Miracle-dz/UniFAR-FTUs huggingface dataset:
Fourier Dense Network to Conduct Plant Classification Using UAV-Based Optical Images

Plant classification is a science that is used to assess the quality of forest resources and has been studied extensively. In this paper, we proposed a novel convolutional neural network known as the Fourier Dense Network (FDN) which is a data-driven method to classify the optical aerial images of plants. To efficiently classify plants, the FDN learns and extracts the features of plants in time and frequency domains from the optical images captured using an unmanned aerial vehicle (UAV). In FDN, we designed a fast Fourier dense block (FF-dense block) that describes the features of the plants by using the magnitude and phase information in the frequency domain. Moreover, we used a transition layer to reduce the dimension of feature maps between two FF-dense blocks in the time domain. The primary contributions of this study are as follows: (1) an FF-dense block that considers frequency information and transfers the information into various layers of a block was designed; and (2) the characteristics between the time and frequency domains were repeatedly extracted and combined to more effectively describe the characteristics of tree species. To evaluate our study, we established a novel dataset comprising the UAV-based optical images of plants-vegetational optical aerial image dataset-for conducting plant classification and information retrieval. The dataset contains more than 21863 images of 12 plants. To the best of our knowledge, this is the largest publicly available dataset of the UAV-based optical images of plants. The experimental results demonstrated that the FDN can achieve state-of-the-art performance in terms of plant classification.
Unified Image Fusion Framework With Learning-Based Application-Adaptive Importance Measure

This paper presents a novel unified image fusion framework based on an application-adaptive importance measure. In the proposed framework, an important area is selected using the importance measure obtained for each image type in each application. The key is to learn this application-adaptive importance measure that can select the important area irrespective of the input image type without manually designing the algorithm for each application. Then, the fused intensity is generated using Poisson image reconstruction. Experimental results demonstrate that the proposed framework is effective for various applications including depth-perceptible image enhancement, temperature-preserving image fusion, and haze removal.
An Improved Supervised Descent Method based Face Alignment Algorithm

Aiming at existing problems about Supervised Descent Method (SDM), such as inaccurate facial feature extraction and the poor final alignment effect resulted by local optimum, an improved SDM (ISDM) based face align- ment algorithm is proposed. Firstly, an improved multi-scale Histograms of Gradient (IMHOG) feature extraction method based on multi-layers is raised, which expresses more refined facial features and makes faces be recognized more easily. Meanwhile, the social spider optimization (SSO) is applied to op- timize the estimated shape in iteration globally, which can avoid the local opti- mal. And it makes the estimated shape closer to the real shape so that the final alignment effect is more precise. Experiments have shown that the proposed al- gorithm can get better results than previous algorithms in LFPW, AFLW and 300-W datasets.
A 55–64-GHz Low-Power Small-Area LNA in 65-nm CMOS With 3.8-dB Average NF and ~12.8-dB Power Gain

This letter presents a low-power small-footprint low-noise amplifier (LNA) that operates over the frequency band of 55–64 GHz. Using a resistor between bulk and substrate (ground) nodes, these two nodes are isolated. This bulk isolation technique is introduced to achieve the maximum gain of the transistor at the desired frequency band. Also, a methodology is proposed to determine the optimal size of transistors to achieve the maximum possible gain. As a proof of concept, the proposed LNA is fabricated in a 65-nm bulk CMOS process, and the design features 12.8 ± 0.5 dB power gain and an average noise figure of 3.8 dB. The output 1-dB compression point of the LNA is −6 dBm. The LNA consumes 8.8 mW from a 1-V supply and excluding the pads occupies a silicon area of 0.23 mm2.
Millimeter Wave Agile Transmitter for IoT Operations

Wireless Power Transfer (WPT) technology has been gathering a large interest in the last decade due to its increasing efficiency. Starting from low-frequency systems via magnetic coupling, the trend has been evolving toward high-frequency solutions, with a particular attention to the emerging 5G wireless communication equipment for the internet of things (IoT). In this respect, WPT has to face new challenges to become competitive at frequencies that will be of utmost importance in the near future, like the 28 GHz band. In this paper, we offer a rigorous combined electromagnetic/nonlinear circuit approach to evaluate the performance of a novel WPT system combining state-of-the-art time-modulated array (TMA) technology and high-performance metal-insulator-metal (MIM) diodes with fast switching time. Merging the advantages of TMAs with MIMs gives rise to a new family of low-power WPT devices, able to efficiently supply energy to the electronics used in IoT networks.
Wearable Finger Tracking and Cutaneous Haptic Interface with Soft Sensors for Multi-Fingered Virtual Manipulation

