Miracle-dz/UniFAR-FTUs huggingface dataset:
Off-Nadir Photogrammetry for Airborne SAR Motion Compensation: A First Step

A photogrammetry system operated simultaneously with a synthetic aperture radar (SAR) from the same aerial platform provides strong sensor fusion possibilities that can improve the accuracy of repeat pass Interferometric SAR (InSAR) processing. Motion compensation is a key step in airborne SAR/InSAR processing, and the availability of accurate external digital elevation models (DEMs) is at the heart of many InSAR applications. Eventual research goals are (1) to produce high precision photogrammetric DEMs as reference for interferometric and tomographic applications, and (2) to use photogrammetric block adjustment parameters to fine-adjust the flight trajectory for enhanced motion compensation in repeat pass InSAR. To meet these goals; as SAR is oblique looking by design, an un-conventional off-nadir photogrammetric field-of-view coinciding with the SAR swath is required for our combined system. In this paper we focus on the accuracy implications of the derived photogrammetric DEMs from off-nadir vs nadir configuration as well as the potential for trajectory refinement from the derived photogrammetric block adjustment parameters. Additionally, we carry out an accuracy comparison of our photogrammetric system with established references (Fairbanks fodar™ and WorldDEM™). Vertical photogrammetric DEMs produced with our system over a test site near Silver City, Yukon Territory, Canada were found to be accurate with a mean height difference of 0.45 m and a corresponding standard deviation of 0.78 m from the reference fodar™ system; whereas produced nadir vs. off-nadir DEMs were found in accordance with each other with a mean difference of 0.29 m and standard deviation of 0.75 m. Furthermore, the estimated flight trajectory refinement for the test data had a mean value of 0.19 m with a standard deviation of 0.09 m.
QoS-Aware Heterogeneous Data Transmission Mechanism for Industrial IoT Systems

Various wireless manufacturing sensors facilitate the monitoring and control process in industrial internet of things (IIoT) systems. Control and image data generated by control and vision-based monitoring systems are transmitted over shared wireless network to edge computing devices for further decisions. Due to limited spectrum resources and sensor transmit power, it is challenging to design efficient quality of service (QoS) aware transmission mechanism for heterogeneous data with diverse real-time and system performance requirements. In this paper, in order to guarantee control system performance and image clarity, the time slot allocation and transmission power for heterogeneous sensors are jointly optimized by formulating a mixed integer nonlinear programming (MINLP) problem. The MINLP problem is decomposed into multiple subproblems which are proved to have strong duality and can be solved in dual domain. Then, according to acknowledge (ACK) signals, we propose a priority-aware heuristic algorithm to solve each subproblem. Finally, the simulation results prove that our proposed mechanism can reduce the number of slots and energy consumption for transmission while guaranteeing performance requirements of heterogeneous data.
Data Compression Algorithms for Wireless Sensor Networks: A Review and Comparison

Energy consumption has risen to be a bottleneck in wireless sensor networks. This is caused by the challenges faced by these networks due to their tiny sensor nodes that have limited memory storage, small battery capacity, limited processing capability, and bandwidth. Data compression has been used to reduce energy consumption and improve network lifetime, as it reduces data size before it can be forwarded from the sensing node to the sink node in the network. In this paper, a survey and comparison of currently available data compression techniques in wireless sensor networks are conducted. Suitable sets of criteria are defined to classify existing data compression algorithms. An adaptive lossless data compression algorithm (ALDC) is analyzed through MATLAB coding and simulation from the reviewed data compression techniques. The analysis aims to discover strategies that can be used to reduce the amount of data further before it is transmitted. From this analysis, it was discovered that encoding residue samples, rather than raw data samples, reduced the bitstream from 112 bits to a range of 30 to 36 bits depending on the sample block sizes. The average length of data samples to be passed to the encoder was minimized from the original 14 bits per symbol to 1.125 bits per symbol. This demonstrated a 0.875 code efficiency or redundancy. It resulted in an energy saving of 67.8% to 73.2%. This work further proposes a data compression algorithm that encodes the residue samples with fewer bits than the ALDC algorithm. The algorithm reduced the bitstream to 26 bits. The average length of the code is equal to the entropy of the data samples, demonstrating zero redundancy and an improved energy saving of 76.8% compared to ALDC. The proposed algorithm, therefore, shows improved energy efficiency through data compression.
Engineering Breakdown Probability Profile for PDP and DCR Optimization in a SPAD Fabricated in a Standard 55 nm BCD Process

