Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled Inductor Based Regenerative Cascaded Multicell Converter for Drives With Multilevel Voltage Operation at Both Input and Output Sides

Cascaded multicell converter (CMC) is one of the effective solutions for medium-voltage high-power drives. The inability to regenerate power back to the source is one of the limitations of this converter. The regeneration capability is obtained by using a pulsewidth-modulated active rectifier instead of a conventional three-phase passive rectifier in each cell. However, due to their two-level voltage operation at the input side, an inductor is required for each cell to filter out the switching frequency components from the input current. This paper proposes a new configuration of regenerative CMC based on coupled inductor to achieve multilevel voltage at the transformer secondary terminals. This allows us to operate the rectifier devices at reduced switching frequency. The coupled inductor reduces the total harmonic distortion (THD) of the transformer secondary current and thus the transformer size. The size of the filter inductor and the transformer ratings are compared. The operating principle and the detailed analysis are presented. A technique to bypass a faulty module is proposed. The working of the converter is verified through simulation and experimental studies.
Decidability of a Sound Set of Inference Rules for Computational Indistinguishability

Computational indistinguishability is a key property in cryptography and verification of security protocols. Current tools for proving it rely on cryptographic game transformations. We follow Bana and Comon's approach, axiomatizing what an adversary cannot distinguish. We prove the decidability of a set of first-order axioms which are computationally sound, though incomplete, for protocols with a bounded number of sessions whose security is based on an IND-CCA_2 encryption scheme. Alternatively, our result can be viewed as the decidability of a family of cryptographic game transformations. Our proof relies on term rewriting and automated deduction techniques.
A New Maximum Power Point Tracking Algorithm for the Photovoltaic Power System

The use of maximum power point tracking (MPPT) techniques is widely discussed and employed for photovoltaic array in micro-grid systems to optimize the PV output power transferred to the load. There are various types of converter models are employed as MPPT circuits such as buck, boost or buck-boost converters. The MPPT circuit is becoming an intermediate component between the load and the PV panel. In this paper, the MPPT boost converter is selected and a new control algorithm for the MPPT controller, namely new hill climbing method, is introduced. This method is also compared with an existing popular MPPT algorithm (P&O) to confirm its superior performance by using the MATLAB simulation.
Design and Test of the BESIII ETOF with MRPC Technology

The Time-of-Flight (TOF) detector is the key detector in the Beijing Spectrometer (BESIII) for charged hadron identification [1], [2]. It is composed of the barrel TOF (BTOF) and the end-cap TOF (ETOF). The BTOF consists of two-layer plastic scintillators and fine-mesh Photo-Multiplier Tubes (PMT) as readout. Its time resolution achieves 80ps for muon samples at 1GeV/c [3]. On the other hand, the previous ETOF is single-layer scintillator detector. There is a significant multiply scattering effect leading to relatively poor time resolution of ~ 140ps, limiting the K/π separation (3σ) momentum range below 1GeV/c [4]. In order to improve its performance, the BESIII-TOF group has upgraded the ETOF based on high time resolution Multi-gap Resistive plate Chamber (MRPC) technology. Experiment and simulation results show that the MRPC-based ETOF can effectively suppress the multiple scattering effect on the intrinsic time resolution and uncertainty of particle hit positions. For the upgraded ETOF system with a proposed time resolution of 80ps, K/π separation can achieve 1.4GeV/c at 95% confidence level.
A Design Framework for Thermal-Aware Power Delivery Network in 3D MPSoCs with Integrated Flow Cell Arrays

Integrated Flow Cell Array (FCA) technology promises to address the power delivery and heat dissipation challenges in three-dimensional Multi-Processor Systems-on-Chips (3D MPSoCs) by providing combined inter-tier liquid cooling and power generation capabilities. In this paper, we present for the first time a design framework to accurately model the temperature-aware power delivery network in 3D MPSoCs, and quantify the effects of FCAs on the voltage drop (IR-drop). This framework estimates the power generation variation along FCAs due to voltage and temperature, in the case of uniform and non-uniform powermaps from several real processor traces. Furthermore, we explore different 3D MPSoC configurations to quantify their power delivery requirements. Our results show that FCAs improve the IR-drop with respect to state-of-the-art design methods up to 53% and 30% for dies with a power consumption of 60W and 190W, respectively, while maintaining their peak temperatures below 52°C, and at no additional Through Silicon Via (TSV) area overhead. In addition, as the presence of high power density regions (hotspots) can decrease the FCAs IR-drop reduction by up to 21% with respect to the average value, we present a scalable TSV placement optimization methodology using the proposed framework. This methodology minimizes the IR-drop at hotspots and guarantees an optimal and uniform exploitation of the IR-drop reduction benefits of FCAs.
iTracker: Towards Sustained Self-Tracking in Dynamic Feature Environment with Smartphones

