Smart Hardware Research Articles

Xiaomi: An Analysis of Market Strategy under the High Cost-Performance Ratio

Abstract: Xiaomi Corporation, which was established in 2010 in Beijing, is well-known for its smartphones along with other smart hardware as well as IoT devices. It is the second-largest manufacturer of smartphones in the world, behind Samsung, with a market share of about 12%, most of which run on the MIUI (now HyperOS) operating system. It mainly focuses on providing products that are of excellent quality and performance but still maintain affordability which attracts customers from around the world. This research delves into Xiaomi's adaptive marketing tactics as a reaction to evolving market dynamics and changing consumer demands, and examines Xiaomis market plan by looking at three clear instances. The first is how Xiaomi keeps its strong position in the smartphone market by bringing out improved models every year with a steady price tag. This helps to maintain their image of being very good value for money. Second, the choice of the company to put $300,000 for three years into remaking its logo with Japanese designer Kenya Hara. This highlights how redesigning has a strategic effect on brand image and worldwide identification. Thirdly, this study talks about Xiaomis start in the car industry when it introduced the first car model called Xiaomi SU7 in month April. It shows a major shift from making only smartphones to also producing electric cars.

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia.

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

2D Bi-doped SnSe ferroelectric memristor integrating all-in-one sensing-memory-computing

Two-dimensional (2D) layered ferroelectric materials are an emerging semiconductor expected to satisfy people's pursuit of post-Moore era of the small-size, high-efficiency, and intellectualization. However, the asymmetric coercive-field of reported 2D ferroelectrics which was not conducive to the linearity and symmetry of conductance regulation, limiting their application. Here, we have confirmed Bi-doped can greatly enhance the symmetry of a coercive voltage (±2.4 V) of 2D-layered SnSe, and designed a multifunctional memristor whose conductance is regulated by the ferroelectric polarization direction. As the synaptic long/short-term plasticity (PPF, PTP, symmetrical STDP), the bio-fire and temporal/spatial integration functions of neurons might be emulated by changing the parameters of electrical pulses. Furthermore, under the synergistic effect of photoelectric field, the “OR/AND” logic computing and store in situ was experimentally demonstrated, meanwhile, 8-level nonvolatile photoresponses and the “LOVE” information of the 3-bit octal number combination was locally sensed, demodulated and stored. This work can provide a new way for the future development of smart hardware in photoelectric neuromorphic and edge computing systems.

Smart transit car design based on STM32 and OPENMV

Under the background of the times, Smart Operating Vehicle has wide application prospects. This paper designs a Smart Car based on STM32F103RCT6 MCU, which combines the functions of OPENMV digital recognition module and infrared route tracing, in order to realize the function of the car to reach the designated location and start its operation. The scheme in this paper breaks the limitation of conventional manual work and can replace manual work in extreme environments such as high temperature and small space, which fully reflects the era significance of smart hardware.

Retraction Note: Smart hardware hybrid secure searchable encryption in cloud with IoT privacy management for smart home system

Retraction Note: Smart hardware hybrid secure searchable encryption in cloud with IoT privacy management for smart home system

Application of New Media Big Data in Visual Performance of Axonometric Illustration.

With the rapid development of Internet technology today, especially in the fields of new media, digital TV, digital movies, smartphones, smart hardware, mobile Internet and big data, new media forms bring us a shocking sensory experience. Axonometric illustration, a traditional and modern artistic expression, is becoming more and more attractive in the new media era. Today, with the continuous progress of science and technology, the continuous innovation of thinking, and the rapid improvement of public aesthetics, traditional axonometric illustrations have been unable to adapt to the gradual improvement of people's needs for artistic aesthetics. This paper will start from the research background of “new media” and combine the characteristics of the axonometric illustration industry itself. On the basis of analyzing related theories at home and abroad, the literature research method and algorithm-driven method are used comprehensively. It can not only effectively improve the visual effect of axonometric illustration by 12.3% but also solve the problems and challenges faced by the current China's axonometric illustration industry.

High-Precision Seedling Detection Model Based on Multi-Activation Layer and Depth-Separable Convolution Using Images Acquired by Drones

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.

Trends in Workplace Wearable Technologies and Connected‐Worker Solutions for Next‐Generation Occupational Safety, Health, and Productivity

The workplace influences the safety, health, and productivity of workers at multiple levels. To protect and promote total worker health, smart hardware, and software tools have emerged for the identification, elimination, substitution, and control of occupational hazards. Wearable devices enable constant monitoring of individual workers and the environment, whereas connected worker solutions provide contextual information and decision support. Here, the recent trends in commercial workplace technologies to monitor and manage occupational risks, injuries, accidents, and diseases are reviewed. Workplace safety wearables for safe lifting, ergonomics, hazard identification, sleep monitoring, fatigue management, and heat and cold stress are discussed. Examples of workplace productivity wearables for asset tracking, augmented reality, gesture and motion control, brain wave sensing, and work stress management are given. Workplace health wearables designed for work‐related musculoskeletal disorders, functional movement disorders, respiratory hazards, cardiovascular health, outdoor sun exposure, and continuous glucose monitoring are shown. Connected worker platforms are discussed with information about the architecture, system modules, intelligent operations, and industry applications. Predictive analytics provide contextual information about occupational safety risks, resource allocation, equipment failure, and predictive maintenance. Altogether, these examples highlight the ground‐level benefits of real‐time visibility about frontline workers, work environment, distributed assets, workforce efficiency, and safety compliance.

