Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things

Network management by using a cognitive approach is an attractive solution for drone-based Internet of Things (IoT) environment to provide many modern facilities to IoT users. In this paper, we try to minimize the networking related issues for drone-based IoT by providing a self-organized cluster-based networking solution. We propose a Hybrid Self-organized Clustering Scheme (HSCS) for drone-based cognitive IoT which utilizes a hybrid mechanism of glowworm swarm optimization (GSO) and dragonfly algorithm (DA). The proposed scheme contains cluster formation and cluster head selection mechanism based on GSO. Furthermore, we propose an effective cluster member tracking methodology using the behavioral study of DA which ensures efficient cluster management. The cluster maintenance is performed by a mechanism to identify dead cluster member which improves the stability of the network. Further routing mechanism is proposed for HSCS in which next hop neighbor for data transmission is selected by using the route selection function which ensures efficient communication. The performance of HSCS is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with existed hybrid bio-inspired clustering algorithm.
Baza noclegowa oraz ruch turystyczny na obszarach przyrodniczo cennych na przykładzie górskich parków narodowych Hiszpanii – stan i perspektywy rozwoju

Accommodation infrastructure and tourist traffic in valuable natural areas on the example of mountain national parks in Spain: present state and perspectives of development Background. Valuable natural areas are significant tourist destinations. However, a large number of tourists is a threat to sensitive mountain ecosystems. Thus the Spain pay particular attention to sustainable development, especially where nature can be mostly endangered. The article is an attempt to assess the state of selected elements of tourism in mountain national parks of Spain and the perspectives of their development. Material and methods. The main method is an analysis of statistical data referring to the accommodation infrastructure of rural tourism, especially of protected mountain areas, as well as data concerning tourist traffic. results. The most important forms of tourism practiced in natural areas are presented; the importance of rural and nature tourism is emphasized in the context of sustainable development. The main issue that influences the development is accommodation infrastructure. Taking into account the perspectives of tourism development in the mountainous national parks, the need to create an offer more open to foreign tourists was underscored. Among the threats, the problem of unifying accommodation units operating in the rural environment was pointed. The solution demands changing legal situation and the competences of the Spanish autonomic regions, which seems to be a difficult task. conclusions. Tourism in the mountain national parks of Spain functions properly based on the tourist attractions, and the development perspectives are good provided that the question of systematizing accommodation unit types is solved, and efforts to attract foreign tourists are undertaken. key words: rural tourism, nature tourism, mountain national parks, Spain, tourist traffic słowa kluczowe: turystyka wiejska, turystyka przyrodnicza, górskie parki narodowe, Hiszpania, ruch turystyczny ROZPRAWY NAUKOWE Akademii Wychowania Fizycznego we Wrocławiu
An Intelligent Control for Synchronous Generator Based Wind-PV-Battery Microgrid System

A new topology of microgrid comprising of a wind turbine driven synchronous generator (SG) and solar photovoltaic (PV) with a battery as an energy storage (ES) for reliable renewable energy harvesting is proposed in this work. The stochastic behavior of the inputs for proposed system, is agitated by the nonlinear time dependent parameters such as variable wind speed and changing solar insolation. The speed variations are absorbed using back to back interfaced voltage source converters (VSCs) namely synchronous generator side VSC (SGVSC) and load side VSC (LSVSC) with a common DC link where a double stage solar PV array with ES is tied directly. The SGVSC uses vector control (VC) for speed control of SG and maintains unity power factor (UPF) at the stator terminals. The hybrid fuzzy precompensated (HFP) proportional integral (PI) controller is used as the speed controller. The battery with LSVSC, absorbs the changes in the load and regulates the DC link voltage. A prototype is developed and tested under different conditions of sudden disconnection of load, wind velocity variations as well as under varying solar PV insolation. Test results validate the efficient functioning of the control algorithms along with keeping the system performance intact under the imposed changes.
Comparative Study of Heart Rate Extraction Methods for a Novel Intelligent Mattress

Unobtrusive monitoring of the heart rate (HR) is essential for improving medical intervention. A new generation of mattress-based fiber optic sensor (FOS) is emerging for HR monitoring. The use of this FOS mattress for medical diagnosis requires appropriate advanced signal processing algorithms. In our study, we aim to weigh the performances of a novel and cheaper microbend FOS mattress by applying ballistocardiogram and HR extraction algorithms. Therefore, our study targets comparing four types of HR extraction algorithms on the FOS mattress, namely MODWT, CEEMDAN, cepstrum and clustering. The goal is to select, based on their accuracy and computational speed, the most suitable one for online or offline application purposes. Results of applying these four chosen algorithms on the FOS mattress show that the cepstrum is the most accurate algorithm with a mean absolute error (MAE) of 4.62 ± 1.68 BPM. However, the cepstrum is more appropriate for offline monitoring with a runtime of 662.9 ms for a 10-second signal segment. The results also show that the Maximal Overlap Direct Wavelet Transform (MODWT) is more efficient with a runtime of 4.1 milliseconds for online purposes, but with a slightly bigger MAE (6.87 ± 1.94 BPM). Both methods proved to be as efficient on the new mattress technology as past intelligent mattresses.
Evaluation of the factors influencing the outcome of trigger points’ treatment with procaine 0,5%: 190.

