Miracle-dz/UniFAR-FTUs huggingface dataset:
Portable and Configurable Implementation of ARINC-653 Temporal Partitioning for Small Civilian UAVs

The ARINC-653 standard defines temporal partitioning that enables multiple avionics applications to execute independently from each other without interference in terms of CPU resources. Though partitioning has been mainly discussed from the viewpoint of manned aircraft, it can also efficiently integrate multiple applications on civilian Unmanned Aerial Vehicles (UAVs) that have even severer limitations on size, weight, power, and cost. In order to employ ARINC-653 temporal partitioning to civilian UAVs, its implementation must be flexible enough to be applied to diverse run-time software environments and computing hardware platforms. In this paper, we suggest a portable and configurable implementation of ARINC-653 for small-sized civilian UAVs aiming for low cost, easy development, and easy extension. Our implementation provides the Operating System (OS) abstraction layer that defines the essential OS-level features and the OS-independent interfaces to the upper layer that actually implements the ARINC-653 standard. Our implementation is also modularized so that the policies of resource management in CPU scheduling and memory allocation can be easily extended and selectively configured. In addition, we implement the advanced resource management schemes to promote the benefits of multi-core processors that are already widely deployed in Commercial Off-The-Shelf (COTS) systems. We show that our ARINC-653 implementation is portable across different OS, such as Linux and RTEMS, reusing the most of source codes thanks to the layered and modular design. We also analyze the overheads of the ARINC-653 APEX interfaces and multi-core scheduling. Moreover, we conduct a case study for a small-sized quad-copter.
Extending Supervoxel-based Abnormal Brain Asymmetry Detection to the Native Image Space

Most neurological diseases are associated with abnormal brain asymmetries. Recent advances in automatic unsupervised techniques model normal brain asymmetries from healthy subjects only and treat anomalies as outliers. Outlier detection is usually done in a common standard coordinate space that limits its usability. To alleviate the problem, we extend a recent fully unsupervised supervoxel-based approach (SAAD) for abnormal asymmetry detection in the native image space of MR brain images. Experimental results using our new method, called N-SAAD, show that it can achieve higher accuracy in detection with considerably less false positives than a method based on unsupervised deep learning for a large set of MR-T1 images.
Theoretical Evaluation of Semiconductor Loss Components Behavior in ISOP-DAB Converters

Input-Series Output-Parallel (ISOP) connected Dual-Active-Bridges (DABs) form an interesting topology which enable step down MVDC to LVDC by employing medium frequency transformers. Si-IGBT semiconductor devices are commonly used in power distribution grid applications such as Smart Transformers (STs) where both MVDC and LVDC energy ports can be presented. In this paper, Si-IGBT semiconductor loss components such as conduction, switching and gate driver losses behavior are theoretically evaluated in a wide range of blocking voltage, switching frequency and phase-shift angle of DAB converters. In addition, gate driver losses are approximated as a continuous function of blocking voltage based on available datasheets. The behavior of the loss components are analyzed for two 50 and 500 kW ISOP-DAB converters where DC voltage ratio is 10 kV/400V.
Emotion-Based Interactive Art Design

This paper proposes a framework of emotion based interactive art design by analyzing the relation of emotion and color, graph, shape, material, and semantics. In this design, it will use six different sounds or music to be the stimulus source to arouse six basic emotions. Then it will generate the corresponding emotion image, which based on visual attention mechanism and cognitive psychology. The most salient part in emotion image will guide the interaction. It is an intelligent and natural interactive mode, which will be used in more fields in the future.
Convolutional Recurrent Neural Networks to Characterize the Circulation Component in the Thoracic Impedance during Out-of-Hospital Cardiac Arrest

Pulse detection during out-of-hospital cardiac arrest remains challenging for both novel and expert rescuers because current methods are inaccurate and time-consuming. There is still a need to develop automatic methods for pulse detection, where the most challenging scenario is the discrimination between pulsed rhythms (PR, pulse) and pulseless electrical activity (PEA, no pulse). Thoracic impedance (TI) acquired through defibrillation pads has been proven useful for detecting pulse as it shows small fluctuations with every heart beat. In this study we analyse the use of deep learning techniques to detect pulse using only the TI signal. The proposed neural network, composed by convolutional and recurrent layers, outperformed state of the art methods, and achieved a balanced accuracy of 90% for segments as short as 3 s.
Recycled polyolefin as dielectric substrate for patch antennas

