Miracle-dz/UniFAR-FTUs huggingface dataset:
A Greedy Heuristic for Cluster Editing with Vertex Splitting

Cluster Editing is one form of correlation clustering that requires a minimal amount of edge-editing operations to transform a given graph into a transitive graph or a disjoint union of cliques. This paper considers the more realistic scenario where data elements can belong to more than one cluster. This problem formulation is typical of real-world data such as social networks where individuals can be members of different communities or have multiple roles/interests. Capitalizing on recent work on Cluster Editing with Vertex Splitting, we present a heuristic approach that, among other steps, evaluates the likelihood of splitting a vertex into two vertices with disjoint neighborhoods and tries to predict how such splitting should be performed. Experimental results show the effectiveness of the splitting operation in giving more insightful clustering and provide empirical evidence of the effectiveness of the proposed heuristic as a promising tool for data analysis in various domains.
Boosting CNN for Hyperspectral Image Classification

In recent years, deep convolutional neural networks (CNNs) have been widely used for hyperspectral image (HSI) classification. Besides, ensemble learning is a useful way to enhance the classification performance. Therefore, in this study, a new method titled Boosting-CNN is proposed for HSI classification, which fully explored the advantages of deep CNN and ensemble learning. Specifically, several deep CNNs are well-designed to classify HSI. The samples are misclassified in a CNN have more weighs in the following CNN through adaptive boosting. The final classification result is obtained by weighted voting of several CNNs. For HSI classification issue, the number of samples in different classes varies greatly, however, traditional classification methods cannot handle this issue well. In order to address imbalance training samples in HSI classification, soft class balanced loss is proposed to mitigate the influence of imbalance training samples. Experimental results on two popular hyperspectral datasets (i.e., Salinas and Pavia University) show that the proposed method obtain better classification accuracy compared to comparison methods.
Design and Verification of Attitude Control System for a Boost-Glide Rocket

The boost-glide rocket needs to maneuver in the atmosphere with complex flight conditions, which is a big challenge for the attitude control system. In this paper, the design process of the control system is analyzed. With specific points of the flight trajectory, the design of an improved proportional-integrative-derivative (PID) controller based on the small perturbation theory is carried out. Design approach of the attitude control system is verified by the six-degree-freedom model simulation. The rocket flight control program is written to the onboard computer so that hardware-in-the-loop simulation experiment is completed. Experimental results show that the mean and standard deviation of the error between simulation value and experiment value are below 0.5°, so the control parameters and design method are reasonable with a great attitude tracking effect. This paper proposes the design and verification method of a boost-glide rocket attitude control system, which can provide a reference for design of the boost-glide rocket around the world.
Attention Based Convolution Autoencoder for Dimensionality Reduction in Hyperspectral Images

Hyperspectral images (HSIs) are being actively used for land use/land cover classification owing to their high spectral resolution. However, this leads to the problem of high dimensionality, making the algorithms data hungry. To resolve these issues, deep learning techniques, such as convolution neural networks (CNNs) based autoencoders, are used. However, traditional CNNs tend to focus on all the features irrespective of their importance, leading to weaker representations. To overcome this, we incorporate attention modules in our autoencoder architecture. These attention modules explicitly focus on more important wavelengths, leading to better transformation of the features in the low dimension. In the proposed method, the attention driven encoder transforms high dimension features to low dimensions, considering their relative importance, while the CNN based decoder reconstructs the original features. We evaluate our method on Indian pines 2010 and Indian pines 1992 hyperspectral datasets, where it surpasses the previous approaches.
Benchmarking Change Detection in Urban 3D Point Clouds

According to the United Nations, 70% of earth population is going to live in cities by 2050. Given this fast urban evolution, urban monitoring is a key process to qualify sustainable development. Vertical changes need to be assessed, and various methods for 3D change detection have been published. However, there is no common quantitative benchmark assessing their performance in urban areas yet. In this paper, we aim to fill this gap and introduce a simulation tool to generate synthetic 3D point cloud data in a well-controlled scenario. These data are then used to compare qualitatively and quantitatively representative 3D change detection methods for urban areas. These methods are based on distance computation (DSMd, C2C, M3C2), traditional machine learning (RF with stability feature) and deep learning (Feed Forward and Siamese networks). We distinguish between binary and multi-class classification of changes at different levels (3D points, 2D pixels, and 2D patches). While deep neural networks have led to numerous success in remote sensing, we show that they do not systematically outperform more simple methods for 3D change detection. Besides, the existing networks are limited to 2D patches while outputs at the pixel or point scale are more attractive.
Polarimetric SAR Image Classification Based on Edge-Aware Dual Branch Fully Convolutional Network

