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6 pages, 198 KiB

Open AccessEditorial

Emerging Sensing Technologies in Consumer Electronics

by Yu-Cheng Fan

Sensors 2021, 21(22), 7689; https://doi.org/10.3390/s21227689 - 19 Nov 2021

Cited by 2 | Viewed by 1836

Abstract

This Special Issue is dedicated to aspects of emerging sensing technologies in consumer electronics [...] Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

Research

Jump to: Editorial

15 pages, 4883 KiB

Open AccessArticle

Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention

by Yuya Moroto, Keisuke Maeda, Takahiro Ogawa and Miki Haseyama

Sensors 2020, 20(8), 2170; https://doi.org/10.3390/s20082170 - 11 Apr 2020

Cited by 10 | Viewed by 3155

Abstract

A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training images, the establishment of a theory using a small [...] Read more.

A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training images, the establishment of a theory using a small number of training images is needed. To tackle this problem, although finding persons who have visual attention similar to that of a target person is effective, all persons have to commonly gaze at many images. Thus, it becomes difficult and unrealistic when considering their burden. On the other hand, this paper introduces a novel adaptive image selection (AIS) scheme that focuses on the relationship between human visual attention and objects in images. AIS focuses on both a diversity of objects in images and a variance of PSMs for the objects. Specifically, AIS selects images so that selected images have various kinds of objects to maintain their diversity. Moreover, AIS guarantees the high variance of PSMs for persons since it represents the regions that many persons commonly gaze at or do not gaze at. The proposed method enables selecting similar users from a small number of images by selecting images that have high diversities and variances. This is the technical contribution of this paper. Experimental results show the effectiveness of our personalized saliency prediction including the new image selection scheme. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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28 pages, 5036 KiB

Open AccessArticle

Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

by Guang Xing Lye, Wai Khuen Cheng, Teik Boon Tan, Chen Wei Hung and Yen-Lin Chen

Sensors 2020, 20(7), 2098; https://doi.org/10.3390/s20072098 - 8 Apr 2020

Cited by 31 | Viewed by 5496

Abstract

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different [...] Read more.

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users’ beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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32 pages, 10830 KiB

Open AccessArticle

Gaze Tracking and Point Estimation Using Low-Cost Head-Mounted Devices

by Ko-Feng Lee, Yen-Lin Chen, Chao-Wei Yu, Kai-Yi Chin and Chen-Han Wu

Sensors 2020, 20(7), 1917; https://doi.org/10.3390/s20071917 - 30 Mar 2020

Cited by 17 | Viewed by 6788

Abstract

In this study, a head-mounted device was developed to track the gaze of the eyes and estimate the gaze point on the user’s visual plane. To provide a cost-effective vision tracking solution, this head-mounted device is combined with a sized endoscope camera, infrared [...] Read more.

In this study, a head-mounted device was developed to track the gaze of the eyes and estimate the gaze point on the user’s visual plane. To provide a cost-effective vision tracking solution, this head-mounted device is combined with a sized endoscope camera, infrared light, and mobile phone; the devices are also implemented via 3D printing to reduce costs. Based on the proposed image pre-processing techniques, the system can efficiently extract and estimate the pupil ellipse from the camera module. A 3D eye model was also developed to effectively locate eye gaze points from extracted eye images. In the experimental results, average accuracy, precision, and recall rates of the proposed system can achieve an average of over 97%, which can demonstrate the efficiency of the proposed system. This study can be widely used in the Internet of Things, virtual reality, assistive devices, and human-computer interaction applications. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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20 pages, 9103 KiB

Open AccessArticle

Thermal Model and Countermeasures for Future Smart Glasses

by Kodai Matsuhashi, Toshiki Kanamoto and Atsushi Kurokawa

Sensors 2020, 20(5), 1446; https://doi.org/10.3390/s20051446 - 6 Mar 2020

Cited by 13 | Viewed by 7135

Abstract

The market for wearable devices such as smart watches and smart glasses continues to grow rapidly. Smart glasses are attracting particular attention because they offer convenient features such as hands-free augmented reality (AR). Since smart glasses directly touch the face and head, the [...] Read more.

