Miracle-dz/UniFAR-FTUs huggingface dataset:
ROSE: Random Opportunistic and Selective Exploration for Cooperative Edge Swarm of UAVs

In this work, we propose a physical approximation scheme - Random Opportunistic and Selective Exploration (ROSE) - for aerial localization of survivors by using a collaborative swarm of IoT-based unmanned aerial vehicles (UAVs). The UAV swarm performs a simultaneous multi-pronged search of a given zone by dividing the search region among the swarm members. This multi-pronged search strategy speeds-up the search, and the division of search areas among the swarm members avoids redundant exploration of an already explored location. As the communication range of the member UAVs is limited, the swarm members communicate opportunistically among themselves to share the information of the visited sites. We formulate the various probabilities associated with opportunistic communication of these aerial IoT nodes and simulate the performance of the approximation algorithm based on these formulations. Simulation results of the proposed approach successfully locate 100% of the ground targets within an acceptable time-frame, and out-performs established searching schemes such as the truncated Levy walk, frontier-based search, and sweep search.
An Online System Dependency Graph Anomaly Detection based on Extended Weisfeiler-Lehman Kernel

Modern operating systems are typical multitasking systems: Running multiple tasks at the same time. Therefore, a large number of system calls belonging to different processes are invoked at the same time. By associating these invocations, one can construct the system dependency graph. In rapidly evolving system dependency graphs, how to quickly find outliers is an urgent issue for intrusion detection. Clustering analysis based on graph similarity will help solve this problem. In this paper, an extended Weisfeiler-Lehman(WL) kernel is proposed. Firstly, an embedded vector with indefinite dimensions is constructed based on the original dependency graph. Then, the vector is compressed with Simhash to generate a fingerprint. Finally, anomaly detection based on clustering is carried out according to these fingerprints. Our scheme can achieve prominent detection with high efficiency. For validation, we choose StreamSpot, a relevant prior work, to act as benchmark, and use the same data set as it to carry out evaluations. Experiments show that our scheme can achieve the highest detection precision of 98% while maintaining a perfect recall performance. Moreover, both quantitative and visual comparisons demonstrate the outperforming clustering effect of our scheme than StreamSpot.
Wideband Automated Planning Tool (WAPT) Prototype Development

Major focus in this software prototype development is the application of automated algorithmic techniques to the planning of communications services on military and commercial satellite systems. The emphasis to date is on priority-based military systems, but the flexibility to accommodate commercial systems with varied service access mechanisms is built in. The overall goal is to decrease scenario planning times from days-to-weeks to minutes on standard, but, capable laptop computers. This level of performance has been met in developmental testing, and will enable resilient operations of satellite networks by supporting fast reallocation of terminal resources. The types of algorithms employed depend on the application. For service planning when terminal endpoints are within antenna beam coverage areas, a deterministic linear programming algorithm is employed. When beam coverage is not provided for new services, i.e., when many uncovered terminal endpoints exist, a probabilistic approach using a genetic algorithm is employed to heuristically search for antenna beam configurations meeting prioritized access requests. Alternatively, when lower volumes of new, uncovered endpoints exist, specialized beam adjustment algorithms are applied. Additionally, a framework has been developed to enable operations in an enterprise environment with automated service access request (SAR) processes, which further advances resilient satellite network operations.
Study on Raman Spectrum Detection Method Based on Surface Enhanced Micro-nano Fiber

In this paper, an optical fiber Raman probe based on surface-enhanced particles is used to study the Raman spectroscopy of liquid trace substances. The optical fiber Raman detection probe uses a hollow-core fiber structure, and its air cavity as the optical excitation region of the surface-enhanced metal particles and the sample to be measured, it can form the high-efficiency action region of the sample and the excitation light, so as to achieve the perfect coincidence of the optical path and the sample cavity. Through the research on the output power of hollow fiber probe and the detection of Raman signal after inhaling different amount of samples and so on, the performance of the fiber Raman detection probe system is improved to the greatest extent. At the same time, the applicability of surface-enhanced metal particles is analyzed, and the best three kinds of metal sol, sample and hollow fiber are formed. The optimum measurement method was studied and tested. The melamine aqueous solution was measured by this method. The detection limit can reach 100 ppb. This Raman spectroscopic method is suitable for biological fluids or high-value fluids because of its small extraction of liquid samples. Its probe has low cost and is easy to mass production, which provides an effiective solution for the realization of all-optical fiber, portable and rapid detection of the whole Raman system.
Application of Computer Vision and Deep Learning in the railway domain for autonomous train stop operation