MaterialMethod: We studied 35 patients suffering from myofascial syndrome ( 25 patients suffered from lowback pain, 10 from cervical pain). After palpation and identification of the Trigger Points according to the Simons and Travel criteria, a 25G spinal needle was inserted and procaine 0,5% was injected in the spot of worst pain as mentioned by the patient. We recorded: a) the depth (length of the inserted needle at which the worst pain appeared), b) the size of each Trigger Point ( estimated by palpation), c) the amount of local anesthetic injected and d) the Visual Analogue Scale pain score (VAS 0-10), during palpation of the Trigger Points, before and after injection of the local anesthetic. In order to compare pain scores, paired ttest was used. The relationship between reduction in pain scores, Trigger Point’s depth and size and the amount of procaine injected, was determined by Person’s correlation tests. Results were expressed as mean values (extent).
Non-Saliency of Interior Permanent Magnet Hybrid Excitation Machine

It is a general perception that the interior permanent magnet machines will have different values of inductances along d-axis and q-axis and hence they are salient pole machines. Though it is true in most of the cases, some interior permanent magnet machines may turn out to be non-salient pole machines. Hence an accurate modeling of magnetic equivalent circuit is very much required in order to estimate the inductance values along d-axis and q-axis. A hybrid excitation machine with an interior configuration of permanent magnets and field winding is proposed in the literature. Though it appears to be a salient pole machine from rotor configuration view point, the actual analytical inductance analysis on this machine proves that it is a non-salient pole machine. A simpler analytical approach is carried out through approximate linear magnetic equivalent circuit. The analytical analysis is validated with analysis carried out through FEM software.
On-line Energy Optimization of Hybrid Production Systems Using Actor-Critic Reinforcement Learning

This paper presents a novel approach for energy optimization in large scale industrial production systems based on an actor-critic reinforcement learning (ACRL) framework. The objective of the on-line capable self-learning algorithm is the optimization of the energy consumption of the whole production process. Our central ACRL framework is realized by artificial neural network (ANN) function approximation using Gaussian radial-basis functions (RBF) for the critic and the actor, respectively, and gives the opportunity to cover not only a discrete but also continuous state and action space, which is necessary for hybrid systems where discrete and continuous actuator behavior is combined. For testing and validation purposes we develop a software model of our bulk good laboratory plant as application example for the developed ACRL algorithm. The model is based on mass-flow equations for a continuous bulk good supply whereas the energy consumption is modeled by functions dependent on the actuators behavior. The capability of our machine learning (ML) approach for energy optimization is underlined by simulation results for the task of supplying bulk good to a subsequent dosing section.
CT-guided percutaneous microwave ablation of lung: A preliminary study

To assess the feasibility and safety of percutaneous microwave ablation of the lung in rabbits, percutaneous puncture and lung microwave ablation were performed in seven New Zealand white rabbits under computed tomography (CT) guidance. Technical feasibility and complications were evaluated. Two rabbits were sacrificed immediately and gross and microscopic pathology was analyzed. The remaining rabbits received CT scans 1 week and 3 months later. CT scans showed the ablation size was 1.1 ~ 1.6 cm immediately after ablation. The lesions shrank slightly 1 week later. After 3 months, the ablation lesions involuted significantly and even disappeared. Mild pneumothorax was found in 2 rabbits but resolved spontaneously 1 week later. Histologic changes in the microwave lesions were observed which were characterized by the central necrosis and edema of surrounding tissues. It is feasible and safe to perform CT-guided percutaneous microwave ablation of the lung in rabbits. Although further study is still required; microwave ablation can be used as a minimally invasive and effective therapeutic technique in patients with lung tumors. Key words: Lung, computed tomography, microwave ablation.
The Influence of Volcanic Aerosol Meridional Structure on Monsoon Responses over the Last Millennium

