Miracle-dz/UniFAR-FTUs huggingface dataset:
A Time-frequency Signal-based Convolutional Neural Network Algorithm for Fault Diagnosis of Gasoline Engine Fuel Control System

This study aims to apply the convolutional neural network algorithm to diagnose the fault of the gasoline engine fuel control system. First, run the system in the Simulink environment and get its operational data, including Fuel, Fuel/air ratio. However, in order to improve the robustness of the proposed method, additive white Gaussian noise is added to the signal. Then the short-time Fourier transform is used to obtain the characteristics of time, frequency and amplitude, and become the data source of neural network modeling. The experimental results show that the 16-layer convolutional neural network architecture can completely diagnose the operating state of the system, including normal and four types of faults. At the same time, it has the advantage of computational efficiency. In the future research work, compound faults, variable speed conditions and algorithm optimization are the key points. It is expected that the operation and maintenance of the plant can be more intelligent, so as to reduce the probability of machine abnormalities and accidents, so that life and property can be better protected.
No Entry: Anti-Noise Energy Detector for Chirp-Based Acoustic Communication

Acoustic communication using microphones and speakers of smart devices is one of the most spotlighted wireless technologies in recent years. In particular, chirp-based acoustic communication is widely adopted for smart device applications because of its robustness to frequency selectivity. Since many chirp-based acoustic applications run in the background on mobile devices, the power consumption of chirp-based acoustic communication is a critically important issue. Using energy detectors (EDs), which determine the existence of a valid acoustic signal based on energy level, applications can reduce power consumption by working only when a valid signal exists. However, conventional ED fails to distinguish between valid signals and high-energy noise in everyday life. In this paper, we propose No Entry, a novel ED for chirp-based acoustic communication systems. No Entry avoids not only high-energy noises but also a different modulation-based acoustic signal by utilizing the frequency sweeping characteristic of chirp signals. We implement prototype Android applications to evaluate the detection accuracy and power consumption. Compared with the state-of-the-art schemes, No Entry reduces energy consumption by 30% while achieving a greater detection performance.
Multi-Label Remote Sensing Scene Classification Using Multi-Bag Integration

For remote sensing (RS) scene classification, most of the existing techniques annotate a scene image with merely a single semantic label. However, with the recent advance of remote sensing technology, more abundant information is contained in high-resolution scenes, making a scene image having multiple semantic meanings (i.e., multilabels). Since multi-label RS scene image annotation is a domain full of challenges due to the ambiguities between complicated scene contents and labels, it motivates us to present a novel algorithm which is based on multi-bag integration. First, to describe the semantic content of RS scene image, we propose to partition a scene image into image patches, defined by a regular grid, and extract the heterogeneous features within each. Second, two kinds of image instance bag, namely segmented instance bag (SIB) and layered instance bag (LIB), are designed to represent the scene image. Third, a Mahalanobis distance-based K-Medoids approach is applied to cluster SIB and LIB, respectively, to convert the multi-instance into single-instance, and then the obtained two single-instances are concatenated to generate more powerful scene-aware representation. At last, a multi-class classification technique is used to make predictions on the class labels. Experiments are performed on real remote sensing images and the results show that the proposed method is valid and can achieve superior performance to a number of state-of-the-art approaches.
Discrimination of the maturity stages of Indian mango using QCM based electronic nose

Mango possesses an elegant smell and attracts consumers due to its high nutritive and medicinal benefits. A proper monitoring of maturity stages of mango is required to increase the shelf life and to boost consumer acceptability. This paper aims to discriminate the maturity stages of three cultivars of mango based on the aroma using quartz crystal microbalance (QCM) based E-nose, which can discriminate the ripening stages.
ESD Analysis and Evaluation of Typical Spacecraft Survival Suit Umbilical Tubing

This document presents an assessment of the potential for occurrence of triboelectrification effects resulting from gas and fluid flow through umbilical tubing attached to a typical spacecraft survival suit. The assessment demonstrates that gas and fluid flow through the umbilicals poses no threat to crew safety over expected operational time periods up to 150 hours.
Bidirectional Undersea Capacitive Wireless Power Transfer System

This paper proposes a bidirectional undersea capacitive wireless power transfer system (BD-UCWPT) for the autonomous underwater vehicles (AUVs). The two H-Bridge circuits are adapted in the proposed BD-UCWPT system to achieve the bidirectional power flow. The H-Bridge circuit is built based on the GaN devices. As a result, the high switching frequency, and low power loss could be reached. The two LCLC compensation networks are respectively used in the transmitter side and receiver side. The simulation work is conducted with the Ansys Maxwell, PSIM and MATLAB. In the experiments, two seawater bags are added in the middle of two metal plates of $\text{C}_{\mathrm {S1}}$ and $\text{C}_{\mathrm {S2}}$ to model the seawater condition, respectively. In order to verify the theory analysis, a 100W experimental BD-UCWPT system is constructed. The experiments have been conducted in air and in seawater, respectively. The simulated and experimental results are compared with each other. It shows that, with this BD-UCWPT system, the power of AUVs could flow bidirectionally. The stable operation and high efficiency of BD-UCWPT system is verified. The research work in this paper could facilitate the application of wireless power transfer (WPT) system for AUVs and other undersea devices.
Assessing Software Quality in High Energy and Nuclear Physics: the Geant4 and ROOT Case Studies and Beyond