As a critical step for Polarimetric Synthetic Aperture Radar (PolSAR) images interpretation, PolSAR classification have attracted growing interests in the field of remote sensing. Recently, many novel ideas and models based on deep learning have emerged to solve the task of PolSAR image classification. The encode-decode network structure of Fully Convolutional Network (FCN) is proved to be effective for this task. However, due to the inherent disadvantages of FCN and the complex high-dimensional feature representation of PolSAR images, there are still some issues need to be addressed. Aiming at the problem that the edge of adjacent regions is not enough finely classified and the regional consistency of the same object class is relatively weak, we propose a novel PolSAR image classification method called DBFCN, which combines a well-designed edge-aware network and the improved FCN. The experimental results verify that it can effectively improve the classification accuracy of PolSAR images.
Hrlinknet: Linknet with High-Resolution Representation for High-Resolution Satellite Imagery

Automatic extraction of buildings from high-resolution remote sensing imagery is very useful in many applications such as city management, mapping, urban planning and geographic information updating. However, due to the general texture of the building and the complexity of the image background, high-precision building segmentation from high-resolution sensing image is still a challenging task. Existing state-of-the-art frameworks use repeated pooling and step operations leading to the loss of detailed information. Thus, high-resolution representations are essential for building extraction. On this basis, our proposed network, named as HRLinkNet, maintains high-resolution representations through the whole process based on the LinkNet. We tested it on WHU Building dataset. Experimental results show that the proposed HRLinkNet is superior to the LinkNet, UNet, DLinkNet, segnet and so on.
Multisensorial robot calibration framework and toolbox

The accuracy of robot models critically impacts their performance. With the advent of collaborative, social, or soft robots, the stiffness of the materials and the precision of the manufactured parts drops and CAD models provide a less accurate basis for the models. On the other hand, the machines often come with a rich set of powerful yet inexpensive sensors, which opens up the possibility for self-contained calibration approaches that can be performed autonomously and repeatedly by the robot. In this work, we extend the theory dealing with robot kinematic calibration by incorporating new sensory modalities (e.g., cameras on the robot, whole-body tactile sensors), calibration types, and their combinations. We provide a unified formulation that makes it possible to combine traditional approaches (external laser tracker, constraints from contact with the external environment) with self-contained calibration available to humanoid robots (self-observation, self-contact) in a single framework and single cost function. Second, we present an open source toolbox for Matlab that provides this functionality, along with additional tools for preprocessing (e.g., dataset visualization) and evaluation (e.g., observability/identifiability). We illustrate some of the possibilities of this tool through calibration of two humanoid robots (iCub, Nao) and one industrial manipulator (dual-arm setup with Yaskawa-Motoman MA1400).
Ship Detection from Optical Remote Sensing Imagery Based on Scene Classification and Saliency-Tuned Retinanet

Due to the variation of ocean scene, such as thin cloud occlusion, thick cloud and sea surface clutter interferences, ship detection from optical remote sensing images often suffer from high false alarm rate. As a consequence, a ship detection method based on background classification and saliency-tuned RetinaNet is proposed to address this problem. First, the ocean scene is divided into four categories: thin cloud and fog occlusion scene, thick cloud interference scene, shore scene and calm sea scene by a designed scene classification module (SCM). Second, a multi-scale saliency feature fusion module (MSFM) is designed to provide more discriminative features for ship detection. In particular, by associating saliency maps and feature maps, our proposed MSFM can effectively suppress backgrounds noise. Finally, the MSFM are integrated into the rotation single-stage detector to effectively identify arbitrary-oriented ships from complex ocean scene. Extensive experiments on optical remote sensing dataset demonstrated that the proposed method can obtain better detection performance than the state-of-the-art methods and achieved a comparable detection speed.
Impact of the Neighbor’s Order on the Capacity of Millimeter-Wave Links with Poisson-Distributed Nodes in Line of Sight Conditions

This paper presents a theoretical model for investigating the average capacity of a millimeter wave (mmWave) communication link in line of sight conditions, when a fixed binary phase-shift keying (BPSK) or a quadrature PSK (QPSK) modulation is used and the nodes are distributed according to a homogeneous Poisson point process (PPP). In particular, as compared to the existing PPP approaches, which often consider the sole nearest neighbor as a possible destination, the proposed analysis enables to evaluate the link performance for a neighbor of any order, thus providing a more complete view of the achievable capacity. Besides, the adoption of the BPSK/QPSK modulations helps to obtain a more realistic estimation with respect to the ideal one provided by the usually adopted Shannon bound. Moreover, the derived formulas, which are expressed in analytical form and checked by extensive simulations, include the influence of all the main mmWave propagation phenomena: path-loss attenuation, small- and mid-scale fading. The developed model is specifically exploited to explore the impact of the average cell radius and of the selected frequency band on the sustainability of the mmWave link as the destination becomes farther from the source.
Retinanet Deep Learning-Based Approach to Detect Termite Mounds in Eucalyptus Forests