The market for wearable devices such as smart watches and smart glasses continues to grow rapidly. Smart glasses are attracting particular attention because they offer convenient features such as hands-free augmented reality (AR). Since smart glasses directly touch the face and head, the device with high temperature has a detrimental effect on human physical health. This paper presents a thermal network model in a steady state condition and thermal countermeasure methods for thermal management of future smart glasses. It is accomplished by disassembling the state by wearing smart glasses into some parts, creating the equivalent thermal resistance circuit for each part, approximating heat-generating components such as integrated circuits (ICs) to simple physical structures, setting power consumption to the heat sources, and providing heat transfer coefficients of natural convection in air. The average temperature difference between the thermal network model and a commercial thermal solver is 0.9 °C when the maximum temperature is 62 °C. Results of an experiment using the model show that the temperature of the part near the ear that directly touches the skin can be reduced by 51.4% by distributing heat sources into both sides, 11.1% by placing higher heat-generating components farther from the ear, and 65.3% in comparison with all high conductivity materials by using a combination of low thermal conductivity materials for temples and temple tips and high conductivity materials for rims. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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16 pages, 4943 KiB

Open AccessArticle

A Location Privacy Attack Based on the Location Sharing Mechanism with Erroneous Distance in Geosocial Networks

by Tu-Liang Lin, Hong-Yi Chang and Sheng-Lin Li

Sensors 2020, 20(3), 918; https://doi.org/10.3390/s20030918 - 9 Feb 2020

Cited by 7 | Viewed by 3200

Abstract

Geographical social networks (GSN) is an emerging research area. For example, Foursquare, Yelp, and WeChat are all well-known service providers in this field. These applications are also known as location-based services (LBS). Previous studies have suggested that these location-based services may expose user [...] Read more.

Geographical social networks (GSN) is an emerging research area. For example, Foursquare, Yelp, and WeChat are all well-known service providers in this field. These applications are also known as location-based services (LBS). Previous studies have suggested that these location-based services may expose user location information. In order to ensure the privacy of the user’s location data, the service provider may provide corresponding protection mechanisms for its applications, including spatial cloaking, fuzzy location information, etc., so that the user’s real location cannot be easily cracked. It has been shown that if the positioning data provided by the user is not accurate enough, it is still difficult for an attacker to obtain the user’s true location. Taking this factor into consideration, our attack method is divided into two stages for the entire attack process: (1) Search stage: cover the area where the targeted user is located with unit discs, and then calculate the minimum dominating set. Use the triangle positioning method to find the minimum precision disc. (2) Inference phase: Considering the existence of errors, an Error-Adjusted Space Partition Attack Algorithm (EASPAA) was proposed during the inference phase. Improved the need for accurate distance information to be able to derive the user’s true location. In this study, we focus on the Location Sharing Mechanism with Maximal Coverage Limit to implement the whole attack. Experimental results show that the proposed method still can accurately infer the user’s real location even when there is an error in the user’s location information. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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17 pages, 547 KiB

Open AccessArticle

White-Hat Worm to Fight Malware and Its Evaluation by Agent-Oriented Petri Nets

by Shingo Yamaguchi

Sensors 2020, 20(2), 556; https://doi.org/10.3390/s20020556 - 19 Jan 2020

Cited by 23 | Viewed by 4498

Abstract

A new kind of malware called Mirai is spreading like wildfire. Mirai is characterized by targeting Internet of Things (IoT) devices. Since IoT devices are increasing explosively, it is not realistic to manage their vulnerability by human-wave tactics. This paper proposes a new [...] Read more.

A new kind of malware called Mirai is spreading like wildfire. Mirai is characterized by targeting Internet of Things (IoT) devices. Since IoT devices are increasing explosively, it is not realistic to manage their vulnerability by human-wave tactics. This paper proposes a new approach that uses a white-hat worm to fight malware. The white-hat worm is an extension of an IoT worm called Hajime and introduces lifespan and secondary infectivity (the ability to infect a device infected by Mirai). The proposed white-hat worm was expressed as a formal model with agent-oriented Petri nets called PN $^{2}$ . The model enables us to simulate a battle between the white-hat worm and Mirai. The result of the simulation evaluation shows that (i) the lifespan successfully reduces the worm’s remaining if short; (ii) if the worm has low secondary infectivity, its effect depends on the lifespan; and (iii) if the worm has high secondary infectivity, it is effective without depending on the lifespan. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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25 pages, 5863 KiB

Open AccessArticle

Applying Mobile Augmented Reality (AR) to Teach Interior Design Students in Layout Plans: Evaluation of Learning Effectiveness Based on the ARCS Model of Learning Motivation Theory

by Yuh-Shihng Chang, Kuo-Jui Hu, Cheng-Wei Chiang and Artur Lugmayr

Sensors 2020, 20(1), 105; https://doi.org/10.3390/s20010105 - 23 Dec 2019

Cited by 76 | Viewed by 11997

Abstract

In this paper we present a mobile augmented reality (MAR) application supporting teaching activities in interior design. The application supports students in learning interior layout design, interior design symbols, and the effects of different design layout decisions. Utilizing the latest AR technology, users [...] Read more.

