Miracle-dz/UniFAR-FTUs huggingface dataset:
28-GHz Channel Characterization for a Short Tunnel

This letter investigates the channel propagation characterization in a short tunnel through a time-domain channel measurement at 28-GHz frequency band. The statistic parameters of the channel, such as root-mean-square delay spread (RMS-DS), K-factor, and shadowing are studied, and the results reveal that the parameters align better to the normal distribution. RMS-DS is positively correlated with the shadowing but negatively correlated with the K-factor. We also deduced that the K-factor is negatively correlated with the shadowing. From the angular measurements, it is inferred that the signal propagation in the tunnel is around 0° and 180° directions, and the maximum number of reflections at 30 and 75 m are 6 and 8, respectively. Furthermore, it is found that the angular spread pursues a decreasing function of transmitter-receiver distance.
An ISAR Imaging Algorithm for Nonuniformly Rotating Targets With Low SNR Based on Modified Bilinear Parameter Estimation of Cubic Phase Signal

For nonuniformly rotating target in the low signal-to-noise ratio (SNR) environment, the inverse synthetic aperture radar (ISAR) imaging is a challenging task due to the Doppler spread induced by time-varying rotation. In this paper, an effective modified bilinear parameter estimation algorithm is proposed and applied to reconstruct the ISAR image of nonuniformly rotating targets with low SNR. First, azimuth echoes of a range cell are modeled as multicomponent cubic phase signals (multi-CPSs) after range alignment and phase adjustment. Then, to estimate the parameter of the CPS (the chirp rate and the quadratic chirp rate, which are identified as causes of the target image defocus), a novel integrated parametric cubic phase function (IPCPF) and a reversing Wigner–Ville distribution processing are developed. Compared to other four representative algorithms, the bilinearity of the IPCPF guarantees a higher antinoise performance and a better suppression on cross terms. Moreover, by utilizing the nonuniform fast Fourier transform and the generalized scaled Fourier transform, the brute-force searching is eliminated and the computational cost is reduced. Finally, a new cross-range scaling method based on a regression analysis is proposed for the ISAR imaging. With the synthetic data and the real radar data, several simulation examples and ISAR imaging results demonstrate the effectiveness and the superiority of the proposed ISAR imaging algorithm.
A Survey on Nonconvex Regularization-Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning. To achieve sparsity and/or low-rankness inducing, the $\ell _{1}$ norm and nuclear norm are of the most popular regularization penalties due to their convexity. While the $\ell _{1}$ and nuclear norm are convenient as the related convex optimization problems are usually tractable, it has been shown in many applications that a nonconvex penalty can yield significantly better performance. In recent, nonconvex regularization-based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization. This paper gives an overview of this topic in various fields in signal processing, statistics, and machine learning, including compressive sensing, sparse regression and variable selection, sparse signals separation, sparse principal component analysis (PCA), large covariance and inverse covariance matrices estimation, matrix completion, and robust PCA. We present recent developments of nonconvex regularization based sparse and low-rank recovery in these fields, addressing the issues of penalty selection, applications and the convergence of nonconvex algorithms. Code is available at https://github.com/FWen/ncreg.git.
Thermal Comparison of Planar Versus Conventional Transformers Used in LLC Resonant Converters

Thermal analysis is an essential design step of high performance power electronics transformers, enabling the designer to estimate temperature behavior of the components and provide a proper cooling system. In this paper, a thorough thermal analysis is carried out for two different types of transformers used in high frequency LLC resonant converters. Two planar and conventional PQ core-based transformers are compared from temperature distributions and thermal time constant points of view. The proposed modeling is carried out using finite element analysis (FEA) so that all the heat transfer mechanisms, including thermal convection, radiation and conduction, are considered. Different scenarios are applied for the studied transformers to have a detailed thermal comparison. In comparison to the traditional core, analysis shows that the planar transformer has a lower hot spot temperature and a desirable temperature distribution, which is the result of having larger surface to volume ratio in planar cores. Furthermore, it is shown that because of the lower volume and greater exchange area of the planar core, shorter time constant is achieved so that the temperature rapidly increases to its steady state condition. Finally, experimental results confirm the validity of the proposed analysis and show the superiority of planar transformers from a heat transfer point of view.
Research on Data Sharing Analysis and Key Technology of Smart City

