Electronics, Volume 9, Issue 3 (March 2020) – 162 articles

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB. Full article

As the energy management system (EMS) participates in the closed-loop control of shipboard integrated power system (IPS), the information network of EMS is closely coupled with the power system and its characteristics affect power system performance significantly. To study the close-coupling relationship between the two systems, a cyber–physical co-simulation platform based on the high level architecture (HLA) framework is constructed in this paper. The proposed platform uses PSCAD and OPNET to simulate shipboard power system and information network respectively, and utilizes OPNET HLA nodes and PSCAD user-defined modules to implement co-simulation interfaces. In order to achieve a higher co-simulation precision without impairing efficiency, an optimized event-driven co-simulation synchronization method is also proposed. By pre-defining power system synchronization points and detecting information network synchronization points in the co-simulation process, both systems can be synchronized in time and the synchronization error is eliminated. Furthermore, the co-simulation efficiency is also improved by optimizing the data transmission in the synchronization process. A co-simulation model of shipboard power distribution network protection based on CAN bus communication is built and analyzed. Simulation results show that the proposed co-simulation platform and synchronization method are feasible and effective. Full article

This paper presents a $K_u$ -band RF receiver front-end with broadband impedance matching and amplification. The major building blocks of the proposed receiver front-end include a wideband low-noise amplifier (LNA) employing a cascade of resistive feedback inverter (RFI) and transformer-loaded common source amplifier, a down-conversion mixer with push–pull transconductor and complementary LO switching stage, and an output buffer. Push–pull architecture is employed extensively to maximize the power efficiency, bandwidth, and linearity. The proposed two-stage LNA employs the stagger-tuned frequency response in order to extend the RF bandwidth coverage. The input impedance of RFI is carefully analyzed, and a wideband input matching circuit incorporating only a single inductor is presented along with useful equivalent impedance matching models and detailed design analysis. The prototype chip was fabricated in 45-nm CMOS technology and dissipates 78 mW from a 1.2-V supply while occupying chip area of 0.29 mm ${}^2$ . The proposed receiver front-end provides 21 dB conversion gain with 7 GHz IF bandwidth, 3.5 dB NF, −15.7 dBm IIP ${}_3$ while satisfying <−10 dB input matching over the whole input band. Full article

The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with issues related to the deployment of the Internet of Things, particularly in terms of performance and communication bottlenecks. Moreover, the associated integration process from the Edge up to the Cloud layer opens new security concerns that are key to assure the end-to-end trustability and interoperability. This work tackles these questions by proposing a novel embedded Edge platform based on an EFR32 SoC from Silicon Labs with Contiki-NG OS that includes an ARM Cortex M4 MCU and an IEEE 802.15.4 transceiver, used for resource-constrained low-power communication capabilities. This IoT Edge node integrates security by hardware, adding support for confidentiality, integrity and availability, making this Edge node ultra-secure for most of the common attacks in wireless sensor networks. Part of this security relies on an energy-efficient hardware accelerator that handles identity authentication, session key creation and management. Furthermore, the modular hardware platform aims at providing reliability and robustness in low-power distributed sensing application contexts on what is called the Extreme Edge, and for that purpose a lightweight multi-hop routing strategy for supporting dynamic discovery and interaction among participant devices is fully presented. This embedded algorithm has served as the baseline end-to-end communication capability to validate the IoT hardware platform through intensive experimental tests in a real deployment scenario. Full article

The ‘You Only Look Once’ v3 (YOLOv3) method is among the most widely used deep learning-based object detection methods. It uses the k-means cluster method to estimate the initial width and height of the predicted bounding boxes. With this method, the estimated width and height are sensitive to the initial cluster centers, and the processing of large-scale datasets is time-consuming. In order to address these problems, a new cluster method for estimating the initial width and height of the predicted bounding boxes has been developed. Firstly, it randomly selects a couple of width and height values as one initial cluster center separate from the width and height of the ground truth boxes. Secondly, it constructs Markov chains based on the selected initial cluster and uses the final points of every Markov chain as the other initial centers. In the construction of Markov chains, the intersection-over-union method is used to compute the distance between the selected initial clusters and each candidate point, instead of the square root method. Finally, this method can be used to continually update the cluster center with each new set of width and height values, which are only a part of the data selected from the datasets. Our simulation results show that the new method has faster convergence speed for initializing the width and height of the predicted bounding boxes and that it can select more representative initial widths and heights of the predicted bounding boxes. Our proposed method achieves better performance than the YOLOv3 method in terms of recall, mean average precision, and F1-score. Full article