In this paper we present a mobile augmented reality (MAR) application supporting teaching activities in interior design. The application supports students in learning interior layout design, interior design symbols, and the effects of different design layout decisions. Utilizing the latest AR technology, users can place 3D models of virtual objects as e.g., chairs or tables on top of a design layout plan and interact with these on their mobile devices. Students can experience alternative design decision in real-time and increases the special perception of interior designs. Our system fully supports the import of interior deployment layouts and the generation of 3D models based on design artefacts based on typical design layout plan design symbols and allows the user to investigate different design alternatives. We applied John Keller’s Attention, Relevance, Confidence, and Satisfaction (ARCS) learning motivation model to validate our solution to examine the students’ willingness and verify the ability of students to improve learning through MAR technology. We compared a sample experimental group of N = 52 test-subjects with a sample of N = 48 candidates in a control group. Learning indicators as learning interest, confidence, satisfaction and effective have been utilized to assess the students’ learning motivation through the use of MAR technology. The learning results have been determined by the independent sample t testing. The significance of the post-test had a p-value < 0.05 difference. The result of the study clearly shows that the reference group utilizing MAR technology as a learning aid show a higher learning effectiveness as the control group. Thus, we conclude that MAR technology does enhance students’ learning ability for interior design and making appropriate design decisions. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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28 pages, 8205 KiB

Open AccessArticle

Efficient CORDIC Iteration Design of LiDAR Sensors’ Point-Cloud Map Reconstruction Technology

by Yu-Cheng Fan, Yi-Cheng Liu and Chiao-An Chu

Sensors 2019, 19(24), 5412; https://doi.org/10.3390/s19245412 - 9 Dec 2019

Cited by 18 | Viewed by 3914

Abstract

In this paper, we propose an efficient COordinate Rotation DIgital Computer (CORDIC) iteration circuit design for Light Detection and Ranging (LiDAR) sensors. A novel CORDIC architecture that achieves the goal of pre-selecting angles and reduces the number of iterations is presented for LiDAR [...] Read more.

In this paper, we propose an efficient COordinate Rotation DIgital Computer (CORDIC) iteration circuit design for Light Detection and Ranging (LiDAR) sensors. A novel CORDIC architecture that achieves the goal of pre-selecting angles and reduces the number of iterations is presented for LiDAR sensors. The value of the trigonometric functions can be found in seven rotations regardless of the number of input N digits. The number of iterations are reduced by more than half. The experimental results show the similarity value to be all 1 and prove that the LiDAR decoded packet results are exactly the same as the ground truth. The total chip area is 1.93 mm × 1.93 mm and the core area is 1.32 mm × 1.32 mm, separately. The number of logic gates is 129,688. The designed chip only takes 0.012 ms and 0.912 ms to decode a packet and a 3D frame of LiDAR sensors, respectively. The throughput of the chip is 8.2105

{\times 10}^{8}

bits/sec. The average power consumption is 237.34 mW at a maximum operating frequency of 100 MHz. This design can not only reduce the number of iterations and the computing time but also reduce the chip area. This paper provides an efficient CORDIC iteration design and solution for LiDAR sensors to reconstruct the point-cloud map for autonomous vehicles. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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14 pages, 63411 KiB

Open AccessArticle

Real-Time Evaluation of the Mechanical Performance and Residual Life of a Notching Mold Using Embedded PVDF Sensors and SVM Criteria

by Ching-Yuan Chang, Tsung-Han Huang and Tzu-Chun Chung

Sensors 2019, 19(23), 5123; https://doi.org/10.3390/s19235123 - 22 Nov 2019

Cited by 2 | Viewed by 3609

Abstract

The geometric tolerance of notching machines used in the fabrication of components for induction motor stators and rotators is less than 50 µm. The blunt edges of worn molds can cause the edge of the sheet metal to form a burr, which [...] Read more.

