Table of Contents

Recently, carbon allotropes have received tremendous research interest and paved a new avenue for optical fiber sensing technology. Carbon allotropes exhibit unique sensing properties such as large surface to volume ratios, biocompatibility, and they can serve as molecule enrichers. Meanwhile, optical fibers possess a high degree of surface modification versatility that enables the incorporation of carbon allotropes as the functional coating for a wide range of detection tasks. Moreover, the combination of carbon allotropes and optical fibers also yields high sensitivity and specificity to monitor target molecules in the vicinity of the nanocoating surface. In this review, the development of carbon allotropes-based optical fiber sensors is studied. The first section provides an overview of four different types of carbon allotropes, including carbon nanotubes, carbon dots, graphene, and nanodiamonds. The second section discusses the synthesis approaches used to prepare these carbon allotropes, followed by some deposition techniques to functionalize the surface of the optical fiber, and the associated sensing mechanisms. Numerous applications that have benefitted from carbon allotrope-based optical fiber sensors such as temperature, strain, volatile organic compounds and biosensing applications are reviewed and summarized. Finally, a concluding section highlighting the technological deficiencies, challenges, and suggestions to overcome them is presented. Full article

This paper proposes a novel parcel delivery system which consists of a drone and public transportation vehicles such as trains, trams, etc. This system involves two delivery schemes: drone-direct scheme referring to delivering to a customer by a drone directly and drone–vehicle collaborating scheme referring to delivering a customer based on the collaboration of a drone and public transportation vehicles. The fundamental characteristics including the delivery time, energy consumption and battery recharging are modelled, based on which a time-dependent scheduling problem for a single drone is formulated. It is shown to be NP-complete and a dynamic programming-based exact algorithm is presented. Since its computational complexity is exponential with respect to the number of customers, a sub-optimal algorithm is further developed. This algorithm accounts the time for delivery and recharging, and it first schedules the customer which leads to the earliest return. Its computational complexity is also discussed. Moreover, extensive computer simulations are conducted to demonstrate the scheduling performance of the proposed algorithms and the impacts of several key system parameters are investigated. Full article

In many applications, modern current-using equipment utilizes power electronic converters to control the consumed power and to adjust the motor speed. Such equipment is used both in industrial and domestic installations. A characteristic feature of the converters is producing distorted earth fault currents, which contain a wide spectrum of harmonics, including high-order harmonics. Nowadays, protection against electric shock in low-voltage power systems is commonly performed with the use of residual current devices (RCDs). In the presence of harmonics, the RCDs may have a tripping current significantly different from that provided for the nominal sinusoidal waveform. Thus, in some cases, protection against electric shock may not be effective. The aim of this paper is to present the result of a wide-range laboratory test of the sensitivity of A-type RCDs in the presence of harmonics. This test has shown that the behavior of RCDs in the presence of harmonics can be varied, including the cases in which the RCD does not react to the distorted earth fault current, as well as cases in which the sensitivity of the RCD is increased. The properties of the main elements of RCDs, including the current sensor, for high-frequency current components are discussed as well. Full article

Wi-Fi technology connects sensor-based things that operate with small batteries, and allows them to access the Internet from anywhere at any time and perform networking. It has become a critical element in many areas of daily life and industry, including smart homes, smart factories, smart grids, and smart cities. The Wi-Fi-based Internet of things is gradually expanding its range of uses from new industries to areas that are intimately connected to people’s lives, safety, and property. Wi-Fi technology has undergone a 20-year standardization process and continues to evolve to improve transmission speeds and service quality. Simultaneously, it has also been strengthening power-saving technology and security technology to improve energy efficiency and security while maintaining backward compatibility with past standards. This study analyzed the security vulnerabilities of the Wi-Fi power-saving mechanism used in smart devices and experimentally proved the feasibility of a battery draining attack (BDA) on commercial smartphones. The results of the experiment showed that when a battery draining attack was performed on power-saving Wi-Fi, 14 times the amount of energy was consumed compared with when a battery draining attack was not performed. This study analyzed the security vulnerabilities of the power-saving mechanism and discusses countermeasures. Full article