Monsoon responses to eruptions over the last millennium (LM) are examined in an ensemble of climate simulations as a function of eruption hemisphere. A composite analysis reveals a particularly strong sensitivity of monsoon rainfall in the year following Northern Hemisphere (NH) extratropical eruptions. Additional analysis focusing on the 18th century eruption of Mt. Laki and idealized simulations representing an analogue Southern Hemisphere eruption (SH‐Laki) reveal monsoon responses that are approximately symmetric across hemispheres, despite exhibiting asymmetries in other aspects of the climate response. We conclude that 1) latitudinally mirrored eruptions result in approximately symmetric monsoon responses, 2) disproportionate weakening (strengthening) of NH (SH) monsoons by NH eruptions over the LM resulted in part from their relatively high latitudes, and 3) uncertainty in eruption latitude fundamentally limits our ability to accurately simulate associated monsoon and tropical precipitation responses in nature.
Memory network for tracking with deep regression

Visual object tracking has been a fundamental topic in recent years and many tracking-by-detection approaches have achieved state-of-the-art performance. However, most of these trackers neglect the temporal information of the targets through sequences. In this paper, we develop a memory network to exploit the history of object locations as well as the distinctive visual features, which then serves as a region predictor for the new frame. In contrast to most existing trackers which use binary classification to score region candidates drawn around the target, this approach gets one specific heuristic proposal from the memory network, followed by bounding box regression for precise tracking results. Specifically, Memory and Regression based Tracker (MRT) consists of a CNN-based network for feature extraction, a stacked Long Short-Term Memory network (LSTM) for memory updating and a set of regression layers for proposal refinement. This model is fully differentiable and can be trained end-to-end. Evaluation of the proposed method is performed on the VOT2018 dataset which shows that the MRT achieves competitive performance.
Blockchain-Based Secure and Trustworthy Internet of Things in SDN-Enabled 5G-VANETs

The modern intelligent transportation system brings not only new opportunities for vehicular Internet of Things (IoT) services but also new challenges for vehicular ad-hoc networks (VANETs). Apart from enhanced network performance, a practical and reliable security scheme is needed to handle the trust management while preserving user privacy at the same time. The emerging 5G mobile communication system is viewed as a prominent technology for ultra-reliable, low-latency wireless communication services. Furthermore, incorporating software-defined network (SDN) architecture into the 5G-VANET enables global information gathering and network control. Hence, real-time IoT services on transportation monitoring and reporting can be well supported. Both pave the way for an innovative vehicular security scheme. This paper investigates the security and privacy issue in the transportation system and the vehicular IoT environment in SDN-enabled 5G-VANET. Due to the decentralized and immutable characteristics of blockchain, a blockchain-based security framework is designed to support the vehicular IoT services, i.e., real-time cloud-based video report and trust management on vehicular messages. This paper explicitly illustrates the SDN-enabled 5G-VANET model and the scheduling procedures of the blockchain-based framework. The numerical simulation results also show that malicious vehicular nodes or messages can be well detected while the overhead and impact on the network performance are acceptable for large-scale scenarios. Through case studies and theoretical analysis, we demonstrate our design substantially guarantees a secure and trustworthy vehicular IoT environment with user privacy preserved.
Experimental Observations on Hybrid RF-Solar Energy Harvesting Circuit for Low Power Applications

This paper investigates the performance of hybrid RF-Solar harvesting circuit that can simultaneously harvest power from solar and radio frequency (RF) sources readily available in the surrounding environment. The proposed work utilizes stand-alone RF and solar harvester circuits to realize a hybrid harvester circuit. The stand-alone RF harvester circuit is a dual-band multi-stage harvester that has been designed to work at GSM 900 MHz and Wi-Fi/WLAN bands. The standalone solar harvester circuit comprises of a solar panel with inbuilt rectifier circuit. The output DC voltage is obtained across the load of the two harvester circuits. The load of the two stand-alone harvester circuits are connected in series to provide DC voltage addition ensuring maximum power transfer at the load resistance of the hybrid harvester circuit. The performance of hybrid circuit has been evaluated for outdoor environment at different times over a day. Even when there is a sudden drop in power coming from standalone RF or solar harvester circuit, the power of hybrid harvester circuit does not fall abruptly due to backup from other dominant harvester circuit. This avoids sudden drop in power requirement by a device ensuring its continued operation and safety.
Control Scheme for Improved Efficiency in a H-bridge Buck-Boost Converter

This paper presents a control scheme for achieving improved efficiency in a H-bridge buck-boost DC-DC converter. The H-bridge buck-boost converter with conventional control scheme operates at lower efficiency compared to buck and boost converter for the same step down/up action, due to the absence of direct power component. A control scheme is presented in this paper to achieve better efficiency by introducing maximum direct power component without affecting the steady state voltage gain. A hardware prototype of 500 W is tested with the proposed control scheme and an efficiency improvement of 4 % is observed as compared to that of the conventional control.
Winding Switching and Turn Switching in Permanent Magnet Vernier Machines for Wide Speed Range Operation and High Efficiency