The elevation detection accuracy of the laser receiver in the laser levelling control system directly affects land-levelling operations. To effectively improve the effect of levelling operations and meet the requirements for the accuracy of elevation detection in different industries, this study optimization designed a multilevel adjustable laser receiver. First, we examined the laser signal detection technology and processing circuit, designed the photoelectric conversion array for the detection of the rotating laser, and converted it into a photocurrent signal. We also designed the filter, amplifier, and shaping and stretching circuits for analogue-to-digital conversion of the photocurrent signal. The digital signal was calculated based on the deviation of the elevation by using a microprocessor and was output by a controller area network (CAN) bus. The laser beam spot diameter transmission and diffusion were then studied, and with the detectable spot diameters were compared and analyzed. Accordingly, an algorithm was proposed to calculate the deviation of laser receiver elevation. The resolution of the elevation deviation was set to ±3 mm; however, this value could be adjusted to ±6 mm, ±9 mm, ±12 mm, and ±15 mm, according to the requirements. Finally, the laser receiver was tested and analyzed, and the test results of the elevation detection accuracy showed that when the laser receiver was within a radius of 90 m, the elevation detection accuracy was within the ±3 mm range. The outcomes of the farmland-levelling test showed that the standard deviation $S_d$ of the field surface decreased from 9.54 cm before levelling to 2.42 cm after levelling, and the percentage of sampling points associated with absolute errors of ≤3 cm was 84.06%. These outcomes meet the requirements of high-standard farmland construction. The test results of concrete levelling showed that within a radius of 30 m, the standard deviation $S_d$ of the elevation adjustment of the left laser receiver was 1.389 mm, and the standard deviation $S_d$ of the elevation adjustment of the right laser receiver was 1.316 mm. Furthermore, the percentage of the sampling points associated with absolute elevation adjustment errors of ≤3 mm in the cases of the two laser receivers was 100% after levelling, whereas the standard deviation $S_d$ of the sand bed surface was 0.881 mm. Additionally, the percentage of the sampling points associated with absolute errors of ≤3 mm was 100%. This met the construction standards of the concrete industry. Full article

It is worldwide known that the most common type of cancer among women is breast cancer. Traditional procedures involve surgery, chemotherapy and radiation therapy; however, these treatments are invasive and have serious side effects. For this reason, minimally invasive thermal treatments like microwave ablation are being considered. In this study, thermal behavior of three types of slot-coaxial antennas for breast cancer microwave ablation is presented. By using finite element method (FEM), all antennas were modeled to estimate the heat transfer in breast tumor tissue surrounded by healthy breast tissue. Experimentation was carried out by using the antennas inserted inside sphere-shaped-tumor phantoms with two different diameters, 1.0 and 1.5 cm. A microwave radiation system was used to apply microwave energy to each designed antenna, which were located into the phantom. A non-interfering thermometry system was used to measure the temperature increase during the experimentation. Temperature increases, recorded by the thermal sensors placed inside the tumor phantom surrounded by healthy breast phantom, were used to validate the FEM models. The results conclude that, in all the cases, after 240 s, the three types of coaxial slot antenna reached the temperature needed produce hyperthermia of the tumor volume considered in this paper. Full article

