Miracle-dz/UniFAR-FTUs huggingface dataset:
Inkjet Printed vs Screen Printed Microstrip Line on LTCC substrates

In this paper we compare the influence of two printing techniques on wideband performance of a microstrip line on LTCC substrate. The inkjet printing and screen printing techniques are compared. The quality of conductive layers is analyzed in terms of material type (paste and ink), their thickness, roughness and shape of pattern edges. Finally, microstrip line performance is characterized for frequencies up to 20 GHz. We show that microstrip lines made of two layers of nanosilver ink performs poorly. In this case the insertion loss can be several times higher (in dB) in comparison to insertion loss introduced by silver paste-made microstrip lines.
Structure and Properties of Madden–Julian Oscillations Deduced from DYNAMO Sounding Arrays

AbstractThe kinematic and thermodynamic characteristics of the October and November 2011 Madden–Julian oscillations (MJOs) that occurred over the Indian Ocean during Dynamics of the MJO (DYNAMO) are investigated. Analyses are presented 1) for two primary sounding arrays, where results are independent of model parameterizations, and 2) on larger scales, including the Indian Ocean, using operational and reanalysis data.Mean precipitation during DYNAMO was characterized by maxima in two east–west bands north and south of the equator. This pattern alternated between two bands during the inactive phase of the MJOs and a single rainfall maximum on the equator during the active phases. Precipitation over the northern sounding array (NSA), where the MJO signal was strongest, was significantly modulated by the MJOs, while the southern array experienced more frequent, briefer episodes of rainfall mostly related to ITCZ convection. Over the NSA the MJOs were characterized by gradual moistening of the low to midtropo.
Learning low level skills from scratch for humanoid robot soccer using deep reinforcement learning

Reinforcement learning algorithms are now more appealing than ever. Recent approaches bring power and tuning simplicity to the everyday work machine. The possibilities are endless, and the idea of automating learning without domain knowledge is quite tempting for many researchers. However, in competitive environments such as the RoboCup 3D Soccer Simulation League, there is a lot to be done regarding humanlike behaviors. Current teams use many mechanical movements to perform basic skills, such as running and dribbling the ball. This paper aims to use the PPO algorithm to optimize those skills, achieving natural gaits without sacrificing performance. We use Simspark to simulate a NAO humanoid robot, using visual and body sensors to control its actuators. Based on our results, we propose an indirect control approach and detailed parameter setups to obtain natural running and dribbling behaviors. The obtained performance is in some cases comparable or better than the top RoboCup teams. However, some skills are not ready to be applied in competitive environments yet, due to instability. This work contributes towards the improvement of RoboCup and some related technical challenges.
MCPL-Based FT-LSTM: Medical Representation Learning-Based Clinical Prediction Model for Time Series Events

Large collections of electronic medical records (EMRs) provide us with a vast source of information on medical practice. However, the utilization of these data to support clinical decisions is still limited. Extracting useful patterns from such data is particularly challenging because the data are variable longitudinal, sparse, and heterogeneous. Therefore, in this paper, we propose the MCPL-based FT-LSTM, a clinical event prediction method based on medical concept representation learning. On one hand, inspired by FASTTEXT, we have developed an interpretative vector representation of medical events in EMRs, which enables us to capture the medical concept information effectively so that the patient’s clinical data can be represented more reasonably. On the other hand, we propose a novel time-controlled long short-term memory (LSTM) prediction model, which adds time-control units to the original LSTM model. The model can describe the variable time intervals in EMRs, better capture long-term, and short-term information, and eliminate the strong dependence of clinical data on timestamps; thus, improving the model’s prediction performance for clinical events. Through extensive experiments on the MIMICIII dataset, we demonstrate that the MCPL-based FT-LSTM achieves higher precision in the field of clinical event prediction, which is of great significance for the medical information research.
Evaluation Criteria for Requirements Engineering Process Improvement Models and Frameworks

Several researchers initiated their work of models and framework development by creating evaluation criteria (also called success criteria or quality aspects). These criteria not only guided development but also were used to help evaluate their models and frameworks. This paper reviews Boehm’s evaluation criteria for assessing a cost model, Beecham’s success criteria for validating a Requirements Engineering (RE) maturity model, Kitchenham’s quality aspects for evaluating a software bidding model, and Niazi’s success criteria for assessing a measurement framework. The paper also explains how these criteria are consolidated, revised and adapted to form a new set of evaluation criteria. The main criteria include:1) completeness, 2) consistency, 3) practicality, 4) usefulness, and 5)verifiability. Each of these criteria is defined with several subcriteria,general rules to guide its assessment. The newevaluation criteriaare proposed to guide evaluations ofexisting and new RE process assessment and improvement models and frameworks. Key-Words: -Evaluation Criteria, Models, Process Improvement, Requirements Engineering, Success Criteria
Widely Linear Complex-Valued Estimated-Input LMS Algorithm for Bias-Compensated Adaptive Filtering With Noisy Measurements

