Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonparametric Specification Testing for Signal Models

Given noisy samples of a signal, the problem of testing whether the signal belongs to a restricted class of parametrically specified class of signals is considered. Using a nonparametric kernel-based approach for reconstructing the signal we compare the parametric signal class to a broad alternative of signal classes that cannot be parametrized. For such a setup, we introduce testing procedures relying on nonparametric kernel-type sampling reconstruction algorithms properly adjusted for noisy data. First, we propose the testing procedures utilizing the $L_{2}$ -distance between the kernel estimate and signals from the target parametric class. Then, we make use of the maximum likelihood ratio test extended to the posed nonparametric signal specification problem. Central limit theorems of the test statistics are derived yielding consistent testing methods. Hence, we obtain testing procedures that keep asymptotically the desired level of the probability of false alarm and showing a power tending to one as the number of samples increases. The finite sample size properties of the introduced tests are also given.
Deep Residual Network for Identifying Bearing Fault Location and Fault Severity Concurrently

Fault diagnosis is composed of two tasks, i.e., fault location detection and fault severity identification, which are both significant to equipment maintenance. The former can indicate where the defective component lies in, and the latter provides evidence on the residual life of the component. However, traditional fault diagnosis methods, like the time-based methods, frequency-based methods and time-frequency-based methods, can only achieve one goal every time. They are not able to produce highly representative features for dealing with above-mentioned two tasks simultaneously. In addition, there is a huge increase in the amount of monitoring data of equipment. There is urgent need for handling this massive data, obtaining highly discriminative features, and further producing accurate diagnosis results in the field of fault diagnosis. Aimed at these problems, a deep residual network based on multi-task learning is proposed, taking detection of fault location and judgment of fault severity into account simultaneously. This network is fed with two kinds of diagnostic information, which is helpful to mine the potential links between two tasks of fault diagnosis and generate very representative features. Moreover, based on maximizing activation value, a visualization method of role of deep neural network is proposed. It can break in the traditional way of using deep neural network as black box. A real bearing experiment validates that the proposed method is reliable and effective in bearing fault diagnosis.
From IoT to Cloud: Applications and Performance of the MQTT Protocol

A study of the MQTT publish/subscribe protocol with different QoS levels is presented with the aim to extend the Internet of Things (IoT) concept across access, edge and transport networks and reach cloud computing facilities. A simple testbed is set up with related software components to measure the end-to-end delivery latency between the publisher and the subscribers and the impact of the network delay caused by network configurations with different service deployments. In particular, the latency is shown to rise up to more than 7 times the average network delay when the QoS 2 level is applied, thus indicating that its adoption must be carefully considered.
Towards Standards for Light Scattering Studies of Proteins Stability and Nanoparticle-Protein Interactions

Protein-nanoparticle suspensions are nowadays widely studied for the development of medical and environmental biosensors. The complexity of interactions between nanoparticles and biological fluids, together with the increasing use of dynamic light scattering (DLS) for their characterization, support the need to develop common standards for DLS measurements and analysis, in order to enable a reliable comparison of measurement results. In this study, we use 3D cross-correlation Dynamic Light Scattering (3Dcc-DLS) for the characterization of gold nanoparticles (Au NP), with spherical and rod shape, stabilized with sodium citrate, and their interaction with bovine serum albumin (BSA). We show that for Au nanorods it is possible to distinguish the rotational and translational motion. Moreover, we monitor the interaction of Au NPs and BSA over time, in order to analyse the first step of protein corona formation. Our results show that Au nanorods interact more than Au nanospheres with BSA. From the evolution of the rotational and translational peaks we conclude that the proteins tend to bind on the long cylindrical side of the nanorods.
Intermodal Raman Scattering between Orbital Angular Momentum Modes in Optical Fibers

This paper presents an experimental and theoretical analysis of Raman amplification of OAM modes i.e. beams carrying orbital angular momentum and spin angular momentum. We show that simple scalar equations are sufficient to describe the Raman interaction between OAM modes when working in a cyclic basis. The equations are similar to those used when considering Raman amplification in conventional fibers used for optical communication. Such fibers are typically single mode and support propagation of LP modes i.e. light that is linearly polarized. In fibers that support propagation of OAM modes as well as in fibers that support propagation of LP modes the scalar equations apply when considering very short- fiber lengths or long fiber lengths, only the proper gain efficiency needs to be used. Here, we discuss the gain efficiency, its material and modal dependencies and we demonstrate that the Raman gain efficiency among OAM modes in comparison with the efficiency among LP modes mainly differs due to a typically larger mode size of OAM modes relative to that of the fundamental LP modes in conventional fibers used for optical communication. In addition, we demonstrate a mode dependent loss in an optical fibers that support propagation of OAM modes.
Neural Networks and FPGA Hardware Accelerators for Millimeter-Wave Radio-over-Fiber Systems