Data sharing among different platform in smart city is a key issue on big data era. This paper focuses on the core issues of heterogeneous data switching, sharing and updating in the construction of smart city. We first review the current research status of data sharing from three aspects which including big data characteristics, technology system and big data sharing platform in smart city. The analysis shows that the current data sharing research in smart city has been only used one or two types of data, and the data utilization mode is relatively simple. The research on the data sharing technology system of heterogeneous data needs to be further development. The research of data sharing is limited to a certain field, few platforms have studied data sharing in more than two fields, and horizontal data sharing methods need some breakthrough. Our work analyzes the data requirement and data source characteristics on the smart city construction platform, analyzes the hierarchical relationship and sharing relationship of various data from different platform, and clarifies the hierarchical relationship, sequence and sharing process of data sharing among different platforms. Then the platform architecture of data sharing, switching, updating and guarantee mechanism between heterogeneous systems based on cloud computing is proposed, and the data sharing and switching technology based on cloud and big data is proposed, which provide the top-level framework reference and technical support for the realization of cross platform data sharing in smart city. Finally, the paper concludes and analyzes the key implementation technology of data sharing in depth.
Ensemble Clustering with Novel Weighting Strategy

The target of ensemble clustering is to improve the accuracy of clustering by integrating multiple clustering results and solve the problem of scalability existed in traditional and single clustering algorithms. In recent years, ensemble clustering has attracted increasing attention due to its remarkable achievements. However, the limitation of most existing ensemble clustering approaches is that all base clusterings are treated equally without considering the validity of them. Some ensemble clustering algorithms are aware of using weighting strategy but also ignoring the negative impact of base clusterings with poor performance. In this paper, we propose an ensemble clustering method based on a novel weighting strategy. Specifically, the validity of each base clustering is measured by the optimal matching score between the base clustering and the whole to obtain the corresponding weight. Then, the weights of base clusterings which have negative contribution are further adjusted to get the final weight vector. Subsequently, a weighted co-association matrix is constructed to serve as the ensemble matrix and a hierarchical clustering algorithm is applied to it to generate the final result. Experimental results on different types of real-world datasets show the superiority of proposed methods.
Proof of Disease: A Blockchain Consensus Protocol for Accurate Medical Decisions and Reducing the Disease Burden

Studies suggest that a significant proportion of the diagnosis in non-communicable diseases (NCD) is erroneous, unwanted, or unnecessary. To reduce the disease burden and improve public health, algorithmic support is essential. To realize this, health data must be computer understandable, secured, ubiquitous, and interoperable. Medical and disease data entered into computers are unstructured natural language texts with medical jargons which a computer normally cannot understand. EMR (Electronic Medical Records) are data silos in the hospital and do not interoperate. In this paper we present Ethereum based future ready Proof of Disease (PoD) consensus protocol with a computer understandable single instance of truth. It will solve many challenges that electronic health records (EHR) or health information exchange (HIE) have failed to address. This medical system will help achieve all the complex needs of P6 (Participatory, Personalized, Proactive, Preventive, Predictive and Precision) medicine and finally reduce the disease burden.
Inspecting Influences on Likes and Comments of Photos in Instagram

Instagram receives millions of photos every day. While many photos get a lot of likes and comments, others do not get much attention. This paper analyzes what factors lead to these differences, and what photos with different levels of popularity look like. To examine the influences to the number of likes and comments, we introduce the publish time, user social context and post content. We then make statistical analysis based on single factor, as well as machine learning based analysis with several combinations of factors. By qualitative and quantitative analysis, we gain some favorable conclusions. The time factors at different scale have different effects. The number of likes and comments vary from year to year while they look similar on each day of a week. Moreover, posts published after noon tend to receive more attention. The number of followers of users plays the most important role in user social context, while the semantic feature of caption and the visual feature of photos are the main factors of post content affecting the number of likes and comments.
Genome Sequence of the Probiotic Strain Bacillus velezensis Variant polyfermenticus GF423

The genome sequence of the commercial probiotic strain “Bacillus polyfermenticus” GF423 was determined. Comparison of the 4.1-Mb genome sequence revealed Bacillus velezensis FZB42 as its closest relative. ABSTRACT The genome sequence of the commercial probiotic strain “Bacillus polyfermenticus” GF423 was determined. Comparison of the 4.1-Mb genome sequence revealed Bacillus velezensis FZB42 as its closest relative. Based on the genome sequence, we propose that this probiotic strain be renamed Bacillus velezensis variant polyfermenticus.
A Microstrip Antenna for Polarized Diversity and Pattern Selectivity Application