CMOS single-photon avalanche diodes (SPADs) have broken into the mainstream by enabling the adoption of imaging, timing, and security technologies in a variety of applications within the consumer, medical and industrial domains. The continued scaling of technology nodes creates many benefits but also obstacles for SPAD-based systems. Maintaining and/or improving upon the high-sensitivity, low-noise, and timing performance of demonstrated SPADs in custom technologies or well-established CMOS image sensor processes remains a challenge. In this paper, we present SPADs based on DPW/BNW junctions in a standard Bipolar-CMOS-DMOS (BCD) technology with results comparable to the state-of-the-art in terms of sensitivity and noise in a deep sub-micron process. Technology CAD (TCAD) simulations demonstrate the improved PDP with the simple addition of a single existing implant, which allows for an engineered performance without modifications to the process. The result is an 8.8 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m diameter SPAD exhibiting <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>2.6 cps/<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> DCR at 20 <inline-formula><tex-math notation="LaTeX">$^{\circ}$</tex-math></inline-formula>C with 7 V excess bias. The improved structure obtains a PDP of 62% and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>4.2% at 530 nm and 940 nm, respectively. Afterpulsing probability is <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>0.97% and the timing response is 52 ps FWHM when measured with integrated passive quench/active recharge circuitry at 3 V excess bias.
Adapting Kernels for Hyperspectral Image Classification

Despite its great potential in a wide range of human activities, hyperspectral remote sensing imaging (HSI) exhibits several challenges that prevent full exploitation of its data. In particular, land-cover classification based on HSI data suffers significant degradation due to problematic data variability. Convolutional Neural Networks (CNNs) ability to extract spectral-spatial features has enabled the development of powerful classifiers, which achieve not yet seen accuracy results. To enhance the feature extraction procedure, this paper presents a novel HSI-CNN model (DKDCNet) which combines adaptive deforming kernels (DK) and convolutions (DC) with the aim of pinpointing the effective receptive field (ERF) on the challenging input data. Experimental results on the University of Houston benchmark show that DKDCNet is able to obtain a more accurate classification than traditional strategies with similar computational cost for HSI classification. Source code: https://github.com/mhaut/DKDCNet.
Combining Task and Motion Planning using Policy Improvement with Path Integrals

Task and motion planning deals with complex tasks that require a robot to automatically define and execute multi-step sequences of actions in cluttered scenarios. In this context, a linear motion is often not sufficient to approach a target object since collisions of the gripper with other objects or the target object might occur. Thus, motion planners should be able to generate collision-free trajectories for every particular configuration of obstacles for grounding the symbolic actions of the task plan. Current approaches either search for feasible motions offline using computationally expensive trial-and-error processes on physically realistic simulations or learn a set of motion parameters for particular object configuration spaces with little generalization. This work proposes an appealing alternative by efficiently generating trajectories for the collision-free execution of symbolic actions in variable scenarios without the need of intensive offline simulations. Our approach combines the benefit of learning from demonstration, to quickly generate an initial set of motion parameters for each symbolic action, with policy improvement with path integrals, to diversify this initial set of parameters to cope with different obstacle configurations. We show how the improved flexibility is achieved after a few minutes of training and successfully solves tasks requiring different sequences of picking and placing actions in variable configurations of obstacles.
Average Weighted Objective Distance-Based Method for Type 2 Diabetes Prediction

Early detection of Type 2 diabetes is necessary for its prevention. The prediction models for detection systems normally employ common factors that may not properly fit all persons having different health conditions. Therefore, this study proposes a method for type 2 diabetes prediction with factors representing personal health conditions. More specifically, this study proposes a novel prediction method named Average Weighted Objective Distance (AWOD) based on the assumption that the individual has diverse health conditions resulting from different individual factors, a requirement for an effective prediction model. AWOD is a modification of Weighted Objective Distance (WOD) by applying information gain to reveal significant and insignificant individual factors having different priorities, which are represented by different weights. For AWOD, the data set is divided into a training set used to determine all relevant thresholds and constant values required for AWOD calculation and the testing set. In particular, AWOD is designed for binary classification problems with a relatively small dataset. To validate the proposed method, two datasets from open sources, Pima Indians Diabetes (Dataset 1) and Mendeley Data for Diabetes (Dataset 2) each containing 392 records, were studied. The prediction performance for both datasets is compared with the machine learning-based prediction methods, including K-Nearest Neighbors, Support Vector Machines, Random Forest, and Deep Learning. The comparison results showed that the proposed method provided 93.22% and 98.95% accuracy for Dataset 1 and Dataset 2, respectively, which are higher than those provided by other machine learning-based methods.
Secure and Fast Image Encryption Algorithm Using Hyper-chaos based Key Generator and Vector Operation

