Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity Optimization for Electromagnetic Measurement Systems by Sensor Selection

We attempt to maximize the sensitivity of a sensor system, where the system sensitivity is expressed by a goal function formulated in terms of the Fisher information matrix. Given a large set of sensor candidates, we formulate the problem as a sensor selection problem by means of introducing a weight for each sensor candidate. Such a weight corresponds to the fraction of measurements that is performed by a specific sensor and it is allowed to vary continuously, which yields a convex optimization problem.
Characteristics of UGC galaxies detected by IRAS

Infrared Astronomy Satellite (IRAS) detection rates at 60 microns were determined for the Uppsala General Catalog of Galaxies (Nilson 1973; the UCG). Late-type spirals, characterized by a normal IR/B ratio of approximately 0.6, are detected to a velocity of approximately 6000 km/s for L sub B = L sub *. Contrary to the situation for IRAS-selected galaxy samples, little evidence was found for a correlation between IR/B and 60/100 microns in this large optically-selected sample. Thus a significant fraction of the IRAS-measured far-infrared flux from normal spirals must originate in the diffuse interstellar medium, heated by the interstellar radiation field. Support was not found for Burstein and Lebofsky's (1986) conclusion that spiral disks are optically thick in the far-infrared.
Highly sensitive measurement of surface charge distribution using the Pockels effect and an image lock-in amplifier

A visualization and a quantification technique for a latent electric charge image on a dielectric material has been developed by using the Pockels effect and a computer imaging technique. An optical phase modulation technique and a computer image lock-in amplifier technique were used to improve the signal-to-noise ratio. The measurement system has several improved performances; sensitivity of surface change of 1 nC cm-2, spatial resolution of 200 mu m and discrimination of charge polarity. The basic principle of advanced electro-optic surface charge measurement is described and the typical surface charge distribution of positive discharge streamer produced by a high impulse voltage (5 kVpeak) is demonstrated. Also, comparison is made between the modulation method and the non-modulation method.
The Cavity Method in Coding Theory

Keywords: spin glass ; cavity method ; dynamical and condensation temperatures ; Bethe free energy ; polymer expansion ; loop calculus ; lossy source coding ; low-density generatormatrix codes ; low-density parity-check codes ; spatial coupling ; threshold saturation ; rate-distortion bound ; belief propagation ; decimation These Ecole polytechnique federale de Lausanne EPFL, n° 6088 (2014)Programme doctoral Informatique, Communications et InformationFaculte informatique et communicationsInstitut de systemes de communicationLaboratoire de theorie des communicationsJury: Prof. E. Telatar (president); Prof. R. Urbanke, Dr N. Macris (directeurs); Dr M. Chertkov, Prof. H. -A. Loeliger, Dr P. Vontobel (rapporteurs) Public defense: 2014-2-21 Reference doi:10.5075/epfl-thesis-6088Print copy in library catalog Record created on 2014-02-18, modified on 2017-05-12
Detector coverage under the r-contiguous bits matching rule

Some computer anomaly detection systems inspired by the immune system rely on the r-contiguous bits matching rule, whereby two strings of length l are said to match if they have at least r contiguous bits in common. In this paper, we derive a recurrence relation and its closed form solution for computing the number of strings matched by a single such string called a detector.
Recognition of paper samples by correlation of their speckle patterns

Buchanan et al. [Nature 436, p. 475 (2005)] have shown that it is possible to recognize paper samples via their speckle pattern by using a line-shaped laser focus, four photo detectors and a scanning mechanism. In this report recognition of five out of ten paper samples is presented. The sample was illuminated by a 2.62 +- 0.02 mm circular spot and the reflected light was measured by a static CCD. We have formulated a criterion for recognition that limits the probability of false recognition to 0.1 % for the experiment with ten samples. We obtained results that show that the probability of false recognition will be negligible for a large amount of samples. The properties of speckle originating from a line and a spot illumination source have been compared to see whether the use of a line results in major advantages for the recognition of samples.
Visual relation detection based on pyramidal convolution and gated recurrent unit

Visual relationship detection, not only needs to identify the targets in the image and their position, but also to identify the interrelationship between the targets, is a comprehensive task including object detection, positioning, image classification. In this paper, pyramid convolutional networks are embedded in the image feature extraction module, and three convolutional kernels of different scales and depths are used to increase the sensing field of the network and ensure multi-scale feature extraction. At the same time, a recurrent neural network is introduced, which combines the background information in the image to improve the accuracy of semantic relationship detection.
Classification Model of 3D Point Cloud Based on Linked Adaptive Graph Convolution

