Miracle-dz/UniFAR-FTUs huggingface dataset:
MEMS-Based RF Oscillators Using SiCer Technology: New Integration, Packaging, and Assembly Concepts

In state-of-the-art RF receiver systems, the number of microelectromechanical systems (MEMS)-based devices is rapidly rising [1]. MEMS devices and circuits have become important for tasks formerly performed solely by microelectronic components, such as switches [2], reference oscillators [3], and filters [4]. Furthermore, they deliver small form factors in the submicron range and are compatible with semiconductor processes [5], which is not the case for all types of mechanical components, such as quartz crystals [6]. Modern thin-film microsystem technologies push the usable frequency range of MEMS components into the gigahertz range and improve their electrical properties, resulting in low-noise multiphysical RF subsystems. This makes MEMS suitable even for mobile communication standards with strict requirements in terms of bit error rate and errorvector magnitude (EVM) [7], [8].
Research on Optimization of Train Energy-Saving Based on Improved Chicken Swarm Optimization

To obtain an operation curve of a train with minimum energy-consumption and improve the utilization of regenerative braking energy without changing equipment and infrastructure, this paper proposes a new optimization method of energy-saving for a train based on an improved chicken swarm optimization algorithm after analyzing the calculation method of energy-consumption for a train and improving the chicken swarm optimization algorithm. On this basis, this paper establishes an optimization model of energy-saving for a train by taking minimum energy-consumption, accurate stopping and punctuality as optimization objectives, and solves the model with the improved chicken swarm optimization algorithm. Finally, a simulation analysis for the method proposed in this paper is performed by using MATLAB software with the running parameters of Nanning metro line 1 in China. The results of the simulation analysis show that the method proposed in this paper is effective in solving the optimization problem of train energy-saving.
Liquid film thickness measurement for gas-liquid two phase flow using ultrasound

Liquid film thickness is one of the most important characteristic parameters for gas-liquid two phase flow, and the measurement technology and method of liquid film thickness are of great significance for studying heat and mass transfer characteristics. The common pulse echo method can’t measure thin thickness and thin film resonance method is affected by multiple echoes and noise. To overcome above defects, a new method called ultrasonic echo resonance main frequency (UERMF) method is proposed to measure liquid film thickness. The experiment is carried out in a 50 mm diameters horizontal tube. The range of superficial velocities covered in the test is 10-30m/s for air and 0.006-0.1m/s for water, and pressure is 0.1-0. 5MPa. Furthermore, a conductance probe method is used to verify the feasibility of this UERMF method under 0.1MPa, and good agreement is found between them and the error is less than ± 10%.
A Fault Diagnosis Model Based on Kernel Auto-encoder and Improved Chaos Firefly Optimization Algorithm

Automatically extracting features from large scale raw data for fault diagnosis is important in the current era of big data. In this paper, a deep neural network based on the kernel function and denoising auto-encoder is proposed. The kernel denoising auto-encoder (KDAE) neural network consists of one KDAE layer and multiple auto-encoder (AE) layers to automatically extract the fault features from raw data. Then, the softmax classifier is added as classifier layer. The improved chaos firefly algorithm is used to optimize the undetermined parameters of the kernel function and the network to obtain the diagnosis model. The proposed method is then verified by the typical failure test data of the aero-engine intermediate bearing, which has achieved higher classification accuracy than the traditional denoising auto-encoder network.
Qualification of additive manufactured trabecular structures using a multi-instrumental approach

Additive manufacturing (AM) is a production process able to create complex structures with trabecular morphology that are of great potential interest in various application areas, such as aerospace and biomedical. In addition to a significant mass reduction, structures with trabecular topology (micro-scale) can be optimized and functionalized depending on the specific properties that the final product (macro-scale) needs to exhibit. The actual structural response of this new class of materials is, however, little known. This paper presents some first results obtained within the framework of a research project of national interest (PRIN) started in 2017, which involves some leading Universities in Italy. The project aims to investigate additive manufactured trabecular structures by developing proper testing protocols based on different non-contact measurement techniques, using a multi-instrumental and multi-competence approach.
A formal analysis approach for verifying the design of respiratory pacing devices