The purpose of this paper is to present the results of the analysis of the application of Deep Learning in the railway domain with a particular focus on a train stop operation. The paper proposes an approach consisting of monocular vision-based and Deep Learning architectures. Even the difficulties imposed by actual regulation, the findings show that Deep Learning architecture can offer promising results in railway localization using techniques like visual odometry, SLAM or pose estimation. Besides, in spite of the many datasets available in the literature needed to train the neural network, none of them have been created for indoor railway environments. Therefore, a new dataset should be created. Furthermore, the paper presents future research and development suggestions for railway applications which contribute to guiding the mid-term research and development.
An Efficient Dilated Convolutional Neural Network for UAV Noise Reduction at Low Input SNR

Acoustic applications on a multi-rotor unmanned aerial vehicle (UAV) have been hindered by its low input signal-to-noise ratio (SNR). Such low SNR condition poses prominent challenges for beamforming algorithms, statistical methods, and existing mask-based deep learning algorithms. We propose the small model on low SNR (SMoLnet), a compact convolutional neural network (CNN) to suppress UAV noise in noisy speech signals recorded off a microphone array mounted on the UAV. The proposed SMoLnet employs a large analysis window to achieve high spectral resolution since the loud UAV noise exhibits a narrow-band harmonic pattern. In the proposed SMoLnet model, exponentially-increasing dilated convolution layers were adopted to capture the global relationship across the frequency dimension. Furthermore, we performed direct spectral mapping between noisy and clean complex spectrogram to cater to the low SNR scenario. Simulation results show that the proposed SMoLnet outperforms existing dilation-based models in terms of speech quality and objective speech intelligibility metrics for UAV noise reduction. In addition, the proposed SMoLnet requires fewer parameters and achieves lower latency than the compared models.
Development of a Dataset to Evaluate SLAM for Fukushima Daiichi Nuclear Power Plant Decommissioning

Simultaneous localization and mapping (SLAM) is not only a key technology for robot to move automatically, but also is a useful technology for humans to understand the state of places. Especially for a site where access is constrained by something harmful, such as Fukushima Daiichi Nuclear Power Plant (1F), understanding of the state of the site is important. In addition, mapping using remotely control robots is one of the ideal solution for investigation such sites. However, there are technical problems to solve for applying SLAM to extreme environments, for example, how to obtain landmarks under severe environmental condition. Furthermore, for extreme environments, we have very limited or no chance for testing SLAM on the actual site, and this makes it difficult to research applying SLAM. For this reason, an evaluation method without testing on the actual 1F site is needed to promote research of SLAM for 1F. This paper introduces the development of a dataset for SLAM evaluation with the mockup field instead of the actual site, specifically the dataset of the mockup field of Primary Containment Vessel (PCV) platform under dark illumination condition.
Integrable bodies in odd-dimensional spaces

V. Arnold's problem 1987-14 asks whether there exist smooth hypersurfaces in $R^N$ (other than the conics in odd-dimensional spaces) for which the volume of the segment cut by any hyperplane from the body bounded by such a hypersurface is an algebraic function of the hyperplane. We desribe very realistic candidates for the role of such new hypersurfaces: in particular, it are examples (additional to Archimedes' conics) of such hypersurfaces, for which the analytic continuation of this volume function is finitely valued.
Containment of Simple Regular Path Queries

Testing containment of queries is a fundamental reasoning task in knowledge representation. We study here the containment problem for Conjunctive Regular Path Queries (CRPQs), a navigational query language extensively used in ontology and graph database querying. While it is known that containment of CRPQs is expspace-complete in general, we focus here on severely restricted fragments, which are known to be highly relevant in practice according to several recent studies. We obtain a detailed overview of the complexity of the containment problem, depending on the features used in the regular expressions of the queries, with completeness results for np, pitwo, pspace or expspace.
The level of empathy and education as factors moderating burnout syndrome in cancer nursing