Brazilian eucalyptus plantations are largely used to produce wood products and play an important role in the country's economy. The presence of termites in these plantations can cause serious damage, requiring their identification and control. Here, we investigated the RetinaNet object detection deep learning method's potential to identify termite mounds in eucalyptus forests automatically. The experiments were conducted using terrestrial images acquired with a GoPro 360, composed of two fisheye cameras, providing a challenging task due to non-conventional geometry. In the tests, the model presented an Average Precision (AP) of 0.721, and after stipulating thresholds from which the objects would be used in training, the AP was 0.867.
Low PAPR OFDM-Chirp Modulation Signaling Scheme

In this paper, we develop a novel OFDM-chirp modulation scheme with low peak-to-average power ratio (PAPR). The OFDM-chirp modulation signaling jointly utilizes the frequency-domain chirp modulation and OFDM signaling technique, which can deal with the disadvantages of traditional OFDM modulation including high PAPR, sensitivity to carrier frequency offset (CFO), poor Doppler tolerance and strong inter-carrier interference (ICI). We also introduce an analytical approach to evaluate the corresponding symbol error rate (SER) under CFO in Rayleigh fading channel. Simulation results verify the superiorities of OFDM-chirp modulation signaling over traditional OFDM signaling schemes.
3D Location Oriented Routing in Flying Ad-Hoc Networks for Information Dissemination

A Flying Ad-hoc Networks (FANETs) is an autonomous technology that creates a self-organized wireless network via Unmanned Arial Vehicles (UAVs). In this network, all UAVs can communicate within a restricted range of wireless communication in the absence of fixed infrastructure. As a result of high mobility, the limited energy, and the communication range of UAVs, network forming and deformation between them are very frequent that causes packet delivery failure. Therefore, a stable route is always needed to ensure effective data dissemination between source and destination in FANETs. Since it has drastically changing network topology, therefore, to maintain the stable route during packet transmission, there is a need for a suitable routing protocol. This paper proposes an Optimized Location-Aided Routing (O-LAR) protocol which is the modified version of Location-Aided Routing (LAR) protocol. Our protocol’s novelty comes from the fact that it established an optimal route between UAVs for information dissemination towards their respective destination UAV by considering weight function. A weighted function is used to decide the best next-hop node selection based on the parameters like residual energy, distance, and UAV movement direction. The performance of the O-LAR is evaluated mathematically and simulated through the NS-2 simulator. The empirical results attest that O-LAR improves the link duration, network lifetime, packet delivery ratio, and average throughput compared with the state-of-the-art protocols: LEPR, D-LAR, and LAR. Further, the proposed scheme reduces the number of next-hops, routing overhead and end-to-end delay compared to the state-of-the-art protocols.
Facial Expression Recognition: A Review of Trends and Techniques

Facial Expression Recognition (FER) is presently the aspect of cognitive and affective computing with the most attention and popularity, aided by its vast application areas. Several studies have been conducted on FER, and many review works are also available. The existing FER review works only give an account of FER models capable of predicting the basic expressions. None of the works considers intensity estimation of an emotion; neither do they include studies that address data annotation inconsistencies and correlation among labels in their works. This work first introduces some identified FER application areas and provides a discussion on recognised FER challenges. We proceed to provide a comprehensive FER review in three different machine learning problem definitions: Single Label Learning (SLL)- which presents FER as a multiclass problem, Multilabel Learning (MLL)- that resolves the ambiguity nature of FER, and Label Distribution Learning- that recovers the distribution of emotion in FER data annotation. We also include studies on expression intensity estimation from the face. Furthermore, popularly employed FER models are thoroughly and carefully discussed in handcrafted, conventional machine learning and deep learning models. We finally itemise some recognise unresolved issues and also suggest future research areas in the field.
Cascaded Deep Neural Networks for Predicting Biases Between Building Polygons in Vector Maps and New Remote Sensing Images

Tremendous historical and recent building vector maps are available from private surveying and mapping departments or open-source platforms such as Open Street Map (OSM). However, there invariably exist offsets between the building vectors and new remote sensing images, caused by geometric registration errors and parallax from hypsography. Consequently, these vector maps cannot be directly used as labels for the up-to-date GIS productions, as well as popular supervised machine learning. As the first work of this kind, this paper proposes an offset correction method, based on deep learning, to predict the bias between a vector building map and a new remote sensing image. The method is based on a dense regression model to predict the offsets between the image and the vector map at the pixel level, which are then processed to retrieve the offsets of each polygon. Two datasets consist of remote sensing images and biased building polygons, the WHU change detection and London datasets, are prepared to test the effectiveness of our approach. In the WHU change detection dataset, approaching 80% polygons can be correctly registered within the tolerance of three pixels, in the London dataset, more than 60% polygons can be corrected by a pre-trained model on an available open-source dataset, both of which demonstrated that our method can contribute to the GIS map updating and more efficient preparation of training samples for a deep learning model.
Sensitivity of Multipath Peak Frequency of Navigation with Indian Constellation (NavIC) towards Surface Soil Moisture over Bare Land