The application of an unmanned vehicle system allows for accelerating the performance of various tasks. Due to limited capacities, such as battery power, it is almost impossible for a single unmanned vehicle to complete a large-scale mission area. An unmanned vehicle swarm has the potential to distribute tasks and coordinate the operations of many robots/drones with very little operator intervention. Therefore, multiple unmanned vehicles are required to execute a set of well-planned mission routes, in order to minimize time and energy consumption. A two-phase heuristic algorithm was used to pursue this goal. In the first phase, a tabu search and the 2-opt node exchange method were used to generate a single optimal path for all target nodes; the solution was then split into multiple clusters according to vehicle numbers as an initial solution for each. In the second phase, a tabu algorithm combined with a 2-opt path exchange was used to further improve the in-route and cross-route solutions for each route. This diversification strategy allowed for approaching the global optimal solution, rather than a regional one with less CPU time. After these algorithms were coded, a group of three robot cars was used to validate this hybrid path programming algorithm. Full article

In the Fifth Generation of telecommunications networks (5G), it is possible to use massive Multiple Input Multiple Output (MIMO) systems, which require efficient receivers capable of reaching good performance values. MIMO systems can also be extended to massive MIMO (mMIMO) systems, while maintaining their, sometimes exceptional, performance. However, we must be aware that this implies an increase in the receiver complexity. Therefore, the use of mMIMO in 5G and future generations of mobile receivers will only be feasible if they use very efficient algorithms, so as to maintain their excellent performance, while coping with increasing and critical user demands. Having this in mind, this paper presents and compares three types of receivers used in MIMO systems, for further use with mMIMO systems, which use Single-Carrier with Frequency-Domain Equalization (SC-FDE), Iterative Block Decision Feedback Equalization (IB-DFE) and Maximum Ratio Combining (MRC) techniques. This paper presents and compares the theoretical and simulated performance values for these receivers in terms of their Bit Error Rate (BER) and correlation factor. While one of the receivers studied in this paper achieves a BER performance nearly matching the Matched Filter Bound (MFB), the other receivers (IB-DFE and MRC) are more than 1 dB away from MFB. The results obtained in this paper can help the development of ongoing research involving hybrid analog/digital receivers for 5G and future generations of mobile communications. Full article

This work presents an energy efficient technique for fabricating flexible thermoelectric generators while using printable ink. We have fabricated thermoelectric composite thick films using two different mesh sizes of n-type bismuth particles, various binder to thermoelectric material weight ratios, and two different pressures, 200 MPa and 300 MPa, in order to optimize the thermoelectric properties of the composite films. The use of chitosan dissolved in dimethylsulfoxide with less than 0.2 wt. % of chitosan, the first time chitosan has been used in this process, was sufficient for fabricating TE inks and composite films. Low temperature curing processes, along with uniaxial pressure, were used to evaporate the solvent from the drop-casted inks. This combination reduced the temperature needed compared to traditional curing processes while simultaneously increasing the packing density of the film by removing the pores and voids in the chitosan-bismuth composite film. Microstructural analysis of the composite films reveals low amounts of voids and pores when pressed at sufficiently high pressures. The highest performing composite film was obtained with the weight ratio of 1:2000 binder to bismuth, 100-mesh particle size, and 300 MPa of pressure. The best performing bismuth chitosan composite film that was pressed at 300 MPa had a power factor of 4009 ± 391 μW/m K² with high electrical conductivity of 7337 ± 522 S/cm. The measured thermal conductivity of this same sample was 4.4 ± 0.8 W/m K and the corresponding figure of merit was 0.27 at room temperature. Full article

The number of forest fires that occurred in recent years in different parts of the world is causing increased concern in the population, as the consequences of these fires expand beyond the destruction of the ecosystem. However, with the proliferation of the Internet of Things (IoT) industry, solutions for early fire detection should be developed. The assessment of the fire risk of an area and the communication of this fact to the population could reduce the number of fires originated by accident or due to the carelessness of the users. This paper presents a low-cost network based on Long Range (LoRa) technology to autonomously evaluate the level of fire risk and the presence of a forest fire in rural areas. The system is comprised of several LoRa nodes with sensors to measure the temperature, relative humidity, wind speed and CO₂ of the environment. The data from the nodes is stored and processed in a The Things Network (TTN) server that sends the data to a website for the graphic visualization of the collected data. The system is tested in a real environment and, the results show that it is possible to cover a circular area of a radius of 4 km with a single gateway. Full article