A three-dimensional point cloud classification model LAGCN based on adaptive graph convolution is proposed to solve the problem that PointNet only extracts feature information from isolated points but not from neighborhood structure. Firstly, the three-dimensional point cloud is transformed into undirected graph structure, which is used to obtain the neighborhood information of point cloud adaptively, and the classification accuracy is improved by incorporating residual structure. In the classification experiment, this paper trains and tests on ModelNet40 dataset, and compares the classification accuracy with VoxNet, PointNet and DGCNN models. The classification accuracy of this model is better than that of the above models.
Functional Groups of Phytoplankton and Their Relationship with Environmental Factors in Lake Maninjau, West Sumatra, Indonesia

Lake Maninjau is a eutrophic lake indicated by the frequent occurrence of cyanobacterial blooms. The phytoplankton functional groups (FGs) reflect the environmental characteristics and can be useful to monitor the trophic state and evaluate lake’s environmental conditions. This study aims to perceive the composition of phytoplankton FGs and their relation to the environmental factors in Lake Maninjau. The data including phytoplankton and environmental factors were collected in 2001, 2005, 2009, 2014, 2015, 2016, and 2018. There were 21 FGs of phytoplankton recorded in Lake Maninjau. FGs of D, J, F, and NA were commonly found in this Lake. The phytoplankton FGs reflected the dynamic of the environmental characteristics of the lake. The FGs NA representative of oligo to mesotrophic habitat type were predominated accounting for 66.3% to 67.69 % in May 2005 with the lake’s conditions characterized by low TP concentration, high Secchi depth and TN: TP ratio. The FGs of M representative of eutrophic to hypereutrophic habitat type were predominated accounting for 74.14 % to 94.96 % in April 2018 with the lake’s conditions characterized by high TP concentration, low TN: TP ratio, and Secchi depth. A canonical correspondence analysis (CCA) showed that temperature, conductivity, and total phosphorus are important environmental factors to have influenced the succession of phytoplankton FGs in Lake Maninjau.
Building extraction algorithm from remote sensing images based on improved DeepLabv3+ network

With the development of deep learning, quickly extracting high-precision building information from remote sensing images has become the research focus of intelligent application and processing of remote sensing data. Aiming at the problems of slow extraction speed and incomplete edge segmentation in building extraction in remote sensing images, a building extraction algorithm of remote sensing images based on an improved deeplabv3+ network is proposed. The more lightweight network MobileNetv3 is used to replace the original deeplabv3+ semantic segmentation model feature extraction backbone network Xception, and the standard convolution in the hole space pyramid pooling module is replaced with deep separable convolution, which reduces the amount of calculation and improves the training speed.DAMM (Dual Attention Mechanism Module) is connected in parallel with ASPP (Atous Spatial Pyramid Pooling) to improve the segmentation accuracy of edge targets. The model is verified on WHU and Massachusetts data sets. The results show that the number of training parameters and training time of the model are reduced, and the accuracy of the building extraction is effectively improved, which can meet the requirements of rapid extraction of high-precision buildings.
Green Sahara tipping points in transient climate model simulations of the Holocene

The ‘Greening’ and subsequent desertification of the Sahara during the early to mid-Holocene is a dramatic example of natural climate change. We analyse a suite of simulations with a newly palaeo-conditioned configuration of the HadCM3 coupled model that is able to capture an abrupt desertification of North Africa during this time. We find that this model crosses a threshold of moisture availability for vegetation at around 6000 years before present. The resultant rapid reduction in vegetation cover acts to reduce precipitation through moisture recycling and surface albedo feedbacks. Precursor drying events which are not directly forced also indicate that the model is close to a critical moisture level. Similar precursor-like events appear in a Holocene record from the East of the continent, hinting that the natural system may resemble some of the properties of this model simulation. The overall response is not fundamentally altered by the inclusion of solar irradiance variations or volcanic eruptions. The simulated timing of the abrupt transition is mostly controlled by orbital forcing and local positive feedbacks, but it is also modulated to some extent by the state of the atmosphere and ocean. Comparisons with proxy records across North Africa show good agreement with the model simulations, although the simulations remain overly dry in the East. Overall, a threshold response may present a useful model of the real transition, but more high-resolution palaeoclimate records would help to discriminate among the predictions of climate models.
A Transformer Network Architecture for Dermoscopy Image Segmentation

Aiming at the problems of irregular shape and blurred boundary of skin lesions in skin lesions images, this paper proposes a skin lesion segmentation algorithm combining CNN and Transformer. Firstly, Resnet is used as the backbone feature extraction network to extract features, and the extracted feature map sequence is used as the input of Transformer. A new structural boundary attention gate is added to Transformer to extract enough local details to deal with fuzzy boundaries. Finally, DenseASPP is used to enhance features Represents and processes multi-scale information, and proposes an improved loss function, the purpose of which is to make the model pay attention to the boundary region when calculating the loss function. The experimental results show that the dice value and JI value of the network on the ISIC2017 dataset are 0.854534 and 0.767901, respectively, and the dice value and JI value on the ISIC2018 dataset are 0.908548 and 0.843689, respectively, which achieves good results compared to other advanced models. Its effectiveness is proved by comparing with different models and showing the effect.
A Novel Multi-Task Face Super-Resolution Framework Embedding Degraded Augmented GAN Networks