This work presents the design of a discrete second-generation voltage conveyor (VCII) and its capability to be used as electronic interface for silicon photomultipliers. The design addressed here exploits directly at the transistor level, with commercial components, the proposed interface; the obtained performance is valuable considering both the discrete elements and the application. The architecture adopted here realizes a transimpedance amplifier that is also able to drive very high input impedance, as usually requested by photons detection. Schematic and circuital design of the discrete second-generation voltage conveyor is presented and discussed. The complete circuit interface requires a bias current of 20 mA with a dual 5V supply voltage; it has a useful bandwidth of about 106 MHz, and considering also the reduced dimensions, it is a good candidate to be used in portable applications without the need of high-cost dedicated integrated circuits. Full article

Counterfeiting of an Integrated Circuit (IC) has become a significant concern for electronics manufacturers, system integrators, and end users. It is necessary to find a robust implementation that is efficient, low cost, and noninvasive in detection and avoidance of ICs counterfeiting. In this paper, we introduce the concept of using a guided radiofrequency (RF) wave technique to authenticate ICs. The approach discussed in this work highlights the use of electromagnetic (EM)/radiofrequency (RF) response that has been further evaluated to assign fingerprint or signature of ICs for the purpose of authentication. Our approach is to use EM/RF guided wave to sense the response of the ICs, extract the manufacturing-based process variation of an IC and finally generate identifier or signature of that IC. As a proof-of-concept, we performed experiments over different field-programmable gate array (FPGA) boards of the same family. The post-processing technique was applied on the measurement results to statistically quantify the error probability of the authentication technique. Full article

Vulnerability mining technology is used for protecting the security of industrial control systems and their network protocols. Traditionally, vulnerability mining methods have the shortcomings of poor vulnerability mining ability and low reception rate. In this study, a test case generation model for vulnerability mining of the Modbus TCP based on an anti-sample algorithm is proposed. Firstly, a recurrent neural network is trained to learn the semantics of the protocol data unit. The softmax function is used to express the probability distribution of data values. Next, the random variable threshold and the maximum probability are compared in the algorithm to determine whether to replace the current data value with the minimum probability data value. Finally, the Modbus application protocol (MBAP) header is completed according to the protocol specification. Experiments using the anti-sample fuzzer show that it not only improves the reception rate of test cases and the ability to exploit vulnerabilities, but also detects vulnerabilities of industrial control protocols more quickly. Full article

In this paper, we optimized Number Theoretic Transform (NTT) and random sampling operations on low-end 8-bit AVR microcontrollers. We focused on the optimized modular multiplication with secure countermeasure (i.e., constant timing), which ensures high performance and prevents timing attack and simple power analysis. In particular, we presented combined Look-Up Table (LUT)-based fast reduction techniques in a regular fashion. This novel approach only requires two times of LUT access to perform the whole modular reduction routine. The implementation is carefully written in assembly language, which reduces the number of memory access and function call routines. With LUT-based optimization techniques, proposed NTT implementations outperform the previous best results by 9.0% and 14.6% for 128-bit security level and 256-bit security level, respectively. Furthermore, we adopted the most optimized AES software implementation to improve the performance of pseudo random number generation for random sampling operation. The encryption of AES-256 counter (CTR) mode used for random number generator requires only 3184 clock cycles for 128-bit data input, which is 9.5% faster than previous state-of-art results. Finally, proposed methods are applied to the whole process of Ring-LWE key scheduling and encryption operations, which require only 524,211 and 659,603 clock cycles for 128-bit security level, respectively. For the key generation of 256-bit security level, 1,325,171 and 1,775,475 clock cycles are required for H/W and S/W AES-based implementations, respectively. For the encryption of 256-bit security level, 1,430,601 and 2,042,474 clock cycles are required for H/W and S/W AES-based implementations, respectively. Full article