Self-tracking at 6 degrees of freedom in real-time is essential in lots of emerging applications such as VR/AR/MR simulation, indoor navigation, and so on. With the development of built-in sensors in smartphones, many self-tracking solutions have appeared. Many researchers try to utilize vision-based approaches combined with an Inertial Measure Unit (IMU) to realize self-tracking with smartphones. After testing these approaches, however, we find that tracking would be lost in four such common scenarios: 1) When the IMU rotates fast or for a long period of time, it will cause serious delays in orientation tracking; 2) The scenes where background features are not distinct enough; 3) When the smartphone moves fast, image features become quite different in successive frames; 4) Unstructured scenes where background features are not static. To address these issues, we propose iTracker, which utilizes Real-time Step-Length Adaption Algorithm to solve the scenario (1) and a Parallel-Multi-State Local Recovery method to deal with scenarios (2)–(4). Extensive experiments show that iTracker realizes robust and accurate self-tracking in these four scenarios with an error of 0.7% throughout the whole trajectory.
Social Cognition Construction of the Avian Flu based on Social Media Big Data

During the high incidence of avian flu, the mainstream media and social media report a lot on the epidemic, mobilizing the people to prevent and control avian flu. This paper collects reports on avian flu from News, Forums, Apps, WeChat and Microblog and forms five data sets. We extract agenda-settings from the News dataset and build agenda-setting networks of the five datasets. Then we use the QAP test to verify the relevance of these agenda-setting networks. We also project the agenda-setting dissimilarity matrices into a two-dimensional space using the MDS method to form cognitive maps, analyzing the cognitive drift of media platforms relative to News. Results show that the agenda-setting networks of Apps and News have the highest correlation coefficient of 0.9193, while Microblog and News have the lowest correlation coefficient of 0.5611. The cognitive maps of Apps, Forum and WeChat have a slight translation and rotation relative to the cognitive map of News. But their relative positional relationship among agenda-settings are similar with News, expect Microblog.
Logic Circuit Based Test Derivation for Microcontrollers

In this paper, an approach to test synthesis based on a logic circuit model for microcontroller based physical systems is described. The formal model of the logic circuit is used to describe the behavior of discrete systems with a finite number of transitions. A model of a higher level of abstraction is a Finite State Machine (FSM) and its modifications, for example, a timed Finite State Machine (TFSM). These formal models are used to synthesize complete test suites for discrete systems with respect to a given fault model. A fault model based on three popular faults in logic circuits is often used to derive complete test suites for logic circuits. According to a previous experimental result, such complete test suites can detect a large number of output faults in the corresponding FSM. The advantage of the logic circuit model is scalability, which allows building tests with sufficiently high fault coverage, in the case when the FSM of the system under test has a large number of states and the test synthesis process becomes difficult. The paper describes the application of this approach to the microcontroller based system of a switching generator, whose behavior is described by a TFSM. The number of states of corresponding FSM abstraction increases significantly so an FSM based test derivation process becomes more complex.
Preferential Multi-Attribute Rank based Forwarding Node Selection in Software Defined Networks

Today’s networks provide a plethora of applications with heterogeneous characteristics. The network generates a huge volume of traffic because of its distributed nature and requires preferential treatment for controlling them. The existing traditional system lacks efficiency because of the diverse nature of the network and the need for the high-performance network is imminent. However, one of the predominant issues that demand high attention is traffic flow management. This paper explores the various traffic management approaches and analyses the significant role played by the next hop neighbor node selection in improving the performance of the network. To address this issue, the emerging software defined networking paradigm (SDN) is introduced to enable flexible traffic flow control. A novel method for traffic flow management by adopting the Simple Multi-Attribute Rating Technique (SMART) ranking method has been proposed for selecting the best next hop neighbor for forwarding the flows. A flow scheduling algorithm has been adopted for minimizing the maximum link utilization and provide improved performance. Simulation result shows that the proposed algorithm performs better in terms of throughput and latency.
Using deep-learning to predict outcome of patients with Parkinson’s disease