The significant increase of the Internet of Things (IoT) devices in smart homes and other smart infrastructure, and the recent attacks on these IoT devices, are motivating factors to secure and protect IoT networks. The primary security challenge to develop a methodology to identify a malicious activity correctly and mitigate the impact of such activity promptly. In this paper, we propose a two-level anomalous activity detection model for intrusion detection system in IoT networks. The level-1 model categorizes the network flow as normal flow or abnormal flow, while the level-2 model classifies the category or subcategory of detected malicious activity. When the network flow classified as an anomaly by the level-1 model, then the level-1 model forwards the stream to the level-2 model for further investigation to find the category or subcategory of the detected anomaly. Our proposed model constructed on flow-based features of the IoT network. Flow-based detection methodologies only inspect packet headers to classify the network traffic. Flow-based features extracted from the IoT Botnet dataset and various machine learning algorithms were investigated and tested via different cross-fold validation tests to select the best algorithm. The decision tree classifier yielded the highest predictive results for level-1, and the random forest classifier produced the highest predictive results for level-2. Our proposed model Accuracy, Precision, Recall, and F score for level-1 were measured as 99.99% and 99.90% for level-2. A two-level anomalous activity detection system for IoT networks we proposed will provide a robust framework for the development of malicious activity detection system for IoT networks. It would be of interest to researchers in academia and industry. Full article

In this paper, a new LLC converter for series-connected, high-voltage LEDs is proposed. The proposed LLC converter consists of two stacked, single-ended rectifiers and one balancing capacitor, to compensate for the current deviation of two individual LED strings. The proposed LLC LED driver can use a diode with low voltage stress, even if the secondary LED is connected in series to have a high driving voltage. In addition, even if several series-connected LEDs are changed into two-stacked structures, the balancing capacitor can compensate for the current deviation of the two separated LEDs, as well as the difference in leakage inductance of the two stacked single-ended rectifiers. The balancing capacitor can be made equal to the voltage tolerance of the stacked, single-ended rectifier diodes. The proposed circuit can be easily extended to a series channel LED driver circuit, without increasing the voltage stress. To verify the characteristics and operation of the proposed LLC LED driver, a 260W high-power LED driver is implemented. Full article

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution. Full article

Inductive power transfer systems (IPTSs) systems are equipped with compensation networks that resonate at the supply frequency with the inductance of the transmitting and receiving coils to both maximize the power transfer efficiency and reduce the IPTS power sizing. If the network and coil parameters differ from the designed values, the resonance frequencies deviate from the supply frequency, thus reducing the IPTS efficiency. To cope with this issue, two methods of tuning the IPTS supply frequency are presented and discussed. One method is aimed at making resonant the impedance seen by the IPTS power supply, the other one at making resonant the impedance of the receiving stage. The paper closes by implementing the first method in an experimental setup and by testing its tuning capabilities on a prototypal IPTS used for charging the battery of an electric vehicle. Full article

A cylindrical joint structure for wireless power transfer (WPT) systems is proposed. The transmitter (Tx) and receiver (Rx) coils were wound on hemicylindrical and cylindrical structures, respectively. The Rx coil rotates freely around the axial direction of the Tx coil. Different methods of winding the Tx and Rx coils are given and discussed. Electromagnetic fields (EMFs) around the WPT windings should be lower than the limits set by WPT standards. Therefore, the WPT windings were designed to reduce EMF level and maintain constant power-transfer efficiency (PTE). The design procedures of the windings are discussed in detail. EMF analysis was done under different rotation angles (α). The selected design reduced the variation of the mutual inductance (M). As a result, it maintained a constant PTE while rotating the Rx coil between 0° and 85°. Moreover, leakage magnetic fields (LMFs) near the WPT coils of the chosen design were reduced by 63.6% compared with other winding methods that have the same efficiency. Finally, a prototype was built to validate the proposed idea. Experiment results were in good agreement with the simulation results. The WPT system maintained constant efficiency in spite of the rotation of Rx coil, where efficiency dropped by only 2.15% when the Rx coil rotated between 0° and 85°. Full article