High-performance antennas operating at ultra-high frequency (UHF) are typically designed and fabricated using specialized radio frequency (RF) materials with low loss and consistent properties. This paper proposes and investigates the use of recycled polyolefin (high-density polyethylene and polypropylene) which is not specifically manufactured for RF applications and thus with unknown RF properties, as dielectric substrate for high-performance UHF patch antennas. As a demonstrative application, a polarization-reconfigurable patch antenna with dual feeds is designed and fabricated using adhesive copper foil on recycled polyolefin substrate. Depending on the feed's magnitude and phase, different types of linear and circular polarizations can be achieved. The point of resonance for both ports is centered at 915 MHz with 10 dB return loss and bandwidth of 28 MHz. Port isolation is as low as −17 dB with peak linear and circular gain of 7.0 dBi, and 7 dBiC, respectively, observed in all polarizations. The resulting antenna not only performs well, but is also lightweight, low moisture absorbent, mechanically sturdy, inexpensive and ecologically friendly arising from its use of recycled materials.
Automatical Segmentation of Pelvic Organs After Hysterectomy by using Dilated Convolution U-Net++

Accurate delineation of the clinical target volume (CTV) and organs at risk (OARs) in pelvic plays a crucial role in the follow-up radiotherapy of the patient after hysterectomy. However, manual delineation is a time-consuming task which is susceptible to inter-observer variation. Automatic organ delineation is reliant on the global context contained in the extracted feature map to segment the original CT image. Currently, numerous deep learning networks, like U-Net++, are widely used in this task. In this paper, we use a standard U-Net++ as a fundamental structure followed by using dilated convolutions to replace its standard convolutions. We observed that dilated convolutions enlarged the size of the receptive field significantly, fusing more global contextual information and increasing the accuracy of segmenting organs. We evaluate the performance of standard U-Net++ and improved U-Net++(with double-deck dilated convolution) on the multiple organs segmentation in pelvic respectively. Our data set consist of CT images of 70 patients after hysterectomy. We use the dice similarity coefficient to quantify the segmentation accuracy. The result of our experiment demonstrates that the improved U-Net++ outperforms the standard U-Net++ on the segmentation of bladder, CTV and rectum. The dice scores show in follow: 93.2%±4.2% vs 91.2%±3.7% for bladder, 89%±3.6% vs 85%±2.6% for CTV and 87.6%±3.6% vs 84.7%±2.1% for rectum. It shows that the segmentation result of the improved U-Net ++ network is closer to the results manually drawn by the doctor than the standard U-Net++.
Multi-phase Induction Machine Speed Estimation Method Using Rotor Slot Harmonics

Multi-phase machines are becoming more popular due to their good performances and are progressively suppressing classical three-phase machines in some applications. Additionally, sensorless control of electrical machines has been thoroughly investigated in the last decades in order to lower the costs associated with the speed sensors used in electrical drives and increase the reliability of the drive. In the literature, rotor slot harmonics were intensively inspected to obtain information about rotor speed and position. In this paper, a speed estimation technique based on rotor slot harmonics in a multi-phase induction machine was proposed, based on the familiar approaches. It relies on the spectral analysis of the stator current. The most important benefit of the method introduced in the paper is lack of any type of the filter for extracting the specific rotor slot harmonics from the current spectrum. Simulation results that verify the effectiveness of the method proposed were obtained using MATLAB/Simulink.
A Portable Ultrasound Imaging System Utilizing Deep Generative Learning-Based Compressive Sensing On Pre-Beamformed RF Signals

Recent advances in the unsupervised and generative models of deep learning have shown promise for application in biomedical signal processing. In this work, we present a portable resource-constrained ultrasound (US) system trained using Variational Autoencoder (VAE) network which performs compressive-sensing on pre-beamformed RF signals. The encoder network compresses the RF data, which is further transmitted to the cloud. At the cloud, the decoder reconstructs back the ultrasound image, which can be used for inferencing. The compression is done with an undersampling ratio of 1/2, 1/3, 1/5 and 1/10 without significant loss of the resolution. We also compared the model by state-of-the-art compressive-sensing reconstruction algorithm and it shows significant improvement in terms of PSNR and MSE. The innovation in this approach resides in training with binary weights at the encoder, shows its feasibility for the hardware implementation at the edge. In the future, we plan to include our field-programmable gate array (FPGA) based design directly interfaced with sensors for real-time analysis of Ultrasound images during medical procedures.
FuzzAttention on Session-based Recommender System

mender system is studied widely and has been implemented successfully into businesses and daily lives. Its primary purpose is to analyze user behaviors to predict the next item of interest. To enrich user information, a session-based recommender system considers the user’s historical records in the session. This information includes the user’s general and current interests as additional information to improve the performance of the model such that the next item can be recommended easily. However, the recommender system not only considers the prediction performance but also regards the interpretability as a major target. For enhancing the interpretability and performance, we propose a novel mechanism, i.e., FuzzAttention, based on the attention mechanism that is applied widely to deep-learning models. In FuzzAttention, we utilize a fuzzy neural network to build a fuzzy inference system; therefore, we adopt joint learning to learn the parameters in the session-based recommendation and the fuzzy neural network jointly. In the experiments, we used two types of session-based recommender systems and conducted them on two datasets including the session-based information to compare the model performance based on the traditional attention mechanism and FuzzAttention. The results indicate that our proposed mechanism can improve the performance to predict the next item and the model’s interpretability.
A review of nanoscaled bulk double gate and triple gate FETs for low standby power application

