Miracle-dz/UniFAR-FTUs huggingface dataset:
Successional changes in plant species diversity in forest chronosequences on southern Vancouver Island, British Columbia.

Six chronosequences were established on southern Vancouver Island, British Columbia, to address the concerns regarding the potential loss of species diversity and increased risk to rare or old-growth–dependent species due to large-scale harvesting of old-growth forests. Each chronosequence comprised three post-harvest seral stages, representing regeneration, immature and mature, and an old-growth forest. Composition and cover of trees, shrubs, herbs, and cryptogams were sampled in each stage. On forested plots, cryptogams (i.e., bryophytes and lichens) made up about half of the species, of which most occurred on coarse woody debris and rock substrates. Sites dominated by Douglas-fir on eastern Vancouver Island exhibited substantial differences among successional stages in the numbers of herbs, shrubs, and cryptogams, but the overall number of species did not change dramatically between stages. On western Vancouver Island, where western hemlock was the dominant species, the regeneration stage supported far more species than later stages did. Mature and old-growth forests do not necessarily maintain larger numbers of species than younger successional stages do, but they may support species that are uncommon or absent in immature forests and regeneration sites. Those species found to be restricted to older forests differed between eastern and western Vancouver Island, suggesting that care must be taken in applying information about species distributions, patterns, and tolerances from one ecosystem type to another. The implications of our findings to forest management are discussed.
Modelling of a Rotating Signal Injection-Based Position Observer for Sensorless Synchronous Electric Drives

A new formulation of the observer transfer function (TF) for rotating injection-based techniques is proposed. The modulation/demodulation theory is exploited to derive the estimator TF in the Laplace-domain. The accuracy of the proposed observer TF is verified with several regulators designed with two approaches, namely, direct synthesis (DS) and internal model principle (IMP). Theoretical considerations and accuracy of the proposed model are verified through an extensive simulation stage. Time response and position tracking of different regulators designed for a wide range of bandwidth are investigated. Finally, the performance of the regulators designed with the conventional observer TF and the proposed one are compared highlighting the flaws of the former approach.
Fracture studies from amplitude versus offset and azimuth and vertical seismic profile data

In this thesis I address the problem of determining fracture properties of subsurface rocks from geophysical surface seismic and vertical seismic profile (VSP) data. In the first part of this thesis I perform multi-attribute analysis, including frequency content, amplitude, travel time and angle of rotation studies on field VSP data from two different carbonate fields, both containing time-lapse surveys. I compare the findings to independent data available in the region and find that the interpreted fracture orientations from the attribute analyses correlate with independent fracture studies in the area, the principal axis of major faults, or the maximum horizontal stress of the area studied. Although I show the existence of these correlations, due to the limited knowledge of the rock properties, these correlations are only qualitative. A more robust inversion of fracture properties requires more knowledge of the physical properties of the medium and forward modelling of the seismic response. A rock physics theory would be required to model the elastic response of the fractured rock; hence a more quantitative fracture characterisation is necessary. In the second part of this thesis I address this need by developing and testing a method for fracture density inversion. Linearised approximations are commonly used in azimuthal amplitude versus offset (AVO) analysis. However, these approximations perform poorly at large angles of incidence where the effect of fractures is more significant. The method proposed here uses a model based approach that does not use these approximations but calculates the exact azimuthal AVO response based on prior knowledge of the elastic constants of the medium, assumed to be known, and a range of fracture densities. A rock physics theory is used for modelling the elastic constants of the fractured rock. I then create a linearized relationship for a specific model that separates the effect due to fracture density from the modelled AVOZ responses. This separation is key to the method, as
Application Research of 3D MSVR-DV-Hop Algorithm Based on Node Filtering

Wireless sensor network has been widely used as an important means of perceiving and monitoring the real environment. Node location algorithm is the key supporting technology for the normal operation of wireless sensor network nodes. To achieve higher positioning accuracy and improve the adaptability to the network, a beacon-based MSVR-DV-Hop (Multidimensional Support Vector Regression-DV-Hop) algorithm is proposed in three-dimensional scenes. The three stages of hop acquisition, distance estimation and coordinate calculation in classical DV-Hop algorithm are improved, and simulation experiments and result analysis are carried out in three-dimensional scene. The positioning accuracy of this algorithm is significantly improved compared with other algorithms in three-dimensional scenes, positioning error fluctuations are significantly improved in different anisotropic scenes, and positioning error fluctuations are stable in different anisotropic scenes, which has better adaptability and accuracy. Positioning errors in three-dimensional scenes are reduced by at least 56% compared to the classical three-dimensional DV-Hop algorithm and 12% compared to the LMSVR algorithm.
Channel Prediction-based Robust Multibeam Precoding Design for LEO Satellite Internet of Things

