Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiband Linearization Technique for Broadband Signal With Multiple Closely Spaced Bands

Under the scenario of noncontiguous carrier aggregation with closely spaced multiple bands, the interband modulation products after power amplifying will overlap with the bands of interest. This leads to linearization performance degradation for the conventional three-band digital predistortion (DPD) method which only accounts for in-band intermodulation (IMD) and cross modulation. To resolve the spectral overlaps, a multiband linearization technique combining the direct power amplifier (PA) model extraction and the indirect DPD learning strategies is proposed. A multidimensional memory polynomial (MD-MP) model is first presented to jointly extract: 1) the in-band IMD; 2) the cross modulation; and 3) the interband modulation products from the observations for the multiple bands of interest to yield an improved frequency/time selective modeling. The extracted MD-MP models are then used in the indirect learning structure instead of the physically filtered observations to estimate the multiband DPD coefficients, given the fact that the observations spectrally overlap with each other which would result in interference for DPD coefficients estimation with direct filtering. Moreover, a novel MD cubic spline (MD-CS) basis is applied for the first time to the considered three-band scenario while the principal component analysis is used to alleviate any ill-conditioning problem during the model extraction. Improved PA modeling and DPD linearization performances are verified through experiments performed on three groups of wide-bandwidth noncontiguous aggregated signals covering a variety of application scenarios.
A Circular Polarized Feed Horn With Inbuilt Polarizer for Offset Reflector Antenna for  $W$ -Band CubeSat Applications

A novel <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band left-hand circular polarized (LHCP) cylindrical waveguide feed horn antenna is proposed. The proposed antenna features an inbuilt polarizer structure and a single side-fed linear polarized input to offer symmetric LHCP radiation pattern. The internal polarizer structure consists of nine pairs of circular cavities to generate a circularly polarized wave, eliminating the need for an orthomode transducer or a complex septum. The side-fed horn reduces the overall length by eliminating the rectangular to circular waveguide transition. The proposed horn antenna’s electrical dimension is <inline-formula> <tex-math notation="LaTeX">$7.2\lambda \times 3.9\lambda \times 1.4\lambda $ </tex-math></inline-formula> at 84 GHz. The antenna has impedance matching (<inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> below −15 dB) and axial ratio below 1.2 dB from 79.5 to 88 GHz. The horn is used as a feed source for an offset parabolic reflector of 10 cm diameter and small <inline-formula> <tex-math notation="LaTeX">$f/D$ </tex-math></inline-formula> ratio 0.25, which can fit inside a CubeSat. The reflector antenna provides the simulated right-hand circular polarization (RHCP) directivity of 36.6 dBic at 86 GHz with spillover loss of 0.83 dB. The feed reflector assembly is also simulated with the complete aluminum CubeSat chassis. The measurement of the prototyped feed horn and the offset parabolic reflector antenna validates the analysis and simulation results.
Coarse to Fine: Multi-label Image Classification with Global/Local Attention

In our daily life, the scenes around us are always with multiple labels especially in a smart city, i.e., recognizing the information of city operation to response and control. Great efforts have been made by using Deep Neural Networks to recognize multi-label images. Since multi-label image classification is very complicated, people seek to use the attention mechanism to guide the classification process. However, conventional attention-based methods always analyzed images directly and aggressively. It is difficult for them to well understand complicated scenes. In this paper, we propose a global/local attention method that can recognize an image from coarse to fine by mimicking how human-beings observe images. Specifically, our global/local attention method first concentrates on the whole image, and then focuses on local specific objects in the image. We also propose a joint max-margin objective function, which enforces that the minimum score of positive labels should be larger than the maximum score of negative labels horizontally and vertically. This function can further improve our multi-label image classification method. We evaluate the effectiveness of our method on two popular multilabel image datasets (i.e., Pascal VOC and MS-COCO). Our experimental results show that our method outperforms state-of-the-art methods.
3-D RPET-NET: Development of a 3-D PET Imaging Convolutional Neural Network for Radiomics Analysis and Outcome Prediction

Radiomics is now widely used to improve the prediction of treatment response and patient prognosis in oncology. In this paper, we propose an end-to-end prediction model based on a 3-D convolutional neural network (CNN), called 3-D RPET-NET, that extracts 3-D image features through four layers. Our model was evaluated for its ability to predict the response to radio-chemotherapy in 97 patients with esophageal cancer from positron emission tomography (PET) images. The accuracy of the model was compared to five other methods proposed in the literature for PET images, based on 2-D CNN and random forest algorithms. The role of the volume of interest on the accuracy of 3-D RPET-NET was also evaluated using isotropic margins of 1–4 cm around the tumor volume. After segmentation of the lesion using a fixed threshold value of 40% of the maximum standard uptake value, the best accuracy of 3-D RPET-NET reached 72% and outperformed the other methods tested. We also showed that using an isotropic margin of 2 cm around the tumor volume improved the performances of 3-D RPET-NET to reach an accuracy of 75%.
Matching Theoretic Beam Selection in Millimeter-Wave Multi-User MIMO Systems