The geometric tolerance of notching machines used in the fabrication of components for induction motor stators and rotators is less than 50 µm. The blunt edges of worn molds can cause the edge of the sheet metal to form a burr, which can seriously impede assembly and reduce the efficiency of the resulting motor. The overuse of molds without sufficient maintenance leads to wasted sheet material, whereas excessive maintenance shortens the life of the punch/die plate. Diagnosing the mechanical performance of die molds requires extensive experience and fine-grained sensor data. In this study, we embedded polyvinylidene fluoride (PVDF) films within the mechanical mold of a notching machine to obtain direct measurements of the reaction forces imposed by the punch. We also developed an automated diagnosis program based on a support vector machine (SVM) to characterize the performance of the mechanical mold. The proposed cyber-physical system (CPS) facilitated the real-time monitoring of machinery for preventative maintenance as well as the implementation of early warning alarms. The cloud server used to gather mold-related data also generated data logs for managers. The hyperplane of the CPS-PVDF was calibrated using a variety of parameters pertaining to the edge characteristics of punches. Stereo-microscopy analysis of the punched workpiece verified that the accuracy of the fault classification was 97.6%. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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21 pages, 9703 KiB

Open AccessArticle

A Single-Chip High-Voltage Integrated Actuator for Biomedical Ultrasound Scanners

by Chin Hsia, Yi-Chi Hsiao and Yen-Chung Huang

Sensors 2019, 19(23), 5063; https://doi.org/10.3390/s19235063 - 20 Nov 2019

Cited by 7 | Viewed by 5164

Abstract

This article presents a high-voltage (HV) pulse driver based on silicon-on-insulator (SOI) technology for biomedical ultrasound actuators and multi-channel portable imaging systems specifically. The pulse driver, which receives an external low-voltage drive signal and produces high-voltage pulses with a balanced rising and falling [...] Read more.

This article presents a high-voltage (HV) pulse driver based on silicon-on-insulator (SOI) technology for biomedical ultrasound actuators and multi-channel portable imaging systems specifically. The pulse driver, which receives an external low-voltage drive signal and produces high-voltage pulses with a balanced rising and falling edge, is designed by synthesizing high-speed, capacitor-coupled level-shifters with a high-voltage H-bridge output stage. In addition, an on-chip floating power supply has also been developed to simplify powering the entire system and reduce static power consumption. The electrical and acoustic performance of the integrated eight-channel pulse driver has been verified by using medical-grade ultrasound probes to acquire the transmit/echo signals. The driver can produce pulse signals >100 Vpp with rise and fall times within 18.6 and 18.5 ns, respectively. The static power required to support the overall system is less than 3.6 mW, and the power consumption of the system during excitation is less than 50 mW per channel. The second harmonic distortion of the output pulse signal is as low as −40 dBc, indicating that the integrated multi-channel pulse driver can be used in advanced portable ultrasonic imaging systems. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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22 pages, 6158 KiB

Open AccessArticle

Maximum Power Point Tracking of Photovoltaic System Based on Reinforcement Learning

by Kuan-Yu Chou, Shu-Ting Yang and Yon-Ping Chen

Sensors 2019, 19(22), 5054; https://doi.org/10.3390/s19225054 - 19 Nov 2019

Cited by 34 | Viewed by 5902

Abstract

The maximum power point tracking (MPPT) technique is often used in photovoltaic (PV) systems to extract the maximum power in various environmental conditions. The perturbation and observation (P&O) method is one of the most well-known MPPT methods; however, it may face problems of [...] Read more.

The maximum power point tracking (MPPT) technique is often used in photovoltaic (PV) systems to extract the maximum power in various environmental conditions. The perturbation and observation (P&O) method is one of the most well-known MPPT methods; however, it may face problems of large oscillations around maximum power point (MPP) or low-tracking efficiency. In this paper, two reinforcement learning-based maximum power point tracking (RL MPPT) methods are proposed by the use of the Q-learning algorithm. One constructs the Q-table and the other adopts the Q-network. These two proposed methods do not require the information of an actual PV module in advance and can track the MPP through offline training in two phases, the learning phase and the tracking phase. From the experimental results, both the reinforcement learning-based Q-table maximum power point tracking (RL-QT MPPT) and the reinforcement learning-based Q-network maximum power point tracking (RL-QN MPPT) methods have smaller ripples and faster tracking speeds when compared with the P&O method. In addition, for these two proposed methods, the RL-QT MPPT method performs with smaller oscillation and the RL-QN MPPT method achieves higher average power. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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16 pages, 2339 KiB

Open AccessArticle

Sensor-Assisted Weighted Average Ensemble Model for Detecting Major Depressive Disorder

by Nivedhitha Mahendran, Durai Raj Vincent, Kathiravan Srinivasan, Chuan-Yu Chang, Akhil Garg, Liang Gao and Daniel Gutiérrez Reina

Sensors 2019, 19(22), 4822; https://doi.org/10.3390/s19224822 - 6 Nov 2019

Cited by 33 | Viewed by 4196

Abstract

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings [...] Read more.

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposed Weighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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23 pages, 8800 KiB

Open AccessArticle

Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns

by Wen-Lin Chu, Chih-Jer Lin and Kai-Chun Kao

Sensors 2019, 19(21), 4806; https://doi.org/10.3390/s19214806 - 5 Nov 2019

Cited by 17 | Viewed by 3704

Abstract

In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of [...] Read more.