The Internet of Things (IoT) has a lot to offer and contribute to the retail industry, from the innovations in retail store experience to the increased efficiency in the store management and supply chain optimization. On its way to real-world applications, Radio Frequency IDentification (RFID) became the main enabler for the final IoT deployment. However, to improve the technology performance even further, it is important to overcome the fundamental limitations of its physical layer and, consequently, to better understand how to use the technology in an optimal way. The analysis provided in this paper employs the simulation/measurement study on RFID technology advancement and the influence of radio propagation in a realistic model of the retail environment. The results are provided for different types of the retail layouts and materials that influence tag responsiveness. Full article

Due to self-motion and sea waves, moving ships are typically defocused in synthetic aperture radar (SAR) images. To focus non-cooperative targets, the inverse SAR (ISAR) technique is commonly used with motion compensation. The hybrid SAR/ISAR approach allows a long coherent processing interval (CPI), in which SAR targets are processed with ISAR processing, and exploits the advantages of both SAR and ISAR to generate well-focused images of moving targets. In this paper, based on hybrid SAR/ISAR processing, we propose an improved rank-one phase estimation method (IROPE). By using an iterative two-step convergence approach in the IROPE, the proposed method achieves accurate phase error, maintains robustness to noise and performs well in estimating various phase errors. The performance of the proposed method is analyzed by comparing it with other focusing algorithms in terms of processing simulated data and real complex image data acquired by Gaofen-3 (GF-3) in spotlight mode. The results demonstrate the effectiveness of the proposed method. Full article

With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). In the front-end of V-INS, image processing extracts information about the surrounding environment and determines features or points of interest. With the extracted vision data and inertial measurement unit (IMU) dead reckoning, the most widely used algorithm for estimating vehicle and feature states in the back-end of V-INS is an extended Kalman filter (EKF). An important assumption of the EKF is Gaussian white noise. In fact, measurement outliers that arise in various realistic conditions are often non-Gaussian. A lack of compensation for unknown noise parameters often leads to a serious impact on the reliability and robustness of these navigation systems. To compensate for uncertainties of the outliers, we require modified versions of the estimator or the incorporation of other techniques into the filter. The main purpose of this paper is to develop accurate and robust V-INS for UAVs, in particular, those for situations pertaining to such unknown outliers. Feature correspondence in image processing front-end rejects vision outliers, and then a statistic test in filtering back-end detects the remaining outliers of the vision data. For frequent outliers occurrence, variational approximation for Bayesian inference derives a way to compute the optimal noise precision matrices of the measurement outliers. The overall process of outlier removal and adaptation is referred to here as “outlier-adaptive filtering”. Even though almost all approaches of V-INS remove outliers by some method, few researchers have treated outlier adaptation in V-INS in much detail. Here, results from flight datasets validate the improved accuracy of V-INS employing the proposed outlier-adaptive filtering framework. Full article

An ultrahigh resolution thickness measurement sensor was proposed based on a single mode–hollow core–single mode (SMF–HCF–SMF) fiber structure by coating a thin layer of material on the HCF surface. Theoretical analysis shows that the SMF–HCF–SMF fiber structure can measure coating thickness down to sub-nanometers. An experimental study was carried out by coating a thin layer of graphene oxide (GO) on the HCF surface of the fabricated SMF–HCF–SMF fiber structure. The experimental results show that the fiber sensor structure can detect a thin layer with a thickness down to 0.21 nanometers, which agrees well with the simulation results. The proposed sensing technology has the advantages of simple configuration, ease of fabrication, low cost, high resolution, and good repeatability, which offer great potential for practical thickness measurement applications. Full article