Multi-Fingered haptics is imperative for truly immersive virtual reality experience, as many real-world tasks involve finger manipulation. One of the key lacking aspect for this is the absence of technologically and economically viable wearable haptic interfaces that can simultaneously track the finger/hand motions and display multi-degree-of-freedom (DOF) contact forces. In this paper, we propose a novel wearable cutaneous haptic interface (WCHI), which consists of 1) finger tracking modules (FTMs) to estimate complex multi-DOF finger and hand motion; and 2) cutaneous haptic modules (CHMs) to convey three-DOF contact force at the finger-tip. By opportunistically utilizing such different types of sensors as inertial measurement units, force sensitive resistor sensors, and soft sensors, the WCHI can track complex anatomically consistent multi-DOF finger motion while avoiding FTM-CHM electromagnetic interference possibly stemming from their collocation in the small form-factor interface; while also providing the direction and magnitude of three-DOF finger-tip contact force, the feedback of which can significantly enhance the precision of contact force generation against variability among users via their closed-loop control. Human subject study is performed with a virtual peg insertion task to show the importance of both the multi-DOF finger tracking and the three-DOF cutaneous haptic feedback for dexterous manipulation in virtual environment.
Detecting and Characterizing Trends in Online Mental Health Discussions

Mental illness is a widespread public health concern that affects many individuals on a daily basis. Increasingly, people are turning to social media to discuss their mental health. The result is a rich dataset of authentic discussions from which to draw insights. In this work, we collected data from multiple mental health forums on the popular social media website, Reddit. We extracted topics from these datasets and then observed the trends of these topics from 2012 to 2018. These trends fall into many recognizable patterns. Some trends are stable, often using common words found in mental health conversations. Other trends are increasing or decreasing. In this work, we found that topics with positive words are becoming less frequently used and topics with negative connotations are becoming more frequently used. Other trends display a periodic pattern, like those associated with the school year. One trend demonstrates a sudden shift in conversation possibly due to a change in how the site is administered. We confirm these qualitative observations through quantitative analysis with statistical tests.
A Scalable Indirect Position-Based Causal Diffusion Protocol for Vehicular Networks

Emerging applications based on vehicular ad-hoc networks (VANETs) are focused on offering new services to intelligent transportation systems (ITSs), such as traffic safety and management. For such kind of services, there are intrinsic spatial and temporal dependencies in the communication among mobile and fixed components. However, the characteristics of VANETs, which include a rapidly changing topology and the lack of global temporal references, make it difficult to satisfy such communication constraints. Commonly, the communication protocols for VANETs assume high coupled communication links; nevertheless, for most ITSs, the entities to whom such information will be useful are determined at run time by the spatial and temporal context. In this paper, a causal position-based indirect communication protocol is proposed, which allows road side units (RSUs) to disseminate messages with an uncoupled communication. The proposed solution uses the vehicles as opportunistic carriers, leveraging the bounded movement in a specific geographical region to exchange data among RSUs even with unknown recipients. To preserve the coherence of the exchange of messages, they are ordered by establishing their causal dependencies without the requirement of global references or synchronized physical clocks. Furthermore, the proposed protocol leverages message redundancy to recover some lost messages during the transmission. It is analytically demonstrated that the protocol is scalable since the size of the control information depends only on the number of RSUs rather than on the total number of entities in the system.
Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems

The intrusion detection systems (IDSs) are essential elements when it comes to the protection of an ICT infrastructure. A misuse IDS is a stable method that can achieve high attack detection rates (ADR) while keeping false alarm rates under acceptable levels. However, the misuse IDSs suffer from the lack of agility, as they are unqualified to adapt to new and “unknown” environments. That is, such an IDS puts the security administrator into an intensive engineering task for keeping the IDS up-to-date every time it faces efficiency drops. Considering the extended size of modern networks and the complexity of big network traffic data, the problem exceeds the substantial limits of human managing capabilities. In this regard, we propose a novel methodology which combines the benefits of self-taught learning and MAPE-K frameworks to deliver a scalable, self-adaptive, and autonomous misuse IDS. Our methodology enables the misuse IDS to sustain high ADR, even if it is imposed on consecutive and drastic environmental changes. Through the utilization of deep-learning based methods, the IDS is able to grasp an attack’s nature based on the generalized feature reconstructions stemming directly from the unknown environment and its unlabeled data. The experimental results reveal that our methodology can breathe new life into the IDS without the constant need for manually refreshing its training set. We evaluate our proposal under several classification metrics and demonstrate that the ADR of the IDS increases up to 73.37% in critical situations where a statically trained IDS is rendered totally ineffective.
Improving LF-MMI Using Unconstrained Supervisions for ASR