The exploitation of GNSS signals for soil moisture as one of the land applications is the current interest of researchers due to its multiple advantages over existing soil moisture retrieval techniques based on traditional radiometer and other datasets. Multipath phase and amplitude of GNSS C/No data have been mostly utilized to determine the sensitivity of soil moisture. However, in this work, we have analyzed the multipath peak frequency to determine its sensitivity for field soil moisture. The multipath frequency is used to consider constant when studies are carried out with multipath phase or amplitude. Here, we have demonstrated that the multipath peak frequency is a function of field soil moisture which can be evaluated efficiently with Lomb Scargle Periodogram (LSP). Navigation with Indian Constellation (NavIC) data has been used to determine the correlation between multipath peak frequency and surface soil moisture. The obtained sensitivity results are very optimistic (correlation coefficient = 0.69), which can be further utilized for developing soil moisture estimation model using NavIC data to cater different land applications.
Features of the Construction and Use of Small Hydroelectric Power Plants for Power Supply of Mountainous Regions of Tajikistan

The article describes the dynamics of the development of power supply systems in Tajikistan in the 21st century. The predominant development of hydropower is shown, based on the geographical features of the relief of the republic, which is rich in hydropower resources. The tendency of a significant expansion of the construction of small hydroelectric power plants, which are the most effective in addressing the issues of power supply in the hard-to-reach high-mountainous regions of the republic, is noted. The features of electricity consumers in rural settlements and their influence on the formation of requirements for control systems of small hydroelectric power plants are considered. At the same time, the main attention is paid to solving the issues of balancing the output voltage of hydroelectric power plants with a significant asymmetry of phase loads, which is typical for household consumers. It is shown that the simplest and most effective method for the implementation of small hydroelectric power plants, taking into account the peculiarities of their use for mountainous regions, is their complete automation with the maximum use of semiconductor power discrete voltage conversion devices. An analysis of the existing methods for balancing phase voltages is given and a description of a new method based on a discrete voltage converter is given.
Snow Water Equivalent Evolution During the 2019/2020 Winter Period in Aemet-Formigal Test Site Using a SFCW Radar

Snow makes a great contribution to the hydrological cycle in cold regions. The parameter to characterize available water from the snow cover is the snow water equivalent (SWE). This paper presents a near-surface based radar for determining SWE from the measured complex spectral reflectance of snowpack. The method is based on a stepped-frequency continuous wave radar (SFCW), implemented in a coherent software defined radio (SDR), in the range from 150MHz to 6GHz. An approximate method to calculate SWE is proposed. Results are compared with those provided by a cosmic-ray neutron (CRN) SWE gauge over the 2019–2020 winter in the experimental AEMet Formigal-Sarrios test site. The results suggest the viability of the approximate method. Finally, the feasibility of an auxiliary snow depth measurement sensor based on a 120GHz radar FMCW sensor (mmWave sensor) is shown.
Automated Artifact Retouching in Morphed Images With Attention Maps

Morphing attack is an important security threat for automatic face recognition systems. High-quality morphed images, i.e. images without significant visual artifacts such as ghosts, noise, and blurring, exhibit higher chances of success, being able to fool both human examiners and commercial face verification algorithms. Therefore, the availability of large sets of high-quality morphs is fundamental for training and testing robust morphing attack detection algorithms. However, producing a high-quality morphed image is an expensive and time-consuming task since manual post-processing is generally required to remove the typical artifacts generated by landmark-based morphing techniques. This work describes an approach based on the Conditional Generative Adversarial Network paradigm for automated morphing artifact retouching and the use of Attention Maps to guide the generation process and limit the retouch to specific areas. In order to work with high-resolution images, the framework is applied on different facial crops, which, once processed and retouched, are accurately blended to reconstruct the whole morphed face. Specifically, we focus on four different squared face regions, i.e. the right and left eyes, the nose, and the mouth, that are frequently affected by artifacts. Several qualitative and quantitative experimental evaluations have been conducted to confirm the effectiveness of the proposal in terms of, among the others, pixel-wise metrics, identity preservation, and human observer analysis. Results confirm the feasibility and the accuracy of the proposed framework.