Metal Oxide Semiconductor or MOS-type gas sensors are resistive sensors which can detect different reducible or volatile gases in atmospheres with oxygen. These gas sensors have been used in different areas such as food and drink industries or healthcare, among others. In this type of sensor, the resistance value changes when it detects certain types of gases. Due to the electrical characteristics, the sensors need a conditioning circuit to transform and acquire the data. Four different electronic topologies, two different MOS-type gas sensors, and different concentrations of a gas substance are presented and compared in this paper. The study and experimental analysis of the properties of each of the designed topology allows designers to make a choice of the best circuit for a specific application depending on the situation, considering the required power, noise, linearity, and number of sensors to be used. This study will give more freedom of choice, the more adequate electronic conditioning topology for different applications where MOS-type sensors are used, obtaining the best accuracy. Full article

A vehicular adhoc network (VANET) recently emerged in the the Internet of Vehicles (IoV); it involves the computational processing of moving vehicles. Nowadays, IoV has turned into an interesting field of research as vehicles can be equipped with processors, sensors, and communication devices. IoV gives rise to handoff, which involves changing the connection points during the online communication session. This presents a major challenge for which many standardized solutions are recommended. Although there are various proposed techniques and methods to support seamless handover procedure in IoV, there are still some open research issues, such as unavoidable packet loss rate and latency. On the other hand, the emerged concept of edge mobile computing has gained crucial attention by researchers that could help in reducing computational complexities and decreasing communication delay. Hence, this paper specifically studies the handoff challenges in cluster based handoff using new concept of dynamic edge-backup node. The outcomes are evaluated and contrasted with the network mobility method, our proposed technique, and other cluster-based technologies. The results show that coherence in communication during the handoff method can be upgraded, enhanced, and improved utilizing the proposed technique. Full article

Sol-gel-processed Mg-doped SnO₂ thin-film transistors (TFTs) were successfully fabricated. The effect of Mg concentration on the structural, chemical, and optical properties of thin films and the corresponding TFT devices was investigated. The results indicated that an optimal Mg concentration yielded an improved negative bias stability and increased optical band gap, resulting in transparent devices. Furthermore, the optimal device performance was obtained with 0.5 wt% Mg. The fabricated 0.5 wt% Mg-doped SnO₂ TFT was characterized by a field effect mobility, a subthreshold swing, and I_on/I_off ratio of 4.23 cm²/Vs, 1.37 V/decade, and ~1 × 10⁷, respectively. The added Mg suppressed oxygen-vacancy formation, thereby improving the bias stability. This work may pave the way for the development of alkaline-earth-metal-doped SnO₂-based thin-film devices. Full article

Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical characteristics of pulse pairs, we present the convergence feature based on the Markov chain to reflect the global characterization of pulse pairs, which is essentially the final state of the Markov transition matrix. Combining the convergence feature with the preceding features, we propose an XGBoost-based steganalysis scheme for AMR speech streams. Finally, we conducted a series of experiments to assess our presented scheme and compared it with previous schemes. The experimental results demonstrate that the proposed scheme is feasible, and can provide better performance in terms of detecting the existing steganography methods based on AMR speech streams. Full article

The convergence of fifth-generation (5G) communication and the Internet-of-Things (IoT) has dramatically increased the diversity and complexity of the network. This change diversifies the attacker’s attack vectors, increasing the impact and damage of cyber threats. Cyber threat intelligence (CTI) technology is a proof-based security system which responds to these advanced cyber threats proactively by analyzing and sharing security-related data. However, the performance of CTI systems can be significantly compromised by creating and disseminating improper security policies if an attacker intentionally injects malicious data into the system. In this paper, we propose a blockchain-based CTI framework that improves confidence in the source and content of the data and can quickly detect and eliminate inaccurate data for resistance to a Sybil attack. The proposed framework collects CTI by a procedure validated through smart contracts and stores information about the metainformation of data in a blockchain network. The proposed system ensures the validity and reliability of CTI data by ensuring traceability to the data source and proposes a system model that can efficiently operate and manage CTI data in compliance with the de facto standard. We present the simulation results to prove the effectiveness and Sybil-resistance of the proposed framework in terms of reliability and cost to attackers. Full article