We present our work on improving the numerator graph for discriminative training using the lattice-free maximum mutual information (MMI) criterion. Specifically, we propose a scheme for creating unconstrained numerator graphs by removing time constraints from the baseline numerator graphs. This leads to much smaller graphs and therefore faster preparation of training supervisions. By testing the proposed un-constrained supervisions using factorized time-delay neural network (TDNN) models, we observe 0.5% to 2.6% relative improvement over the state-of-the-art word error rates on various large-vocabulary speech recognition databases.
Self-supervised Railway Pantograph Image Component Retrieval with Geometry Prior

The pantographs are important infrastructures in the railway traction power supply, therefore, whose serving status are frequently monitored, in order to detect any faults or anomaly as early as possible. To visually understand the inspection images, the pantograph pixels should be grouped with markings to indicate specific components. In this paper, a novel unsupervised image component retrieval method is proposed for pantograph visual inspection. To fully utilized the prior knowledge of the interested artificial objects, predefined 3D models are used to estimate latent geometric pose parameters, so as to assist the retrieval of the specified component. Particular deep Q-network based reinforcement learning is designed and trained with the help of an environmental simulator to interactively search optima in high-dimensional parameter space with a global envision. Experiments on the synthesis and real datasets proved the effectiveness of the proposed method in pantograph monitoring.
ResNet with Global and Local Image Features, Stacked Pooling Block, for Semantic Segmentation

Recently, deep convolutional neural networks (CNNs) have achieved great success in semantic segmentation systems. In this paper, we show how to improve pixel-wise semantic segmentation by combine both global context information and local image features. First, we implement a fusion layer that allows us to merge global features and local features in encoder network. Second, in decoder network, we introduce a stacked pooling block, which is able to significantly expand the receptive fields of features maps and is essential to contextualize local semantic predictions. Furthermore, our approach is based on ResNet18, which makes our model have much less parameters than current published models. The whole framework is trained in an end-to-end fashion without any post-processing. We show that our method improves the performance of semantic image segmentation on two datasets CamVid and Cityscapes, which demonstrate its effectiveness.
Combining Local and Global Models for Robust Re-detection

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual tracking. However, these methods still struggle in occlusion and out-of-view scenarios due to the absence of a re-detection component. While such a component requires global knowledge of the scene to ensure robust re-detection of the target, the standard DCF is only trained on the local target neighborhood.In this paper, we augment the state-of-the-art DCF tracking framework with a re-detection component based on a global appearance model. First, we introduce a tracking confidence measure to detect target loss. Next, we propose a hard negative mining strategy to extract background dis-tractors samples, used for training the global model. Finally, we propose a robust re-detection strategy that combines the global and local appearance model predictions. We perform comprehensive experiments on the challenging UAV123 and LTB35 datasets. Our approach shows consistent improvements over the baseline tracker, setting a new state-of-the-art on both datasets.
A Novel Receiver for Dual-side Controlled Wireless Power Transfer System

Recently, dual-side phase control (PC) is introduced to regulate the output power and track the maximum efficiency of the wireless power transfer system (WPT). Since an active receiver is utilized on the secondary side, the control is complex in nature. In addition, short through may occur and the reliability of the system cannot be guaranteed. In this paper, a novel one-switch receiver is proposed for the dual-side controlled WPT system. The proposed receiver can take full advantage of low switching losses of silicon carbon (SiC) metal-oxide-semiconductor field effect transistor (MOSFET), whereas avoid its high forward voltage loss without paralleling any freewheeling diode. In addition, the proposed receiver advances in easy design and realization, cost effective, free from short through and small size. Experiments are added to verify the proposed plan.
Multimapper: Data Density Sensitive Topological Visualization

Mapper is an algorithm that summarizes the topological information contained in a dataset and provides an insightful visualization. It takes as input a point cloud which is possibly high-dimensional, a filter function on it and an open cover on the range of the function. It returns the nerve simplicial complex of the pullback of the cover. Mapper can be considered a discrete approximation of the topological construct called Reeb space, as analysed in the 1-dimensional case by [Carri et al.,]. Despite its success in obtaining insights in various fields such as in [Kamruzzaman et al., 2016], Mapper is an ad hoc technique requiring lots of parameter tuning. There is also no measure to quantify goodness of the resulting visualization, which often deviates from the Reeb space in practice. In this paper, we introduce a new cover selection scheme for data that reduces the obscuration of topological information at both the computation and visualisation steps. To achieve this, we replace global scale selection of cover with a scale selection scheme sensitive to local density of data points. We also propose a method to detect some deviations in Mapper from Reeb space via computation of persistence features on the Mapper graph.
An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components

