d] linear code over Fq, where q is a prime power. However, the existence of binary and ternary Euclidean LCD codes has not been totally characterized. Hence it is interesting to construct binary and ternary Euclidean LCD codes with new parameters. In this paper, we construct new families of binary and ternary leading-systematic Euclidean LCD codes from some special functions including semibent functions, quadratic functions, almost bent functions, and planar functions. These LCD codes are not constructed directly from such functions, but come from some self-orthogonal codes constructed with such functions. Compared with known binary and ternary LCD codes, the LCD codes in this paper have new parameters.

Study of cyclic codes from low differentially uniform functions and its consequences

Cyclic codes have many applications in consumer electronics, communication and data storage systems due to their efficient encoding and decoding algorithms. An efficient approach to constructing cyclic codes is the sequence approach. In their articles (Ding and Zhou 2014 [11]) and (Ding, 2013 [7]), Ding and Zhou constructed several classes of cyclic codes from almost perfect nonlinear (APN) functions and planar functions over finite fields and presented some open problems on cyclic codes from highly nonlinear functions. This article focuses on these exciting works by investigating new insights in this research direction. Specifically, its objective is twofold. The first is to provide a complement with some former results and present correct proofs and statements on some known ones on the cyclic codes from the APN functions. The second is studying the cyclic codes from some known functions processing low differential uniformity. Along with this article, we shall answer some open problems presented in the literature. The first concerns Open Problem 1, proposed by Ding and Zhou (2014) [11]. The others are Open Problems 5.16 and 5.25, raised by Ding (2013) [7].

Two-dimensional van der Waals heterostructure for ultra-sensitive nanoelectromechanical piezoresistive pressure sensing

Micro electromechanical systems (MEMS) micro-pressure sensors have wide applications in consumer electronics, industrial monitoring, and biomedicine. However, as the next-generation portable and wearable devices bloom in recent years, existing design of pressure sensors faces great challenges to break the trade-off between the sensitivity and nonlinearity in the miniaturization process. Here, we report a sandwich-like Graphene/h-BN/Graphene-resistor heterostructure sensing element for micro-pressure detection. This novel design decouples pressure-to-strain translation process and strain-to-electrical signal sensing process by the insulation of bottom graphene structure layer and top graphene piezoresistive layer. We reveal the effects of the heterostructure dimensions, layer number and pretension on the mechanical performance utilizing finite element method (FEM) simulation, and propose the design principle of the piezoresistors and patterning strategy of the heterostructure. Remarkably, the heterostructure exhibits ultra-high sensitivity per unit area, outperforming state-of-art graphene and silicon-based sensors by up to five orders of magnitude, as well as a significant reduction of the nonlinearity. Our work shows tremendous potential of 2D material heterostructure as a promising platform to break the miniaturization limitation of ultrasensitive pressure sensors.

Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review.

Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.

Blockchain: Applications, Challenges, and Opportunities in Consumer Electronics

One of the most beneficial and original solutions for cybersecurity is blockchain. Although the technology has only been on the market for a short time, it has already effectively replaced economic transaction systems in a number of businesses in its early stages and is capable of modernizing various business models in numerous consumer electronic applications. It is critical for planners and decision makers to thoroughly evaluate the suitability of Blockchain for their particular use cases and commercial applications, even as it promises a secure distributed framework for sharing, exchanging, and integrating information across users and third parties. Blockchain should only be deployed if it is suitable, offers security, and provides better opportunities to drive revenue and save costs. In this paper, you will get an overview of this technology that can be used to achieve security in consumer electronic applications. We also discuss the difficulties and potential benefits of using blockchain technologies in consumer electronics applications.

Wearable ultrasound technology

The use of wearable electronic devices that can acquire vital signs from the human body noninvasively and continuously is a significant trend for healthcare. The combination of materials design and advanced microfabrication techniques enables the integration of various components and devices onto a wearable platform, resulting in functional systems with minimal limitations on the human body. Physiological signals from deep tissues are particularly valuable as they have a stronger and faster correlation with the internal events within the body compared to signals obtained from the surface of the skin. In this presentation, I will demonstrate a soft ultrasonic technology that can noninvasively and continuously acquire dynamic information about deep tissues and central organs. I will also showcase examples of this technology's use in recording blood pressure and flow waveforms in central vessels, monitoring cardiac chamber activities, and measuring core body temperatures. The soft ultrasonic technology presented represents a platform with vast potential for applications in consumer electronics, defense medicine, and clinical practices.

Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework

The 5 G networks are effectively deployed worldwide, and academia and industries have begun looking at 6 G network communication technology for consumer electronics applications. 6 G will be built on pervasive artificial intelligence (AI) to enable data-driven Machine Learning (ML) applications in massively scalable and heterogeneous networks. Conventional ML technique involves centralizing train data in data centers where centralized ML algorithms can be employed for data inference and analysis. The data inference and analysis are frequently inconvenient or impracticable for the devices to submit information to the preset sever because of privacy concerns and inadequate communication capabilities in wireless networks. However, privacy limitations and restrictions in wireless network communication capacity are frequently impractical or undesirable for the devices to acquiesce data to the parameter server. Federated learning (FL) enables the devices to train a practical and standard model while needing data exchange and transfer, which might solve these issues. This paper presents an overview of FL, 6 G, and FL enables 6 G communication technology. In particular, 6 G requirements and applications, and the proposed FL framework algorithm with evaluation are described. Finally, FL-enabling 6 G communication technologies open challenges, and research directions are discussed to help future researchers improve the FL-enabled 6 G network.

