Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed Multilevel Inverter Topology for High Power Medium Voltage Applications

In this paper, a mixed multilevel inverter scheme for high power, median voltage (HPMV) application is proposed. The major contribution of this work is an implementation of Diode clamped H-Bridge (DC/H-Bridge) inverter topology which is a fusion of diode clamped & cascaded H-bridge inverter. The fundamental challenges in applying the inverter topology were high device switching frequency which causes high power loss and excessive heating of switches (IGBT/GTO) and harmonics are present in output. Our research is to reduce switching frequency of devices without hampering the inverter switching frequency. This helps to overcome the two major drawbacks of conventional inverter topologies. A SPWM scheme is used. An inductor filter (L-filter) is used to remove harmonic content present in the inverter output.
Accelerating garbage collection for 3D MLC flash memory with SLC blocks

3D MLC NAND Flash is more appreciated for its massive capacity and significant performance. It's common to configure a portion of flash blocks to SLC-mode to further shorten the requests latency at a small cost of capacity. However, the limited SLC-mode blocks provoke GC (Garbage Collection) procedures more frequently and the GC penalty is heavier for 3D Flash than that for 2D Flash. As the block of 3D Flash consists of much more pages, the increment in the block size prolongs the erase operation latency and increases the number of migrated pages during GC. Existing works focus on reducing the number of migrated pages with sub-block GC strategies, which deeply depends on the distribution of valid pages across the sub-blocks in the victim block. In this paper, we propose a set of schemes called DCD which consists of Dual-mode Handler, Compensated GC and Dynamic Data Distribution. The key idea is to exploit the shorter operation delays of SLC-mode to accelerate the valid pages' migrations during GC, which doesn't rely on the distribution of valid pages inside the victim block. Experimental results show that compared with state-of-the-art designs, DCD shortens the average read response time by 19.8% and 37.3% for block-level and page-level FTL, respectively.
Diabetic Retinopathy Grade and Macular Edema Risk Classification Using Convolutional Neural Networks

Diabetic retinopathy (DR) is one of the major causes of blindness in the western world. Effective treatment of DR is available, when detected early enough, which makes this a vital process. Computers are able to obtain much quicker classifications once trained, giving the ability to aid clinicians in real-time classification. This work employed a deep convolutional neural network (CNN) based method for diabetic retinopathy classification. Three independent CNNs were employed for the classification of DR grade, macular edema risk and multi-label, which included the combination of both grade and risk classes. A fusion method was used to combine all features extracted by the CNNs and make the final classification result. The classification accuracy of the grade and risk were 0.65 and 0.72, respectively. The classification results showed the proposed network fusion method can improve the performances on both task - DR grading and macular edema risk.
Data Aided Channel Estimation for OFDM Wireless Systems using Reliable Carriers

Channel estimation is an essential and crucial research problem in orthogonal frequency division multiplexing (OFDM) based wireless communication systems. Several techniques have been applied and investigated for wireless channel estimation but each of them has its own limitations. The major drawback in current channel estimation techniques is that a large pilot overhead is needed to yield a satisfactory performance which reduces the spectral efficiency. To overcome this shortcoming, we suggest a method of data aided channel estimation which minimizes the pilot overhead without compromising the quality of channel estimation in OFDM systems. The proposed technique is based on reliable data carrier's selection algorithm in which reliable data carriers are used as virtual pilots, thus eliminating the need for additional pilots. Simulation results show a significant improvement in terms of mean square error (MSE) and bit error rate (BER) of the proposed algorithm.
Image Enhancement and Quality Assessment Methods in Turbid Water: A Review Article

The underwater imaging systems are drawing attention during the past few decades as more and more applications such as ocean exploration, underwater vehicles, intelligent fishing are becoming popular. As a result, newer and superior techniques are in need to fulfill the demands of receiving clearer, finer and better-quality underwater images with improved information within it. In this paper, recent underwater image enhancement systems which are capable of improving quality of images that suffers from low light and unbalanced colors are revised. Moreover, a variety of objective assessment methods regarding to desired category of information evaluation are highlighted.
Information Filtering Via Trust Relationship for Better Personalized Recommendation

Recommender system is one of the most promising techniques with widest application in solving the problem of information overload. Aimed at providing better performance personalized recommendation, this paper improves a new hybrid recommender method based on social trust relationship and benchmark information filtering algorithm. Epinions and Friendfeed show that the hybrid trust method has better performance with high accuracy. Moreover, trust relationship has positive effect on the personalized recommendation performance.
Computed tomographic pulmonary angiography vs ventilation-perfusion lung scanning in patients with suspected pulmonary embolism: a randomized controlled trial.

