Miracle-dz/UniFAR-FTUs huggingface dataset:
Extrinsic Visual–Inertial Calibration for Motion Distortion Correction of Underwater 3D Scans

Underwater 3D laser scanners are an essential type of sensor used by unmanned underwater vehicles (UUVs) for operations such as navigation, inspection, and object recognition and manipulation. Scanners that acquire 3D data by sweeping a laser plane across the scene can provide very high lateral resolution. However, their data may suffer from rolling shutter effect if the change of pose of the robot with respect to the scanned target during the sweep is not negligible. In order to compensate for motion-related distortions without the need for point cloud postprocessing, the 6-DoF pose at which the scanner acquires each line needs to be accurately known. In the underwater domain, autonomous vehicles are often equipped with a high-end inertial navigation system (INS) that provides reliable navigation data. Nonetheless, the relative pose of the 3D scanner with respect to the inertial reference frame of the robot is not usually known a priori. Therefore, this paper uses an ego-motion-based calibration algorithm to calibrate the extrinsic parameters of the visual-inertial sensor pair. Simulations are performed to quantify how miscalibration affects motion-related distortion. The method is also evaluated experimentally in laboratory conditions.
A 0.8V, 152 μW, 433 MHz Mixer-First Receiver with a Self-Adjusted Frequency Tracking Loop

This paper presents a 433 MHz low-power receiver utilizing an N-path filter technique and a self-frequency tracking mechanism. Without the front-end amplifier, the mixer-first architecture can reduce power consumption significantly. A self-adjusted frequency tracking loop (SA-FTL) adjusts local oscillator (LO) frequency to approach input RF frequency automatically, thereby enhancing conversion gain and lowering return loss. The receiver, implemented in a <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS process, achieves a sensitivity of −80 dBm at a bit error rate (BER) of 10<sup>−3</sup> and a data rate of 10 kb/s, while consuming <inline-formula> <tex-math notation="LaTeX">$152~\mu \text{W}$ </tex-math></inline-formula> from a 0.8V voltage supply.
The Motion Controller Based on Neural Network S-Plane Model for Fixed-Wing UAVs

Aiming at the attitude control problem of fixed wing UAV, this paper introduces S-plane control, which has good control effect in the field of underwater UAV, into the attitude control of UAV. At the same time, aiming at the problem that the coefficient setting of parameters in S-plane control completely depends on experience and cannot be adjusted adaptively, the radial basis function neural network (RBFNN) is introduced, and a neural network S-plane control model which can realize on-line adaptive adjustment of the coefficient of parameters in S-plane control is proposed. The simulation results based on the data of a certain UAV show that compared with the S-plane control, the proposed neural network S-plane control model has the characteristics of fast response speed, strong anti-interference ability, and strong robustness. In addition, it also has the function of adaptive adjustment, which shows good control performance.
Stuck Query Point Processing Of Multi-point Query For Image Retrieval With Relevance Feedback

Many previous techniques were designed to retrieve semantic images in the entire feature space but with low precision because of bypassing some points in stuck the multi-point query. In this article, we propose a semantic-related image retrieval method, which can retrieve semantic images spread in the entire feature space with high precision, and the method is based on dealing with stuck points in multi-point queries called STUP (Stuck Query Point Processing). Our method combines user feedback with information that is about similarity among the neighbors to choose “the best” alternative point for stuck query points. We also provide experimental results based on two feature databases to demonstrate the accuracy of our method.
Building Height Estimation using Street-View Images, Deep-Learning, Contour Processing, and Geospatial Data

In the recent years, there has been an increasing interest in extracting data from street-view images. This includes various applications such as estimating the demographic makeup of neighborhoods to building instance classification. Building height is an important piece of information that can be used to enrich two-dimensional footprints of buildings, and enhance analysis on such footprints (e.g., economic analysis, urban planning). In this paper, a proposed algorithm (and its open-source implementation) for automatic estimation of building height from street-view images, using Convolutional Neural Networks (CNNs) and image processing techniques, is presented. The algorithm also utilizes geospatial data that can be obtained from different sources. The algorithm will ultimately be used to enrich the Open Database of Buildings (ODB), that has been published by Statistics Canada, as a part of the Linkable Open Data Environment (LODE). Some of the obtained results for building height estimation are presented in this paper. Furthermore, current and future improvements, some challenging cases and the scalability of the system are discussed.
Secure Vertical Handover to NEMO Using Hybrid Cryptosystem

Department of Computer Science and Engineering, NMAM Institute of Technology, NITTE, Deralakatte 574110, India Department of CSE, Manipal University Jaipur, Jaipur 303007, India Department of Information Technology, Dean Technology and Innovation Cell, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India Department of Computer Science College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Informing Agility in the Context of Organizational Changes

