Control Of Smart Devices Research Articles

EchoTap: Non-Verbal Sound Interaction with Knock and Tap Gestures

The growing demand for highly accessible interaction technologies to effectively interact with smart devices has led to the increasing popularity of voice user interfaces (VUIs). However, VUIs face interpretation challenges stemming from the variability of natural language input, such as speech clarity issues, linguistic variability, and speech impediments. As an alternative, non-verbal sound-based interaction techniques emerge as highly advantageous for smart device control, mitigating the inherent challenges of VUIs. In this article, we introduce EchoTap, a novel audio interface that harnesses the distinctive sound responses generated by knock and tap gestures on target objects. Employing deep neural networks, EchoTap recognizes both the type and location of these gestures based on their unique sound signatures. Through offline evaluation, EchoTap demonstrated competitive classification accuracy (88% on average) and localization precision (93% on average). Moreover, a user study involving 12 participants validated EchoTap’s practical effectiveness and user-friendliness in real-world scenarios. This study highlights EchoTap’s potential for various daily interaction contexts and discusses further design implications for leveraging auditory interfaces based on simple gestures.

Laser‐induced graphene formation on recycled woods for green smart furniture

AbstractIn the pursuit of carbon neutrality policies, the development of eco‐friendly and intelligent furniture commands a significant role. However, the integration of non‐biodegradable electronic components in smart furniture fabrication has led to substantial electronic waste. Here, we report a straightforward approach, the rapid production of Laser‐Induced Graphene (LIG) on medium‐density fiberboard (MDF), a prevalent recycled wood in furniture production. This LIG electrode is crafted with negligible material ablation in ambient air with the aid of femtosecond laser pulses, without requiring any additional materials, showcasing the highest electrical conductivity (2.781 Ω sq−1) among previously reported lignocellulosic materials‐based LIG. The application of this LIG electrode for lighting, heating, and touch sensors displays sufficient performance for smart furniture implementation. For eco‐conscious furniture, LIG‐based human‐machine interfaces are demonstrated on recycled woods for the facile control of smart devices, which will readily enable IoT‐oriented smart sustainable furniture.image

LiveProbe: Exploring Continuous Voice Liveness Detection via Phonemic Energy Response Patterns

Voice assistants support contactless smart device control and thus act as a holy grail of human-computer interaction. However, recent studies reveal that an adversary can manipulate devices by vicious voice commands. This security risk is caused by only executing one-time liveness detection and lacking safeguard modules after service activation. Therefore, identifying speaker type (i.e., human articulators or loudspeakers) is critical in protecting voice-driven services during an entire interaction session. In this paper, we propose a continuous voice liveness detection approach LiveProbe, leveraging unique energy response patterns in frequency bands induced by distinct voice generation mechanisms. The rationality behind LiveProbe is presented in two aspects: human articulator reshapes initial voices by exquisitely coordinated movements of vocal organs, which act as band-pass filters generating unique energy responses; nevertheless, the internal modules of loudspeakers are position-fixed and cannot reproduce this response characteristic. To that end, we first work on voice generation mechanisms behind two-type speakers that cause spectrum differences. Then we elaborately construct signal processing and deep-learning modules to extract liveness features. Especially, our approach doesn’t interfere with normal voice interaction and needn’t to carry customized sensors. The experiment presents its effectiveness against potential attacks with a false acceptance rate of 0.51%.

Variations in Students’ Learning Habits and Behavior Patterns in Light of the Technological Innovations and Connectivist Theory of Learning

The “Tsunami "of spread of knowledge and modern technological means led to the emergence of new mental skills and modes of learning among learners. Consequently, students’ learning was affected by the new knowledge networks. As such, this study aimed at investigating the change in the learning habits of learners in light of the technological development, and to study the impact of digital instruments and applications on the development/change of the learners’ study habits. The study utilized a qualitative approach with a descriptive analytical design, where semi-structured interviews were created and conducted with three age groups: three adult learners who studied before technological development, five secondary school students, and four tertiary university students. Research results revealed the persistence of some traditional learning habits among school students, the development of study habits linked to modern technological means, and the development of modern technical skills such as Internet search. Some factors affecting learning habits appeared; specifically, the nature of the study task, teaching strategies, and student specialization. Some digital skills have been diagnosed for each category, like online searching skills, staying connected to networks, communication skills, the skills of self-organization, and the control of smart devices. In light of its findings, the study ended up with a set of recommendations for future research, and other recommendations for practice. Keywords: Learning habits, connectivism, digital learners, learning skills, learning instruments.