In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of motor control and measurement systems. The motor control system provides a set fixed speed, and the measurement system uses an accelerometer to measure the vibration, and the collected signal data are sent to a PC for analysis. This paper gives the details of the decomposition of vibration signals, using discrete wavelet transform (DWT) and computation of the features. It includes the classification of the features after analysis. Two major methods are used for the diagnosis of malfunction, the support vector machines (SVM) and general regression neural networks (GRNN). For visualization and to input the signals for visualization, they were input into a convolutional neural network (CNN) for further classification, as well as for the comparison of performance and results. Unique experimental processes were established with a particular hardware combination, and a comparison with commonly used methods was made. The results can be used for the design of a real-time motor that bears a diagnostic and malfunction warning system. This research establishes its own experimental process, according to the hardware combination and comparison of commonly used methods in research; a design for a real-time diagnosis of motor malfunction, as well as an early warning system, can be built thereupon. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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15 pages, 2664 KiB

Open AccessArticle

A Computer Mouse Using Blowing Sensors Intended for People with Disabilities

by Hsin-Chuan Chen, Chiou-Jye Huang, Wei-Ru Tsai and Che-Lin Hsieh

Sensors 2019, 19(21), 4638; https://doi.org/10.3390/s19214638 - 25 Oct 2019

Cited by 2 | Viewed by 3238

Abstract

The computer is an important medium that allows people to connect to the internet. However, people with disabilities are unable to use a computer mouse and thus cannot enjoy internet benefits. Nowadays, there are various types of assistive technologies for controlling a computer [...] Read more.

The computer is an important medium that allows people to connect to the internet. However, people with disabilities are unable to use a computer mouse and thus cannot enjoy internet benefits. Nowadays, there are various types of assistive technologies for controlling a computer mouse, but they all have some operational inconveniences. In this paper, we propose an innovative blowing-controlled mouse assistive tool to replace the conventional hand-controlled mouse. Its main contribution is that it uses microphones to induce small signals through the principle of airflow vibration, and it then converts the received signal into the corresponding pulse width. The co-design of software programming enables various mouse functions to be implemented by the identification of the blowing pulse width of multiple microphones. The proposed tool is evaluated experimentally, and the experimental results show that the average identification rate of the proposed mouse is over 85%. Additionally, compared with the other mouse assistive tools, the proposed mouse has the benefits of low cost and humanized operation. Therefore, the proposed blowing control method can not only improve the life quality of people with disabilities but also overcome the disadvantages of existing assistive tools. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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19 pages, 6547 KiB

Open AccessArticle

An Advanced ICTVSS Model for Real-Time Vehicle Traffic Applications

by You-Shyang Chen, Chien-Ku Lin and Yao-Wen Kan

Sensors 2019, 19(19), 4134; https://doi.org/10.3390/s19194134 - 24 Sep 2019

Cited by 6 | Viewed by 2466

Abstract

From the accident news, it is found that the occurrences of traffic accidents every year and the numbers of deaths and injuries have raised continually and have become a specific issue concerned in society in Taiwan. More seriously, the number of traffic accidents [...] Read more.

From the accident news, it is found that the occurrences of traffic accidents every year and the numbers of deaths and injuries have raised continually and have become a specific issue concerned in society in Taiwan. More seriously, the number of traffic accidents is positively increased with the increasing motorized vehicles. Thus, to reduce the incidence of traffic accidents through by some advanced real-time technologies is an important and interesting work. However, some serious problems against traffic safety are facing, such as the low-quality video saved by a camera, low efficiency facilities supported, inefficient management of surveillance resources, and low definition resolution for cameras, which is resulted in a dilemma problem caused from providing evidence-based images to a local authority either for criteria for judgment or basis for improvement. As a big effort to deal with the above defects for constructing a smart city, this paper makes a main purpose to develop an advanced system of intelligent cloud-based transportation vehicle surveillance (called ICTVSS) for license plate identification. This existing identification algorithm was studied and developed from a combination of improved differential algorithm and improved active contour algorithm. Given such a combination, a novel algorithm of dynamic license identification for smart monitoring was fully realized for constructing a well-defined smart city. The experimental results showed good performance and experienced that the proposed algorithm performed well in locating multi-license plate and differential methods, removing image noise of license plate, and processing constant-inconstant light source from complex environment cases, and guaranteed effective license plate identification from the benefit of high resolutions of digital cameras. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2019 ICCE-TW))

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