The electrical characteristics of bulk double gate (DG) and triple gate (TG) FETs for low standby power application are reviewed using various peer reviewed literature. The study is focused on nanoscale devices up to channel length of 10nm. Junctionless (JL) and inversion mode (IM) structures together with other device improvement techniques have been explored from different research publications to optimize the device for low standby power application. The effect of device parameters e.g., channel length, fin dimensions, fin doping, gate dielectric and fin materials have been summarized using the findings reported till recent past. Further, the effects of variation in device parameters for the cases of inclusion and exclusion of Quantum Mechanical Effects are studied. Since the domain is wider than the reporting of a single literature, sometimes a few of the findings get scattered and appear to be uncorrelated in different literature. The implication of high k dielectric with quantum mechanical effects is one such important aspect. This work presents correlated study of such findings, and establishes their meaning with the overall functioning of the device. Altogether, the work establishes meaningful relation among various device characteristics of scaled bulk multi-gate devices reported in isolated literatures over the past decade.
Study of Variation of Q-Factor Using Minimum Power Under Different Weather Conditions

In recent scenario, Free Space Optical (FSO) communication technique plays pivotal role in high data rate cost effective communication system. But the major threat of such transmission technique is atmospheric weather conditions. In this paper, a study is performed on six different weather conditions keeping the Laser source power as minimum as possible to overcome the hindrance of atmospheric turbulence in plain area. The diameters of the transmitting and receiving apertures of the FSO link are varied to obtain superior Q-Factor and height of eye diagram for a particular wavelength of 1550nm using 0.1mW power for a fixed link distance of 500m. For establishing the novelty of this technique six different atmospheric conditions are considered. The simulation of the performance analysis is done using OPTISYSTEM 7.0.
A Review of Delay Tolerant Protocol for Data Aggregation in WBAN Application

Wireless Body Area Network is an essential part of IOT (internet of thing) which benefits in gathering data from the body surface area. The wireless body area network is the type of network in which sensor devices can sense the body conditions and pass to base station. It has number of applications for health monitoring. To maintain the quality of dissemination of the observed data, data aggregation techniques are immensely used. The foremost aim of data aggregation method is to assemble and cluster data packets in a well-organized and cos-effective way so as to minimize power consumption, to intensify network life time, delay, traffic congestion etc. In wireless body area network data aggregation with high delay tolerance is of utmost importance. In WBAN, delay tolerant network means in the network data storage node is formed which is used to the information transmission. This paper work is based on the study of different data aggregation methods and delay tolerant network to reduce delay in the network.
A Novel Causality Method for Reconstruction of Process Topology in Multivariable LTI Dynamical Systems

Reconstruction of process topology from cause-effect analysis of measurements finds applications in root-cause analysis, identification of disturbance propagation pathways, estimation of fault propagation times, and interaction assessment. A widely used technique based on the notion of Granger causality (GC), but is well-suited only for stationary stochastic processes. The GC-based measures and methods, while being useful in certain cases, can be highly restrictive and produce misleading results in engineering applications since changes in process variables are frequently deterministic. The lack of sufficient excitation and presence of measurement errors further restricts their applicability. In this respect, we present (i) a new definition of direct causality for deterministic linear time-invariant (LTI) dynamical systems, and (ii) a novel causality detection method based on delay estimation from noisy multivariable measurements. Efficient estimates of time delays are obtained from recently developed non-parametric frequency domain method based on partial coherence and Hilbert transform relations. In addition to the ability of handling deterministic variations, the proposed causality detection method is well-suited to handle low excitation signals and does not require the specification of any model structure. Case studies involving data from synthetic and benchmark processes are presented to illustrate the utility and efficacy of the proposed method.
Shape, Pose and Density Statistical Model for 3D Reconstruction of Articulated Structures from X-Ray Images