High speed data streaming has been highly demanded by mobile end users and millimetre-wave (mm-wave) radio-over-fiber (RoF) optical communications have been studied to satisfy the users' demands. To solve various impairments existing in mm-wave RoF systems, neural networks have been proposed and studied due to their capability in solving nonlinear effects and multiple impairments simultaneously. However, previous studies mainly focused on the fully-connected neural network (FC-NN), which has relatively complicated architecture and a large number of parameters to be learnt. To solve this issue, we have proposed the convolutional neural network (CNN) and binary convolutional neural network (BCNN) based decision schemes. In addition, the neural networks in previous studies are typically implemented offline using high-end CPUs or GPUs, which are not practical for optical communication applications. To solve this issue, we have proposed the field-programmable gate array (FPGA) based hardware accelerators, which have the key advantages of reconfigurability, low power consumption, and parallel computation capability. A novel inner parallel optimization method has also proposed to improve the latency with minimal additional power consumption. Results show that the CNN/BCNN implemented with GPU and with the FPGA-based hardware accelerator achieves similar BER performance within the forward-error-correction (FEC) limit, while the FPGA-based hardware accelerators reduce the power consumption significantly.
Metal-Embedded Chiplet Assembly for Microwave Integrated Circuits

This letter provides an overview of the metal-embedded chiplet assembly for microwave integrated circuits (MECAMICs) technology. MECAMIC is a 2.5-D wafer-level packaging approach that provides seamless heterogeneous integration capabilities of compound semiconductor transistors (e.g., GaN HEMTs) with passive components and interconnects using a backside metal embedding process. The chiplets are embedded in through-interposer cavities, and the backside metallization locks the chiplets into position while providing dc and RF ground for the high-frequency circuits. With this hybrid manufacturing approach, high-performance RF ICs can: 1) be designed using the best transistor technology for the function; 2) be fabricated in short cycle times through hybrid manufacturing; and 3) compete performancewise with monolithic circuits. We demonstrated an <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-band power amplifier circuit realized in MECAMIC with a saturated output power of 31 dB and a peak power added efficiency of 54%, which compared favorably to its monolithic GaN monolithic microwave integrated circuit (MMIC) counterpart. The MECAMIC process cycle time was <inline-formula> <tex-math notation="LaTeX">$4.5\times $ </tex-math></inline-formula> faster than the monolithic process.
Cache Management with Fadvise Based on LFU

Virtualization is used for various purposes such as cloud computing and a virtualized environment has been one of the most important platforms. In many virtualized environments, access to its persistent storage device, e.g. hard disk drive (HDD) or solid-state drive (SSD), is performed via two caches, which are page caches of guest and host operating systems. Both caches are usually managed based on the least recently used (LRU) algorithm. Previous work demonstrated that accesses to the host operating system page cache have a negative temporal locality of reference in these cases. Namely, an accessed block will probably not be accessed again in the near future. This locality severely decreases the hit ratio of a host operating system page cache. For addressing this issue, a method for improving the locality of reference in the accesses in the host operating system page cache by fixing data stored in the guest operating system page cache was proposed. However, the method assumed that the hot spot, which is the data area aggressively accessed, was given. Because of this limitation, the method can rarely be used. In this paper, we propose a method for improving the hit ratio of a host operating system page cache by enhancing the locality of reference at the cache. First, we explain the negative temporal locality of reference in virtualized environments. Second, we propose a method for enhancing the locality of reference in the host operating system page cache for improving its hit ratio without an assumption that its hotspot is given. This method observes file accesses in the system using an operating system kernel function and automatically detects the hotspot. This then stores the area in the guest operating system page cache. As a result, the locality of reference in the host operating system cache increases. Lastly, we evaluate this proposed method, then show that this method outperforms the normal method and can provide comparable performance with an existing method even if the hot spot is not given.
Label‐free molecular detection of antibiotic susceptibility for Mycobacterium smegmatis using a low cost electrode format