The training method that uses predefined degradation methods to provide training data for deep convolutional neural networks suffers from overfitting and image distortion when performing face super-resolution reconstruction. In this paper, we propose a multi-task facial super-resolution reconstruction framework embedded in a degraded augmented GAN network. Compared with the commonly used method of generating low-quality face images through pixel interpolation, this paper inversely embeds a generative adversarial network composed of residual coding blocks in the network. Through the feature learning ability of the network, the feature fitting of the face images collected in the natural scene is performed to generate training data that can more fully represent the real noise in the natural scene, thereby improving the noise reduction and image reconstruction capabilities of the network. In order to take full advantage of the task-specific features, the semantic features of the dataset are extracted through a pre-trained facial organ semantic detection framework and the data is used to add face semantic information in the super-resolution reconstruction network. The experimental results show that the model proposed in this study can achieve super-resolution reconstruction of ultra-low-resolution face images simply and efficiently, and achieve super-resolution face images with lower visual quality than other advanced methods with lower model complexity. It solves the problem of single style and distortion of face reconstruction images caused by the training method of the data set obtained by the usual predefined degradation.
Trajectory tracking control of wheeled mobile robot based on improved LSTM-DDPG algorithm

This paper proposed a deep deterministic policy gradient (DDPG) algorithm based on the clipped double Q-learning method and long short-term memory (LSTM) neural network for the trajectory tracking control of wheeled mobile robots (WMR). Firstly, aiming at solving the problem of overestimating state-action value in the DDPG algorithm, a double critic network is introduced to approximate the value function, and the minimum value will be taken to calculate the target value of actor network update, avoiding the algorithm falling into local optimal. Then, to overcome the difficulty caused by partial observability in reinforcement learning control, the historical states information will be used to generate the current action by an actor, which adopts an LSTM neural network processing the temporal relations in those historical states. Finally, the reinforcement learning agent based on the improved LSTM-DDPG algorithm is designed as the kinematic controller of WMR and provides reference values for the dynamic controller (two independent PI controllers). Simulation results verify the effectiveness of this control scheme based on the proposed algorithm for WMR trajectory tracking control, even in the presence of external disturbances and model parameter uncertainties.
Prediction Technology of Uncontrolled Ammunition Landing Point Based on Bi-LSTM

Landing point prediction is a commonly used algorithm in the field of guidance and control, and has high control accuracy. However, for the prediction of landing points, most algorithms do not realize end-to-end calculation, and the real-time calculation is poor. Aiming at the problem of poor real-time performance of landing calculation, a landing state prediction model based on Bi-LSTM (Bi-directional Long Short-Term Memory) neural network is proposed, and a variable step learning strategy is introduced on this basis. The model is built by taking the state of the ammunition at a certain moment (ballistic inclination, velocity, height) as the model input, and taking the landing point and landing velocity as the output of the model. And carry out simulation verification, and compare the training data fitting accuracy with the Back Propagation (BP) neural network and the LSTM neural network, as well as the complete ballistic fitting accuracy. The results show that the proposed model has a good effect in solving the problem of ammunition drop state prediction.
EV-VINS: An Efficient and Versatile Binocular Visual-Inertial SLAM Algorithm

To address the problem that the current optimization-based visual-inertial SLAM algorithms have difficulty in achieving high accuracy, high robustness and high efficiency at the same time, an efficient and versatile SLAM algorithm (EV-VINS) based on the improvement of VINS-Mono is proposed in this paper. EV-VINS tightly coupling the binocular and inertial measurement unit is divided into decoupled front-end, which detects GFTT corners and performs optical flow tracking, and back-end, which mainly performs initialization, nonlinear optimization and outlier rejection. The dense map module is added for subsequent navigation functions. In particular, a feature classification strategy is proposed based on the analysis of the existing problems in the initialization, which not only greatly reduces the number of outliers during initialization, but also makes the points used for back-end optimization have good initial values. Therefore, the computational efficiency and the stability of the back-end optimization are significantly improved while ensuring the accuracy. The simulation experiments based on the EuRoC datasets and actual test results show that EV-VINS has better real-time performance while having the basically same location accuracy compared to the classical algorithms.
Research on the Application of Object Detection in the Installation of Spacer Bars