CONTEXT Ventilation-perfusion (V(dot)Q(dot) lung scanning and computed tomographic pulmonary angiography (CTPA) are widely used imaging procedures for the evaluation of patients with suspected pulmonary embolism. Ventilation-perfusion scanning has been largely replaced by CTPA in many centers despite limited comparative formal evaluations and concerns about CTPA's low sensitivity (ie, chance of missing clinically important pulmonary embuli). OBJECTIVES To determine whether CTPA may be relied upon as a safe alternative to V(dot)Q(dot scanning as the initial pulmonary imaging procedure for excluding the diagnosis of pulmonary embolism in acutely symptomatic patients. DESIGN, SETTING, AND PARTICIPANTS Randomized, single-blinded noninferiority clinical trial performed at 4 Canadian and 1 US tertiary care centers between May 2001 and April 2005 and involving 1417 patients considered likely to have acute pulmonary embolism based on a Wells clinical model score of 4.5 or greater or a positive D-dimer assay result. INTERVENTION Patients were randomized to undergo either V(dot)Q(dot scanning or CTPA. Patients in whom pulmonary embolism was considered excluded did not receive antithrombotic therapy and were followed up for a 3-month period. MAIN OUTCOME MEASURE The primary outcome was the subsequent development of symptomatic pulmonary embolism or proximal deep vein thrombosis in patients in whom pulmonary embolism had initially been excluded. RESULTS Seven hundred one patients were randomized to CTPA and 716 to V(dot)Q(dot scanning. Of these, 133 patients (19.2%) in the CTPA group vs 101 (14.2%) in the V(dot)Q(dot scan group were diagnosed as having pulmonary embolism in the initial evaluation period (difference, 5.0%; 95% confidence interval [CI], 1.1% to 8.9%) and were treated with anticoagulant therapy. Of those in whom pulmonary embolism was considered excluded, 2 of 561 patients (0.4%) randomized to CTPA vs 6 of 611 patients (1.0%) undergoing V(dot)Q(dot scanning developed venous thromboembolism in follow-up (difference, -0.6%; 95% CI, -1.6% to 0.3%) including one patient with fatal pulmonary embolism in the V(dot)Q(dot group. CONCLUSIONS In this study, CTPA was not inferior to V(dot)Q(dot scanning in ruling out pulmonary embolism. However, significantly more patients were diagnosed with pulmonary embolism using the CTPA approach. Further research is required to determine whether all pulmonary emboli detected by CTPA should be managed with anticoagulant therapy. TRIAL REGISTRATION isrctn.org Identifier: ISRCTN65486961.
ProMiSi Architecture - A Tool for the Estimation of the Progression of Multiple Sclerosis Disease using MRI

The aim of this work is to present the architecture of the ProMiSi tool, a software for the analysis of magnetic resonance imaging and the extraction of information on the progression of multiple sclerosis disease. ProMiSi is based on the automatic processing, segmentation and post-processing of MRI for the automatic labeling, visualization and volumetric quantification of segmentable brain structures from magnetic resonance image. The combination of the above mentioned volumetric results with other type of information (e.g. clinical, demographic etc.), through autonomous learning intelligent techniques, allows the evaluation of the severity and the progress prediction of the multiple sclerosis and consequently the personalized management of the disease. A proof of concept study with 30 patients will take place for the validation of the algorithms, while ProMiSi will be evaluated in terms of functionality, usability, reliability, performance and supportability.
UGLRnet: A Classification Model for Upper Gastrointestinal Lesion Image

Convolutional Neural Networks (CNNs) have been widely applied in biomedical image classification, but the classification of upper gastrointestinal diseases, especially gastric cancer, has not been further studied. In this paper, we compare the difference between the medical image and the natural image. We have found that simply making the network structure deeper does not improve the performance of medical image classification model, and sometimes it will be worse. Inspired by the residual connection, we propose a shallow network model, which can converge quickly and achieve high accuracy. In an upper digestive tract classification dataset, we demonstrate the proposed model can achieve high accuracy.
Performance Enhancement of Power Converters for PV-Based Microgrid using Model Predictive Control

PV-based renewable energy systems are integrated in microgrids (MGs) throughout power electronic converters. During external disturbances of abrupt load variation and PV generation fluctuation, controllers play the most key role in regulating the system performance. In this paper, a proposed combination control method of model predictive control (MPC) and sliding mode control (SMC) for moderating the power converters is presented. The interlinking inverter is controlled via applying MPC while DC-DC converter is controlled by SMC. MPC is well known as most reliable and modern approach for non-linear systems to compensate noise and handle modeling inaccuracies for efficient load voltage profile. On the other hand, SMC is considered as the most stable controller with highly robust operation for DC-DC converters. Intermittency nature of renewable energy systems and abrupt load variation have a severe impact on load voltage quality. Keeping in view the sophisticated control attributes of MPC and SMC, inner and outer control loops with primary droop control are designed for interlinking converter by using MPC while SMC is designed for the DC-DC boost converter. Controller performance is analyzed through simulations with fluctuating generation, and variable loads. Unlike conventional cascaded PI controller, proposed MPC strategy is simple, robust with fast dynamic response.
A P2P Network Framework for Interactive Streaming Media