Quality of software is the key to successful software development and physics results transparency. An original method to assess the quality of scientific software systems objectively has been developed. A set of established software metrics is combined with statistical analysis methods, such as trend analysis and inequality and diversity measures, to quantify various characteristics of the code, such as maintainability. This methodology, previously applied to Geant4, has now been employed to assess the quality of ROOT, a software system for big data processing, statistical analysis, visualisation and storage frequently used in particle and nuclear physics experiments. Preliminary results of the analysis of ROOT software quality are presented.
Sensitivity Estimation and Image Reconstruction for Sparse PET with Deep Learning

The use of a sparse crystal setting would reduce the cost of the PET scanner and has advantages such as less RF shielding in PET/MR. It also allows a longer axial field of view (FOV) using the same crystal volume. In this paper, the sensitivities of the coincidence events of PET systems with the sparse crystal configuration, thin crystal setting, and the conventional design using a fixed total crystal volume were analytically estimated. The sinograms of a sparse system (with 50% crystal removed and fixed axial FOV) were simulated using patient data. Reconstruction algorithms were developed by modeling the effects of reduced crystals in the system matrix. A convolutional neural network (CNN) based noise reduction approach was used for post-processing. A total of 14 patient data were included and were truncated to 3 minutes scan for consistency. Leave-one-out cross-validation was used for evaluation purpose. A patch based data input/output was used for model training to increase the number of training samples. Images reconstructed using OSEM followed by Gaussian denoising was also used as a comparison. The percentage summed square difference (SSD) between images of sparse crystal configuration and non-sparse systems were used for quantitative evaluation. When using the same total volume of crystals, the difference of sensitivity at the center of FOV was within 10% among three different settings, with the rank from highest to lowest being the thin detector, sparse detector, and conventional detector. When using the same axial FOV, reconstructed images of the sparse crystal configuration showed increased noise due to reduced sensitivity. The percentage SSD for image processed with the Gaussian filter was 30% on average and was reduced to 16% with CNN on average. The results show with the same amount of crystal, the use of sparse crystal configuration provides a slightly larger sensitivity and much larger axial FOV. CNN processed images was able to partially recover lost image quality due to the removal of certain crystals.
Autonomous Passive Localization Algorithm for Shared Bikes

Opportune and accurate position information of the ubiquitous shared bikes can help users rapidly find the available bicycle around them. In order to remove the professional locating device of bicycles and reduce industrial costs, a passive positioning method for shared bikes named PSA is proposed in this paper with the support of activity recognition based on deep learning. First, an action recognition module based on LSTM algorithm is constructed through training three-axis accelerator data collected by commercial mobile phone to distinguish user’s riding state from other actions like walking or running; then, the GPS module embedded in the user’s mobile phone will record the location when the state changes from riding to non-riding, which is regarded as the bike’s return location. Furthermore, considering the variety of state changing during riding procedure, a duration threshold mechanism is added for the sake of fault tolerance processing. Several simulations and test-bed experiments were conducted to verify the feasibility and practicality of the proposed method, where the accuracy of activity recognition in practical data set can reach more than 95% and the detection time in public data set is less than 0.2s. In addition, this method reduces phone operations and enhances the convenience of the experience.
Intercomparison of MR-Informed Methods for PET Image Reconstruction

Numerous PET reconstruction methods incorporating MR information have been proposed in the literature that seek to utilise the shared PET-MR boundaries to supress noise and reduce the partial volume effect. The inclusion of prior MR information into the PET reconstruction algorithm can be achieved using either an MR weighted penalty e.g. maximum a posteriori expectation maximisation (MAPEM) [1] , or MR derived spatial basis functions to reparameterise the emission image e.g. kernel expectation maximisation (KEM) [2] . MR information can be included into MAPEM with a quadratic penalty function through MR dependent weighting factors. Prominent choices of these weighting factors include Bowsher [3] and Gaussian weights. The kernel method can also be used to determine the weighting factors for the MR-guided MAPEM quadratic penalty. The limitation of all of these aforementioned methods lies in their susceptibility to suppress PET unique features. Therefore, many have been extended to incorporate PET information into the weighting factors or basis functions, with current state of the art methods including methods which use an anato-functional quadratic prior [4] , and methods like hybrid KEM [5] .
Improving Human Pose Estimation With Self-Attention Generative Adversarial Networks