In recent years, the reliability and safety requirements of ship systems have increased drastically. This has prompted a paradigm shift toward the development of prognostics and health management (PHM) approaches for these systems’ critical maritime components. In light of harsh environmental conditions with varying operational loads, and a lack of fault labels in the maritime industry generally, any PHM solution for maritime components should include independent and intelligent fault detection algorithms that can report faults automatically. In this paper, we propose an unsupervised reconstruction-based fault detection algorithm for maritime components. The advantages of the proposed algorithm are verified on five different data sets of real operational run-to-failure data provided by a highly regarded industrial company. Each data set is subject to a fault at an unknown time step. In addition, different magnitudes of random white Gaussian noise are applied to each data set in order to create several real-life situations. The results suggest that the algorithm is highly suitable to be included as part of a pure data-driven diagnostics approach in future end-to-end PHM system solutions.
Magnetic Shielding of Open and Semi-closed Bulk Superconductor Tubes: The Role of a Cap

In this paper we investigate the magnetic shielding of hollow and semi-closed bulk superconducting tubes at 77 K. We first consider the properties of a commercial Bi-2223 tube closed by a disk-shaped cap placed against its extremity. The results are compared with those obtained on a bulk large grain Y-Ba-Cu-O (YBCO) tube produced by buffer-aided top seeded melt growth. In this process, the disk-shaped pellet and the tubular sample are grown together, resulting in a tube naturally closed at one extremity. The field to be shielded is either parallel or perpendicular to the main axis of the tube. The experimental results are compared with the results of finite element numerical modeling carried out either in two dimensions (for the axial configuration) or three dimensions (for the transverse configuration). In the axial configuration, the results show that the shielded volume can be enhanced easily by increasing the thickness of the cap. In the transverse configuration, the results show the critical role played by the superconducting current loops flowing between the tube and the cap for magnetic shielding. If the tube and the cap are separated by a non-superconducting joint or air gap, the presence of a cap leads only to a small improvement of the transverse shielding factor, even for a configuration where the gap between the cap and the tube contains a 90° bend. The cap leads to a significant increase in the transverse shielding when the cap and the tube are naturally grown in the same process, i.e., made of a continuous superconducting material. The experimental results can be reproduced qualitatively by 3-D numerical modeling.
Compiler-aided Type Tracking for Correctness Checking of MPI Applications

MUST, a dynamic MPI correctness checker, is extended with a type and memory allocation tracking sanitizer called TypeART for C/C++ codes based on the LLVM compiler framework. The sanitizer extracts type information and inserts instrumentation to track memory allocations and deallocations relevant to MPI communication. This allows MUST to check for type compatibility between the type-less communication buffer and the declared MPI datatype at all phases of the MPI communication, namely message assembly, message transfer and message disassembly into the receiving buffer. We evaluate our approach on benchmarks taken from SPEC MPI 2007 and two CORAL mini applications. The results show that our approach typically exhibits acceptable runtime and memory overheads. In particular, the memory overhead was below 20% in all cases.
Fuzzy transform and support vector machine for pedestrian detection

This paper focuses on a task of automatic pedestrian detection. There are many possible solutions with several ideas behind. Histogram of oriented gradients is among the popular ones. The former is used as a descriptor of the input image together with support vector machine. In this initial study, we propose to create a feature vector using fuzzy mathematics and test its effectiveness. The effectiveness is evaluated by comparison with original histogram of oriented gradient descriptors in support vector machine.
Farsi Handwriting Digit Recognition Based on Convolutional Neural Networks

In this paper, a convolutional neural network (CNN) is exploited for Farsi handwritten digit recognition. For training and evaluating the CNN, the "HODA" dataset was used which consists of 80000 images of Farsi handwritten digits. In the proposed method, we focused on the efficient and unique feature of Farsi digits that is using just the half upper part of the digits for recognition purpose. The proposed method, despite of a 50% reduction in the data size which fed to the CNN, yielded an acceptable reduction in time consuming for training and evaluate CNN of about 50 % compared when using the full image of the digits (full data), and just a 1.5% increase in recognition error.
Learning Network Representations with Neighboring Affinity

This paper studies the problem of representing networks in a low-dimensional vector space, which is critical in network node classification, link prediction, and recommender systems. We observe that neighbors of a given node often contain rich contextual information which reveals the shared patterns that could be used to learn sparse network representations. Under this observation, we define a new similarity function to describe the similarity between nodes in networks for utilizing the aggre- gated effect of neighboring information. Furthermore, we propose Neighboring Affinity based neTwork Embedding (NATE), which explicitly incorporates the new similarity into a loss function for network embedding learning by effectively representing the node relationships in networks. NATE efficiently represents the node relationship in both homogeneous and heterogeneous networks. Experimental results demonstrate that NATE outperforms the state-of-the-art network embedding techniques on several types of real-world networks in terms of rating prediction and multi- label classification, yet with lower computational complexity.