To protect image data privacy, a secure and fast image encryption algorithm using hyper-chaos based key generator and vector operation is proposed. Firstly, we introduce a novel post-process method to create a key matrix for significantly reducing the iterations of the hyperchaotic system from W×H/4 to 2W for an image size of W×H. And within the post-process, a random selector driven by the piecewise linear chaotic map ensures the good randomness and unpredictability of the generated key matrix. Secondly, the vector operation is employed to accelerate the cipher block chaining based diffusion process, so the plain image can be parallel encrypted row by row and column by column with high efficiency. Thirdly, we utilize a Logistic map to quickly produce an initial vector for the vectorized diffusion process. Finally, the proposed algorithm is evaluated by some common security and performance tests. Experimental results show that the cipher image can pass all tests of NIST SP 800-22 with P-values>>0.01, its correlation coefficient between pixels is close to 0, and the entropy is greater than 7.999. Combined with other results of security tests, we can safely conclude that the proposed image encryption algorithm provides adequate protection against statistical, brute-force, chosen-plaintext, and other common types of attacks. In addition, the time complexity is in the order of O(W+H) and the average encryption time of 512×512 images is only 0.023s. The results indicate that our algorithm with high security and fast speed can meet the requirements of real-time confidential transmission of massive image data.
Utilizing the SAR, GIS, and Novel Hybrid Metaheuristic-GMDH Algorithm for Flood Susceptibility Mapping

On 14 March 2019, Tropical Cyclone Idai generated a devastating flood incident in Mozambique with considerable socioeconomic consequences. 1.85 million people were affected, and 602 others were killed. Generally, this region has been prone to flood disasters. Hence, preparing flood susceptibility maps can play a significant role in reducing the negative impacts of floods on lives and properties of people as well as the critical infrastructures. In this study, 10 influential factors on flood occurrence were analyzed using the geographic information system (GIS) coupled with the synthetic-aperture radar (SAR) data and the hybrid gray wolf optimizer-group method of data handling (GWO-GMDH). The results indicated that the hybrid GWO-GMDH model outperformed GMDH in mapping flood susceptibility. According to the GWO-GMDH model, 19.40% and 15.52% of the study area was found to be high and very high susceptible to flooding, respectively. The generated maps are beneficial for decision makers in mitigating flood damages and risk management.
Low-Rank Subspace Unmixing of Remotely Sensed Hyperspectral Image

Spectral unmixing is an important technique for hyperspectral image application, which aims to estimate the pure spectral signatures in each mixed pixel and their corresponding fractional abundances. However, due to the influence of factors such as illumination, topography change and atmosphere, spectral variability is inevitable, which will lead to inaccurate unmixing results. Traditional unmixing methods fail to handle this problem, especially the complex spectral variability in the image. To address this limitation, a new technique called low-rank subspace unmixing (LRSU) was established, which aims to jointly estimate a subspace projection and abundance maps. For the proposed LRSU approach, the original data is projected into a low-rank subspace to deal with various spectral variabilities in spectral unmixing. Meanwhile, the spectral-spatial weighted sparse regularization term is introduced to upgrade the sparsity of the solution and capture the piecewise smooth structure of the data. The experimental results, conducted using synthetic data sets, quantitatively indicate that the proposed LRSU strategy produces better results than other advanced spectral unmixing methods.
Unsupervised Band Selection for Hyperspectral Datasets by Double Graph Laplacian Diagonalization

The vast amount of spectral information provided by hyperspectral images can be useful for different applications. However, the presence of redundant bands will negatively affect application performance. Therefore, it is crucial to select a relevant subset that preserves the information of the original set. In this paper, we present an automatic and accurate band selection method based on Graph Laplacians. Unlike existing band selection methods, this method exploits two similarity measures simultaneously. Furthermore, it is performed on a superpixel level, so it allows us to preserve not only global but contemporaneously local particularities of original data. Experiments show the importance of measuring the relevance of the bands at local and global scales and the ability of the method to minimize intercorrelation among selected bands, hence improving the selection of the most informative spectral channels.
Early Detection of Wildfires with GOES-R Time-Series and Deep GRU Network

In recent years, wildfires have become major devastating hazards that affect both public safety and the environment. Thus, agile detection of the wildfires is desirable to suppress wildfires in the early stage. Owing to the high temporal resolution, GOES-R satellites offer capabilities to obtain images every 15 minutes enabling a near real-time monitoring of wildfires. In this research, a time-series-based deep learning framework, composed of Gated Recurrent Units (GRU), is proposed to capture the emerging of the wildfire at early stage. By feeding the embedding of the coarse satellite imagery to Deep GRU network, the active fires are segmented out from the remote sensing imagery. The preliminary results show that proposed network can detect the wildfires earlier than the state-of-the-art fire product for 2020 wildfires in California and British Columbia, at the same time provide sufficiently high accuracy on the burned areas.
EasyNet: 100 Gbps Network for HLS

