Miracle-dz/UniFAR-FTUs huggingface dataset:
Aperture-coupled stacked microstrip antenna with dual polarization and low back-radiation for X-band SAR applications

The design of a X-band single radiator for a wide-band dual polarized-aperture coupled stacked patch microstrip antenna is introduced. A bandwidth of 2 GHz with enhanced port decoupling, improved front to back ratio and high gain can be obtained by using thin substrates for the feeding network. Simulated and measured results of S-parameters and measured radiation patterns are presented.
Text automation tool to improve pharmacist productivity and work capacity in a digital hospital: a pilot study

Hospitals and health services are increasingly using digital systems to improve medication safety. Yet, despite many gains, negative user experiences and increased documentation time have been recognised as the main barriers to successful adoption of an electronic medication management (EMM) system in routine practice. The objective of this study was to describe the results of a preliminary trial in which clinical pharmacists used a customised pharmacist text and reference automation tool (PARAT). The clinic pharmacist then completed a survey to determine if they found that the tool affected their productivity or documentation consistency when used with an EMM system. Fifteen pharmacists completed the training, the four‐week trial, and the follow‐up survey. The main outcome measure was self‐reported clinical pharmacist productivity and user experience. Overall, they indicated that PARAT had a positive impact on their productivity, work capacity and the EMM user experience. Fourteen (93.3%) participants indicated that the tool was either ‘easy to use’ or ‘easy to use – training required’. No participant stated that the tool was hard to use. Agreement was highest with the statements that PARAT improved clinical workflow and improved clinical work capacity (mean of 4.1). Automation of text creation for common intervention recording was listed as second most useful among participants. The findings from this study demonstrate that PARAT is acceptable to pharmacists and is likely to enhance clinical pharmacist workflows.
Automatic Detection and Recognition of the First Arrival Phase of Seismic Event Signals Contaminated by Noise. The Curious Case of the Missing Explosion

Abstract : Both of these papers are based upon data collected at the Lajitas Seismic Station. In the first paper a historical perspective on the methods that have been used to automatically detect event signals and pick first arrival times is developed. Then the data set used to characterize and test the detection techniques is described and the features which discriminate seismic signals effectively from the background noise are characterized. Tow automatic detection methods are investigated: 1) The AR (8)-Spectral estimates of the signal and noise are used to develop the AR(p)-Spectral estimate for a synthetic waveform; and 2) Several non-parametric tests are employed in a time domain detector to discriminate event signals from background. The non-parametric detector chosen, RANK 2700, employs a modified rank sum test to locate the seismic event and pick its first arrival time. Errors in the automatic first arrival picks for 152 of the event traces in the data set are used to analyze the performance. The second paper presents a scenario for a clandestine, decoupled nuclear explosion in southeastern New Mexico based upon data recorded at Lajitas and other stations at regional distances from the hypothetical clandestine test. Keywords: Seismic data; Seismic detection; Nuclear explosion detection; Pattern recognition; Seismometers; Texas seismological stations; Seismic wave spectral analysis; Signal to noise ratio.
A Scalable Generator for Massive MIMO Baseband Processing Systems with Beamspace Channel Estimation

This paper describes a scalable, highly portable, and energy-efficient generator for massive multiple-input multiple-output (MIMO) baseband processing systems. This generator is written in Chisel and produces hardware instances for a scalable massive MIMO system employing distributed processing. The generator is parameterized in both the MIMO system and hardware datapath elements. Coupled with a Python-based system simulator, the generator can be adapted to implement other baseband processing algorithms. To demonstrate the adaptability, several generator instances with different parameter values are evaluated by FPGA emulation. In addition, a beamspace calibration and channel denoising algorithm are applied to further improve the channel estimation performance. With those algorithms, the error vector magnitude can be reduced by up 9.2%.
Direct Identification of Elasticity from Attenuated Spectrum in Resonant Ultrasound Spectroscopy

Resonant ultrasound spectroscopy (RUS) is an experimental measurement method for obtaining stiffness coefficients from the free resonant frequencies of a sample. The resonant frequencies are commonly extracted from the swept spectrum in a RUS experiment, which is not a trivial task for the high damping materials with broad and overlapped resonant peaks. Though several specific signal processing methods could help find resonant peaks, manual operation is a requisite for identifying the true-positive peaks, laboriously and empirically. To overcome these challenges, we proposed to obtain the elasticity from attenuated spectrum directly without explicit peak extraction. The feasibility of the method was assessed using the spectrum of a cortical bone specimen. The stiffness coefficients calculated by the proposed method showed a good agreement with the reference value (errors less than 4%).
Shear Wave Speed Estimation for Crawling Wave Sonoelastography Using the Short-Time Fourier Transform