Today, the emergence of antibiotic resistance in pathogenic bacteria is considered an important problem for society. Excessive consumption of antibiotics, long‐term treatments, and inappropriate prescriptions continually increase the severity of the problem. Improving antibiotic stewardship requires improved diagnostic testing, and, therefore, in vitro antibiotic susceptibility testing is becoming increasingly important. This research details the development of an antibiotic susceptibility test for Mycobacterium smegmatis using streptomycin as antibiotics. This strain was selected because it is a member of the slow growing Mycobacterium genus and serves as a useful surrogate organism for M. tuberculosis. A commercially available and low‐cost screen‐printed gold electrode in combination with a specifically developed nucleic acid probe sequence for the 16SrRNA region of the mycobacterial genome was employed to monitor M. smegmatis nucleic acid sequences using the techniques of square‐wave voltammetry and electrochemical impedance spectroscopy. The results show that it was possible to detect M. smegmatis sequences and distinguish antibiotic‐treated cells from untreated cells with a label‐free molecular detection. As a result, the in vitro antibiotic susceptibility test revealed that M. smegmatis showed sensitivity to streptomycin after a 24‐H incubation, with the developed protocol representing a potential approach to determining antibiotic susceptibility more quickly and economically than current methods.
Real-Time Automatic Video Inspection System for Piece Products Marking

The paper considers the problem of real time automated video inspection of piece products. The architecture of automatic video inspection system is proposed, the temporal characteristics of its action are investigated. The possibility of analyzing images of objects after transformations in grayscale and Lab space, the use of Hough transforms and second-order moments to frame object localization is considered. The possibility of using machine learning algorithms for real time automatic video inspection of industrial products was studied.
Processor Energy–Performance Range Extension Beyond Voltage Scaling via Drop-In Methodologies

This work introduces reconfiguration for energy–performance adaptation beyond conventional voltage scaling in microcontroller-based systems. Coordinated thread-level processor and row-level memory reconfiguration are enabled by an architecture-agnostic methodology. The latter requires low design and integration effort while reusing existing macros, including third-party intellectual properties (IPs) in an obfuscated or encrypted form. The methodology represents a drop-in solution that is applicable to extend the energy–performance tradeoff in existing designs. The proposed approach was demonstrated with a testchip implementing an ARM Cortex-M0 processor with 16-KB SRAM in 40 nm. The system demonstrates 1.8X throughput boost at nominal voltage (1.1 V) and 1.3X energy reduction at the minimum energy point (0.51 V) compared to voltage scaling with no reconfiguration. Such energy–performance range extension is achieved at 10.3% area overhead, which is mostly due to processor reconfiguration. Overall, the proposed approach reduces energy in the common case and increases performance when demanded, surpassing the capabilities of conventional voltage scaling, while reusing existing IPs and retaining the original software stack.
Evolutionary Algorithms for Vulnerability Coverage

We present a novel idea on adequacy testing called "vulnerability coverage. " The introduced coverage measure examines the underlying software for the presence of certain classes of vulnerabilities often found in the National Vulnerability Database (NVD) website. The thoroughness of the test input generation procedure is performed through the adaptation of evolutionary algorithms namely Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The methodology utilizes the Common Vulnerability Scoring System (CVSS), a free and open industry standard for assessing the severity of computer system security vulnerabilities, as a fitness measure for test inputs generation. The outcomes of these evolutionary algorithms are then evaluated in order to identify the vulnerabilities that match a class of vulnerability patterns for testing purposes.
Intelligent Application Switch and Key-Value Store Accelerated by Dynamic Caching

Recent programmable switches allow developers to profoundly optimize network elements. In this study, we introduce a concept of an application switch that supports a network application based on programmable switches. A developer can optimize not only server computers but also application switches to improve the application performance by implementing functions of the network application. We then introduce a method to apply an application switch to a database management system using the transmission control protocol (TCP). The method migrates a TCP connection from a server computer to the application switch when the switch replies to a query. The method reversely migrates it to the server computer again when a query is processed by the server. The application switch manages the TCP sequence and the Ack numbers for these migrations. In the existing work, we implemented a prototype key-value store (KVS) system based on the proposed system without TCP Ack management. We then showed the potential of the method. In this paper, we show the problem of the prototype system of Ack management and propose a method for solving the problem. In addition, we evaluate the resolved method and demonstrate its effectiveness.
Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations

Consider the problem of clustering objects with temporally changing multivariate variables, for instance, in the classification of cities with several changing socioeconomic indices in geographical research. If the changing multivariate can be recorded simultaneously as a multivariate time series, in which the length of each subseries is equal and the subseries can be correlated, the problem is transformed into a multivariate time series clustering problem. The available methods consider the correlations between distinct time series but overlook the shape of each time series, which causes multivariate time series with similar correlations and opposite shapes to be clustered into the same class. To overcome this problem, this paper proposes a two-phase multivariate time series clustering algorithm that considers both correlation and shape. In Phase I, the discrete wavelet transform is applied to capture the wavelet variances and the correlation coefficients between each pair of variables to realize the initial clustering of multivariate time series, where time series with a similar correlation but opposite shape may be assigned to the same cluster. In Phase II, multivariate time series are clustered based on shape via the symbolic aggregate approximation (SAX) method. In this phase, time series with similar correlations but opposite morphologies are differentiated. The method is evaluated using multivariate time series of incoming and outgoing passenger volumes from Beijing IC card data; these volume data were collected between March 4, 2013 and March 17, 2013. Based on the silhouette coefficient, our approach outperforms two popular multivariate time series clustering methods: a wavelet-based method and the SAX method.
Age-Related Child Sexual Abuse Experiences

The study aimed to describe differences in the types of sexual abuse injuries between two age groups of children presenting to an emergency department. This descriptive correlational study explored the relationships between sexual abuse type and child’s age using data from the hospital health records of 95 children ages 6–14 years. Descriptive analysis indicated that sexually abused child victims’ ages were significantly associated with different types of penetrations and mucosal tears or injuries (p < 0.05). These findings highlighted the need for increased awareness about the different types of physical trauma associated with the ages of the child victims.
Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine

Land cover information depicting the complex interactions between human activities and surface change is critically essential for nature conservation, social management, and sustainable development. Recent advances have shown great potentials of remote sensing data in generating high-resolution land cover maps, but it remains unclear how different models, data sources, and inclusive features affect the classification results, which hinders its applications in regional studies requiring more accurate land cover data. Informing these issues, here we developed a robust framework to improve the mapping results of 10 m resolution land cover classification in Guangdong Province, China using thousands of manually collected samples, multisource remote sensing data (Sentinel-1, Sentinel-2, and Luojia-1), machine learning algorithms, and a free cloud-based platform of Google Earth Engine. Results showed that an overall accuracy of 86.12% and a Kappa coefficient of 0.84 could be achieved for land cover classification in Guangdong for 2019. We found that random forest models achieved better performance than classification and regression trees, minimum distance, and support vector machine models. We also found that features derived from Sentinel-1 data and Sentinel-2 spectral indices greatly contributed to the classification process, while the feature of Luojia-1 data was not as much important as other configurations. A comparison between our results and several existing land cover products in terms of classification accuracy and visual interpretation further validated the effectiveness and robustness of the proposed framework. Our experiments and findings not only systematically elucidate the role of classification methods and data sources in deriving more accurate and reliable land cover maps but also provide certain guidelines for future land cover mapping from regional to global scales.
FSO Communication with Nonzero Boresight and Jitter over Málaga Atmospheric Turbulence

In free-space optical (FSO) communication, the pointing errors are widely considered as a combination of boresight and jitter. In this paper, we obtain a closed-form probability density function (PDF) for the composite Málaga atmospheric turbulence and pointing errors with nonzero boresight and jitter. Furthermore, we provide exact and asymptotic closed-form expressions for symbol error rate (SER) and outage probability of M-ary amplitude shift keying modulation (M-ASK) over Málaga atmospheric turbulence. Finally, using Monte-Carlo simulations and numerical analysis, we validate the accuracy of the proposed analytical derivations. The results show perfect matching between the exact expressions, and their corresponding asymptotic expansions are clearly observed.
Design of Cross-border Network Crime Detection System Based on PSE and Big Data Analysis

In order to speed up the detection of cybercrime worldwide, a new cross-border cybercrime detection system is designed by introducing the PSE theory and the principle of big data analysis. In the TFTP server, the U-boot network development board and OpenStack crime information detection component are connected to build the hardware running environment of the cross-border network crime detection system. On this basis, through the analysis of the characteristics of detection information, the calculation of cross-border network detection domain and the directional planning of network crime information, the software operating environment of the detection system is built, and the cross-border network crime detection system based on PSE and big data analysis is designed with the basis of hardware implementation. The comparative experimental results show that, compared with the conventional case detection situation, the average detection time of criminal cases is basically maintained between 3–5 days after the application of cross-border cyber crime detection system, and the accuracy of criminal location is maintained at more than 90%.