The massive deployment of FPGAs in data centers is opening up new opportunities for accelerating distributed applications. However, developing a distributed FPGA application remains difficult for two reasons. First, commonly available development frameworks (e.g., Xilinx Vitis) lack explicit support for networking. Developers are, thus, forced to build their own infrastructure to handle the data movement between the host, the FPGA, and the network. Second, distributed applications are made even more complex by using low level interfaces to access the network and process packets. Ideally, one needs to combine high performance with a simple interface for both point-to-point and collective operations. To overcome these inefficiencies and enable further research in networking and distributed application on FPGAs, we first show how to integrate an open-source 100 Gbps TCP/IP stack into a state-of-the-art FPGA development framework (Xilinx Vitis) without degrading its performance. Further, we provide a set of MPI-like communication primitives for both point-to-point and collective operations as a High Level Synthesis (HLS) library. Our point-to-point primitives saturate a 100 Gbps link and our collective primitives achieve low latency. With our approach, developers can write hardware kernels in high level languages with the network abstracted away behind standard interfaces. To evaluate the ease of use and performance in a real application, we distribute a K-Means algorithm with the new stack and achieve a 1.9X and 3.5X throughput increase with 2 FPGAs and 4 FPGAs respectively.
Comparing CNN Architectures for Land Cover Classification on Multispectral Images

We investigate the benefits of using multispectral images for land cover classification. To perform this comparative analysis, we present a novel model, LandNet which is configurable with multiple deep residual networks to extract features on several combinations of bands. We consider both the classification accuracy of the various LandNet configurations. We perform this study on the EuroSAT and BigEarthNet datasets, both of which contain multispectral images from the Sentinel-2 mission. On EuroSAT, we convincingly demonstrate marked improvements in the accuracy of around 1% to 97.815% when using additional bands compared to merely using the RGB bands. On BigEarthNet we show the additional bands are able to improve the recall of the LandNet by 0.04. We achieve a precision score of 0.85, recall of 0.80 and an F-score of 0.82. The precision, recall and F-score we achieve outperform prior results achieved on BigEarthNet.
From Space to Earth – Relative-CoM-to-Foot (RCF) control yields high contact robustness

This paper introduces the Simplest Articulated Free-Floating (SAFF) model, a low-dimensional model facilitating the examination of controllers, which are designed for free-floating robots that are subject to gravity. Two different state-of-the-art control approaches, namely absolute CoM control accompanied by an assumption about the foot acceleration, and a controller combining absolute CoM and foot control objectives, are shown to yield exponential stability in the nominal case, while becoming unstable if the foot contact is lost. As an improvement over the state of the art, the so-called Relative-CoM-to-Foot (RCF) controller is introduced, which again yields exponential stability nominally, while preserving a BIBO stable behavior even in case of a complete contact loss. The controller performance is validated in various simulations.
Sparsity Constrained Convolutional Autoencoder Network for Hyperspectral Image Unmixing

Hyperspectral images (HSIs) contain a large number of mixed pixels due to low spatial resolution, which poses great challenges to the analyses and applications of HSIs. In recent years, convolutional neural networks (CNNs) have attained promising performance in HSI field. However, few CNN-based methods are proposed to solve the hyperspectral unmixing (HU) problem because of insufficient labeled samples. In this paper, we propose a novel unsupervised method, sparsity constrained convolutional autoencoder network (SC-CAE), for the HU problem. The data are preprocessed by principal component analysis (PCA) and then fed into the encoder network to obtain low dimensional representations. The decoder network is to reconstruct the original data from these low dimensional representations. Under the sparse constraint, the endmember matrix and the abundance matrix are obtained after many training epochs. The experiment results on synthetic dataset and real dataset show that our method has evident advantages compared with several state-of-the-art methods.
Detection of Collaboration and Collision Events during Contact Task Execution

This work introduces a contact event pipeline to distinguish task-contact from Human-Robot interaction and collision during task execution. The increasing need for close proximity physical human-robot interaction (pHRI) in the private, health and industrial sector demands for new safety solutions. One of the most important issues regarding safe collaboration is the robust recognition and classification of contacts between human and robot. A solution is designed, that enables simple task teaching and accurate contact monitoring during task execution. Besides an external force and torque sensor, only proprioceptive data is used for the contact evaluation. An approach based on demonstrated task knowledge and the offset resulting from human interaction is designed to distinguish contact events from normal execution by a contact event detector. A contact type classifier implemented as Support Vector Machine is trained with the identified events. The system is set up to quickly identify contact incidents and enable appropriate robot reactions. An offline evaluation is conducted with data recorded from intended and unintended contacts as well as examples of task-contacts like object manipulation and environmental interactions. The system’s performance and its high responsiveness are evaluated in different experiments including a real world task.