There are currently no established disease modifying therapies for PD, and prediction of outcome in PD to power clinical studies is a very important area of research. Assessment of PD is informed by imaging the dopamine system with dopamine transporter (DAT) single-photon emission computed tomography (SPECT) imaging and by the presence of key symptoms. Recently, deep-learning based methods have shown promise for medical image analysis tasks and disease detection. The purpose of this study was to develop a deep-learning based approach to predict outcome of patients with PD using longitudinal clinical data containing imaging and non-imaging information. Features were first extracted from the clinical data by the proposed deep-learning based approach and then combined to predict motor performance (MDS-UPDRS-III) in year 4. The performance of the proposed approach was evaluated via a 10-fold cross-validation. We evaluated the performance of the network on the basis of mean absolute error (MAE) between the predicted and true MDS-UPDRS part III scores in year 4. The proposed approach yielded a MAE of 4.33±3.36 when given only imaging features, 3.71±2.91 when given only non-imaging features, and 3.22±2.71 when given all input data. While the approach given only non-imaging input data outperformed the approach given only imaging data, we found that the performance of the proposed approach substantially improved when given both imaging and non-imaging information. Our results indicate that the addition of imaging data to non-imaging clinical data is helpful for the prediction of outcome in patients with PD. The proposed approach that incorporated both imaging and non-imaging clinical data shows significant promise for prediction of outcome in patients with PD.
Attenuation correction in a combined, single-gantry breast PET-Tomosynthesis scanner

We are currently building a breast PET-Tomosynthesis scanner integrated in a single gantry providing spatially co-registered 3D PET-Tomosynthesis images. We hypothesize the breast tomosynthesis scanner can aid in improving PET quantitation by improving attenuation correction (AC) based on identification of the tissue-types present in the breast tomosynthesis image. The PET detector consists of a 32 x 32 array of 1.5x1. 5x15 mm3 LYSO crystals. The PET scanner utilizes two detector heads separated by 9 cm, with each detector head having a 4x2 arrangement of the PET detectors. GATE simulations were performed with anthropomorphic breast phantom that under clinical compression measured 6.8 x 12.6 x 6.15 cm3 in size. While the Virtual Clinical Trial (VCT) environment permits the definition of a variety of tissues, our study limited the tissue classification to air, breast tissue, adipose, & skin. Four FDG-avid lesions, each 5 mm in diameter were also simulated within the breast. GATE simulation data was converted to list-mode format & image reconstruction performed with a blob-based iterative algorithm using a 300 ps TOF kernel. Reconstruction either excluded AC, or, utilized tissue segmentation & classification to perform AC. Initial studies demonstrate that AC is necessary to measure the true tracer uptake. It also demonstrates that a homogeneous tissue model provides a relatively close estimate of the true radiotracer uptake. The lesion activity concentration was overestimated by only 1.9% when AC was performed using the DBT image, demonstrating the feasibility to perform accurate quantitation with the combined breast PET-DBT scanner.
Recognition Of Fish Categories Using Deep Learning Technique

Nowadays the most crucial aspect in Fisheries is the classification and localization of Fish. Due to Segmentation problems, noise and changes in the environmental conditions, it is difficult to classify the images with good accuracy. However, the object recognition with greater accuracy is in great demand. So, in order to solve this problem, the Convolutional Neural Network is used which helps to classify and localize the images with better accuracy. Fish classification is made by training the dataset and the level of accuracy has been improved by using different activation functions. Several comparisons have been made and finally a better method has been identified, thereby the accuracy is improved. Localization helps to generate Region proposals and class labels successfully. Finally, image classification and localization has been made with greater accuracy.
A Novel Method for Emotion Extraction From Paintings Based on Luscher’s Psychological Color Test: Case Study Iranian-Islamic Paintings

Paintings evoke certain emotions in the viewers. Colors, shape, texture, and many other factors affect the feeling conveyed by paintings, but colors seem to have a stronger effect due to a century-long study of color-emotion association in various fields of psychology, art, and color science. There are many color-emotion theories and most of them have been implemented, however, the Luscher Color Test is untouched amongst them. Based on several reasons, discussed in detail inside the paper, we believe this theory can cover problems in this domain of emotion extraction. The main motivation for choosing the Luscher test was that this method is designed for personality and mood analysis and it can better study abstract paintings. In this paper, a set of paintings from Iranian-Islamic cultural heritage is chosen as a dataset. We have proposed the L-EEP method based on Culture Technology (CT) concept to extract emotions from paintings with image processing techniques and psychology knowledge. This method extracts specific colors from paintings and by performing the Luscher test automatically, is able to determine eight emotions. For this matter, paintings are assessed in two moods: 1. The full extent of the painting 2. Cropped interest area of the painting that attracts more attention. Then, the color palette which is extracted colors ordered based on their coverage extent enters search engine. The search engine performs the searching process in the 3D knowledge base of Luscher color-emotion layers to extract relative values of emotions in both scenarios. For the evaluation of the results, three steps were taken. First, we compared the output results of ancient Persian painting with literature and text of their background stories. Then a viewer evaluation is done to compare the results with human viewpoint. Finally, a set of modern abstract paintings peer-rated in the IAPS standard system to further examine the proposed method. The results of the three forms of evaluation indicate the applicability of the L-EEP approach.
BAGH – Comparative Study