Two party authentication schemes can be good candidates for deployment in Internet of Things (IoT)-based systems, especially in systems involving fast moving vehicles. Internet of Vehicles (IoV) requires fast and secure device-to-device communication without interference of any third party during communication, and this task can be carried out after registration of vehicles with a trusted certificate issuing party. Recently, several authentication protocols were proposed to enable key agreement in two party settings. In this study, we analyze two recent protocols and show that both protocols are insecure against key compromise impersonation attack (KCIA) as well as both lack of user anonymity. Therefore, this paper proposes an improved protocol that does not only resist KCIA and related attacks, but also offers comparable computation and communication. The security of proposed protocol is tested under formal model as well as using well known Burrows–Abadi–Needham (BAN) logic along with a discussion on security features. While resisting the KCIA and related attacks, proposed protocol also provides comparable trade-of between security features and efficiency and completes a round of key agreement in just $13.42$ ms, which makes it a promising candidate to be deployed in IoT environments. Full article

This paper proposes an external breaking vibration identification method of transmission line tower based on a radio frequency identification (RFID) sensor and deep learning. The RFID sensor is designed to obtain the vibration signal of the transmission line tower. In order to achieve long-time monitoring and longer working distance, the proposed RFID sensor tag employs a photovoltaic cell combined with a super capacitor as the power management module. convolution neural network (CNN) is adopted to extract the characteristics of vibration signals and relevance vector machine (RVM) is then employed to achieve vibration pattern identification. Furthermore, the Softmax classifier and gradient descent method are used to adjust the weights and thresholds of CNN, so as to obtain a high-precision identification structure. The experiment results show that the minimum sensitivity of the proposed solar-powered RFID sensor tag is −29 dBm and the discharge duration of the super capacitor is 63.35 h when the query frequencies are 5/min. The optimum batch size of CNN is 5, and the optimum number of convolution cores in the first layer and the second layer are 2 and 4, respectively. The maximum number of iterations is 10 times. The vibration identification accuracy of the proposed method is over 99% under three different conditions. Full article

The development of autonomous driving cars is a complex activity, which poses challenges about ethics, safety, cybersecurity, and social acceptance. The latter, in particular, poses new problems since passengers are used to manually driven vehicles; hence, they need to move their trust from a person to a computer. To smooth the transition towards autonomous vehicles, a delicate calibration of the driving functions should be performed, making the automation decision closest to the passengers’ expectations. The complexity of this calibration lies in the presence of a person in the loop: different settings of a given algorithm should be evaluated by assessing the human reaction to the vehicle decisions. With this work, we for an objective method to classify the people’s reaction to vehicle decisions. By adopting machine learning techniques, it is possible to analyze the passengers’ emotions while driving with alternative vehicle calibrations. Through the analysis of these emotions, it is possible to obtain an objective metric about the comfort feeling of the passengers. As a result, we developed a proof-of-concept implementation of a simple, yet effective, emotions recognition system. It can be deployed either into real vehicles or simulators, during the driving functions calibration. Full article

Data resources in open computing environments (including big data, internet of things and cloud computing) are characterized by large scale, wide source, and strong dynamics. Therefore, the user-permission relationship of open computing environments has a huge scale and will be dynamically adjusted over time, which enables effective permission management in the role based access control (RBAC) model to become a challenging problem. In this paper, we design an evolution mechanism of access control roles for open computing environments. The mechanism utilizes the existing user-permission relationship in the current system to mine the access control role and generate the user-role and role-permission relationship. When the user-permission relationship changes, the roles are constantly tuned and evolved to provide role support for access control of open computing environments. We propose a novel genetic-based role evolution algorithm that can effectively mine and optimize roles while preserving the core permissions of the system. In addition, a role relationship aggregation algorithm is proposed to realize the clustering of roles, which provides a supplementary reference for the security administrator to give the role real semantic information. Experimental evaluations in real-world data sets show that the proposed mechanism is effective and reliable. Full article