Human pose estimation in images is challenging and important for many computer vision applications. Large improvements in human pose estimation have been achieved with the development of convolutional neural networks. Even though, when encountered some difficult cases even the state-of-the-art models may fail to predict all the body joints correctly. Some recent works try to refine the pose estimator. GAN (Generative Adversarial Networks) has been proved to be efficient to improve human pose estimation. However, GAN can only learn local body joints structural constrains. In this paper, we propose to apply Self-Attention GAN to further improve the performance of human pose estimation. With attention mechanism in the framework of GAN, we can learn long-range body joints dependencies, therefore enforce the entire body joints structural constrains to make all the body joints to be consistent. Our method outperforms other state-of-the-art methods on two standard benchmark datasets MPII and LSP for human pose estimation. Our code is available at: https://github.com/idotc/Hg-SAGAN.
Estimation of individualized counting efficiency by means of numerical simulation on the assessment of iodine-131 activity in thyroid

It is necessary to calibrate the thyroid monitoring system to properly assess internal contamination in thyroid. The conventional calibration performed by the neck physical phantom is widely used for calibrating monitoring systems, but the neck physical phantom cannot reflect the individual characteristic such as the size of thyroid, the position of thyroid, and overlay tissue thickness. The physical variations between the neck physical phantom and the human body anatomy can influence on the measurement of radioiodine in thyroid, especially the counting efficiency. Therefore, this study describes the estimation of counting efficiency considering individual characteristics of subject in thyroid measurement for iodine-131 by using the Monte Carlo technique. In this study, the NaI(Tl)-based and high purity germanium-based thyroid monitoring systems in operation at the Korea Institute of Radiological and Medical Sciences were used and embodied with Monte Carlo code. Based on the computational modelling of monitoring systems, simulations were performed using the ICRP adult reference computational phantoms and the UF/NCI pediatric computational phantoms (1, 5, 10, and 15 years) to consider the anatomical variations, including the age and gender of subjects in thyroid measurement. The overlay tissue thickness of subject could be estimated from the counting ratio of two energy peaks at 80.2 and 364 keV of iodine-131 due to the attenuation of gammas. Thus, the overlay tissue thicknesses of each phantom were modified to evaluate the relationships between the above counting ratio and counting efficiency. From these relationships, the equations for estimating the individualized counting efficiencies for each age and gender group were derived. Through these equations, it is possible to estimate the individualized counting efficiencies with considering the age, gender, and overlay tissue thickness of subject in thyroid measurement for iodine-131.
Robust Observer Design and Fault Reconstruction for Wind Energy Conversion System: SOS Approach

This paper proposes a new approach for designing a robust polynomial observer to estimate the unmeasurable states and reconstruct the faults of the Wind Energy Conversion Systems (WECSs). The WECSs in this study are affected by the uncertainties and faults. In this paper, firstly, the WECSs are modeled under the framework of the polynomial systems instead of linearizing at operating points to reduce the modeling errors. Secondly, the WECSs are reformulated in a new form then a polynomial observer is synthesized for this system to eliminate the influences of uncertainties and faults, estimate the unknown states, and reconstruct the faults. On the basis of Lyapunov theory and Sum-Of-Square approach, the conditions for observer design are derived in the main theorems. Finally, the simulation results are provided to prove the effectiveness of the proposed method.
Modeling Thermoelectric Performance in Nanoporous Nanocrystalline Silicon

Introducing hierarchical disorder from multiple defects into materials through nanostructuring is one of the most promising directions to achieve extremely low thermal conductivities and thus improve thermoelectric performance. The success of nanostructuring relies on charge carriers having shorter mean-free-paths than phonons so that the latter can be selectively scattered. Nevertheless, introducing disorder into a material often comes at the expense of scattering charge carriers as well as phonons. In order to determine the tradeoff between the degradation of the lattice thermal conductivity and of the power factor due to this, we perform a theoretical investigation of both phonon and electron transport in nanocrystalline, nanoporous Si geometries. We use molecular dynamics for phonon transport calculations and the non-equilibrium Green's function method for electronic transport. We report on the engineering tradeoff that the porosity (number of pores and their in-between distance) has on the overall thermoelectric performance for the material optimization. We indeed find that the reduction in thermal conductivity is stronger compared to the reduction in the power factor, for the low porosities considered in this study (up to 5%), and that the ZT figure of merit can experience a large increase, especially when grain boundaries are included, compared to just nanoporosity.
Multiplexing Kernelized Expectation Maximization Reconstruction for PET-MR

