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Road speed is an important indicator of traffic congestion. Therefore, the occurrence of traffic congestion can be reduced by predicting road speed because predicted road speed can be provided to users to distribute traffic. Traffic congestion prediction techniques can provide alternative routes to users in advance to help them avoid traffic jams. In this paper, we propose a machine-learning-based road speed prediction scheme using road environment data analysis. The proposed scheme uses not only the speed data of the target road, but also the speed data of neighboring roads that can affect the speed of the target road. Furthermore, the proposed scheme can accurately predict both the average road speed and rapidly changing road speeds. The proposed scheme uses historical average speed data from the target road organized by the day of the week and hour to reflect the average traffic flow on the road. Additionally, the proposed scheme analyzes speed changes in sections where the road speed changes rapidly to reflect traffic flows. Road speeds may change rapidly as a result of unexpected events such as accidents, disasters, and construction work. The proposed scheme predicts final road speeds by applying historical road speeds and events as weights for road speed prediction. It also considers weather conditions. The proposed scheme uses long short-term memory (LSTM), which is suitable for sequential data learning, as a machine learning algorithm for speed prediction. The proposed scheme can predict road speeds in 30 min by using weather data and speed data from the target and neighboring roads as input data. We demonstrate the capabilities of the proposed scheme through various performance evaluations.The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22-64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland-Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen's kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR r = 0.81, |bias| = 0.23 beats/min; BR r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR r = 0.94, |bias| = 0.15 beats/min; BR r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions.Security has always been the main concern for the internet of things (IoT)-based systems. Blockchain, with its decentralized and distributed design, prevents the risks of the existing centralized methodologies. Conventional security and privacy architectures are inapplicable in the spectrum of IoT due to its resource constraints. To overcome this problem, this paper presents a Blockchain-based security mechanism that enables secure authorized access to smart city resources. The presented mechanism comprises the ACE (Authentication and Authorization for Constrained Environments) framework-based authorization Blockchain and the OSCAR (Object Security Architecture for the Internet of Things) object security model. The Blockchain lays out a flexible and trustless authorization mechanism, while OSCAR makes use of a public ledger to structure multicast groups for authorized clients. Moreover, a meteor-based application is developed to provide a user-friendly interface for heterogeneous technologies belonging to the smart city. The users would be able to interact with and control their smart city resources such as traffic lights, smart electric meters, surveillance cameras, etc., through this application. To evaluate the performance and feasibility of the proposed mechanism, the authorization Blockchain is implemented on top of the Ethereum network. The authentication mechanism is developed in the node.js server and a smart city is simulated with the help of Raspberry Pi B+. Furthermore, mocha and chai frameworks are used to assess the performance of the system. Experimental results reveal that the authentication response time is less than 100 ms even if the average hand-shaking time increases with the number of clients.Intent sensing-the ability to sense what a user wants to happen-has many potential technological applications. Assistive medical devices, such as prosthetic limbs, could benefit from intent-based control systems, allowing for faster and more intuitive control. The accuracy of intent sensing could be improved by using multiple sensors sensing multiple environments. As users will typically pass through different sensing environments throughout the day, the system should be dynamic, with sensors dropping in and out as required. An intent-sensing algorithm that allows for this cannot rely on training from only a particular combination of sensors. It should allow any (dynamic) combination of sensors to be used. Therefore, the objective of this study is to develop and test a dynamic intent-sensing system under changing conditions. A method has been proposed that treats each sensor individually and combines them using Bayesian sensor fusion. This approach was tested on laboratory data obtained from subjects wearing Inertial Measurement Units and surface electromyography electrodes. The proposed algorithm was then used to classify functional reach activities and compare the performance to an established classifier (k-nearest-neighbours) in cases of simulated sensor dropouts. Results showed that the Bayesian sensor fusion algorithm was less affected as more sensors dropped out, supporting this intent-sensing approach as viable in dynamic real-world scenarios.Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods. Early face mask detection is very important to prevent the spread of COVID-19. For this purpose, we investigate several deep learning-based architectures such as VGG16, VGG19, InceptionV3, ResNet-101, ResNet-50, EfficientNet, MobileNetV1, and MobileNetV2. After these experiments, we propose an efficient and effective model for face mask detection with the potential to be deployable over edge devices. Our proposed model is based on MobileNetV2 architecture that extracts salient features from the input data that are then passed to an autoencoder to form more abstract representations prior to the classification layer. The proposed model also adopts extensive data augmentation techniques (e.g., rotation, flip, Gaussian blur, sharping, emboss, skew, and shear) to increase the number of samples for effective training. The performance of our proposed model is evaluated on three publicly available datasets and achieved the highest performance as compared to other state-of-the-art models.A compact flexible multi-frequency antenna for smart portable and flexible devices is presented. The antenna consists of a coplanar waveguide-fed slotted circular patch connected to a rectangular secondary resonator (stub). A thin low-loss substrate is used for flexibility, and a rectangular stub in the feedline is deployed to attain wide operational bandwidth. A rectangular slot is etched in the middle of the circular patch, and a p-i-n diode is placed at its center. DNA Repair inhibitor The frequency reconfigurability is achieved through switching the diode that distributes the current by changing the antenna's electrical length. For the ON state, the antenna operates in the UWB region for -10 dB impedance bandwidth from 2.76 to 8.21 GHz. For the OFF state of the diode, the antenna operates at the ISM band (2.45/5.8 GHz), WLAN band (5.2 GHz), and lower X-band (8 GHz) with a minimum gain of 2.49 dBi and a maximum gain of 5.8 dBi at the 8 GHz band. Moreover, the antenna retains its performance in various bending conditions. The proposed antenna is suitable for modern miniaturized wireless electronic devices such as wearables, health monitoring sensors, mobile Internet devices, and laptops that operate at multiple frequency bands.A three-dimensional finite element analysis model of surface acoustic wave (SAW) torque sensor based on multilayer structure is proposed in this paper. Compared with the traditional saw torque sensor with quartz as piezoelectric substrate, the SAW torque sensor with multilayer structure has the advantages of fast propagation speed and high characteristic frequency. It is a very promising torque sensor, but there is very little related research. In order to successfully develop the sensor, it is essential to understand the propagation characteristics and torque sensing mode of SAW in multilayer structure. Therefore, in this study, we first established a multi-layered finite element analysis model of SAW device based on IDT/128° Y-X lithium niobate/diamond/Si (100). Then, the effects of different film thicknesses on the characteristic frequency, electromechanical coupling coefficient, s parameter, and mechanical quality factor of SAW device without changing the wavelength are analyzed. Then, based on the finite element analysis, a three-dimensional research model of a new SAW torque sensor suitable for small diameter torsion bar (d = 10 mm) is established, and the relationship between saw device deformation and torque under the condition of small torque (±40 Nm) is tested. The shape variable is introduced into the finite element analysis model of multi-layer SAW device. Finally, the relationship between saw torque sensor with multi-layer structure and torque is established by using the deformation relationship, which shows the perfect curve of sensor performance.Multiple sensors are embedded in wearable devices [...].Wireless sensor networks (WSNs) have received considerable interest in recent years. These sensor nodes can gather information from the surrounding environment and transmit it to a designated location. Each sensor node in WSN typically has a battery with a limited capacity. Due to their large number and because of various environmental challenges, it is sometimes hard to replace this finite battery. As a result, energy-efficient communication is seen as a critical aspect in extending the lifespan of a sensor node. On the other hand, some applications that require large coverage and generate various sorts of data packets require multi-hop routing and quality of service (QoS) features. Therefore, in order to avoid network failure, these applications need an energy-efficient QoS MAC protocol that can support multiple levels of data packet priority and multi-hop routing features while focusing on energy conservation. An energy-aware QoS MAC protocol based on Prioritized Data and Multi-hop routing (EQPD-MAC) is proposed in this article.
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