Beamspace multiple-input multiple-output (MIMO) with beam selection offers an attractive solution to reduce the number of radio-frequency chains in a high-dimensional millimeter-wave MIMO system. Considering a multi-user MIMO system in which an access point, having an <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> element antenna array, communicates with <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> users (<inline-formula> <tex-math notation="LaTeX">$K < N$ </tex-math></inline-formula>), each having a single antenna, we address the problem of selecting <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> beams with sum rate as the performance metric. We model beam selection as a two-sided <italic>matching</italic> between the two sets of players (users and beams) and consider two different ways of modeling players preferences. When the interdependences between the players’ preferences are considered, it becomes <italic>matching with externalities</italic>. The proposed algorithm, which finds a stable matching in such a case, outperforms the existing beam selection algorithms.
Time-Series Features for Predictive Policing

Forecasting when and where crimes are more likely to occur based on years of historical record analysis is becoming a task which is increasingly helping cities’ safety departments with capacity planning, goal setting, and anomaly detection. Crime is a geographically concentrated phenomena and varies in intensity and category over time. Despite its importance, there are serious challenges associated with producing reliable forecasts such as sub-regions with sparse crime incident information. In this work, we address these challenges proposing a crime prediction model which leverages features extracted from time series patterns of criminal records based on spatial dependencies. Our results benchmarked against the state of the art and evaluated on two real world datasets, one from San Francisco, US, and another from Natal, Brazil, show how crime forecasting can be enhanced by leveraging Spatio-Temporal dependencies improving our understanding of such models.
A Novel Solid-State Circuit Breaker for On-Board DC Microgrid System

The development of the dc microgrid system on-board has promoted the development of the dc circuit breaker, but short circuit fault may cause serious damage to the system. Many problems exist in the traditional dc circuit breaker such as long periods of fault interruption, complex circuit structure, existing arc, low reliability, and low anti-interference. Aiming to solve these problems, a novel solid-state dc circuit breaker is proposed in this paper. First, this paper analyzes the working principle and process of the circuit in detail according to the results of the simulation based on Saber. Second, this paper gives the criteria on how to choose the parameters of the components correctly in the circuit. The concept of the maximum fault resistance according to the minimum fault current is also introduced. Moreover, this paper analyzes the influence of the minimum fault ramp rate on the circuit performance. Finally, experimental results verify that the performance of this novel solid-state circuit breaker agree with the theoretical analysis.
A Flux Constrained Predictive Control for a Six-Phase PMSM Motor With Lower Complexity

This paper proposes a low-complexity model predictive flux control (MPFC) with current harmonics and torque ripple reduced for a six-phase permanent magnet synchronous machine (PMSM) motor. First, the virtual vectors in two different magnitudes are adopted for the sake of harmonic currents and torque ripple reduction, as well as simplifying the predictive model by eliminating the z1–z2 variables from the cost function. Then, a look-up table is developed to exclude the useless voltage vectors in advance. In this way, the number of prediction vectors is reduced and heavy computation burden is alleviated. Moreover, to avoid the tedious tuning work of the weighting factor in the cost function, the torque and flux amplitude are transformed into an equivalent reference flux vector. Thus, the complexity of the cost function is significantly reduced. Meanwhile, the current and torque performance are highly improved using the proposed method. Also, the fast dynamic response of predictive control is retained. Finally, simulation and experimental results are offered to verify the effectiveness of the proposed MPFC method.
A Framework for Debugging Java Programs in a Bytecode

In the domain of Software Engineering, program analysis and understanding has been considered to be a very challenging task since decade, as it demands dedicated time and efforts. The analysis of source code may occasionally be comparatively easier due to its static nature, however, the back-end code (Bytecode), especially in terms of Java programming, is complicated to be analysed. In this paper, we present a methodological approach towards understanding the Bytecode of Java programs. We put forward a framework for the debugging process of Java Bytecode. Furthermore, we discuss the debugging process of Bytecode understanding from simple to multiple statements with regards to data flow analysis. Finally, we present a comparative analysis of Bytecode along with the simulation of the proposed framework for the debugging process.
A Framework for Teaching evaluation

This research to practice full paper presents a framework for teaching evaluation. Teaching Evaluation is required for increased student learning, to measure the effectiveness of teaching, and to review courses and programs to effectively meet the needs of students. This evaluation must have a multidimensional approach to address various aspects of teaching and learning. Teaching evaluation can be done as a formative or summative evaluation, or a combination of both. Some combination of formative and summative evaluation about student learning can help in improving the teaching methodology to improve student learning. It is also known that evidence based instruction strategies improve the learning levels in the teaching learning process. This paper proposes a framework for teaching evaluation incorporating evaluation of evidence based teaching strategies.
Student Attendance System in Crowded Classrooms Using a Smartphone Camera