A Smart Hardware Security Engine Combining Entropy Sources of ECG, HRV, and SRAM PUF for Authentication and Secret Key Generation

Securing personal data in wearable devices is becoming a crucial necessity as wearable devices are being deployed ubiquitously, which inadvertently exposes them to more sophisticated adversarial attacks. Although authentication systems using a single-entropy source, such as fingerprint or iris, are being used widely, successful spoofing attacks have been made, which show such systems’ vulnerability. To mitigate these issues, new biometric modalities [e.g., electrocardiogram (ECG) and photoplethysmogram (PPG)], as well as multifactor authentication/security engine designs, are being investigated. In this work, we present a new smart hardware security engine that combines three different sources of entropy, ECG, heart rate variability (HRV), and SRAM-based physical unclonable function (PUF) to perform real-time authentication and generate unique/random signatures. Such hybrid signatures vary person-to-person, device-to-device, and over time, which significantly reduces the scope of an attack and enables secure personal device authentication as well as secret random key generation. The prototype chip fabricated in 65-nm LP CMOS consumes $4.04~\mu \text{W}$ at 0.6 V for real-time authentication. Compared with ECG-only authentication, the average equal error rate of multi-source authentication is reduced by $7\times $ down to 0.2375% for a 741-subject in-house ECG database. The generalization capability of the hardware was also tested by evaluating equal error rate (EER) values using other ECG databases available online. Also, 256-bit keys generated by optimally combining ECG, HRV, and PUF values fully pass nine NIST randomness tests.

A wearable device for health management detection of multiple physiological parameters based on ZigBee wireless networks

Abstract Detecting physiological signals in the daily life of the population can effectively grasp the health status of the people in the area, and provide an important reference for the diagnosis and prevention of chronic diseases. This paper demonstrates that ZigBee technology meets the requirements of this system. A wireless sensor network system of human physiological parameters based on Zig Bee protocol is built. This wearable device consists of smart hardware and wearables. Intelligent hardware consists of multiple physiological signal detection modules and core processing platforms, and is mainly responsible for the collection and signal processing of multiple physiological signals. Based on the system platform, the entire system was tested for joint debugging. The system's wireless transmission stability and power consumption were tested, and various physiological parameters and motion signals were collected and tested. The test results verify the effectiveness of this system.

Smart hardware integration with advanced robot programming technologies for efficient reconfiguration of robot workcells

The manufacturing industry is seeing an increase in demand for more custom-made, low-volume production. This type of production is rarely automated and is to a large extent still performed manually. To keep up with the competition and market demands, manufacturers will have to undertake the effort to automate such manufacturing processes. However, automating low-volume production is no small feat as the solution should be adaptable and future proof to unexpected changes in customers’ demands. In this paper, we propose a reconfigurable robot workcell aimed at automating low-volume production. The developed workcell can adapt to the changes in manufacturing processes by employing a number of passive, reconfigurable hardware elements, supported by the ROS-based, modular control software. To further facilitate and expedite the setup process, we integrated intuitive, user-friendly robot programming methods with the available hardware. The system was evaluated by implementing five production processes from different manufacturing industries.

Real-time electricity pricing to balance green energy intermittency

The presence of consumers able to respond to changes in wholesale electricity prices facilitates the penetration of renewable intermittent sources of energy such as wind or sun power. We investigate how adapting demand to intermittent electricity supply by making consumers price-responsive - thanks to smart meters and home automation appliances - impacts the energy mix. We show that it almost always reduces carbon emissions. Furthermore, when consumers are not too risk-averse, demand response is socially beneficial because the loss from exposing consumers to volatile prices is more than offset by lower production and environmental costs. However, the gain is decreasing when the proportion of reactive consumers increases. Therefore, depending on the costs of the necessary smart hardware, it may be non-optimal to equip the whole population.

A new smart safety navigation system for cycling based on audio technology

Cycling as a green and environmentally friendly way of traffic is gradually favored by more and more people, but there is few safe navigation system suitable for cycling currently. Therefore, the paper designs a real intelligent navigation system based on “audio navigation” with simple structure, practical functions and high reliability. The system adopts the integrated solution of “smart hardware + APP + data platform”. It solves the shortcomings of traditional auditory navigation, ensures that users pay attention to the road surface, reduces traffic hazards, and provides users with a safe and convenient travel experience.