Aim/Purpose. This paper, although conceived earlier than the emergence of COVID-19 pandemic, addresses the problem of informing agility as part of organizational agility that has become a rather important issue for business survival. Background. While the general issues of business informing, and business intelligence (BI) in particular, have been widely researched, the dynamics of informing, their ability to act in accord with changes in business and preserve the key competencies has not been widely researched. In particular, the research on BI agility is rather scattered, and many issues need to be clarified. Methodology. A series of in-depth interviews with BI professionals to determine relations between organizational agility and BI agility, and to round up a set of key factors of BI agility. Contribution. The paper clarifies a candidate set of key factors of BI agility and gives ground for future research in relations with areas like corporate and BI resilience and culture. Findings. The interview results show the relations between organizational changes, and changes in BI activities. BI has limited potential in recognizing important external changes but can be rather helpful in making decision choices and detecting internal problems. Lack of communication between business and IT people, existence of data silos and shadow BI, and general inadequacy of organizational and BI culture are the key factors impairing BI agility. Recommendations for Practitioners. There are practical issues around BI agility that need solving, like the reason-able coverage of standards or creation of a dedicated unit to care about BI potential. Recommendations for Researchers. The research is still in its starting phase, but additional interesting directions start to emerge, like relations between BI agility, resilience and corporate agility, or the role of informing culture and BI culture for BI agility issues. Impact on Society. Agile business, especially in times of global shocks like COVID-19, loses less value and has more chances to survive. Future Research. Most likely this will be focused on the relations between BI agility, resilience, and corporate agility, and the role of informing culture and BI culture for BI agility issues.
Demystifying the Dark Side of Social Networking Sites through Mindfulness

Over the last few decades, social networking sites (SNS) have evolved as an effective medium of communication for the world. They are instrumental in connecting people across time and space with just a click. However, the darker side of SNS has resulted in a deteriorated human connection between individuals in real life. The current study is an attempt to examine the compulsive usage of SNS in detail. It utilizes a sequential mixed method design to examine the negative outcome of compulsive SNS usage and the effect of mindfulness in overcoming them. Findings of the study suggest that compulsive usage mediates the relationship between mindfulness and exhaustion; further, this relationship is moderated by extroversion personality traits.
A Linear Programming Approach to Computing Safe Sets for Software Rejuvenation

Software rejuvenation was born to fix operating system faults by periodically refreshing the run-time code and data. This mechanism has been extended to protect control systems from cyber-attacks. This letter proposes a software rejuvenation design method in discrete-time where invariant sets for the safety and mission controllers are designed to schedule the timing of software refreshes. To compute a minimal robust positively invariant (min-RPI) set and the bounded time between software refreshes to ensure system safety, an LP based approach is proposed for stable and unstable systems. Finally, the designed approach is illustrated by the case study of a simulated lab-scale microgrid.
Optimal Setting for Hurst Index Estimation and Its Application in Chinese Stock Market

The Hurst index is widely used to describe the long memory process of time series in economics, finance, and other fields. The setting for Hurst index estimation has not been thoroughly investigated by current literature. Many scholars choose the estimation settings subjectively without comparing the results of different settings or just choose the settings providing the desirable results. In this research, the impact of various settings such as correlation processing, trend processing, scale range selection, subsequence division, fitting method, and tail-cut method on the Hurst index estimation models, such as R/S and DFA, are evaluated through the Monte Carlo simulation. The results show that the settings have significant impact on the accuracy of the Hurst index estimation. The detailed optimal six setting for Hurst index estimation is summarized for future researchers. In addition, the results show that the comprehensive performance of the DFA estimation is better than the R/S-type estimation. On the basis of the optimal setting, the Hurst indexes of China’s stock market are estimated. The daily volatility in the Chinese stock market show long memory by the Hurst index. Our contribution is to identify the optimal model setting for the Hurst index estimation by comparing all the six optional settings in every step of estimation. Our stepwise analysis for model effectiveness may shed light on challenges for various approaches to market trend analysis.
Efficient Concurrent L1-Minimization Solvers on GPUs

Given that the concurrent L1-minimization (L1-min) problem is often required in some real applications, we investigate how to solve it in parallel on GPUs in this paper. First, we propose a novel self-adaptive warp implementation of the matrix-vector multiplication (Ax) and a novel self-adaptive thread implementation of the matrix-vector multiplication (Ax), respectively, on the GPU. The vector-operation and inner-product decision trees are adopted to choose the optimal vector-operation and inner-product kernels for vectors of any size. Second, based on the above proposed kernels, the iterative shrinkage-thresholding algorithm is utilized to present two concurrent L1-min solvers from the perspective of the streams and the thread blocks on a GPU, and optimize their performance by using the new features of GPU such as the shuffle instruction and the read-only data cache. Finally, we design a concurrent L1-min solver on multiple GPUs. The experimental results have validated the high effectiveness and good performance of our proposed methods.
Certificateless Network Coding Ring Signature Scheme