To follow the attendance of students is a major concern in many educational institutions. The manual management of the attendance sheets is laborious for crowded classrooms. In this paper, we propose and evaluate a general methodology for the automated student attendance system that can be used in crowded classrooms, in which the session images are taken by a smartphone camera. We release a realistic full-annotated dataset of images of a classroom with around 70 students in 25 sessions, taken during 15 weeks. Ten face recognition algorithms based on learned and handcrafted features are evaluated using a protocol that takes into account the number of face images per subject used in the gallery. In our experiments, the best one has been FaceNet, a method based on deep learning features, achieving around 95% of accuracy with only one enrollment image per subject. We believe that our automated student attendance system based on face recognition can be used to save time for both teacher and students and to prevent fake attendance.
Stability Control of Wheelchair System Using Interval Type-2 Fuzzy Logic Control (IT2FLC)

Existing control techniques of mobile robot specifically for four to two wheeled wheelchair transformation are lack of adequate mobility, comfort and independence for their users due to highly nonlinear and complex system. This paper presents a four to two wheeled wheelchair transformation system that based on the fundamental of double-link inverted pendulum. SimWise 4D CAD software is used to model and visualize the system movement while evaluating Matlab. This CAD software is integrated with Matlab/Simulink for designing control algorithm. In this work, the controller used to perform transformation and balancing the wheelchair is IT2FLC. It shows that IT2FLC produce better result in terms of lifting and stabilizing the wheelchair in an upright position compared to previous Fuzzy Logic Type-l control.
Pedestrian Detection based on Deep Fusion Network using Feature Correlation

Since most of the pedestrian detection method focus on color images, the detection accuracy is lower when the images are captured at night or dark. In this paper, we propose a deep fusion network based pedestrian detection method. We utilize deconvolutional single shot multi-box detector (DSSD) fused at halfway stage. Also, we apply feature correlation for two image modality feature maps to produce a new feature map. For the experiment, we use KAIST dataset to train and test the proposed method. The experiment results show that the proposed method gains 22.46% lower miss rate compared to the KAIST pedestrian detection baseline. In addition, the proposed method shows at least 4.28% lower miss rate compared to the conventional halfway fusion method.
Improvement of 4K/8K multi-channel IP multicast using DOCSIS over in-building coaxial cable network

In Japan, 4K/8K satellite broadcasting is scheduled to start in December 2018 and ultra high definition video service is expected to be spreading rapidly. In the future, 4K/8K multichannel broadcasting by cable television operators is also expected. The challenge in introducing this service is that the transmission method in older apartment buildings is not fiber to the home (FTTH). Therefore, we investigated 4K/8K multichannel IP multicast distribution using data over cable service interface specifications (DOCSIS), which utilizes in-building coaxial cable networks. We have proposed a transmission method which can distribute with a smaller number of QAM signals than conventional methods.
Society for Maternal-Fetal Medicine ( SMFM ) Consult Series : # 38 : Hepatitis B in pregnancy screening , treatment , and prevention of vertical transmission Society for Maternal-Fetal Medicine ( SMFM )

All authors and Committee members have filed conflict of interest disclosure delineating personal, professional, and/or business interests that might be perceived as a real or potential conflict of interest in relation to this publication. Any conflicts have been resolved through a process approved by the Executive Board. The Society for Maternal-Fetal Medicine has neither solicited nor accepted any commercial involvement in the development of the content of this publication.
A comparative analysis between an hybrid MUSIC-LS algorithm and the standard MUSIC for volume height estimation in tomography-SAR

This paper deals with an application of Multi-baseline-SAR (Synthetic Aperture Radar) that estimates the biomass in forested area. Tomography is a technique that acquires N images of the study area at different altitudes to retrieve the backscattering signal parameters. This work focuses on inversion algorithms applied to SAR signal. The proposed procedure is based on the fusion of MUSIC (Multiple Signal Classification) and LS (Least Squares) inversions. We used the MUSIC inversion as a reference for the evaluation of the algorithm while the LIDAR height was exploited as ground truth for the inversion. Results of the inversion are validated qualitatively and quantitatively by comparing between the estimated volume backscatter signal (MUSIC and MUSIC-LS) and the DSM (Digital Surface Model) obtained from LIDAR (Laser Detection and Ranging) data.
Protein phosphatase 2A is requisite for the function of regulatory T cells this access benefits you. Your story matters