Multibeam precoding is widely recognized as an enabling technique to support wide-area massive connectivity with limited spectrum in LEO satellite Internet of Things (IoT). However, timely channel state information (CSI) acquisition for multibeam precoding is a critical issue due to fast mobility of LEO satellites. To this end, we propose a novel channel prediction method to obtain current CSI by using past estimated CSI through an autoregressive model. Then, we design an effective robust multibeam precoding algorithm for LEO satellite IoT by maximizing the weighted sum rate of IoT devices based on predicted CSI. Both theoretical analysis and simulation results confirm the effectiveness of the proposed algorithm.
Study on the Self-Explosion Defect Detection Methods in Glass Insulators Based on Modified YOLOv7

With the increasing utilization rate of glass insulators in transmission lines, in order to adapt to the current mode of unmanned aerial vehicle (UAV) -assisted inspection, a modified YOLOv7 glass insulator self-explosion defect detection method is proposed to meet the demand for glass insulator defect detection in the process of transmission line inspection. ST attention mechanism was introduced, and its module was added to the 11th layer of YOLOv7 network model to strengthen the detection effect of small targets and incomplete insulators. The coordinate loss function was modified, the formula of SIOU loss function was defined, and the position of detection box was corrected. Some glass insulator images in the data set CPLID and the glass insulator images taken by the UAV are used to train the modified network. The detection results show that the detection accuracy and speed are significantly modified.
Fortified-Edge 2.0: Machine Learning based Monitoring and Authentication of PUF-Integrated Secure Edge Data Center

A collaborative edge computing model involves multiple edge devices, such as Edge Data Centers (EDCs) and Edge Routers. Data can be stored and processed at the Edge in Collaborative Edge Computing (CEC). Edge security is very crucial as it is prone to external attacks at different layers of its architecture. In this work, a novel machine-learning-based approach is proposed to improve the security of EDCs using Security-by-Design (SbD) principle in CEC framework. This research aims to provide a security scheme for EDCs in a collaborative edge computing environment through machine-learning-based monitoring and authentication. Through this method, any drastic change in the device’s behavior will be considered critical, and the device can be removed from the network, securing the network from further harm. This research proposes a support vector machine (SVM)-based algorithm to monitor the EDC authentication process and detect an intrusion or malicious authentication requests during load balancing.
Improving Performance of Photovoltaic Solar Pumps Using PSO-Based Maximum Power Point Tracking

This research project aims to enhance the efficiency and performance of photovoltaic (PV) solar pumps through the implementation of a Particle Swarm optimization (PSO)-based Maximum Power Point Tracking (MPPT) technique. The primary objective of this study is to design and model the Cuk converter for application in a PV water pump system, integrating the PSO-based MPPT technique to achieve optimal power extraction from the PV array. The MPPT algorithm, based on the PSO method, was developed and thoroughly validated through comprehensive simulations conducted in MATLAB/Simulink. Key highlights of the project include the achievement of over 80% accuracy for the solar module combined with a Brushless DC (BLDC) water pump, while effectively increasing the pumping speed by more than 50%. Through the incorporation of the PSO-based MPPT strategy, the system demonstrates superior power extraction and transfer capabilities, ensuring the maximum utilization of PV energy for water pumping operations. This research contributes to the advancement of sustainable energy solutions by enhancing the operational efficiency of PV water pumps. The proposed PSO-based MPPT technique, integrated with the Cuk converter, offers an innovative and effective approach to harnessing solar energy for water-pumping applications. The findings of this study provide valuable insights for researchers and practitioners working towards the integration of renewable energy systems in various applications
A Low-Complexity PAPR Reduction Scheme for Multi-User MIMO-OFDM Systems