The electroencephalogram (EEG) has great attraction in emotion recognition studies due to its resistance to deceptive actions of humans. This is one of the most significant advantages of brain signals in comparison to visual or speech signals in the emotion recognition context. A major challenge in EEG-based emotion recognition is that EEG recordings exhibit varying distributions for different people as well as for the same person at different time instances. This nonstationary nature of EEG limits the accuracy of it when subject independency is the priority. The aim of this study is to increase the subject-independent recognition accuracy by exploiting pretrained state-of-the-art Convolutional Neural Network (CNN) architectures. Unlike similar studies that extract spectral band power features from the EEG readings, raw EEG data is used in our study after applying windowing, pre-adjustments and normalization. Removing manual feature extraction from the training system overcomes the risk of eliminating hidden features in the raw data and helps leverage the deep neural network’s power in uncovering unknown features. To improve the classification accuracy further, a median filter is used to eliminate the false detections along a prediction interval of emotions. This method yields a mean cross-subject accuracy of 86.56% and 78.34% on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED) for two and three emotion classes, respectively. It also yields a mean cross-subject accuracy of 72.81% on the Database for Emotion Analysis using Physiological Signals (DEAP) and 81.8% on the Loughborough University Multimodal Emotion Dataset (LUMED) for two emotion classes. Furthermore, the recognition model that has been trained using the SEED dataset was tested with the DEAP dataset, which yields a mean prediction accuracy of 58.1% across all subjects and emotion classes. Results show that in terms of classification accuracy, the proposed approach is superior to, or on par with, the reference subject-independent EEG emotion recognition studies identified in literature and has limited complexity due to the elimination of the need for feature extraction. Full article

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HD^TM and Osypka ICON-Core^TM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation. Full article

The effect of deep-level defects is a key issue for the applications of CdZnTe high-flux photon counting devices of X-ray irradiations. However, the major trap energy levels and their quantitive relationship with the device’s performance are not yet clearly understood. In this study, a 16-pixel CdZnTe X-ray photon counting detector with a non-uniform counting performance is investigated. The deep-level defect characteristics of each pixel region are analyzed by the current–voltage curves (I–V), infrared (IR) optical microscope photography, photoluminescence (PL) and thermally stimulated current (TSC) measurements, which indicate that the difference in counting performance is caused by the non-uniformly distributed deep-level defects in the CdZnTe crystals. Based on these results, we conclude that the CdZnTe detectors with a good photon counting performance should have a larger ${\mathrm{Te}}_{\mathrm{cd}}^{2+}$ and Cd vacancy-related defect concentration and a lower A-center and Te_i concentration. We consider the deep hole trap Te_i, with the activation energy of 0.638–0.642 eV, to be the key deep-level trap affecting the photon counting performance. In addition, a theoretical model of the native defect reaction is proposed to understand the underlying relationships of resistivity, deep-level defect characteristics and photon counting performance. Full article

A single-photon CMOS image sensor (CIS) design based on pinned photodiode (PPD) with multiple charge transfers and sampling is described. In the proposed pixel architecture, the photogenerated signal is sampled non-destructively multiple times and the results are averaged. Each signal measurement is statistically independent and by averaging, the electronic readout noise is reduced to a level where single photons can be distinguished reliably. A pixel design using this method was simulated in TCAD and several layouts were generated for a 180-nm CMOS image sensor process. Using simulations, the noise performance of the pixel was determined as a function of the number of samples, sense node capacitance, sampling rate and transistor characteristics. The strengths and limitations of the proposed design are discussed in detail, including the trade-off between noise performance and readout rate and the impact of charge transfer inefficiency (CTI). The projected performance of our first prototype device indicates that single-photon imaging is within reach and could enable ground-breaking performances in many scientific and industrial imaging applications. Full article