Traditional certificateless ring signature is not suitable for the network coding environment. In this view, a new certificateless network coding ring signature (CL-NCRS) is devised by using the techniques of multisource network coding and certificateless ring signature. Network coding is a kind of information interaction technology and can improve system robustness and save wireless network resources. CL-NCRS satisfies the unforgeability and unconditional anonymity; moreover, it has relatively higher communication and computational efficiency.
UAV Clustering Scheme for FANETs using Elbow-Hybrid Metaheuristic Techniques

Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles (UAVs) within the commercial domain, which demands a proper coordination and reliable communication among the UAVs. UAVs suffer from limited time of flight. Conventional techniques suffer from high delay, low throughput, and early node death due to aerial topology of UAV networks. To deal with these issues, this paper proposes a UAV parameter vector which considers node energy, channel state information and mobility of UAVs. By intelligently estimating the proposed parameter, the state of UAV can be predicted closely. Accordingly, efficient clustering may be achieved by using suitable metaheuristic techniques. In the current work, Elbow method has been used to determine optimal cluster count in the deployed FANET. The proposed UAV parameter vector is then integrated into two popular hybrid metaheuristic algorithms, namely, water cycle-moth flame optimization (WCMFO) and Grey Wolf-Particle Swarm optimization (GWPSO), thereby enhancing the lifespan of the system. A methodology based on the holistic approach of parameter and signal formulation, estimation model for intelligent clustering, and statistical parameters for performance analysis is carried out by the energy consumption of the network and the alive node analysis. Rigorous simulations are run to demonstrate node density variations to validate the theoretical developments for various proportions of network system sizes. The proposed method presents significant improvement over conventional stateof-the-art methods.
Modification of existing permeation models of mixed matrix membranes filled with porous particles for gas separation

Gas separation methods have received much attention, as the process plays a key role in various industries. Among the gas separation methods, membrane-based methods, particularly those employing mixed matrix membranes (MMMs), are important. MMMs are formed by modifying the properties of polymeric membranes by fabricating them with inorganic particles. This paper presents the gas transport behaviour in MMMs fabricated with porous particles, as described by two-phase (ideal) and three-phase (non-ideal) models. The effect of particle porosity on gas permeability was incorporated into existing models through the J parameter, which adjusts the particle loading percentage. J-modified models were verified against existing models and experimental data for gas permeability were obtained with various MMMs. It was found that the proposed modified models provide a better prediction of the gas transport behaviour in MMMs.
A Stock Trend Forecast Algorithm Based on Deep Neural Networks

As a recognized complex dynamic system, the stock market has many influencing factors, such as nonstationarity, nonlinearity, high noise, and long memory. It is difficult to explain it simply through mathematical models. Therefore, the analysis and prediction of the stock market have been a very challenging job since long time. Therefore, this paper adopts an encoder-decoder model of attention mechanism, adding attention mechanism from two aspects of feature and time. Both encoder and decoder use LSTM neural network. This method solves two problems in time series prediction; the first problem is that multiple input features have different degrees of influence on the target sequence, the feature attention mechanism is used to deal with this problem, and the weights of different input features can be obtained. A more robust feature association relationship is obtained; the second problem is that the data before and after the sequence have a strong time correlation. The time attention mechanism is used to deal with this problem, and the weights at different time points can be obtained to obtain more robustness and good timing dependencies. The simulation and experimental results show that the introduction of the attention mechanism can obtain lower forecast errors, which proves the effectiveness of the model in dealing with stock forecasting problems.
Study on desorption properties of single amine and mixed amines in natural gas decarbonization process

The desorption properties of five kinds of amines MEA, DEA, TEA, DETA, TETA and their mixed amines with MDEA in the process of CO2 removal from natural gas were analyzed by the comparative experiments. The change regulation of desorption rate of different types of single amine and mixed amines was investigated. The results showed that the desorption properties of two kinds of enamines DETA, TETA and their mixed amines with MDEA were relatively poor. While the desorption properties of the above amines could be improved by increasing the absorption saturation.
Development of Acetylcysteine Jelly for the Prevention of Radiocontrast-induced Reductions in Renal Function and its Evaluation