To overcome the shortcomings of interactive streaming media in real-time transmission, control overhead, stability and scalability in general P2P network, we propose a hybrid P2P network framework called PPISM (P2P Interactive Streaming Media) for interactive streaming media. The framework adopts a hierarchical structure, which integrates CDN (Content Distribution Network), P2P network and the combine of tree-mesh structure. After building a mechanism of super-node selection and super-nodes tree construction, streaming media data are pushed to the super-nodes tree, which reduces the load of edge servers. Meanwhile, in the aspect of real-time streaming media transmission, the combination of push-pull is adopted. The edge server pushes streaming media data to the requesting node, and the requesting node pulls the missing streaming media data to the super-nodes tree, which improves the real-time transmission of data. The simulation shows that compared with ordinary P2P and simple CDN-P2P architecture, PPISM can significantly decrease the end-to-end delay, streaming media distortion and control overhead.
Zazz: Variant Annotation and Exploration of Next Generation Sequencing Variants

Over the last 10 years, Next-Generation Sequencing (NGS) has become a powerful tool in clinical genetics and precision medicine. Techniques like Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES) and Target Sequencing are commonly used for the elucidation of common and rare variants in mendelian and complex diseases. One of the most vital parts of NGS pipelines is the prioritization of annotated variants according to their clinical significance. During this process, a clinical geneticist is presented with annotation information from external databases for each of the thousands of potential variants. The vast amounts of data and the vague nature of existing guidelines for variant reporting, like ACMG (American College of Medical Genetics) can make this procedure very cumbersome and time consuming. Here we present the main computational challenges and existing solutions for this task. We also present Zazz, an online environment for variant annotation, query and exploration. Zazz can efficiently support the submission of complex and dynamically generated queries to hundreds of millions of variants each having hundreds of annotation fields. Zazz also leverages the capabilities of modern browsers to dynamically filter, explore and visualize multidimensional data.
RSS-Based Coverage Deployment Method Under Probability Model in 3D-WSN

Due to the wider application of wireless sensor networks in real life, 3D coverage closer to the actual application environment has become a research hotspot of current sensor networks. To this end, this paper proposes a three-dimensional coverage deployment method based on RSS (Received Signal Strength) under a probabilistic model. According to the path loss of the wireless signal in the propagation process, the distance between the nodes can be roughly calculated, and the maximum distance between the nodes is defined by setting a threshold of the path loss, thereby further ensuring network connectivity and network quality. The probability coverage model is used and the cube-based network coverage is constructed. Based on this, the optimal coverage deployment problem in 3D-WSN is explored. Combining and improving the traditional particle swarm optimization algorithm can converge faster and avoid falling into local optimum. The simulation results show that the proposed method can converge quickly to improve network coverage and effectively reduce network energy consumption. In addition, we built a real experimental environment to verify the network quality by observing the RSSI (Received Signal Strength Indicator) changes. The experimental results verify the effectiveness of the proposed method.
Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization

Joint moment is an important parameter for a quantitative assessment of human motor function. However, most existing joint moment prediction methods lacking feature selection of optimal inputs subset, which reduced the prediction accuracy and output comprehensibility, increased the complexity of the input sensor structure, making the portable prediction equipment impossible to achieve. To address this problem, this paper develops a novel method based on the binary particle swarm optimization (BPSO) with the variance accounted for (VAF) as fitness function to reduce the number of input variables while improves the accuracy in joint moment prediction. The proposed method is tested on the experimental data collected from ten healthy subjects who are running on a treadmill with four different speeds of 2, 3, 4 and 5m/s. The BPSO is used to select optimal inputs subset from ten electromyography (EMG) data and six joints angles, and then the selected optimal inputs subset be used to train and predict the joint moments via artificial neural network (ANN). Prediction accuracy is evaluated by the variance accounted for (VAF) test between the predicted joint moment and multi-body dynamics moment. Results show that the proposed method can reduce the number of input variables of five joint moment from 16 to less than 11. Furthermore, the proposed method can better predict joint moment (mean VAF: 94.40±0.84%) in comparison with the state-of-the-art methods, i.e. Elastic Net (mean VAF: 93.38±0.96%) and mutual information (mean VAF: 86.27±1.41%). In conclusion, the proposed method reduces the number of input variables and improves the prediction accuracy that may allow the future development of a portable, non-invasive system for joint moment prediction. As such, it may facilitate real-time assessment of human motor function.
UNIVERSIDADE FEDERAL DO PARÁ NÚCLEO DE MEDICINA TROPICAL PROGRAMA DE PÓS-GRADUAÇÃO EM DOENÇAS TROPICAIS