Driving is a task that puts heavy demands on visual information, thereby the human visual system plays a critical role in making proper decisions for safe driving. Understanding a driver’s visual attention and relevant behavior information is a challenging but essential task in advanced driver-assistance systems (ADAS) and efficient autonomous vehicles (AV). Specifically, robust prediction of a driver’s attention from images could be a crucial key to assist intelligent vehicle systems where a self-driving car is required to move safely interacting with the surrounding environment. Thus, in this paper, we investigate a human driver’s visual behavior in terms of computer vision to estimate the driver’s attention locations in images. First, we show that feature representations at high resolution improves visual attention prediction accuracy and localization performance when being fused with features at low-resolution. To demonstrate this, we employ a deep convolutional neural network framework that learns and extracts feature representations at multiple resolutions. In particular, the network maintains the feature representation with the highest resolution at the original image resolution. Second, attention prediction tends to be biased toward centers of images when neural networks are trained using typical visual attention datasets. To avoid overfitting to the center-biased solution, the network is trained using diverse regions of images. Finally, the experimental results verify that our proposed framework improves the prediction accuracy of a driver’s attention locations. Full article

The development of 3D anthropomorphic head and neck phantoms is of crucial and timely importance to explore novel imaging techniques, such as radar-based MicroWave Imaging (MWI), which have the potential to accurately diagnose Cervical Lymph Nodes (CLNs) in a neoadjuvant and non-invasive manner. We are motivated by a significant diagnostic blind-spot regarding mass screening of LNs in the case of head and neck cancer. The timely detection and selective removal of metastatic CLNs will prevent tumor cells from entering the lymphatic and blood systems and metastasizing to other body regions. The present paper describes the developed phantom generator which allows the anthropomorphic modelling of the main biological tissues of the cervical region, including CLNs, as well as their dielectric properties, for a frequency range from 1 to 10 GHz, based on Magnetic Resonance images. The resulting phantoms of varying complexity are well-suited to contribute to all stages of the development of a radar-based MWI device capable of detecting CLNs. Simpler models are essential since complexity could hinder the initial development stages of MWI devices. Besides, the diversity of anthropomorphic phantoms resulting from the developed phantom generator can be explored in other scientific contexts and may be useful to other medical imaging modalities. Full article

Presently, the adoption of Internet of Things (IoT)-related technologies in the Smart Farming domain is rapidly emerging. The ultimate goal is to collect, monitor, and effectively employ relevant data for agricultural processes, with the purpose of achieving an optimized and more environmentally sustainable agriculture. In this paper, a low-cost, modular, and Long-Range Wide-Area Network (LoRaWAN)-based IoT platform, denoted as “LoRaWAN-based Smart Farming Modular IoT Architecture” (LoRaFarM), and aimed at improving the management of generic farms in a highly customizable way, is presented. The platform, built around a core middleware, is easily extensible with ad-hoc low-level modules (feeding the middleware with data coming from the sensors deployed in the farm) or high-level modules (providing advanced functionalities to the farmer). The proposed platform has been evaluated in a real farm in Italy, collecting environmental data (air/soil temperature and humidity) related to the growth of farm products (namely grapes and greenhouse vegetables) over a period of three months. A web-based visualization tool for the collected data is also presented, to validate the LoRaFarM architecture. Full article

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today’s world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios. Full article

This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application. Full article

State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53% which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process. Full article

Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible. Full article

This paper analyzes, through computational simulations, which spectral filters increase the number of discernible colors (NODC) of subjects with normal color vision, as well as red–green anomalous trichromats and dichromats. The filters are selected from a set of filters in which we have modeled spectral transmittances. With the selected filters we have carried out simulations performed using the spectral reflectances captured either by a hyperspectral camera or by a spectrometer. We have also studied the effects of these filters on color coordinates. Finally, we have simulated the results of two widely used color blindness tests: Ishihara and Farnsworth–Munsell 100 Hue (FM100). In these analyses the selected filters are compared with the commercial filters from EnChroma and VINO companies. The results show that the increase in NODC with the selected filters is not relevant. The simulation results show that none of these chosen filters help color vision deficiency (CVD) subjects to pass the set of color blindness tests studied. These results obtained using standard colorimetry support the hypothesis that the use of color filters does not cause CVDs to have a perception similar to that of a normal observer. Full article