Acetylcysteine is used for the prevention of radiocontrast-induced reductions in renal function. However, as an oral solution, acetylcysteine has an unpleasant taste and smell, which can cause discomfort to patients. To solve this problem, we prepared an oral jelly of acetylcysteine consisting of acetylcysteine, gelatin, flavoring agent and purified water. The unpleasant taste and smell of acetylcysteine were greatly reduced through the use of flavoring agents like apple.We expect our acetylcysteine jelly to be a highly effective preparation for the prevention of radiocontrast-induced reductions in renal function.
Recent Advances in Blockchain and Artificial Intelligence Integration: Feasibility Analysis, Research Issues, Applications, Challenges, and Future Work

Blockchain constructs a distributed point-to-point system, which is a secure and verifiable mechanism for decentralized transaction validation and is widely used in financial economy, Internet of +ings, large data, cloud computing, and edge computing. On the other hand, artificial intelligence technology is gradually promoting the intelligent development of various industries. As two promising technologies today, there is a natural advantage in the convergence between blockchain and artificial intelligence technologies. Blockchain makes artificial intelligence more autonomous and credible, and artificial intelligence can prompt blockchain toward intelligence. In this paper, we analyze the combination of blockchain and artificial intelligence from a more comprehensive and three-dimensional point of view. We first introduce the background of artificial intelligence and the concept, characteristics, and key technologies of blockchain and subsequently analyze the feasibility of combining blockchain with artificial intelligence. Next, we summarize the research work on the convergence of blockchain and artificial intelligence in home and overseas within this category. After that, we list some related application scenarios about the convergence of both technologies and also point out existing problems and challenges. Finally, we discuss the future work.
Autologous Activated Omental versus Allogeneic Adipose Tissue-Derived Mesenchymal Stem Cells in Corneal Alkaline Injury: An Experimental Study

Purpose: To compare the effects of two types of mesenchymal stem cells (MSCs), activated omental cells (AOCs), and adipose tissue-derived stem cells (ADSCs) in the healing process of animal model of ocular surface alkali injury. Methods: An alkaline burn was induced on the ocular surfaces of eighteen rats divided randomly into three groups. The first and second groups received subconjunctival AOCs and ADSCs, respectively. The control group received normal saline subconjunctival injection. On the 90th day after the injury, the eyes were examined using slit-lamp biomicroscopy. Corneal neovascularization and scarring were graded in a masked fashion. Histological evaluation of the corneal scar was performed, and the number of inflammatory cells was evaluated. Results: Corneal neovascularization scores revealed higher neovascularization in the control (0.49 ± 0.12) than the AOC (0.80 ± 0.20, P = 0.01) and ADSC groups (0.84 ± 0.24, P = 0.007). There were no statistically significant differences between the neovascularization score of the AOC and ADSC groups (P > 0.05). According to histologic evaluation, stromal infiltration was significantly more in the control group compared to AOC and ADSC groups (P < 0.05). Conclusions: Our results suggest that MSCs, even with different sources, can be used to promote wound healing after corneal chemical burns. However, the ease of harvesting ADSC from more superficial fat sources makes this method more clinically applicable.
Considering medical education research: Selection into medical school

Medicine as a science and as a profession has made great progress in the last 150 years. The infection epidemic was mastered by the invention of antibiotics, cardiovascular disease became treatable, and our knowledge of cancer advances rapidly. However, at the same time, tens of thousands of people die worldwide every year as a result of medical errors. “To err is human, building a safer healthcare system,” an influential report from the Institute of Medicine, estimates that in the US alone, every year, at least 44,000 and perhaps even 98,000 patients die as a result of medical errors. [1] In short, more people die in a given year as a result of medical errors than from motor vehicle accidents, breast cancer, or acquired immunodeficiency syndrome.
A new approach for Content Based Image Retrieval – A Review

1,2,3 Department Of ECE, IKPTU, Ramgarhia Institute of Engineering and Technology, Phagwara ---------------------------------------------------------------------***--------------------------------------------------------------------ABSTRACTThe content based image retrieval (CBIR) methods are used to discover the similar images in accordance with the input image from the database. The image retrieval applications plays a vital role in the case of big databases, where thousands of millions or more images are stored. In the case of media sharing platforms such as Instagram, Whatsapp, Facebook, Picasa, etc, a very large number of data is uploaded on these portals on the daily basis, which makes it impossible to discover the relevant image data manually. Hence there is a strong requirement of versatile information based image retrieval engines from such databases, which can discover the relevant images out of the given database. In this paper, an innovative model for the image retrieval on the basi s of color and texture features has been proposed, which is expected to resolve the issue related to the accuracy of image retrieval engines. The performance of the model would be analyzed by using the accuracy metrics such as recall, precision, F1measure and overall accuracy.