Immune homeostasis depends on the proper function of regulatory T (T reg ) cells. Compromised T reg cell suppressive activity leads to autoimmune disease, graft rejection and promotes anti-tumor immunity. Here we report the previously unrecognized requirement of the serine/threonine phosphatase Protein Phosphatase 2A (PP2A) for the function of T reg cells. T reg cells exhibited high PP2A activity and T reg cell-specific ablation of the PP2A complex resulted in a severe, multi-organ, lymphoproliferative autoimmune disorder. Mass spectrometric analysis revealed that PP2A associates with components of the mTOR pathway and suppresses mTORC1 activity. In the absence of PP2A, T reg cells altered their metabolic and cytokine profile and were unable to suppress effector immune responses. Therefore, PP2A is requisite for the function of T reg cells and the prevention of autoimmunity.
18.2 A 16fJ/Conversion-Step Time-Domain Two-Step Capacitance-to-Digital Converter

Capacitive sensors are widely used to measure various physical quantities, including pressure, humidity [1], and displacement [2]. Ultra-low-power capacitance-to-digital converters (CDCs) are required for sensors with limited battery capacity or powered by energy harvesters. A SAR CDC is simple to design and well-suited for low-to-medium resolution applications. However, to reach high resolution, it requires a low-noise comparator [3] or OTA-based active charge transfer [4], resulting in degraded power efficiency. The $\delta \Sigma $ CDC [1] is suitable for high-resolution applications, but it requires OTAs and repeated charging of the sensing capacitor, leading to high power consumption. The Zoom CDC in [5] achieves high resolution with only one-time charging, but its energy efficiency is still limited by power-hungry OTAs. The open-loop SAR-VCO CDC in [6] achieves low power consumption by eliminating the OTA; however, the VCO gain variation causes inter-stage gain error and requires background calibration, which increases the design complexity and makes it unsuitable for single-shot measurement in sensor node applications due to the long convergence time.
Self-Adjustable Speech Enhancement and Recognition System

We have developed a self-adjustable speech enhancement and recognition (SSER) system to make automatic speech recognition (ASR) robust to variation of acoustic conditions. The SSER system enhances speech components in observed signals, switching parameter values in accordance with the acoustic conditions. Candidates for the parameter value are automatically generated using a real-coded genetic algorithm (GA) in advance. The experimental results show that the performance of the parameter-set values is improved by the proposed method.
Decoupled Power Control for Direct-Modulation-Based Modular Multilevel Converter With Improved Stability

Direct modulation principle is widely used in modular multilevel converters (MMCs) for its simplicity and natural stability. However, it results in strong active and reactive power coupling. This paper carefully analyzes and identifies four power coupling paths and two power influence factors with the direct modulation principle. Full power decoupling of the MMC can be realized by cutting off the derived coupling paths and compensating the influence factors. However, this will further deteriorate the already poor damping of the MMC and significantly increase dc-bus current fluctuations. To solve this problem, the sources resulting in poor damping are identified and then a new closed-loop dc-bus current controller is incorporated into the control system. Finally, the proposed power decoupling strategy with the newly designed current controller realizes completely decoupled power regulation, avoids overload operation, and obviously improves the dynamic performance of the MMC. Furthermore, it increases the stability of the MMC system under all possible operation conditions. The proposed control strategy is confirmed by experimental results.
High-Voltage Regrown Nonpolar  ${m}$ -Plane Vertical p-n Diodes: A Step Toward Future Selective-Area-Doped Power Switches

We report high-voltage regrown nonpolar <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>-plane p-n diodes on freestanding GaN substrates. A high blocking voltage of 540 V at ~1 mA/cm<inline-formula> <tex-math notation="LaTeX">$^{\textsf {2}}$ </tex-math></inline-formula> (corresponding to an electric field of E ~ 3.35 MV/cm), turn- ON voltages between 2.9 and 3.1 V, specific on-resistance of 1.7 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \textsf {cm}^{\textsf {2}}$ </tex-math></inline-formula> at 300 A/cm<inline-formula> <tex-math notation="LaTeX">$^{\textsf {2}}$ </tex-math></inline-formula>, and a minimum ideality factor of 1.7 were obtained for the regrown diodes. Our results suggest that Si, O, and C interfacial impurity levels up to <inline-formula> <tex-math notation="LaTeX">$\textsf {2}\times \textsf {10}^{\textsf {17}}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {3}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\textsf {8}\times \textsf {10}^{\textsf {17}}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {3}}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\textsf {1}\times \textsf {10}^{\textsf {19}}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {3}}$ </tex-math></inline-formula>, respectively, at the metallurgical junction of <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>-plane, p-n diodes do not result in very early breakdown in the reverse bias although the off-state leakage current in the forward bias is affected. The impact of the growth interruption/regrowth on diode performance is also investigated.