Several conditions such as central sleep apnoea and chronic hypoventilation syndrome detrimentally affect respiratory drive. A decreased or absent respiratory drive results in inadequate ventilation and undesirable changes to blood gas levels that are detrimental to health. Respiratory pacing devices are used to regulate respiratory rhythm during times of reduced respiratory drive by delivering stimulation through electrodes placed on phrenic nerve motor points in the diaphragm muscle. These devices mostly operate in an openloop fashion, and provide rhythmic stimulation without sensing the intrinsic parameters that affect breathing. This paper develops a model driven approach for the design of closed-loop pacing devices for the treatment of illnesses that affect respiratory drive. Respiratory gas exchange and pacing systems are abstracted into high-level mathematical models that are validated through formal analysis with a focus on the overall timing behaviour. Using a network of timed automata we model normal and abnormal breathing through a lung model. This model is then composed in closed loop with a pacemaker model that paces the diaphragm when abnormal breathing is detected. We then verify and express a set of safety properties in Uppaal, which establishes that the designed pacemaker may fail to maintain the correct breathing rate for certain values of the timing parameters.
An Intelligent Compound Fault Diagnosis Method Using One-Dimensional Deep Convolutional Neural Network With Multi-Label Classifier

Intelligent compound fault diagnosis technology is significantly important to ensure that rotating machinery works in high-efficiency, security and reliability, and it remains a great challenge in this field. A lot of traditional intelligent fault diagnosis techniques have been developed with certain achievements in recent years, however, these methods inherently suffer from the obvious shortcoming that the traditional classifier only outputs one label for a testing sample of compound fault, rather than multiple labels. Consequently, it cannot classify a compound fault as two or more single faults. To solve this problem, a novel method named 1D DCNN-MLC, One-Dimensional Deep Convolutional Neural Network (1D DCNN) with a Multi-Label Classifier (MLC), is proposed for intelligent compound fault identification. 1D DCNN is employed to learn the representations from the vibration raw signals effectively. MLC is then designed to discriminate and predict the single or compound fault by outputting single or multiple labels. The proposed method is validated by a gearbox dataset with bearing fault, gear fault and compound fault. The experimental results demonstrate that the proposed method can effectively detect and identify compound fault. To the best knowledge of the authors, this work is the first effort to identify compound fault for rotating machinery via outputs multiple labels.
Verification for Electrical Tomography in Flame Monitoring by Ion Probe

Electrical tomography has been used to monitor the processes of flame ignition, fluctuation and extinction. To obtain more quantitative and key information of the flame from images of conductivity and permittivity distributions, a calibration method is necessary for studies on different combustors. In this paper, the ion probe sensor is proposed as a means of verification for electrical tomography in flame monitoring. From the impedance data of the ion probe, the local conductivity and permittivity can be calculated. Both an ion probe sensor and an electrical tomography system were experimented on an alcohol lamp. The ion probe experiment results are that the conductivity increased from $2\times 10^{-6}$ to $9\times 10^{-6} \rm{{S}/{m}}$, while the relative permittivity increased from 1 to 4 in the flame. The ranges of electrical tomography images are that conductivity from 0 to $6\times 10^{-6} \rm{{S}/{m}}$ and relative permittivity from 1 to 4.5, which are consistent with the data acquired from the ion probe.
Watermarking Technique Based on Harris-Laplace Feature Point Detector Capable of Resisting Geometric Attacks

In this paper, a strong watermarking technique is proposed that first selects the more stable feature points of an image by using the Harris–Laplace detector and then adjusts the included angles among these feature points, centers of mass, and the most salient feature point to embed the secret message. Triangles are formed among the feature points before adjustment through Delaunay triangulation, and subsequently, the adjusted feature points are further connected. Finally, each triangle is fine-tuned through affine transformation to embed the watermarking. According to the experimental results, the proposed method possesses strong resistance to both geometric attacks and general image processing attacks.
Travel Time Prediction-Based Routing Algorithms for Automated Highway Systems

This study investigates routing algorithms for automated highway systems (AHS). In AHS, the central system manages decisions regarding routing for all vehicles and the distribution of traffic volume. We define an automated highway routing problem, of which the objective is to minimize the average travel time of vehicles through the target highway network. We propose four routing approaches considering (1) distance, (2) current traffic conditions, (3) predicted travel time, and (4) probabilistic route selection with predicted travel time. In the third and fourth approaches, the predicted travel time is obtained from an empirical speed–density relationship. AnyLogic, an agent-based simulation software, is used to simulate the behavior of individual cars. Four approaches are tested on a sample highway network and we found that the routing approach considering the predicted travel time difference exhibits the best performance.
A Novel Boost Algorithm Exploiting Adjacent Sparsity Pattern