Introduction: The burnout syndrome affects people working in various professions focused on helping others. It is an important issue especially for those nurses who work with oncology patients. The author of this study focused on relationship between levels of empathy and education of oncology nurses and burnout syndrome development. Association of empathy, education of burnout reported in this study confirms the need to develop empathy and its competence in the education process, which leads to lower stress, which is associated with particularly contact with a patient suffering , dying or being in a different situation traumatic. Material and methods: The study included 121 oncology nurses working in different departments of oncology including radiotherapy, chemotherapy, brachytherapy, transplantation, surgery and others. The study used the questionnaire method with the following research tools: Maslach Burnout Inventory (MBI) by C. Maslach and the empathy scale (EMP) taken from the PKIE questionnaire (Popularny kwestionariusz inteligencji emocjonalnej) by A. Jaworska and A. Matczak. Results: The relation between level of education and the emergence of burnout syndrome. There was no correlation between the level of empathy and the level of burnout oncology nurses. In contrast, the relationship shown a low level of empathy with the lack of professional satisfaction. Conclusions: The main factor moderating burnout syndrome among oncology nurses is their level of education. Moreover, the educational level effects the development of empathy.
Improved Harvested RF Energy for Cyber-Physical Energy Systems

Cyber-physical systems need to harvest renewable energy sources to work properly. Energy harvesting comes from sources such as solar radiation, ambient RF, wind, rain, or vibrations. RF energy can be harvested in the dark, or when no solar energy is available. RF is readily available, consistent, and reliable compared with energy harvesting from other sources. One major disadvantage of RF energy harvesting is the relatively low density of ambient RF energy in most locations. The goal of this work was to enhance the RF harvested power level by introducing a simple design and a small size. The proposed system included two circuits. Each circuit consisted of an RF power source, L matching element, and a single- stage voltage multiplier. The two circuits were connected in parallel via the filter and the resistor load. The ADS simulation tool was utilized to simulate the proposed design for the applied low input power range of -50dBm to 5dBm at 900 MHz. The load values were varied between 1K and 12K to determine the suitable load which achieved the optimum performance. Results showed that 3.11 mW as the top harvested power for the input power of 5dBm and output voltage of 3.5 V at the optimum load of 4 K . The maximum efficiency accomplished by the proposed system was 57%.
Interference Mitigation Via Cross-Tier Cooperation in Heterogeneous Cloud Radio Access Networks

In this paper, a cross-tier cooperation framework in H-CRAN is proposed to mitigate inter-tier interference. Specifically, a small cell remote radio head (S-RRH) acts as the relay for multiple macrocell users (MUEs), and obtains a fraction of time slot from multiple MUEs as a reward. Through the cooperation, the S-RRHs can obtain extra spectrum resource for serving small cell users (SUEs) which are secondary users, while the MUEs which are primary users can improve their data transmission utility. The cooperation problem is formulated as a mixed integer programming, which is NP-hard. To solve this problem, we transform the cooperation problem into a joint cooperation time and power allocation problem and a cooperator selection problem. First, we solve the joint cooperation time and power allocation problem to obtain the maximum cooperation utility of all the possible cooperating pairs. We derive the closed-form solution for the allocation problem by using linear independence constraint qualification and Karush-Kuhn-Tucker conditions. Then, we solve the cooperator selection problem by a two-sided matching algorithm. Extensive simulation results show that the utility of macrocell networks and small cell networks can be improved by adopting the proposed cooperation framework, and the cooperator selection result is stable and close-to-optimal.
Development of Backdrivable Servovalve With Feedback Spring for Enhanced Electro-Hydraulic Torque Actuator

This letter proposes a novel backdrivable servovalve to implement a torque-controlled electro-hydraulic actuator. The proposed backdrivable servovalve simultaneously contains the characteristics of a flow-control servo valve (=feedback spring) and a pressure-control servovalve (=pressure feedback port). Consequently, the torque control performance of electro-hydraulic torque actuators (EHTAs), which consist of the backdrivable servovalve and rotary vane-type hydraulic actuators, can be enhanced. First, the effective torque bandwidth, which represents the region where the torque output remains constant within a 90<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> phase lag, is improved. Second, torque-based control algorithms developed for electric motors are realized in a wide frequency region. Finally, although the effect of viscous friction increases, the effect of static friction is reduced. With these advantages, we achieved an enhanced EHTA with calculated torque output of −372<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>355 Nm; this could facilitate in the development of a high-performance interactive robot system. The proposed backdrivable servovalve and enhanced EHTA were evaluated through experiments.
A Power Converter Circuit of Co-Planar Waveguide RF Harvester Antenna for Low Power Wireless Applications

This research is focusing on a performance of a single-stage AC-DC power converter circuit for a Wi-Fi energy harvesting system. The system capable of collecting radio frequency energy and converting it into direct current supply. A co-planar waveguide antenna is designed to collect the incoming ambient RF energy source. The output from antenna is connected to a converter circuit for rectification process. The proposed circuit combines impedance matching and voltage boosting circuit that act as a harvester circuit. The simulation result shows that the simple harvester circuit can boost up voltage to 1.9 V dc with conversion efficiency of 61.4%.
Toward Distributed Streaming Data Sharing Manager for Autonomous Robot Control