A new reconfigurable center-feeding microstrip antenna is presented. By controlling the different states of the PIN diodes, the antenna can operate in either normal patch or monopolar patch mode for the number of the grounded shorted probes in different frequency bands. That is to say, with eight shorted probes connected to ground, a right-hand circular polarization (RHCP) for broadside radiation pattern mode is excited at 2.5GHz. Otherwise, a linear polarization (LP) for conical radiation pattern mode is achieved at 3.5GHz by four shorted probes. The gains of the two modes are 9.0 dBic and 5.0 dBi, respectively. Compared with the former designs, the proposed antenna owns good radiation properties and no extra complicated networks are needed. To verify the feasibility, a prototype is fabricated and measured, and the reasonable agreement between the simulated and measured results is obtained.
Age Estimation with Local Statistical Biologically Inspired Features

Biologically inspired features (BIF) have proven to be robust object representation technique for image visual artifacts. Derived from primates visual cortex, BIF extraction consist of alternating layers of simple (S1) and complex (C1) cells. Although studies reveal that there exist spatial frequency processing in primates visual cortex, BIF techniques mostly employ scale and orientation selectivity in feature extraction. A series of processor intensive pooling operations above C1 cells significantly limit application of BIF in real-time computer vision problems. This paper proposes local statistical BIF (LS-BIF) for age estimation. MAX pooling is applied to S1 units for each frequency band to obtain frequency tolerant features. Local means and standard deviations in $n \times n$ grid search are used to obtain orientation tolerance features. AVG pooling is applied to orientation tolerance features to obtain scale-tolerant features. Adopting hierarchical age estimation approach that consists of between group classification followed by within group regression, LS-BIF achieves better age estimation accuracies compared to BIF with scale and orientation selectivity only and Multi frequency BIF (MF-BIF) with holistic (global) pooling in C1 layer.
Evaluation of antioxidant, diuretic, and wound healing effect of Tulkarm honey and its effect on kidney function in rats

Aim: The composition and activity of honey depend on its floral origin. Honey collected from Tulkarm was evaluated for physicochemical property and antioxidant content as well as a diuretic and wound healing activity. Its effect on kidney function was evaluated and compared with furosemide. Materials and Methods: Honey was collected in Tulkarm, Palestine, and its phenol, flavones, and flavonol content were assessed. The antioxidant activity was determined with the use of colorimetric assays, 1,1-diphenyl-2-picrylhydrazyl, ferric reducing antioxidant power, and 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid). Two sets of experiments were conducted. First experiment: 18 rats were used for the evaluation of diuretic activity of honey. The rats received either honey or furosemide. Renal function test, uric acid, and serum and urine electrolytes assay were performed. Second experiment: 18 male mice were used to evaluate the wound healing property of honey. Wounds were created on mice skin and treated daily with honey or Madecassol. Measurements of wounds were performed over a period of 12 days. Results: The physical and chemical parameters of Tulkarm honey are within the limits of the European legislation and fulfilling the criteria described in the standard codex for honey. It contains antioxidant compounds and shows antioxidant activity. Oral honey increased creatinine clearance and urine volume, sodium, and chloride without causing hypokalemia or affecting blood urea, uric acid, or serum creatinine level. The diuretic activity of furosemide was associated with hypokalemia. Topical honey application enhanced wound closure when compared with the Madecassol application. Conclusion: The study is the first to report that honey collected from Tulkarm has a considerable diuretic effect without affecting serum electrolytes or kidney function test and exhibits strong antioxidant activity and wound healing property.
Small-Signal Modeling and Controller Design Considerations for Dyna-C AC-DC Converter

The Dyna-C ac-dc converter is a current-source-based topology featuring medium-frequency galvanic isolation. This paper presents the state-space based average and small signal modeling of Dyna-C ac-dc converter. A space vector based modulation strategy is developed, presented and validated by simulation. Based on the proposed small signal model for Dyna-C ac-dc converter, a two stage closed loop control approach is developed. The proposed approach comprises of an outer loop to regulate the transformer magnetizing-current and an inner $\boldsymbol{d}\boldsymbol{q}$-based vector control strategy for regulating the grid currents. Circuit simulation results are presented to validate the proposed models and the closed loop control scheme for the Dyna-C ac-dc converter.
CS for ALL: Introducing Computational Thinking with Hands-On Experience in College