Covalently-Bonded Laminar Assembly of Van der Waals Semiconductors with Polymers: Toward High-Performance Flexible Devices.

Van der Waals semiconductors (vdWS) offer superior mechanical and electrical properties and are promising for flexible microelectronics when combined with polymer substrates. However, the self-passivated vdWS surfaces and their weak adhesion to polymers tend to cause interfacial sliding and wrinkling, and thus, are still challenging the reliability of vdWS-based flexible devices. Here, an effective covalent vdWS-polymer lamination method with high stretch tolerance and excellent electronic performance is reported. Using molybdenum disulfide (MoS2 )and polydimethylsiloxane (PDMS) as a case study, gold-chalcogen bonding and mercapto silane bridges are leveraged. The resulting composite structures exhibit more uniform and stronger interfacial adhesion. This enhanced coupling also enables the observation of a theoretically predicted tension-induced band structure transition in MoS2 . Moreover, no obvious degradation in the devices' structural and electrical properties is identified after numerous mechanical cycle tests. This high-quality lamination enhances the reliability of vdWS-based flexible microelectronics, accelerating their practical applications in biomedical research and consumer electronics.

Progress in polyacrylate‐based electrically conductive adhesives: Featured properties, preparation, applications, and perspectives

AbstractPolyacrylate‐based electrically conductive adhesives (PA‐based ECAs) have attracted much attention due to their wide applications in precise instruments and consumer electronics. To obtain high‐performance and environmentally friendly PA‐based ECAs, their main properties and corresponding mechanisms to performance, chemical constitutions and structure–function relationships should be well understood, and the applications of recent PA‐based ECAs should be comprehensively summarized. This review focuses on the progress of PA‐based ECAs in the past two decades and discusses the featured properties, preparation of PA, the selection of conductive fillers and the adhibition of the ECAs. The aim of the review is to find the challenges of high‐performance ECAs and offer some measures and foresee their future development.Highlights Preparation methods of polyacrylate (PA)‐based composites are presented. Properties of PA‐based electrically conductive adhesives (ECAs) are outlined. Selection of conductive fillers and the adhibition of ECAs are discussed. Representative applications of PA‐based ECAs are exhibited. Present main challenges and future solutions are highlighted.

Federated-ANN-Based Critical Path Analysis and Health Recommendations for MapReduce Workflows in Consumer Electronics Applications

Federated-ANN-Based Critical Path Analysis and Health Recommendations for MapReduce Workflows in Consumer Electronics Applications

ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State-of-Charge Estimation

Accuracy and speed of Lithium-ion battery state of charge (SOC) estimation determine the reliability and stability of electric vehicle (EV), as well as promoting the development of smart home energy management system. Existing SOC estimation approaches embedded in commercial EVs still suffer from limitations of low precision, effectiveness, and relatively low robustness. To address these issues, we propose an internal cascaded neuromorphic computing system (ICNCS) via memristor circuits for EV SOC estimation. Specifically, we use three circuit modules to facilitate the design of the proposed ICNCS. Firstly, a bidirectional gated recurrent unit (Bi-GRU) circuit module is designed, enabling adequate feature extraction from the time-contextual battery information. Secondly, an attention circuit module is proposed to distinguish the useful and unimportant information related to SOC estimation. Thirdly, the Kalman filter module is constructed to eliminate the random noise caused by data transmission and processing. Finally, a series of experiments and analysis demonstrate that the proposed ICNCS has good performances in terms of accuracy (the lowest achievable RMSE and MAE are 0.853 and 0.711, respectively), time efficiency (approximately 16 20 times), and robustness (anti-noise capacity), indicating an advancement in consumer electronics applications.

Guest Editorial of the Special section on Emerging Technologies and Applications of Consumer Electronics for Healthy and Sustainable Life

Guest Editorial of the Special section on Emerging Technologies and Applications of Consumer Electronics for Healthy and Sustainable Life

SMoSE: Artificial Intelligence-Based Smart City Framework Using Multi-Objective and IoT Approach for Consumer Electronics Application

SMoSE: Artificial Intelligence-Based Smart City Framework Using Multi-Objective and IoT Approach for Consumer Electronics Application

Latent Vector Optimization-Based Generative Image Steganography for Consumer Electronic Applications

Latent Vector Optimization-Based Generative Image Steganography for Consumer Electronic Applications

Multi-View Ensemble Federated Learning for Efficient Prediction of Consumer Electronics Applications in Fog Networks

Multi-View Ensemble Federated Learning for Efficient Prediction of Consumer Electronics Applications in Fog Networks

Poultry Health Monitoring with Advanced Imaging: Towards Next-Generation Agricultural Applications in Consumer Electronics

Poultry Health Monitoring with Advanced Imaging: Towards Next-Generation Agricultural Applications in Consumer Electronics