In this paper, a novel widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) is first proposed for processing noisy input and output data in the complex domain. The WLC-EIAF consists of two steps: (i) estimation of noise-free input and (ii) update of the weight vector, which is realized by alternating the minimization of an instantaneous perturbation with both input and output data. Based on the WLC-EIAF method and adopting the least mean-square (LMS) scheme, a widely linear complex-valued estimated-input LMS (WLC-EILMS) algorithm is developed. It is able to achieve an unbiased parameter estimation and, thus, outperforms the widely linear complex-valued LMS (WL-CLMS) algorithm in the presence of noisy input and output. In particular, for Gaussian signals, closed-form expressions are derived for its steady-state excess mean-square error performance. Furthermore, the linear complex-valued estimated-input LMS and linear real-valued estimated-input LMS algorithms are presented, which are two simplified versions of the WLC-EILMS for circular and real-valued signals, respectively. Simulation results demonstrate that the proposed methods achieve significantly improved performance in terms of mean-square deviation and mean-square error when compared to the WL-CLMS and CLMS algorithms.
Convolutional Neural Network for Sound Processing - Study of Deployed Application

Pest birds are considered as a special kind of vermin, since, in most of countries, their legal position does not enable their direct extermination. Therefore, in order to protect the agricultural areas indirectly from pest birds, the robust and highly selective pest bird sensor is necessary to design. In this contribution, the pest bird detection unit, based on a convolutional neural network, is presented. The convolutional neural network itself is used for the decision making about the pest bird occurrence, while sound recordings are used as input data. The testings, presented at the end of the contribution, proved a very high accuracy of the detection unit, with the results indispensably improved in comparison to previously presented approaches.
Harmonics and Interharmonics of Top and Mudpump Variable Frequency Drives in Drilling Rigs

Drilling rigs are large loads temporary in nature. Nowadays, drilling of a well can be done in the range of a few hours to a few weeks. Given the cost and complexity of building, mobilizing and operating, and space constraints, oil and gas companies rarely own their drilling rig and contract out specialized drillers. Their rigs often are operated for decades and contain rugged Variable Frequency Drives (VFDs), most times of six-pulse thyristor bridge topology or SCR-driven DC motors. The dynamics of the drives show steep ramp-up/ramp-down, rich harmonic content, and a wide power factor range. To make matters worse, drilling operation is often conducted in isolated, weak systems, leading to large background voltage distortion when grid-connected. This paper presents simulations and measurements of top-drive and mud-pump VFDs in a large drill rig operating in Alberta, Canada. It was observed that the VFDs draw currents very high in harmonic and interharmonic contents. To further investigate the true impact of these harmonic emissions, the paper also quantifies the harmonic Attenuation Effect of the VFDs, which can lead to harmonic content overestimated, if ignored. Due to the rich harmonic emissions, care must be taken to appropriately size the interconnection transformer when grid connected, and this is covered in the paper as well. As expected, these loads must be managed accordingly by the electric utility to avoid issues to nearby customers.
INVITED COMMENTARY TRAINING GENERAL PRACTITIONERS*

Nationally there is no standardized system for the education and training of general practitioners (GPs) within the UK in relation to prevention, early detection and management of alcohol problems. A number of surveys over the past 20 years have confirmed GPs' role legitimacy concerning working with excessive drinkers, but identified a lack of role adequacy and role support for this work. However, there are considerable constraints present in current UK general practice which limit opportunities for education and training of GPs. A possible way ahead may be to encourage general practitioners to utilize a screening and early intervention programme, coupled with ongoing support and imaginative use of other primary health care workers, community workers and the secondary services.
Probability-Interval based Optimal Planning of Integrated Energy System with Uncertain Wind Power

Owing to higher energy supply efficiency and operational flexibility, integrated energy system (IES), including power, heating and gas systems, will be the primary form of energy supply in the future. However, with the increase of large-scale stochastic wind power integrated, the IES operation will face significant challenge as same as traditional power system. In view of the above problems, considering the probability distribution characteristics of wind power, a conditional value-at-risk (CVaR) based probability-interval method is proposed to describe the uncertain wind power in this paper. Then, besides traditional facilities, electricity storage system (ESS) is introduced to improve the flexibility of IES. Furthermore, a planning model is established based on minimizing the total cost including investment, operation, CVaR cost and unserved energy cost. Finally, an IEEE14-NGS14 system is constructed and its planning model is solved by GAMS/CPLEX. The numerical results illustrate the correctness and effectiveness of the proposed method.
Distributed Event-Triggered Control for Islanded Microgrids: Cyber-Physical Design and Implementation