CS for ALL is a new education initiative launched in 2016 to empower a generation of American students with the computer science skills they need to thrive in a digital economy. In order to keep up with trends in the growing technology-driven world, students should have the ability to analyze and consider the consequences of computing problems critically. However, introducing CS and computational thinking skills to the first year students in college is a difficult task because the nature of the subject tends to be dry and conceptual. Thus, we introduce a computer science course that helps all undergraduate students to prepare for digital life as well as enhance their critical thinking skills through hands-on learning experiences. The course contents introduce the general concept of computer science such as computing system, basic networking, algorithms and programming with Scratch and mBot robot exercises. Our student feedback shows a high level of enthusiasm and engagement among the students. The strong hands-on learning nature of the course helped our students to have more engaging and interactive classroom experiences.
Unsupervised Approach for Detecting Low Rate Attacks on Network Traffic with Autoencoder

Most approaches to network intrusion detection look only at the header part of network packets. These approaches are able to detect high-rate attacks, such as Denial of Service or probing, with high degrees of accuracy. However, it remains to be seen whether they are also able to detect more subtle attacks, such as when adversaries try to exploit a vulnerability or plant a backdoor. In these cases, the attributes of network packets are usually very similar to the legitimate traffic which presents a limitation for header-only intrusion detection methods. Such attacks present an increasing problem to network security, especially given the rise of Internet of Things (IoT) and the rapidly increasing number of devices that can be exploited through low-intensity attacks. To address this problem we propose the use of the Autoencoder method for network intrusion detection. Autoencoder is a deep learning architecture that has the capability to identify outliers in a dataset. Thus it does not need labelled datasets which contain both legitimate and malicious traffic for training purposes. Through our experiments, we show that the proposed approach was able to detect 100% of low rate attack traffic with an average false positive rate of 8.01%. To demonstrate the improvement over the state of the art we have compared our results to a number of other similar works and our proposed method gave at least 32.81% better in detection rate.
Position and Capacitor Voltage Sensorless Control of High-Speed Surface-Mounted PMSM Drive with Output Filter

In this paper, a new position sensorless drive method for high-speed surface-mounted permanent magnet synchronous motors (SPMSMs) with LCL output filter is proposed. Position sensor and voltage sensors of capacitor voltage are eliminated. Firstly, the discrete-time model of the SPMSM with LCL output filter is developed in the complex region. Then, a fully discretized state-space current regulator and a reduced-order observer of the three-phase capacitor voltages and the back-EMF are developed. With the observed parameters, full-state feedback of the current control is achieved. The dynamic response of the current controller is designed by pole placement, the resonance damping is achieved at the same time. Moreover, a phase-locked-loop is implemented to obtain the estimated rotor position from the estimated back-EMF. Simulation and experimental results running up to 100 kr/min with 20 kHz sampling frequency validate the effectiveness of the proposed method.
Augmenting LSPIV Surface Current Measurement with Drifting ASVs

Large Scale Particle Image Velocimetry (LSPIV) has been successfully used in the field to gather water surface flow data, which is critical to understanding complex geomorphic, hydrologic, and ecological river processes. Its success, however, depends on lighting conditions and adequate flow seeding with trackable debris or visual features, that can be hampered by environmental disturbances such as wind or wildlife. Instead, this research proposes augmenting traditional LSPIV methods by incorporating autonomous surface vehicles (ASVs) as pieces of actuated debris. This addressses the shortcomings of artificial seeding with tracers that may not be recoverable and impractical. Our method uses initial image velocimetry flow estimates to guide an ASV to sparsely seeded or poorly illuminated regions within the survey area. There it can be set adrift and tracked from above to capture additional measurements to improve the surface flow field reconstruction. We compare conventional techniques with our augmented LSPIV system in simulation and in field tests. The results showcase performance and capability enhancements ASVs can provide.
Thermal Analysis of a Hybrid Excitation Linear Eddy Current Brake

In this paper, the thermal analysis of a hybrid excitation linear eddy current brake is presented. The air gap flux of the hybrid excitation linear eddy current brake is provided by the excitation windings and the permanent magnets. Therefore, the braking force produced by the eddy current brake is large and controllable. But, on the other hand, there is a high heating value in the excitation windings and conductor plate; hence, it is necessary to analyze thermal characteristics of the eddy current brake to avoid the declination of braking performance or even damage of the device. First, the structure and working principle of the eddy current brake are described. Second, the analytical model of the eddy current brake is built, and then the thermal network model of the eddy current brake is built taking the actual airflow around the eddy current brake into consideration. Finally, the validity of the thermal network model is verified by steady and dynamic thermal experimental measurements.