Since the kernel method was first proposed in positron emission tomography (PET), it has been successfully used for different applications. PET-MR scanners allow the acquisition of multiple MR sequences, in parallel to PET, for the same subject, to improve PET image reconstruction and lesion detectability. In this work we propose and investigate a version of the kernel method which exploits the information contained in multiple MR images and potentially CT or PET images. To do so, we modify the non-hybrid and hybrid kernel methods (KEM and HKEM respectively) such that the kernel matrix is obtained using additional Gaussian terms carrying the features from multiple MR images. We call these new methods multiplexing-HKEM (MHKEM) when the PET iterative information is included and "multiplexing-KEM" (MKEM) when only static images are included. We hypothesized a case where two MR images are beneficial using a simulated torso dataset. The technique was implemented in the open source Software for Tomographic Image Reconstruction (STIR) library. The proposed algorithm uses images from two different MR sequences, called MR1 and MR2, and PET update images to create multiple feature vectors for each voxel in the image which contains the information about the local neighborhood. The simulated dataset is reconstructed with 10 iterations and 23 subsets using HKEM, KEM, MHKEM and MKEM in three possible cases:1)HKEM, KEM with MR1;2)HKEM, KEM with MR2;3)MHKEM, MKEM with MR1 and MR2.The results show that HKEM is more reliable than KEM due to its ability to exploit functional and anatomical information together. Although the standard HKEM can work well when we focus on a specific region, it can over-smooth other regions which might also be of interest. The MHKEM can provide good quantification, resolution and noise suppression for different areas where the disease might appear compared to the standard HKEM and KEM where the improvement is restricted to a local area. Whenever the two MR images contain information that complement each other, the MHKEM improved results for the two areas of interest. This techniques can potentially become very useful in oncology, as the detection of primary and metastatic lesions of different type and soft-tissue environment in PET images might benefit from the utilization of multiple MR sequences.
A New Floating and Tunable Capacitance Multiplier With Large Multiplication Factor

This paper presents a CMOS floating and tunable capacitance multiplier with a very large multiplication factor. The proposed design uses CCII and OTAs designed using MOSFETs biased in subthreshold region to provide low power consumption and high multiplication factor. TANNER TSPICE simulation tool is used to confirm the functionality of the design in <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> TSMC CMOS technology. The circuit is powered using ±0.75V DC supply voltage. Simulation results indicate that the maximum multiplication factor is 3600 and the maximum error is 8.6%.
Voxel-based partial volume correction of amyloid PET images incorporating non-local means regularization

Amyloid PET imaging is increasingly utilized to assess Alzheimer’s disease. Nonetheless, PET imaging can be significantly degraded by the partial volume effect (PVE). This issue has been tackled via a number of post-reconstruction partial volume correction (PVC) methods. In our work, we proposed a voxel-based PVC method using non-local means (NLM) regularization under the weighted least squares framework that models the point-spread function of the PET system. The NLM algorithm has been proposed to suppress image noise while preserving edge information for natural images. This algorithm utilizes the high degree of information redundancy that typically exists in images and reduces image noise by replacing each pixel intensity with a weighted average of its non-local neighbors. Based on its advanced property, we propose to employ NLM as a regularization term in PET PVC. For a penalized weighted least squares (PWLS) objective function, we used the Gauss-Seidel (GS) optimization algorithm regularized with one-step-late (OSL) framework. Under the assumption of independent, identically-distributed (iid) Gaussian noise, the PWLS framework becomes standard least squares. When the steepest descent scheme is applied to the problem, it leads to the iterative ‘reblurred’ Van Citter (VC) method. We tried both the VC method, and GS which involves a more sophisticated step-size method. In any case, the iid assumption is especially violated in OSEM reconstruction where the variance image is roughly proportional to the image (thus not uniform as in FBP). In the present work, we assessed the impact of appropriate variance weighting, as well as added NLM regularization. Our results demonstrate that statistical weighting improved quantitative bias vs. noise performance; and also, NLM regularization method exhibits improved performance. These were especially the case in the small regions relevant in Alzheimer’s disease research.
A bionic in vitro bioelectronic tongue based on cardiomyocytes for high-specificity detection of bitter and umami compounds

Gustation, one of the most important senses, is very important for feeding and survival of mammals. Umami and 7 kinds of bitterness receptors are specifically expressed in rat cardiomyocytes. Combined with MEA sensor chip, a novel bioelectronic tongue (BioET) based on cardiomyocytes is designed and constructed for the detection of bitter and umami compounds in this paper. The results show that this BioET has specific response to bitter and umami among the five taste compounds. In the detection of different bitter compounds, the sensor only has specific response to the bitter compounds that can activate the bitter receptors expressed by cardiomyocytes. The specific detection of two bitter and one umami compounds are realized.