Using robots is demanding for supporting our lives and/or covering works that are not suitable for human beings. The robot software implementation requires a variety of knowledge and experiences. Thereby developing cost for such software systems is now increasing. Middleware systems such as Robot Operating System (ROS) are being developed to decrease such cost and widely used. Streaming data Sharing Manager (SSM) is one of such middleware systems that allow developers to write and read sensor data with timestamps using a common PC. This feature enables developers to control a robot by taking account of measured time. This control is important because using multiple sensor data with different timestamp cannot allow developers to control a robot correctly. SSM assumes that only one PC is used to control a robot, therefore if it exists a process that consumes much CPU resource, other processes cannot finish their assumed deadlines, leading to the unexpected behavior of a robot. This paper proposes an architecture called Distributed Streaming data Sharing Manager (DSSM) that enables to distributing each process on existing SSM to different PCs. We investigate the current architecture and behavior of SSM, then propose a new architecture that can achieve our goal. Finally, we apply DSSM to an existing SSM based robot control system that autonomously controls an unmanned vehicle, then confirm the effectiveness of DSSM by measuring the resource usages.
Leaf Recognition Based on Elliptical Half Gabor and Maximum Gap Local Line Direction Pattern

Plant identification via leaf images is very meaningful to agricultural information. The existing methods were based on one or two kinds of the three distinct characteristics in leaf images including leaf contours, textures and veins. This limits their recognition performance and scope of application. This paper describes a novel counting-based leaf recognition method, which can directly and effectively combine all of the three kinds of significant characteristics in leaf images. In order to obtain the stable and independent local line responses from leaf contour, texture and vein, elliptical half Gabor is introduced and convoluted with the raw grayscale leaf images, and then maximum gap local line direction patterns are extracted from the local line responses and normalized in direction by cyclically right shifting these patterns until the most numerous bit plane with a value of 1 to the left bit. The histogram of the normalized patterns is calculated and regarded as the counting-based local structure descriptor, and support vector machine is utilized as the classifier. Experimental results on three frequently used leaf databases show that the proposed approach yields a better performance in terms of the classification accuracy, applicability and feasibility in comparison with the state of the art methods.
Artificial neural networks to estimate the forecast of tourism demand in Peru

Service companies, for the most part, do not have physical inventories that allow them to cushion demand variability. The high logistics costs of each process of the company are the reflection of these differences in the forecast. For this reason, having a successful demand forecast will generate a competitive advantage in companies that take these processes with interest. In the present work it is possible to estimate the amount of tourist packages that will be sold in the next three months using ANN (artificial neural networks) that present a decrease in the error of the current situation of 9.89% which, together with a forecast management system of adequate demand, it was thus possible to reduce the logistics costs of the services company by up to 33%.
Research and Application of Regularized Sparse Filtering Model for Intelligent Fault Diagnosis Under Large Speed Fluctuation

The speed of mechanical rotating parts often fluctuates during the working process. Vibration signals collected under constant speed have a strong correlation with the corresponding fault types. However, the mapping relationship becomes complex under large speed fluctuation, which is an urgent research subject in intelligent fault diagnosis. As an effective unsupervised learning method, sparse filtering (SF) has been successfully used in intelligent fault diagnosis. However, the generalization capability of this method to deal with large speed fluctuation remains poor. To overcome this deficiency, this study adds regularization to the loss function of SF to obtain regularized SF methods. The frequency domain signals under large speed fluctuation are directly input to regularized SF for feature extraction, and softmax regression is used as a classifier for fault type identification. Experimental results of gearbox and bearing datasets show that L1/2 regularized sparse filtering (L1/2-SF) model can solve the problem of large speed fluctuation more effectively than other regularized SF models can.
System Identification of a DC Servo Motor Using ARX and ARMAX Models

System identification is a major preoccupation in most scientific disciplines. Its use in the robotics field has grown exponentially in recent years, due to the high interest shown to movement accuracy in the industry. Therefore, this article describes an application of system identification to a DC Servomotor widely used in robots. The aim of such an experiment is to reproduce a mathematical model of the system using experimental data. To collect the inputs and output and to meet the application requirements, we have built a microcontroller-based acquisition card. Afterward, we focused our study on identifying two model structures ARX and ARMAX of the system to test and compare their results based on validation criterions. Finally, the ARMAX model has reproduced a better adjustment to the measurements compared to ARX model.