Research on IEEE 802.11 OFDM Packet Detection Algorithms for Household Wireless Sensor Communication

Millions of smart home devices equipped with wireless sensors have gradually entered people’s homes and improved the quality of life. Wireless communication with sensors is crucial for remote automatic control of smart devices. Packet detection is one of the key technologies in wireless communication systems and faces the challenges of detection accuracy and power consumption. In households, WiFi devices are widely deployed. Therefore, we focus on IEEE 802.11 OFDM packet detection algorithms for household wireless sensor communication. We first introduce four packet detection algorithms and verify their feasibility by simulation experiments. Then, we identify two important factors that affect detection accuracy and provide suggestions for algorithm improvement. Further, we compare and discuss the performance of the four algorithms from three dimensions in detail. The results show that each algorithm has its own advantages, and the auto-correlation algorithm has the best overall performance. Finally, we point out the open challenges and future research directions in this field.

Helically Intersected Conductive Network Design for Wearable Electronic Devices: From Theory to Application.

Flexible and stretchable strain sensors are crucial components for wearable electronics that can detect and quantify the stimuli from the environment and thus realize the rapid feedback and control of smart devices. However, reconciliation of the conflict between resourceful design of conductive networks and large-scale production in the industry still faces a huge challenge. Herein, we present a new flow-manipulated strategy to prepare a wearable strain sensor featuring a helically intersected conductive network, which exhibited easy integration, multidimensional sensibility, and robust mechanical properties. From visualization of simulation and verification of experimental results, the helically intersected conductive network formed in an elastomer ring can simultaneously reflect the static and dynamic mechanical responses with a tunable gauge factor (10.41-31.12), wide linear region (0-40o), mechanical robustness (σs = ∼7 MPa, ε = ∼1400%), and rapid response time (∼300 ms). We further constructed a control system based on smart rings and demonstrated its application in controlling industrial robotic arms and remote-controlled cars. Looking ahead, this kind of a smart ring will be more widely used in space and underwater exploration, intelligent robotics, and human-machine interface technologies.

A Smart Home System based on Microcontroller using Android Application and MySQL Database

In recent years, people may disremember to turn off various devices or not be sure open or closed. Predefined scenarios for these people, home comfort and efficiency in the workplace will help a lot. Most people spend time with mobile phones throughout the day. Therefore, the control of smart devices by phones would be an appropriate solution. In this study, it is designed with microcontroller based intelligent building automation system hardware and software to provide energy efficient, security and comfort. The programming of the microcontroller, writing the android application program for the mobile phone and MySQL database software were realized. Microcontroller with GSM shield, it is possible to control the devices connected to the system by an android application automatically and manually by means of the internet connection by the MySQL database on the remote server. It is ensured that all devices are adjusted with a single touch according to predefined scenarios.

A trusted feature aggregator federated learning for distributed malicious attack detection

With the rapid development of IoT technology, millions of physical devices embedded with electronics or software are put into regular production. Each IoT device is connected to the user’s life and property privacy. Without a credible intrusion detection and defense mechanism installed on the device, it may be attacked by hackers, such as monitoring events of home cameras and control of smart devices. These attack events will have a serious impact on users’ production and life. This paper proposes a Blockchained-Federated Learning based cloud intrusion detection scheme. The scheme sends the local training parameters of the IoT intrusion alarm set to the cloud computing center for global prediction, and stores the model training process information and behavior on the blockchain. In order to solve the high probability of false alerts affecting the accuracy of the federated learning model, the scheme proposes an alert filter identification module. At the same time, through the erasure code-based blockchain storage solution, the traditional blockchain storage performance is improved to meet the storage needs of a large number of alert training data in real scenarios.

Cyber security of electric networks with energy storages

Modern Energy Power Systems (EPS) are characterized by a rather high share of distributed generation, renewable energy sources (RES) and energy storage systems (ESS) operating under the control of smart devices. For transition to a qualitatively new level of power industry management, the developed countries create Smart Grids. An effect of sudden changes of loads, power flows in the lines, and changes in the generation, as well as other unexpected factors can negatively affect on stability and reliability of state of EPS as well as lower EPS flexibility that is one of the most relevant features of future energy systems. Use of ESS is one of the recognized options of maintaining the energy system flexibility. At the same time executing energy storages at an energy object and, moreover, their coordination using Internet-technologies globally increase cyber vulnerability of electrical network. At this paper different aspects of cyber security of electrical networks with energy storage units incorporated into them are shown and ways of their cyber resilience are considered. By arranging PMUs in power system nodes using ESS, awareness of the absence of cyber attacks can be increased.