This paper proposes to turn to switch along with winding switching for high-efficiency of permanent magnet vernier machines (PMVM) during wide speed range operation for electric vehicles application. For winding switching, the three-phase winding of the machine is divided into two sets of windings, namely, winding set A, B, C and winding set X, Y, Z. For turn switching, the number of turns for one set of winding is changed. With the different number of turns for winding set X, Y, Z, the efficiency of the machine can be significantly improved. Initially, the basic electromagnetic characteristics, such as the flux linkage, back electromotive force (EMF), torque, and phase voltages of the machine are investigated using winding switching and turn switching. Then, the output power, torque-speed curve, core losses, efficiency, and power factor of the machine are analyzed in detail using a different number of turns. In addition, the transient effects of winding switching and turn switching have been analyzed. Finally, a control strategy is proposed which combines turn switching and winding switching to ensure high-efficiency of PMVMs during wide speed range operation. A PMVM is designed and driven in accordance with the proposed control strategy (PCS) to show the characteristics of PCS. The PMVM driven with PCS is compared to a recently presented PMVM. The recently presented PMVM is driven by a different control strategy and is analyzed with two different kinds of steel laminations that are general steel (50TW470) and special steel (20JHF1300).
Predictive Model for Fire Outbreaks: A Case Study in the Ashanti Region, Ghana

The current changes in ecosystem functioning and climate systems are having major impact on Fire Outbreaks conditions globally. It is worrying that not much work appears to have been done in Ghana regarding the formulation of statistical and other models for predicting Fire Outbreaks. Due to this, actuarial and insurance practitioners are unable to effectively help manage the risk of Fire Outbreaks. This study sought to predict monthly fire outbreaks by employing the Box-Jenkins approach to model fire outbreaks using time series data from 1997 to 2014. Several SARIMA (Seasonal Auto Regressive Integrated Moving Average) models were tested and the model with lowest Akaike Information Criterion (AIC) was selected. The analysis revealed that ARIMA (4, 1, 1) (1, 1, 1)12 model was the best SARIMA model for the Fire Outbreaks since diagnostic checks revealed its adequate for predicting the monthly number of fire outbreaks in Ashanti Region of Ghana. The sixteen years forecast with this model revealed that the number of fire outbreaks will continue to increase with time. This continuous increase in the pattern of the number of fire outbreaks as evident from the forecast results could be a great danger to the economy of the country. The results achieved for fire forecasting will help to estimate number of fire events which can be used in planning the fire activities in that region. The study recommends that, stakeholders and management of fire should make use of this formulated SARIMA model for the purpose predicting, mitigating and insuring against fire outbreaks in Ghana.
A Robotic Grasping Method using ConvNets

In the near future, most of the industrial robots will serve as assistants involved in targeted complex manufacturing tasks which are difficult to be automated. To achieve this, it is crucial to enhance the ability of manipulators to pick and place objects from the assembly line. Reorienting and picking up pieces for assembly are difficult to be done by manipulators since, for different pieces, shapes and physical properties vary. In this work, we use Convolutional Neural Networks for recognizing a selected production piece on a cluster. Once the selected piece has been recognized, a grasping algorithm estimates the best gripper configuration so that the robot is able to pick the piece up. We tested our algorithm on grasping experiments with an ABB robot and using a common webcam as image input. We found that our implementations perform well and the robot was able to pick up a variety of objects.
A New Soft-Switched High Gain Boost Converter

This paper presents a new soft-switched high gain coupled-inductor boost converter operating within permissible range of duty ratio. All semiconductor switches of the proposed converter change their states under soft switched condition and the diodes commutate softly. Operation of the proposed converter is presented and analyzed in detail. A hardware prototype of the proposed converter rated with nominal 16V-24V input, 220V/500 W output and 100 kHz switching frequency was designed, fabricated and tested in the laboratory. Switching behavior of the converter was found to be matching with the predictions.
Image-based 3D Reconstruction: Neural Networks vs. Multiview Geometry

Methods using multiple view geometry (MVG), like Structure from Motion (SfM), are still the dominant approaches for image-based 3D reconstruction. These reconstruction methods have become quite robust and accurate. However, how robust and accurate can artificial neural networks (ANNs) reconstruct a priori unknown 3D objectsƒ Exceed the restriction of object categories this paper evaluates ANNs for reconstructing arbitrary 3D objects. With the use of a synthetic scalable cube dataset for training, testing and validating ANNs, it is shown that ANNs are capable of learning mathematical principles of 3D reconstruction. As inspiration for the design of the different ANNs architectures, the global, hierarchical, and incremental key-point matching strategies of SfM approaches were taken into account. Based on these benchmarks and a review of the used dataset, it is shown that voxel-based 3D reconstruction cannot be scaled. Thus, voxel-based reconstruction might be misleading for capturing the complexity of real-world images. Also, the benchmark results show that ANNs have the same limitation by reconstructing unknown object categories as current MVG approach.