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results. Full article

We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 $\mathsf{\mu }$ W power and occupies 0.003 mm ${}^2$ die area. Full article

In this paper, we introduce the three-dimensional Maximum Range-Sum (3D MaxRS) problem and the Maximum Spatiotemporal Range-Sum Change (MaxStRSC) problem. The 3D MaxRS problem tries to find the 3D range where the sum of weights across all objects inside is maximized, and the MaxStRSC problem tries to find the spatiotemporal range where the sum of weights across all objects inside is maximally increased. The goal of this paper is to provide efficient methods for data analysts to find interesting spatiotemporal regions in a large historical spatiotemporal dataset by addressing two problems. We provide a mathematical explanation for each problem and propose several algorithms for them. Existing methods tried to find the optimal region over two-dimensional datasets or to monitor a burst region over two-dimensional data streams. The majority of them cannot directly solve our problems. Although some existing methods can be used or modified to solve the 3D MaxRS problems, they have limited scalability. In addition, none of them can be used to solve the MaxStRS-RC problem (a type of MaxStRSC problem). Finally, we study the performance of the proposed algorithms experimentally. The experimental results show that the proposed algorithms are scalable and much more efficient than existing methods. Full article

The achievement of robot autonomy has environmental perception as a prerequisite. The hazards rendered from uneven, soft and slippery terrains, which are generally named non-geometric hazards, are another potential threat reducing the traversing efficient, and therefore receiving more and more attention from the robotics community. In the paper, the vibration-based terrain classification (VTC) is investigated by taking a very practical issue, i.e., lack of labels, into consideration. According to the intrinsic temporal correlation existing in the sampled terrain sequence, a modified Laplacian SVM is proposed to utilise the unlabelled data to improve the classification performance. To the best of our knowledge, this is the first paper studying semi-supervised learning problem in robotic terrain classification. The experiment demonstrates that: (1) supervised learning (SVM) achieves a relatively low classification accuracy if given insufficient labels; (2) feature-space homogeneity based semi-supervised learning (traditional Laplacian SVM) cannot improve supervised learning’s accuracy, and even makes it worse; (3) feature- and temporal-space based semi-supervised learning (modified Laplacian SVM), which is proposed in the paper, could increase the classification accuracy very significantly. Full article

Sleep disorder is a medical disease of the sleep patterns, which commonly suffered by the elderly. Sleep disorders diagnosis and treatment are considered to be challenging due to a time-consuming and inconvenient process for the patient. Moreover, the use of Polysomnography (PSG) in sleep disorder diagnosis is a high-cost process. Therefore, we propose an efficient classification method of sleep disorder by merely using electrocardiography (ECG) signals to simplify the sleep disorders diagnosis process. Different from many current related studies that applied a five-minute epoch to observe the main frequency band of the ECG signal, we perform a pre-processing technique that suitable for the 30-seconds epoch of the ECG signal. By this simplification, the proposed method has a low computational cost so that suitable to be implemented in an embedded hardware device. Structurally, the proposed method consists of five stages: (1) pre-processing, (2) spectral features extraction, (3) sleep stage detection using the Decision-Tree-Based Support Vector Machine (DTB-SVM), (4) assess the sleep quality features, and (5) sleep disorders classification using ensemble of bagged tree classifiers. We evaluate the effectiveness of the proposed method in the task of classifying the sleep disorders into four classes (insomnia, Sleep-Disordered Breathing (SDB), REM Behavior Disorder (RBD), and healthy subjects) from the 51 patients of the Cyclic Alternating Pattern (CAP) sleep data. Based on experimental results, the proposed method presents 84.01% of sensitivity, 94.17% of specificity, 86.27% of overall accuracy, and 0.70 of Cohen’s kappa. This result indicates that the proposed method able to reliably classify the sleep disorders merely using the 30-seconds epoch ECG in order to address the issue of a multichannel signal such as the PSG. Full article