Patients with chronic kidney disease are at high risk of acquiring hepatitis C virus (HCV). The prevalence of hepatitis C in hemodialysis units is high. The study aimed to assess the presence of HCV and its different genotypes in patients with chronic kidney disease who underwent hemodialysis in Belém and metropolitan area in the state of Pará, Brazil, determining the prevalence of the virus genotypes and epidemiological characteristics of patients with chronic kidney disease. We conducted cross-sectional study, hemodialysis units in seven cities of Belém and metropolitan area in the period from October 2011 to April 2012. A questionnaire was applied to social data, epidemiological and on the presence of risk factors for viral hepatitis. Biological material was collected from the patients for HCV PCR and ELISA tests. Patients with the presence of viral RNA were evaluated for genotypes. The prevalence of antibodies to HCV between the groups was 8.4%, while 5.4% had viral RNA, with 0.1% among those who were negative. Genotype 1 was the most prevalent, with 86.1%, followed by type 2, with 11.6%. The type 3 was only 2.3%. Whereas epidemiological analysis showed male predominance, age 49, married or in a stable, low education level and family income of up to two minimum wages. The main cause of CKD was diabetes mellitus (34.4%), followed by glomerulonephritis (18.6%) and hypertension (17.1%). The duration of hemodialysis was significantly important risk factor for acquiring HCV (p = 0.012), with the majority of HCV patients who acquired the disease during hemodialysis were above 5 years of treatment (p = 0.0001). Another risk factor associated with HCV was prior organ transplant. We conclude that, in Belém and the metropolitan area, the prevalence of HCV in hemodialysis was high and the most frequent genotype is the same as the general population in northern Brazil.
Edge-Coupling of O-Band InP Etched-Facet Lasers to Polymer Waveguides on SOI by Micro-Transfer-Printing

O-band InP etched facets lasers were heterogeneously integrated by micro-transfer-printing into a <inline-formula> <tex-math notation="LaTeX">$1.54~\mu \text{m}$ </tex-math></inline-formula> deep recess created in the <inline-formula> <tex-math notation="LaTeX">$3~\mu \text{m}$ </tex-math></inline-formula> thick oxide layer of a 220 nm SOI wafer. A <inline-formula> <tex-math notation="LaTeX">$7\times 1.5\,\,\mu \text{m}^{2}$ </tex-math></inline-formula> cross-section, 2 mm long multimode polymer waveguide was aligned to the ridge post-integration by e-beam lithography with <inline-formula> <tex-math notation="LaTeX">$ < 0.7~\mu \text{m}$ </tex-math></inline-formula> lateral misalignment and incorporated a tapered silicon waveguide. A 170 nm thick metal layer positioned at the bottom of the recess adjusts the vertical alignment of the laser and serves as a thermal via to sink the heat to the Si substrate. This strategy shows a roadmap for active polymer waveguide-based photonic integrated circuits.
Smart Cage Bird Lovebird Based on Arduino Using Internet of Thing

The traditional design of the bird lovebird’s cage has been implementing for many years. Typically, a healthy bird lovebird shows behaviors like actively whistle at any moment. Some factors are affecting the health of the bird lovebird, like temperature and humidity. In normal conditions, the temperature and humidity of the lovebird’s cage around should continuously be in 250C-300C, with the humidity around is 77% - 80%. In irregular cases, the temperature and humidity are increased significantly higher or lower than should it be. In this inconsistency situation, it is causing the lovebird’s health in some problems like increasing or decreasing its appetite, inactive movement, and unnormal whistling behavior. The new lovebird’s cage designed has been implemented in order to improve the health’s quality of the lovebird. It is complemented with microcontroller Arduino as a controller of the lamp and fan in order to keep the temperature and humidity around the cage in constant value using a fuzzy logic control method. It also integrated with the internet of things using things view in case of real-time monitoring using a smartphone. The result, the new cage design shows the cage around is kept continuously in normal. So that, the breeding of the bird lovebird is significantly improved.
An Application-Oriented Autoscaler Based on Deep Learning

In order to meet the performance requirements and reduce resource consumption, researchers have proposed many autoscaling schemes. However, most of them only considered current states of the servers or application that limits the applicability of autoscaler. This paper presents an application-oriented autoscaler based on Long Short Term Memory network and Multi-Layer perceptron. The autoscaler includes a workload prediction model, a response time prediction model and a resource adjusting model. With these models, workload and response time of service are accurately predicted, and mean absolute percentage error of workload prediction is reduced to 3.3 × 10-4. The autoscaler can also provide appropriate adjustment suggestion to server managers. The experiments compared our autoscaler with other models, and results show that our autoscaler had better performance in workload prediction and server adjusting.