In this paper we exploit the adjacent property to boost the sparse pattern recovery in the framework of modulated wideband converter. A frequency banded grid is defined to quantify the multiband sparsity. Any active band occupies two consecutive sparse grid cells with high probability, which we call the adjacent property. Once an atom is greedily chosen, the next promising atom can be selected from the two adjacent cells instead of performing full column searching, which can reduce the computational complexity, asymptotically half of the commonly used simultaneous orthogonal matching pursuit. This strategy turns out to be optimal because greedy searching is exploited in the subset with reduced dimension. The proposed modification performs much better especially when number of channels is less than required. Numerical results validate the analysis.
Non-Destructive Characterization of Glass Laminated Electronics

By integrating electronics inside the laminated glass, the windows and structural glass elements can be transformed to be functional and interactive. Once the electronics is embedded into the glass in lamination process, the electronics is exposed to different type of stresses, having an influence on performance and reliability. In order to understand the consequences of lamination and to explain the reason for failures, non-destructive optical coherence tomography (OCT) based method was used in this study. In addition, thermo-mechanical simulations were done to find possible causes for observed failures in light emitting device (LED) chips and wirings. Combining the analyzed OCT data with simulations, was shown to be very effective tool to select right materials and optimize the lamination process for glass embedded electronics.
Classification of short unsegmented heart sound based on deep learning

Heart-related ailments are one of the primary causes of death worldwide. Hence the early investigation of a heart for such ailments is crucial. Recent approaches for automated analysis of the heart sounds require segmentation of Phonocardiograms (PCG) signal. However, segmentation of PCG adds up to the complexity and expanded computational difficulty in the algorithm. Thereby, the main aim of this paper is to eliminate the segmentation process and to measure the benefit for accurate and detailed classification of short unsegmented 5 second PCG recordings. A novel approach for the classification of heart sounds that had been provided by PhysioNet2016 challenge, based on the convolutional neural network using a pre-trained (AlexNet) model has been analyzed in this study. After pre-processing short 5 second PCG recordings accompanied by continuous wavelet transform (CWT) results to the generation of 2D scalogram images. The scalogram images have been used to train and test Convolutional neural network based on deep learning. The proposed design has obtained comparable performance compared to the state-of-the-art methods. Test results have demonstrated that the proposed technique presents excellent performance outcomes by reducing segmentation complexity.
PASNIC: a thermal based privacy-aware sensor node for image capturing

In this paper, we propose PASNIC, a lightweight privacy-aware sensor node for image capturing. PASNIC masks the region of faces and private screens (smartphone and portable computer displays) in captured visible images before sending them to remote sites. Thermal information, which can also be captured with a thermal image sensor, is used to detect the regions of faces and private screens. Our masking algorithms are so simple that even sensor nodes with low processing power can sufficiently process them.
Progress Report: Exploring API Design for Capabilities for Programming with Arrays

In on-going work, we are exploring reference capabilities for arrays, with the intention of carrying over previous results on statically guaranteed data-race freedom to parallel array algorithms. Reference capabilities typically restrict incoming pointers to an object to one (uniqueness), or restrict operations via multiple pointer to a single object (e.g., to only read). Extending such a design to arrays involve operations such as logically partitioning an array so that even though there are multiple pointers to a single array, these pointers cannot access the same elements. In this paper, we report on the on-going work of a prototype implementation of array capabilities, focusing in particular on the "array capability API design", meaning the native operations on capabilities such as splitting and merging arrays. Using our prototype implementation, we translate several existing array algorithms into using array capabilities and qualitatively study the result. In addition to identifying the need for additional operations, we study what features are commonly exercised, what are the recurring patterns, and how reliance on direct element addressing using indexes can be reduced. We end by discussing a possible design for a more performant implementation once the API is fixed.
DeepNavi

Indoor navigation plays a crucial role in indoor location-based services. Single signal-based navigation systems, however, are prone to sensor noises, signal ambiguities and are specific to trial sites. To address these, existing work fuses different signals with user trajectories. Despite their accuracy, many of them are specific to input signals and navigation modes (e.g., spot-based or sequence-based) and are computationally expensive in large sites. Additionally, they do not give predictive uncertainty estimations, leading to a lack of trust in navigation instructions. In this paper, we propose a unified framework for accurate indoor navigation in various modes with different inputs, termed DeepNavi. We exploit either convolutional or recurrent neural networks for initial feature extraction. Afterwards, we insert fully connected layers to generalize extracted signal-dependent features to a shared domain before fusion. Then, we leverage state-of-the-art ensemble learning to learn multiple predictive models. By combining them together, we further reduce the impact of signal noises and achieve high accuracy. Finally, we insert mixture density networks to model more generalized data distributions and provide uncertainty estimations. We have implemented DeepNavi and conducted extensive experiments in two different trial sites with different signal combinations. Experimental results show that DeepNavi reduces location errors by more than 20% with comparable orientation accuracy.
Beyond Respiration