Process mining is a new emerging research trend over the last decade which focuses on analyzing the processes using event log and data. The raising integration of information systems for the operation of business processes provides the basis for innovative data analysis approaches. Process mining has the strong relationship between with data mining so that it enables the bond between business intelligence approach and business process management. It focuses on end-to-end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs readily available in today’s information systems. The discovered process models can be used for a variety of analysis purposes. Many companies have adopted Process-aware Information Systems for supporting their business processes in some form. These systems typically have their log events related to the actual business process executions. Proper analysis of Process Aware Information Systems execution logs can yield important knowledge and help organizations improve the quality of their services. This paper reviews and compares various process mining algorithms based on their input parameters, the techniques used and the output generated by them.
Feature Oriented Deep Convolutional Neural Network for PET Image Denoising

Deep convolutional neural networks have demonstrated superior performance in natural image denoising. Trainable network weights have been typically optimized by minimizing a loss function that computes pixel-wise discrepancies between the noisy image and the clean target image. In this study, we investigate an alternative solution that utilizes more prior knowledge to highlight the features of interest by modifying their contributions to the global loss function. We propose a feature-oriented deep convolutional neural network (FeaOri-DCNN) for PET image denoising that uses weight maps in order to steer the training toward contrast preservation for small features. To obtain the weight maps, we first manually segment the lesions in the target images to create lesion masks. Lesion voxels are assigned stronger weights than the background voxels followed by a Gaussian smoothing. This weight map is then incorporated into the loss function optimization. We first trained the proposed FeaOri-DCNN and a conventional DCNN built on a five-layer residual network architecture with simulated and phantom images containing hot spheres with various size and contrast. We then trained an eight-layer network with 8 patient studies and 1 phantom study. We evaluated the five-layer network on phantom studies and the eight-layer network on 2 patient studies inserted with GATE simulated lesions. The results of the phantom studies show that FeaOri-DCNN improved contrast recovery on small and low contrast spheres by up to 36% while performing similarly in terms of noise reduction in the background. Similar results were also observed in the patient studies.
NCFET-Aware Voltage Scaling

Negative Capacitance Field-Effect Transistor (NCFET) has recently attracted significant attention. In the NCFET technology with a thick ferroelectric layer, voltage reduction increases the leakage power, rather than decreases, due to the negative Drain-Induced Barrier Lowering (DIBL) effect. This work is the first to demonstrate the far-reaching consequences of such an inverse dependency w.r. t. the existing power management techniques. Moreover, this work is the first to demonstrate that state-of-the-art Dynamic Voltage Scaling (DVS) techniques are sub-optimal for NCFET. Our investigation revealed that the optimal voltage at which the total power is minimized is not necessarily at the point of the minimum voltage required to fulfill the performance constraint (as in traditional DVS). Hence, an NCFET-aware DVS is key for high energy efficiency. In this work, we therefore propose the first NCFET-aware DVS technique that selects the optimal voltage to minimize the power following the dynamics of workloads. Our experimental results of a multi-core system demonstrate that NCFET-aware DVS results in 20% on average, and up to 27% energy saving while still fulfilling the same performance constraint (i.e., no trade-offs) compared to traditional NCFET-unaware DVS techniques.
Material Decomposition of Energy Spectral CT by AUTOMAP

Energy spectrum CT can perform spectral imaging of objects. In the clinical and industrial fields, spectral images are often converted into a distribution image of the base material, ie, material decomposition. Traditional image domain material decomposition algorithms are often parsed point by point. This time we try to use the end-to-end AUTOMAP for fitting solution. AUTOMAP can perform image domain mapping and achieve certain effects on MRI image reconstruction, but our attempts at spectral CT material decomposition are not satisfactory.
Hybrid Local Route Generation Combining Perception and a Precise Map for Autonomous Cars

Autonomous cars are currently widely studied in the automotive and robotics industries because autonomous driving can satisfy the needs of human drivers regarding safety, efficiency, and comfortable driving. Behavior and motion planning for an autonomous car is one of the main requirements of autonomous driving. For autonomous cars, a local route along with its road geometry and attributes of the area is required. There are two ways to obtain the local route: perception-based local route (PBLR) and precise map-based local route (MBLR) methods. First, this paper analyzes the characteristic of both the PBLR and MBLR inference methods. Then based on this analysis, this paper proposes a hybrid local route generation algorithm that chooses the best local route between the PBLR and MBLR options, according to the perceived performance and the precise map availability. To effectively create an expensive precise map, a mapping region classification algorithm is presented to selectively choose the mapping area, where the precise map must be constructed for the MBLR inference. The hybrid local route generation algorithm with the mapping region classification allows the area used for autonomous driving to be extended while reducing the cost due to the precise map. The advantages of the proposed algorithm were verified with experiments in real traffic conditions.