A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition

Gestures play an important role in human-computer interaction, providing a potentially intuitive way to bridge the gap between human intention and the control of smart devices. Electromyography (EMG) and force myography (FMG) are two commonly-adopted wearable sensing modalities for gesture recognition. Previous research approaches utilize only a single modality (EMG or FMG) at any given muscle location, thus limiting the amount of potentially-useful biological hand gesture information. We thus propose a novel co-located approach (EMG and FMG) for capturing both sensing modalities, simultaneously, at the same location. We developed a novel hand gesture recognition armband consisting of 8 co-located EMG-FMG sensing units (size and weight of each EMG-FMG sensing unit was 11 × 13 × 6 mm and 0.90 g, respectively). Five subjects performed a hand gesture recognition experiment for American Sign Language digits 0–9 while wearing the co-located EMG-FMG armband on the forearm. Hand gesture classification accuracy was 81.5 % for EMG only, 80.6 % FMG only, and 91.6 % for co-located EMG-FMG. These results suggest that co-located EMG-FMG may lead to higher hand gesture classification accuracy than sensing approaches using either EMG or FMG in isolation. To the best of our knowledge, this is the first prototype that measures EMG and FMG simultaneously at the same muscle location for hand gesture recognition. Implications of this work could positively impact a variety of muscle activity monitoring research applications including biomechanics modeling, prosthesis control, and gesture recognition.

Transient response of magnetorheological elastomers to step magnetic field

The time-dependent performance of magnetorheological elastomers (MREs) under a magnetic field is important for the precise control of smart devices based on MREs. Here, the transient responses of anisotropic magnetorheological elastomers, including magnetic field-induced storage modulus (ΔG′) and loss modulus (ΔG″), were investigated under different step magnetic fields. The results indicated that ΔG′ increased over time while ΔG″ decreased after the initial jump in the loading stage of the step field. ΔG′ quickly dropped to 0, while ΔG″ continued to decrease gradually in the removal stage of the step field. A comprehensive model based on magnetic dipolar and viscoelastic parameter models was proposed to explain the transient responses.

Improving Self-Consumption of Green Energy Using Linear Programming for Reactive Control of Smart Devices

Improving Self-Consumption of Green Energy Using Linear Programming for Reactive Control of Smart Devices

An Assistive VLC Technology for Smart Home Devices Using EOG

In recent years, assistive technology has been an emerging area of research to improve the quality of life, especially for the disabled and elderly people. In this paper, a novel electro-oculogram (EOG) signal based assistive visible light communication (VLC) in a smart home environment is presented. The eye movement is captured using silver chloride (AgCl) surface electrodes placed around eyes. The captured signal is further processed and transmitted using a VLC link to control smart home devices. To validate the proposed EOG-VLC based smart device control scheme, experiments were conducted. For the ease of the experiments, instead of an actual smart home device, we employed a digital door lock to verify its accurate control operation. Experiment results demonstrate that the proposed smart device control scheme operates accurately and reliably. Therefore, since the proposed scheme is based on a less hazardous VLC link, it is envisioned that the scheme can pave the way for greener and safer smart homes, particularly benefiting disabled and elderly people.

Design and implementation smart device control system based on indoor positioning

With the rise of smart phone usage, smart phone users are the cause of many problems. They talk loudly in public spaces, divulging private information and taking photos in secure areas where cameras are not permitted. In this paper, we design and implement a smart phone control system to resolve such problems. The proposed system is based on an indoor positioning system and controls smart phone functions automatically by setting up restricted functions for the distinct zones.

High sensitivity wrist-worn pulse active sensor made from tellurium dioxide microwires

We have demonstrated the practicality of an ultrasensitive wrist-wear pulse active sensor that was first made using tellurium dioxide triangular microwires. The unusual growth direction of the α-TeO2 triangular microwires was governed by a [110] axis. The α-TeO2 microwires belong to the tetragonal structure with a space group of P41212. Due to the gravitational force effect, the triangular microwires were encapsulated by polydimethylsiloxane (PDMS) and underwent a natural gravity-driven settling process during the PDMS solidification process, leading to the triangular microwires being placed on the substrate with a randomly horizontal orientation. When a compressive stress was applied to the α-TeO2–PDMS composite film, the shear forces were formed on the microwires׳ surface along with the [110] and/or [1¯1¯0] poling axis. As a result of the antisymmetric element of e14 and −e25 in the α-TeO2 structure, the triangular microwires have created an obviously piezoelectric potential (piezopotential) ~3.2V that was perpendicularly established along with the poling axis. On the basis of the unique triangular morphology and its physical properties, the piezopotential of the α-TeO2 will not be canceled out even if the microwires׳ tips are pointed towards each other. Thus, the piezopotential can be accumulated across the top and bottom electrodes of the nanogenerator (active sensor). The α-TeO2 triangular microwires can be acted upon as a useful active sensor for detecting the tiny physical motions, for instance, a pulse driven sensor, air pressure, and touchless control of smart devices.