Brain computer interface for smart hardware device

Brain computer interface for smart hardware device

Direct-to-Consumer Telemedicine

The telemedicine industry and adoption of services have grown exponentially in the last 5 years, and the market is expected to reach more than $130 billion by 2025. Most US health institutions and hospital systems are currently using some form of telehealth, and more than 90% of health care executives surveyed across the United States have virtual care on their roadmap for growth. Telemedicine has been proposed as a way to expand the reach of allergy services and allow more patients to manage their disease with an allergy specialist. Technology can help address fragmentation of allergy care, which is currently provided in multiple clinical settings beyond the allergist's office including in primary care, pulmonary, dermatology, ear, nose, and throat, urgent care, and the emergency room. Remote monitoring, specialist second opinions, and synchronous and asynchronous encounters offer opportunities to streamline routine care, especially as smart hardware such as digital inhalers hit the market and reimbursement for telehealth services evolve. To date, allergy care has been a significant area of interest for direct-to-consumer telemedicine solutions, although the care has mainly been offered by nonallergists. Quality assurance and adherence to evidence-based standards, particularly in the self-pay direct-to-consumer space, warrant attention.

Guest Editorial

The latest generation of mobile networks, the 5G, is expected to unlock many new services and to provide a platform for growth in many industries. The key requirements for 5G are to provide ubiquitous connections to billions of devices and to enable multi-Gb/s wireless data transmission aiming to achieve 1000 times greater capacity compared with the existing systems. To bring 5G to reality, there are countless challenges lying ahead, ranging from spectral regulation to system architecture through to network management. Many of the most difficult scientific challenges are located at the “physical layer,” due to some fundamental limitations imposed by the nature of the electromagnetic energy used. As a result, significant research is required to enable advanced signal processing, energy-efficient RF and microwave circuits and systems, and smart hardware implementations to achieve a working system, in particular within the transceiver front end.

Guest Editorial: Special Issue on New Trends in Smart Chips and Smart Hardware

The articles in this special section aim to promote novel research investigations in low power and low latency of smart chips and smart hardware for machine learning and biologically-plausible learning.

Understanding the path to smart home adoption: Segmenting and describing consumers across the innovation-decision process

The burgeoning smart home market brings opportunities for home energy management systems (HEMS). Despite hundreds of smart HEM products on the market and many invested stakeholders, consumer adoption is lagging behind expectations. Past research in this space has focused on smart home technology (SHT) in general, rather than particular products with HEM potential. Conflating smart HEMS with all SHT is problematic because there is a wide range of smart home products and functions, toward which consumers may have varying attitudes. Past work has also rarely distinguished between various stages of the adoption process that lead up to smart HEMS purchase (Knowledge, Persuasion, and Decision Stages). This research used a Diffusion of Innovations framework and survey data from 709 California utility customers to assess the current market and barriers to HEM smart hardware adoption. Cluster analysis based on consumer awareness, interest, and ownership of HEMS revealed four consumer segments at different positions along the path to adoption: Unfamiliar, Unpersuaded, Persuaded, and Owners. Each group had a unique demographic and psychographic profile with implications for different sets of relevant barriers to adoption.

Quasi-exact logic functions through classification trees

Over the last few years machine learning (ML) has evolved becoming pervasive in many scientific and industrial fields. Such success is ignited by the possibility of applying algorithms that can solve complex problems through generic rule formulations. With such premise, ML surely represents a new opportunity to improve existing design paradigms.In this paper we propose a novel ML-driven synthesis methodology that allows to describe generic Boolean functions through a representative subset of core expressions using Classification Trees (CTs). Obtained circuits are able to mimic Boolean functions to a certain degree of accuracy, hence the name quasi-exact logic functions. The proposed synthesis flow enables a smart hardware mapping of quasi-exact logic functions by means of reduced and ordered decision diagrams. Experiments conducted on a subset of open-source benchmarks demonstrate that CTs are indeed able to cover rather complex Boolean functions with a very high degree of accuracy, 88% on average, still requiring 3× less area over standard multi-level circuit counterparts.

A Review on Ambient Assisted Living in a Maltese Context

The number of senior citizens is continually increasing and they are enjoying a longer life span due to improved quality of life and healthcare. Old age brings with it a whole new set of challenges ranging from the physical to the cognitive. Medical advances have made possible a good quality of life, albeit at a high economic cost to relocate to a residential care facility. One of the main objectives is to keep senior citizens living and participating in their local communities where they have lived and contributed for decades. Another issue faced by moving to residential care is the rising cost of such services. Assisted living allows citizens to continue living in the community whilst using technology to provide a set of services that enable independent living while keeping in contact with care providers. This is a rapidly evolving multi-disciplinary area utilizing techniques ranging from smart hardware, communications, data mining, robotics, Artificial Intelligence, and ICT technologies. This paper discusses a summary of potential applications and the technological disciplines needed to create an independent living space together with issues arising from the application of such technology. Finally a discussion of the application of assisted living in the Maltese context is presented.