Microgrid is a complex cyber-physical system with interacted power and communication networks. Conventionally, the secondary control of microgrids relies on a central controller and periodic communications, which may limit the efficiency and resilience of the cyber systems. In this paper, a distributed event-triggered secondary control scheme is proposed for efficient communication in islanded microgrids. The proposed control scheme only takes effect when there are microgrid states change ‘events' (e.g load changes and communication failures). The secondary control including frequency/voltage regulation and accurate real/reactive power sharing are decoupled into two timescales. The triggering conditions for only power sharing control in slower timescale are defined to avoid overlap. The proposed method only requires communications among neighbour controllers at the event-triggered time, which dramatically reduces the communication burden in the cyber system. A cyber-physical microgrid platform has been built with the microgrids in OPAL-RT and communications in Raspberry Pis. The proposed secondary controller has been implemented on this platform and the hardware-in-the-loop (HiL) experiment results demonstrate the performance of our controller design.
Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

Automatic seizure detection has been often treated as a classification problem that aims at determining the label of electroencephalogram (EEG) signals by computer science, as the EEG monitoring is a helpful adjunct to the diagnosis of epilepsy. In most existing work, the traditional signal energy of the EEG has been applied for classification, since the energy pattern of epileptic seizures differs from that of non-seizures. Although they are effective, the accuracy either heavily depends on additional information besides energy or is limited by the shortcoming of energy-based features. To address this issue, the proposed approach achieves the classification based on the instantaneous energy of the EEG signals instead. The proposed approach first measures the instantaneous energy related to changes in the EEG signals. Then, energy behavior over time is characterized by instantaneous energy-based features from different aspects. Finally, the classification is carried out on the features to produce output labels. By processing instantaneous energy, the information of energy evolution is involved. As such, the accuracy is improved without bringing in extra information besides energy, or complicated transformation. In multi-class problems, the proposed approach has obtained promising results for identifying the ictal EEG, which indicates the tremendous potential of the proposed approach for epileptic seizure detection.
Area-Aware Optimizations for Resource Constrained Branch Predictors Exploited in Embedded Processors

Modern embedded processors (e.g., Intel's XScale) use small and simple branch predictors to improve performance. Such predictors impose little area and power overhead but may offer low accuracy. As a result, branch misprediction rate could be high. Such mispredictions result in longer program runtime and wasted activity. To address this inefficiency, we introduce two optimization techniques: first, we introduce an adaptive and low-complexity branch prediction technique. Our branch predictor removes up to a maximum of 50% of the branch mispredictions of a bimodal predictor. This results in improving performance by up to 16%. Second, we present front-end gating techniques and reduce wasted activity up to a maximum of 32%
Efficient PRNG Design and Implementation for Various High Throughput Cryptographic and Low Power Security Applications

Pseudo-Random Number Generators (PRNGs) are an integral part of cryptographic applications, such as key generations, digital signatures, Internet-of-Things (IoT) security, etc. These applications require low-power and high-throughput PRNGs along with statistically secure random numbers capability. In this paper, we propose two PRNGs by combining/modifying Blum-Blum-Shub (BBS), Xorshift and Permuted Congruential PRNGs. The first one is for general purpose applications and the second one is for low-power IoT applications. We implement the proposed PRNGs on FPGA ZedBoard Zynq™–7000. Our implementation results show that the proposed PRNGs generate 4.83 × 107 and 4.29 × 107 random numbers per-second, respectively. The total dynamic power consumption of the proposed PRNGs is 17mW at 48.31Mhz and 16mW at 42.90Mhz with a maximum throughput of 184.288MBps and 163.651MBps, respectively. We test the proposed PRNGs on Diehard battery suite and US National Institute of Standard and Technology (NIST) SP 800 − 22 suite for the analysis of randomness quality.
DNN Based Music Emotion Recognition from Raw Audio Signal

Music emotion is an important component in the field of music information retrieval and computational musicology. Considering the complexity of the state-of-the-art methods and challenges brought by lack of information available in existing public databases, we propose novel approach to emotion recognition based on convolutional and recurrent neural networks for feature mining. The proposed method utilizes a latest findings in deep learning by stacking convolution layer with bidirectional gated recurrent unit. The method was evaluated on the MediaEval Emotion in Music dataset and has shown exceptional performance using only raw audio signals and without any need for pre-processing.
Poster Abstract: Synchronous Automatic Training for Wearable Sensors via Knowledge Distillation