Crawling Wave Sonoelastography is quantitative ultrasound-based elastography approach that provides tissue stiffness information. Two mechanical sources generates an interference pattern on a tissue with obtaining a shear wave speed (SWS) map. To do this task, many estimators have been developed; however, one of the most common non solved issues is the present of artifacts on the SWS map reconstruction. In this study, a new SWS estimator based on the Short-Time Fourier Transform is proposed. Testing is made with data acquired from experiments on a gelatin phantom with a circular inclusion vibrating on a range between 200 Hz and 360 Hz with steps of 20 Hz. The results were compared with the Phase Derivative estimator. The proposed estimator generated a SWS image with lower coefficient of variation and higher Contrast-to-Noise Ratio, which may allow better discrimination between regions.
A Comprehensive Review on Machine Learning Models for Medical Data Classification

The content of multi-modal medical features representing medical data was researched and many modes of learning were analyzed for the risk assessment of diseases in order to gather information from medical health data and generate intelligent application relevant inquiries. The representation of medical data employs the method of deep learning. To attain model adaptability, the study and extraction of different disease features employ the same procedure. Classification of medical data is an important data mining challenge that has interested various academics throughout the world for a decade. Various data mining algorithms are utilized for health data sorting such as classification, clustering, regression, soft computing and more. Feature selection algorithms play a key part in every challenge in machine learning. The selection of the best method produces an optimal subset of properties, which enhances accuracy and reduces training time. It is also beneficial to remove the useless characteristics for high-dimensional datasets. This paper provides a brief review on medical data classification models to provide a brief assessment of the common feature selection and classification models specifically utilized for medical data classification. Various aspects, such as the medical data sets available, selection of features, choice of classification, problems in the identification and analysis of the key methods of selecting features and detailed mechanisms are represented in this survey paper.
Distributed Denial of Service Attack Prevention from Traffic Flow for Network Performance Enhancement

Customer Relationship Management (CRM), Supply Chain Management (SCM), banking, and e-commerce are just a few of the internet-primarily based commercial enterprise programmes that make use of distributed computing generation. These programmes are the principal target of large-scale attacks known as DDoS attacks, which cause the denial of service (DoS) of resources to legitimate customers. Servers that provide dependable services to real consumers in distributed environments are vulnerable to such attacks, which send phoney requests that appear legitimate. Flash crowd, on the other hand, is a massive collection of traffic generated by flash events that imitate Distributed Denial of Service assaults. Detecting and distinguishing between Distributed Denial of Service assaults and flash crowds is a difficult problem to tackle, as is preventing DDoS attacks. Existing solutions are generally intended for DDoS attacks or flash crowds, and more research is required to have a thorough understanding. This study presents a technique for distinguishing between different types of Distributed Denial of Service attacks and Flash Crowds. This research work has suggested an approach to prevent DDOS attacks in addition to detecting and discriminating. The performance of the suggested technique is validated using NS-2 simulations.
Flashover performance and voltage correction of iced and polluted HV insulator string at high altitude of 4000 m and above

Based on the test results carried out on several types porcelain and AC composite insulators as well as a DC insulator in the artificial climate chamber, this paper puts forward the flashover performance of tested insulators at the atmospheric pressure of the altitude of 4000-meter and above concomitant with the contamination and the icing, and analyzes the effect of the pressures and the contaminations as well as the ice amount accreted on the AC and the DC flashover voltages of the insulators respectively. And the flashover voltage correction factors of the tested insulators in the coexisting conditions of the high altitude and the contamination as well as the icing and the corresponding characteristic index are given. According to test results, it indicates that the flashover voltage of tested insulators decrease by the exponential function law with the increase of the ice amount, by the power function with the decrease of the altitude and the contamination, and the special exponents, which show the effects of the altitude, the contamination and the ice respectively, are various with the type of the insulators, the kind of voltages, and the contamination level and the ice amount.
Nonlinear Ultrasonic Phased Array Imaging based on Amplitude Modulation