The correct installation of spacer bars is an important part of ensuring people’s daily life. This paper studies and implements a spacer bar installation confirmation system based on a deep learning object detection algorithm, which makes up for the deficiencies in the installation. Among the object detection algorithms, Faster-RCNN has higher detection accuracy, is more accurate than one-stage and can detect multi-scale and small objects. Through historical use, it is found that Faster-RCNN is excellent in detecting multi-class image sets tasks. For personal data sets, only fine-tuning is needed to achieve better results, and it also has a fast detection rate. Therefore, this paper is based on the Faster-RCNN algorithm to realize the detection and confirmation of the plug and rubber during the installation of the spacer bar.
Breakdown kinetics of Pr2O3 films by conductive-atomic force microscopy

The dielectric breakdown (BD) kinetics of praseodymium thin films has been determined by comparison between current-voltage measurements on large-area (up to 78.54μm2) metal-oxide-semiconductor structures and conductive-atomic force microscopy (C-AFM). C-AFM clearly images the weak BD single spots under constant voltage stresses. The stress time on the single C-AFM tip dot was varied from 2.5×10−3 to 8×10−2s. The density of BD spots, upon increasing the stress time, exhibits an exponential trend. The Weibull slope and the characteristic time of the dielectric BD have been determined by direct measurements at nanometer scale.
Why and how do journals retract articles? An analysis of Medline retractions 1988 e 2008

Background Journal editors are responsible for what they publish and therefore have a duty to correct the record if published work is found to be unreliable. One method for such correction is retraction of an article. Anecdotal evidence suggested a lack of consistency in journal policies and practices regarding retraction. In order to develop guidelines, we reviewed retractions in Medline to discover how and why articles were retracted. Methods We retrieved all available Medline retractions from 2005 to 2008 and a one-in-three random selection of those from 1988 to 2004. This yielded 312 retractions (from a total of 870). Details of the retraction including the reason for retraction were recorded by two investigators. Results Medline retractions have increased sharply since 1980 and currently represent 0.02% of included articles. Retractions were issued by authors (63%), editors (21%), journals (6%), publishers (2%) and institutions (1%). Reasons for retraction included honest error or non-replicable ﬁndings (40%), research misconduct (28%), redundant publication (17%) and unstated/unclear (5%). Some of the stated reasons might have been addressed by corrections. Conclusions Journals’ retraction practices are not uniform. Some retractions fail to state the reason, and therefore fail to distinguish error from misconduct. We have used our ﬁndings to inform guidelines on retractions.
Low coronal signatures of coronal mass ejections : coronal 'waves' and dimmings

Coronal mass ejections (CMEs) are vast eruptions of magnetised plasma that explode from the solar atmosphere. This thesis focuses on understanding the nascent stages of CMEs, and their magnetic development as they expand into the interplanetary space of our solar system. This is an important part of our effort to understand the space weather environment that we live in, and increasingly interact with through satellite communications technologies. Predominantly through combining extreme ultra-violet imaging and magnetogram data, two low coronal signatures of CMEs, namely coronal waves and dimmings, are studied. A comprehensive list of observational properties of EIT coronal waves is compiled and potential counterparts in radio, H! , soft X-rays and He II wavelengths are also discussed. New observational constraints on EIT coronal waves are presented, most notably diffuse coronal waves are shown to have a magnetic nature. Finding that many observational constraints are not satisfactorily explained by current theories, a new model for understanding the physical nature of diffuse coronal waves is developed. The new model interprets diffuse coronal “wave” bright fronts to be the low coronal magnetic footprint of CMEs. Implications for developing our understanding of how CMEs become large-scale in the low corona are discussed. Application of the model demonstrates how an understanding of the formation of complex global-scale coronal dimmings can be derived. For the first time it is shown that study of the evolution and magnetic nature of coronal dimming regions can be used to probe the post-eruptive evolution of the CME. Finally, a study is presented regarding why and how CME-related dimmings recover, despite the “open” magnetic connectivity of the ejecta to the Sun being maintained as indicated by electron heat flux measurements at 1 AU.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

A growing number of studies acknowledge the role of Enterprise Architecture (EA) in integrating Big Data Analytics (BDA) towards BDA value creation. However, a holistic view of how EA practice contributes to BDA value realization is currently limited. This study aims to synthesize EA roles for BDA value creation that have been addressed in the previous studies and identify current research gaps to guide future study directions. Our investigation identifies 14 EA roles categorized into strategy, technology, collaboration and governance. It also highlights significant gaps relating to fragmented EA roles, limited research on EA contributions for BDA value creation in strategy and governance aspects, and limited studies incorporating intangible EA resources and all possible strategies used for BDA implementation. We propose a conceptual model that explains EA contributions to BDA value creation. It also shows the importance of deployment of appropriate EA strategy and EA resources to establish BDA capabilities.