Since wearable sensors often have high substitutability, it is imperative that newly added wearable sensors have automatic training capabilities. In this paper, we propose a novel automatic model training method called Synchronous Automatic Training (SAT) for wearable sensors, which adopts a knowledge distillation technique to fully exploit the knowledge of the existing trained sensors and a synchronous mechanism to ensure the knowledge is transferred to the untrained sensors to assist the automatic model construction. The automatically trained model by using our proposed SAT method has higher accuracy than existing methods.
The Application of the Integration-Differentiation Method for the Measurement of the Fundamental Component of the Reactive Power

At present, the following concepts of reactive power are observed: reactive power of the fundamental component, reactive power of harmonics and reactive power in a given frequency range. The most important parameter is the reactive power of the fundamental spectral component. In this article the method of the measuring of the reactive power of sinusoidal signal, based on the integration and differentiation of one of the input signal is implemented. The implementation of this measurement method based on the first-order digital integrator and the first-order digital differentiator is taken into consideration. The digital differentiator based on the algorithm of approximation by two samples is considered. The comparison of the considered method with popular method of integration and popular method of continues time delay from the point of view of the measurement error is carried out. The analytical dependence for the calculation of the additional measurement error of the reactive power caused by deviation of signal frequency is obtained. An expression to determine the gain factors of the integrator and differentiator, which allows to minimize the measurement error of the reactive power near the nominal value of the frequency of input signal is obtained. Simulation mathematical modeling by the software package Matlab 7.0 and Simulink 8.0 is performed.
Multicarrier Constant-Envelope Multiplexing Technique by Subcarrier Vectorization for New Generation GNSSs

Multicarrier constant-envelope multiplexing (CEM) techniques are those that combine different signals at several adjacent carrier frequencies into a composite signal with constant envelope, which is an important characteristic in global navigation satellite systems (GNSSs). In this letter, we propose a multicarrier CEM technique based on equivalent signal vectors. This new technique simplifies the multicarrier CEM design by vectorizing the sinusoidal subcarriers, and achieves both high multiplexing efficiency and high flexibility. Performance analysis and application examples are also given in this letter. The technique proposed in this letter could be used in new generation GNSSs.
Cost‐utility analysis of meaning‐centered group psychotherapy for cancer survivors

Meaning‐centered group psychotherapy for cancer survivors (MCGP‐CS) improves meaning, psychological well‐being, and mental adjustment to cancer and reduces psychological distress. This randomized controlled trial was conducted to investigate the cost‐utility of MCGP‐CS compared with supportive group psychotherapy (SGP) and care‐as‐usual (CAU).
Management of optic pathway gliomas: Role of magnetic resonance imaging with diffusion-tensor imaging tractography in planning surgical resection

Optic pathway gliomas (OPGs) are histological low grade but widely infiltrative tumors that arise along the optic nerves, optic chiasm, tracts, diencephalic structures, and white matter radiations to the visual cortex. When OPGs progress, management strategies may differ between pre-chiasmatic and chiasmatic lesions. Radiation therapy is often utilized as the primary treatment modality, but it is typically postponed till the age of 5–7 years because of the potential for late risks such as endocrinopathy, vasculopathy, and cognitive decline. No definitive studies establish the superiority of one specific chemotherapeutic agent over others. However, a favorable side effect profile is often opted for. Surgery is considered in patients with (1) single nerve is involved, (2) progressive and disfiguring proptosis, (3) blindness, and (4) significant mass effect or hydrocephalus. Encouragingly, diffusion-tensor imaging (DTI) has emerged as a useful tool for the assessment of white matter structures, including the visual pathways. We report an interim analysis of 14 patients with OPG, at our center, a tertiary referral center. We report that magnetic resonance imaging (MRI)-DTI studies provided additional information about visual fiber arrangement in relationship to the tumor that was not evident by conventional MRI methods.
AN ADAPTIVE ESTIMATION METHOD FOR SEMIPARAMETRIC MODELS AND DIMENSION REDUCTION

Xia, Tong, Li and Zhu (2002) proposed a general estimation method termed minimum average variance estimation (MAVE) for semiparametric models. The method has been found very useful in estimating complicated semiparametric models (Xia, Zhang and Tong, 2004; Xia and Hardle, 2006) and general dimension reduction (Xia, 2008; Wang and Xia, 2008). The method is also convenient to combine with other methods in order to incorporate additional statistical requirements (Wang and Yin, 2007). In this paper, we give a general review on the method and discuss some issues arising in estimating semiparametric models and dimension reduction (Li, 1991 and Cook, 1998) when complicated statistical requirements are imposed, including quantile regression, sparsity of variables and censored data.