A computationally efficient target parameter estimation algorithm for frequency agile radar (FAR) under jamming environment is developed. First, the barrage noise jamming and the deceptive jamming are suppressed by using adaptive beamforming and frequency agility. Second, the analytical solution of the parameter estimation is obtained by a low-order approximation to the multi-dimensional maximum likelihood (ML) function. Due to that, fine grid-search (FGS) is avoided and the computational complexity is greatly reduced. Full article

Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated. Full article

In this study, a photoacoustic microscopy (PAM) system based on a multifocal point (MFP) transducer was fabricated to produce a large depth-of-field tissue image. The customized MFP transducer has seven focal points, distributed along with the transducer’s axis, fabricated by separate spherically-focused surfaces. These surfaces generate distinct focal zones that are overlapped to extend the depth-of-field. This design allows extending the focal zone of 10 mm for the 11 MHz MFP transducer, which is a great improvement over the 0.48 mm focal zone of the 11 MHz single focal point (SFP) transducer. The PAM image penetration depths of a chicken-hemoglobin phantom using SFP and MFP transducers were measured as 5 mm and 8 mm, respectively. The significant increase in the PAM image-based penetration depth of the chicken-hemoglobin phantom was a result of using the customized MFP transducer. Full article

This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows corresponding to different time intervals. In our approach, in order to reconstruct true sources, we proposed a novelty idea of grouping statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA. The TFD components are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the β distance of Gaussian distribution introduced by as. In addition, the TFD components are grouped by minimizing the negentropy of reconstructed constituent signals. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The quality of obtained separation results was evaluated by perceptual tests. The tests showed that the automated separation requires qualitative information about time-frequency characteristics of constituent signals. The best separation results were obtained with the use of the β distance of Gaussian distribution, a distance measure based on the knowledge of the statistical nature of spectra of original constituent signals of the mixed signal. Full article

School bullying is a serious problem among teenagers. School violence is one type of school bullying and considered to be the most harmful. As AI (Artificial Intelligence) techniques develop, there are now new methods to detect school violence. This paper proposes a video-based school violence detecting algorithm. This algorithm first detects foreground moving targets via the KNN (K-Nearest Neighbor) method and then preprocesses the detected targets via morphological processing methods. Then, this paper proposes a circumscribed rectangular frame integrating method to optimize the circumscribed rectangular frame of moving targets. Rectangular frame features and optical-flow features were extracted to describe the differences between school violence and daily-life activities. We used the Relief-F and Wrapper algorithms to reduce the feature dimension. SVM (Support Vector Machine) was applied as the classifier, and 5-fold cross validation was performed. The accuracy was 89.6%, and the precision was 94.4%. To further improve the recognition performance, we developed a DT–SVM (Decision Tree–SVM) two-layer classifier. We used boxplots to determine some features of the DT layer that are able to distinguish between typical physical violence and daily-life activities and between typical daily-life activities and physical violence. For the remainder of activities, the SVM layer performed a classification. For this DT–SVM classifier, the accuracy reached 97.6%, and the precision reached 97.2%, thus showing a significant improvement. Full article

MicroRNAs regulate and control the growth and development of cells and can play the role of oncogenes and tumor suppressor genes, which are involved in the occurrence and development of cancers. In this study, DNA fragments obtained by target-induced rolling-circle amplification were constructed to complement with self-cleaving deoxyribozyme (DNAzyme) and release fluorescence biomolecules. This sensing approach can affect multiple signal amplification permitting fluorescence detection of microRNAs at the pmol L⁻¹ level hence affording a simple, highly sensitive, and selective low cost detection platform. Full article