Sleep sound-activities including snore, cough and somniloquy are closely related to sleep quality, sleep disorder and even illnesses. To obtain the information of these activities, current solutions either require the user to wear various sensors/devices, or use the camera/microphone to record the image/sound data. However, many people are reluctant to wear sensors/devices during sleep. The video-based and audio-based approaches raise privacy concerns. In this work, we propose a novel system TagSleep to address the issues mentioned above. For the first time, we propose the concept of two-layer sensing. We employ the respiration sensing information as the basic first-layer information, which is applied to further obtain rich second-layer sensing information including snore, cough and somniloquy. Specifically, without attaching any device to the human body, by just deploying low-cost and flexible RFID tags near to the user, we can accurately obtain the respiration information. What's more interesting, the user's cough, snore and somniloquy all affect his/her respiration, so the fine-grained respiration changes can be used to infer these sleep sound-activities without recording the sound data. We design and implement our system with just three RFID tags and one RFID reader. We evaluate the performance of TagSleep with 30 users (13 males and 17 females) for a period of 2 months. TagSleep is able to achieve higher than 96.58% sensing accuracy in recognizing snore, cough and somniloquy under various sleep postures. TagSleep also boosts the sleep posture recognition accuracy to 98.94%.
Transportation recognition with the Sussex-Huawei Locomotion challenge

Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge provides a large dataset including train, validation and test data. Our team name is ICT-BUPT League. Moreover, the main work is to recognition eight transportation modes. Our method combines the CNN and LSTM. In this system, CNNs allow us to learn suitable features representation for recognition that are robust against transportation mode recognition. We make use of LSTM units on the CNN output, which play the role of a structured dimensionality reduction on the feature vector leading to drastic improvements in transportation mode recognition performance. As a result, the CNN+LSTM transportation mode recognition system could predict eight classes with a success rate of 76%.
Deep Probabilistic Models for Camera Geo-Calibration

OF DISSERTATION Deep Probabilistic Models for Camera Geo-Calibration The ultimate goal of image understanding is to transfer visual images into numerical or symbolic descriptions of the scene that are helpful for decision making. Knowing when, where, and in which direction a picture was taken, the task of geo-calibration makes it possible to use imagery to understand the world and how it changes in time. Current models for geo-calibration are mostly deterministic, which in many cases fails to model the inherent uncertainties when the image content is ambiguous. Furthermore, without a proper modeling of the uncertainty, subsequent processing can yield overly confident predictions. To address these limitations, we propose a probabilistic model for camera geo-calibration using deep neural networks. While our primary contribution is geo-calibration, we also show that learning to geo-calibrate a camera allows us to implicitly learn to understand the content of the scene.
Designing Drones

The last few years have seen a revolution in aerial robotics where personal drones are becoming pervasive to our environments and can be bought by anyone anywhere, including at local supermarkets. As they become ubiquitous to our lives, it is crucial to understand how they are perceived and understood by people. The robotics community has extensively theorized and quantified how robotic agents are perceived as social creatures and how this affects users and passersby. However, drones present different form factors that are yet to be systematically explored. This work aims to fill this gap by understanding people's perceptions of drones and how drones physical features correlate to a series of dimensions. We explored the quadcopters available on the 2018 market and built a dataset of 63 images that were evaluated in a user study (N=307). Using the study results, we present a model of how people understand drones based on their design and which physical features are better suited for people wanting to interact with drones. Our findings highlight that safety features have a negative effect on several dimensions including trust. Our work contributes a set of design guidelines for future personal drones and concludes on the implications for ubiquitous computing.
A deep autoencoder model for pollution map recovery with mobile sensing networks

Air pollution is a global health threat. Nowadays, with the increasing amount of air pollution monitoring data from either conventional official stations or mobile sensing systems, the role of deep learning methods in pollution map recovery becomes gradually apparent. To address the challenges including the irregular sampling from mobile sensing and the non-interpretability of deep learning models, we proposed a deep autoencoder framework based inference algorithm. By separating the process of pollution generation and data sampling, this framework is able to deal with sampling under any irregular intervals in time and space. Also, we adopt a convolutional long short-term memory (ConvLSTM) structure to model the pollution generation after revealing its internal connections with an atmospheric dispersion model. Our algorithm is evaluated over a three-month real-world data collection in Tianjin, China. Results show our method can obtain up to 2x performance improvement over existing methods, benefited from its high robustness against different background pollution level and accidental sensor errors.