This article proposes a joint statistical model, to describe the volumetric shape + pose + density information, and a reconstruction algorithm to simultaneously recover the volumetric information of several anatomical structures from biplanar radiographs. A PCA-based representation is proposed as compact model representation and a hybrid AAM search and genetic optimization is used to perform the reconstruction. A study was conducted to recover a 3D volume grid containing a human knee mesh from 2 orthogonal simulated radiographs. The model was computed on a data set of 200 subjects and the reconstruction test was performed on 18 subjects, leading to a surface distance RMSE of 0.7 ± 0.31 mm for the distal femur, 0.9 ± 0.3 mm for the proximal tibia and 0.8 ± 0.3 mm for the fibula. These results demonstrate the feasibility and the pertinence of the proposed approach, the next step being its application in a clinical context.
Universal Functions Originator—Part II: Evaluation

In the first part of this study, a brief overview about the basic design of the universal functions originator (UFO) is introduced without conducting any numerical experiment to evaluate its performance and explore its capabilities. This part of the study is allocated to cover the practical side of the proposed new AI computing system (patent-pending). For this mission, a highly advanced graphical user interface (GUI) has been designed with multiple options to see how UFO can perform against different data size problems. The current version of the GUI is based on the one output stream structure presented in the first part of the study. Two “function approximation” problems are given in this paper. After being solved by UFO, they are also solved by regression analysis, support vector machines (SVMs), and artificial neural networks (ANNs); using different approaches and configurations. Based on the results obtained from these experiments, there is an obvious evidence that UFO is a very competitive computing system; and thus it could open new promising research areas.
Py4JFML: A Python wrapper for using the IEEE Std 1855-2016 through JFML

JFML is an open source Java library aimed at facilitating interoperability of fuzzy systems by implementing the IEEE Std 1855-2016 – the IEEE Standard for Fuzzy Markup Language (FML) that is sponsored by the IEEE Computational Intelligence Society. We developed a Python wrapper for JFML that enables to use all the functionalities of JFML through a Python 3.x module. The bridge between Python and Java is accomplished through the use of the Py4J framework. As a result, the possibility of using the IEEE standard for representing fuzzy systems is enlarged to a wider community of developers and knowledge engineers, with minimal code redundancy. Experiments show full interoperability between Python programs and JFML without any tangible overhead. We illustrate the use of Py4JFML in a beer style classification case study.
FPGA Implementation and Evaluation of an Approximate Hilbert Transform-Based Envelope Detector for Ultrasound Imaging Using the DSP Builder Development Tool

In this paper, we present the FPGA implementation of an approximate Hilbert Transform-based envelope detector to compute the magnitude of the received ultrasound echo signals in real-time using a Model-based design flow. The proposed architecture exploits the negative odd-symmetry and interleaved zero-valued coefficients of a Hilbert Transform-based FIR filter to reduce hardware resource requirements and complexity. The hardware design is modeled using the DSP Builder development tool allowing the automatic generation of HDL algorithms directly from the Matlab/Simulink environment. The generated VHDL code was synthesized for an Intel Stratix IV FPGA and validated on a Terasic DE4-230 board. The accuracy and performance of the envelope detector are analyzed with real ultrasound phantom data for different filter orders, coefficient length and two filter design methods: Equiripple and Least-Squares. The normalized residual sum of squares (NRSS) and the normalized root mean square error (NRMSE) cost functions are used for comparison with the reference absolute value of the Matlab Hilbert function. The results demonstrate that the proposed method yields similar results to conventional envelope detection methods, while being simpler to implement and requiring lower computational cost.
Blood Pressure Estimation Using Time Domain Features of Auscultatory Waveforms and GMM-HMM Classification Approach

This paper presents a novel method to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time domain features extracted from auscultatory waveforms (AWs) and using a Gaussian Mixture Models and Hidden Markov Model (GMM-HMM) classification approach. The three time domain features selected include the cuff pressure (CP), the energy of the Korotkoff pulses (KE), and the slope of the KE (SKE). The proposed GMM-HMM can effectively discover the latent structure in AW sequences and automatically learn such structures. The SBP and DBP points are then detected as the cuff pressures at which AW sequence changes its structure. We conclude that the proposed GMM-HMM estimation method is a very promising method improving the accuracy of automated non-invasive measurement of blood pressure.
Detection of Sleep and Wake States Based on the Combined Use of Actigraphy and Ballistocardiography

This paper presents a combined use of actigraphy and ballistocardiography to measure sleep stages without a disruptive sleep environment. Although polysomnography (PSG) is considered the gold standard for measuring sleep stages, the intrusive setup may lead to an unnatural sleep and impact the actual sleep quality. To address this issue a novel approach to measure sleep stages is presented by combining the acceleration measurements of actigraphy with the cardiological measurements of ballistocardiography. The combined measurements are compared with PSG for verification of the closest match possible with minimal interference for the sleeping individual. The experimental results of the study show that sleep/wake states can be classified accurately using the integrated approach.