Ultrasonic phased array is an advanced ultrasonic detection and imaging technology with powerful applicability, which is widely used in the non-destructive testing and evaluation of material defects in the field of industrial non-destructive testing. However, the requirements for the detection accuracy of damage and defects in complex industrial structures are deeply increasing nowadays. When the crack in the equipment is partially closed or smaller than half wavelength, the crack may be underestimated or missed, which often results in serious consequences. Nonlinear ultrasonic method has attracted considerable attention for its high sensitivity to early material damage and micro defects, and therefore its application in ultrasonic phased arrays has broad potential and value. On the other hand, an amplitude modulation (AM) method is proposed, which is more efficient than studying specific high-order harmonics as nonlinear component. In this work, we utilize nonlinear ultrasonic phased array imaging based on AM method to realize the detection of fatigue cracks. The fatigue cracks were made from compact tensile specimens of 7075 aluminum alloy by fatigue tensile test. The imaging result shows that compared with conventional ultrasonic phased array, the nonlinear ultrasonic phased array imaging based on the AM method can not only detect and evaluate fatigue cracks in the specimen, but also has the sensitivity and selectivity to the nonlinear response. This work provides theoretical guidance for nonlinear ultrasonic phased array imaging detection in industrial structures.
Machine Learning Based Approach For Prediction Of Suicide Related Activity

Suicidal ideation is a significant public health issue that kills a large number of people every year all around the world. Every year, over 800,000 people commit suicide all over the world. As their use of social networking sites has grown, users have utilized them to discuss personal problems, including exchanging suicide plans. Suicide can be prevented by analyzing such posts available on social media sites, either manually or automatically. Suicide-related subjects are gathered from social media sites, which leads to a spike in suicide ideation. Prior research has found individual terms or phrases from tweets that predict suicide risk factors, but this study will use the n-gram model to compute scores, which combines Unigram, Bigram, and Trigram with a dictionary. Suicide attempts will be categorized into three groups. : low, medium, and high risk. The purpose of this research is to use a range of important data, such as linguistic and location-based factors, as well as emoticons, to better identify cases of suicide. Machine learning-based approaches such as Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), as well as Extra Tree, will be used to categories suicidal cases.
MatMat: Matrix Factorization by Matrix Fitting

Matrix factorization is a widely adopted recommender system technique that fits scalar rating values by dot products of user feature vectors and item feature vectors. However, the formulation of matrix factorization as a scalar fitting problem is not friendly to side information incorporation or multi-task learning. In this paper, we replace the scalar values of the user rating matrix by matrices, and fit the matrix values by matrix products of user feature matrix and item feature matrix. Our framework is friendly to multitask learning and side information incorporation. We use popularity data as side information in our paper in particular to enhance the performance of matrix factorization techniques. In the experiment section, we prove the competence of our method compared with other approaches using both accuracy and fairness metrics. Our framework is an ideal substitute for tensor factorization in context-aware recommendation and many other scenarios.
Reverse-Blocking IGBTs with V-Groove Isolation Layer for Three-Level Power Converters

Multilevel power converters are among the most effective approaches to reduce power loss and to improve efficiency in power conversion systems. Reverse-blocking IGBTs (RB-IGBTs) have been improved and extended to higher breakdown voltage to be used as bidirectional switches in multilevel converter applications. In this work, a hybrid isolation process by combining thermal diffusion and V-Groove etching is developed to form 1200–1700-V RB-IGBTs. The details on 1700-V RB-IGBTs are presented in this paper. Compared with that of full diffusion, the thermal budget of the frontside surface deep boron diffusion has been reduced to less than one-third. Sufficient reverse-blocking capability and switching robustness have been successfully demonstrated. At the same switching loss level, on-state voltage of a 50 A-rated planar gate RB-IGBT is reduced to approximately 1.9 V compared with that of serially connected trenchgate field-stop IGBT (FS-IGBT) and free-wheeling diode (FWD). Experimental benchmarking on 1200-A module demonstrated that the energy loss in three-level inverter was reduced to 18% by using RB-IGBTs instead of IGBT and FWD pairs at typical switching frequencies for high-power, medium-voltage applications.
Submerged antenna performance

This paper gives the results of a theoretical and experimental study of the characteristics of antennas in lossy environments, such as wires buried in the earth or sea. A criterion for the evaluation of such antennas (for surface waves or any other single mode of propagation), called relative communication efficiency, is defined and a mathematical formula for its calculation is derived. A number of specific evaluations are presented, with emphasis on horizontal wire antennas. The results of experiments to determine the input impedance and current distribution on wires of different sizes and different insulation thicknesses are summarized and compared to a simple theory. The results of measurements of the field strength of horizontal wires and arrays are presented and compared to the theoretical results. The signal to noise ratios observed with submerged receiving antenna structures are discussed.
6 GHz Lithium Niobate MEMS Resonator with Square Spiral Electrodes

Due to the increasing demand of higher frequencies for RF front end technologies, mainly for 5G smartphones, there is a need to develop filters for higher frequencies and large bandwidths. One of the promising candidates for satisfying those demands is the recently emerged laterally-excited bulk acoustic wave resonators. In this study, we aim to realize a resonator operating at 6 GHz based on the first-order antisymmetric (A1) mode utilizing Z-cut LiNbO3 thin films and square spiral electrodes.
The Feasibility and Design of Multifrequency Acoustic Traps

Single frequency acoustic traps are widely applied to microparticle trapping, filtering, sorting and manipulation. This work considers how additional frequencies can be used to achieve better trapping. The acoustic radiation force on a small (sub-wavelength) particle in a rigid walled cavity vibrating at multiple harmonic frequencies is calculated analytically and applied to developing a trap with large regions of near constant force and a stiff local trap at the cavity center. A 1D transmission line model is used to demonstrate that these multi-frequency traps should be realizable in a straight forward acoustic system and that the trap stiffness can be increased more than 2.5 times compared to a single frequency trap without increasing power requirements or the acoustic intensity in the chamber.
Attaining Meta-self-awareness through Assessment of Quality-of-Knowledge

Self-awareness is a crucial capability of autonomous service-based systems that enables them to self-adapt. There are different types of self-awareness whereby certain types of knowledge are captured at various levels. We argue that effective management of the trade-offs of dependability requirements can be achieved through “seamless” switching between different levels of awareness. However, the assessment of the quality of knowledge to enable dynamic switching between self-awareness levels has not been tackled yet. We propose a general architecture that exploits symbiotic simulation in order to tackle the complexity of assessing the quality of knowledge and attaining the meta-self-awareness property, wherein the system can reflect on its different levels of awareness. We conduct a thorough real-world study in the context of volunteer services. We conclude that a system made meta-self-aware using our approach achieves optimal performance by activating the most suitable awareness level. This comes at the cost of a modest computational overhead.
Correction of Designed Compressive Spectral Imaging Measurements Using a Deep Learning-Based Method

Spectral imaging offers useful additional information to improve or expand imaging applications such as biomedical images, identification of cultures, and surveillance. These applications take advantage of features involved in a spectral scene captured using, for instance, the Coded Aperture Snapshot Spectral Imagers (CASSI), that naturally embodies the compressing sensing principles, whose potential is diminished because in practice, sensing matrix loses the ideal characteristics. This paper uses a deep learning based method in order to correct the real-compressed measurements and estimate the ideal-corrected measurements. The correction is estimated from a matrix form of compressed measurements, with the representation of compressed spatial dimensions and the number of projections captured as shots. The performance of the model is measured using peak signal-to-noise ratio, and the structural similarity index, upon the recovered data cube with the gradient projection for sparse reconstruction algorithm. The outcomes show how the deep learning-based method improves the quality in reconstruction against image ground truth when the noise is not concerning.
Human Emotions Detection using Kernel Nonlinear Collaborative Discriminant Regression Classifier : Human Emotions Detection using KNCDRC

This paper has proposed a non-linear subspace classifier that uses a linear collaborative discriminant regression classification method on a kernel version called Kernel Nonlinear Collaborative Discriminant Regression Classification method (KNCDRC). The kernel method is generally considered helpful in determining non-linear Structure. Unlike traditional classification methods, the proposed model use the kernel function to plot the test points from the low dimensional kernel space into the high dimensional kernel space. The proposed method is to detect human emotion through facial feature recognition. To improve recognition rate and accuracy, a future subspace has been created on the dataset. It enhances the connection between sample points and the dimensions of the sample points. The experiments show that KNDCRC achieves a better recognition rate than existing state-of-art recognition systems on large-scale standard datasets like Multi-PIE and AffectNet.
Integrated Switching Circuit for Low-Noise Self-Referenced Mid-Infrared Absorption Sensing Using Silicon Waveguides

We report a new method of accurately measuring the mid-infrared absorption of fluid analytes using on-chip waveguides that allows measurements to be referenced for noise and drift reduction whilst using only a single external detector. A silicon-on-insulator waveguide circuit integrated with a microfluidic channel is made in which a pair of switches alternate light between an evanescent absorption sensor waveguide and a reference waveguide at a rate of a few kHz, thus allowing low frequency common noise components to be removed from the measurement, and leading to a reduction in the noise floor by a factor of 11 compared to a simple waveguide absorption sensor. The device was verified by using it to detect an Isopropyl alcohol (IPA) absorption peak at a wavelength of 3.77 µm.