Multi-user multiple-input multiple-output orthogonal frequency division multiplexing (MU-MIMO-OFDM) is an attractive technique for high-data-rate transmission. However, one main disadvantage of the MU-MIMO-OFDM system is the high peak-to-average power ratio (PAPR), which causes inefficient power amplifier utilization and limits the transmit power. To address this issue, some effective PAPR reduction schemes, such as the conventional active constellation extension (ACE), can be applied for the MU-MIMO-OFDM systems, which limits the maximum amplitude of the signals by iteratively modifying the signal constellation. However, the iterative signal processing procedures induce excessive computation complexity and delay, which is prohibited in high data rate communications. In this paper, we propose a low-complexity PAPR reduction method, where an improved tone reservation (ITR) scheme based on least square approximation (LSA) is first performed, and then the modified ACE scheme is appiled to further decrease the PAPR. Compared to the conventional ACE scheme, simulation results show that the proposed method significantly reduces computational complexity without sacrificing BER, and achieves better PAPR.
Identification of Mechanical Impedance of an Electric Machine Drive for Drivetrain Design

This paper proposes a method for identifying mechanical impedance of an electric machine drive for drivetrain design and analysis. The mechanical impedance describes the dynamics of an electric machine as seen from its mechanical terminals. It depends not only on the electric machine but also on the control system, which should be taken into account when analyzing torsional vibrations. If the black-box input-output time-domain models of the electric machine and its control system are available, the mechanical impedance can be extracted from simulations. The proposed identification method is based on signal injection in time-domain simulations. At steady-state operating points, an excitation signal is injected into the rotor speed and the response is observed in the electromagnetic torque. The method is validated by comparing the identified mechanical impedances with the corresponding analytical solutions.
Inspection Path of UAV Power Tower Based on Multi-Sensor

Electric energy, as an efficient and clean energy for production, plays a very critical role in social and economic development. In order to improve the power supply capacity, under the situation of reform and opening up, my country's electric power industry has not only rapidly increased the installed capacity of power generation, but also greatly improved its power transmission capacity. In this paper, fault inspection and analysis of power towers are carried out. In transmission lines of different levels, power poles and towers often have different types and have different functions, which also makes the manual daily inspection work more complicated. Based on the multi-sensor UAV technology, this paper discusses the types of transmission towers and their functional characteristics, analyzes the types of possible faults, and proposes the locations and methods of detecting different faults. Image processing technology is used to perform various processing on pictures or video information containing gestures to achieve the purpose of recognition. It mainly refers to the steps of image segmentation, hand positioning, feature extraction and recognition. This method is a more intuitive method. The final results of the research show that with the increase of fault test points, the fault detection rate shows a trend of changing in the opposite direction. When the number of faults is 56, the detection accuracy rate is 94.56%, and when the number of faults is large, the detection accuracy there are some errors.
Parameter Identification Methods for Multi-Phase Permanent Magnet Synchronous Machines

This paper presents and assesses parameter estimation methods for multi-phase permanent magnet synchronous machines. More specifically, three different methods are considered, namely, a step-response method for identifying the stator resistance and absolute inductance values, a Fourier analysis-based method to obtain the harmonics in the permanent magnet flux-linkage, and a methodology to acquire the flux-linkage maps. To verify the effectiveness of the methods in question, experimental tests based on a six-phase PMSM drive are performed, and the results are compared with those acquired with finite-element-analysis simulations. Finally, to promote open science and make the findings of this work publicly available, all results are published within the open-source UltraZohm project.
Peculiarities of Supporting Anti-Crisis Decision-Making in the Conditions of the Epidemic Danger of the Spread of COVID-19

The work examines the peculiarities of the functioning of situational centers in the event of an epidemic danger of the spread of COVID-19. A procedure has been developed to support the adoption of anti-crisis decisions at the regional management level regarding the implementation of targeted actions aimed at minimizing the consequences of medical and biological emergency situations in the conditions of the epidemic danger of the spread of COVID-19.
A Robust and Efficient Measurement Method of Lumbar Lordosis Based on Three Dimensional Curvature Line

Lumbar lordosis (LL) is an important physiological parameter used to evaluate the degree of curvature of human lumbar in the sagittal plane. The accurate and reliable measurement of lumbar lordosis is of great significance for diagnosis, clinical treatment and rehabilitation evaluation in the care of degenerative lumbar diseases such as lumbar disc herniation and lumbar spondylolisthesis. Cobb and Centroid are the standard methods for measuring LL in clinical practice but show poor reliablity and low efficiency since the accuracy of the methods depends on the subjective judgment and clinical experience of operators. Here, we present a robust and efficient method for measuring LL. The core of the method involves an algorithm based on the variable step Runge-Kutta (RK) integral, extracting the three-dimensional curvature line (3DCL) on the lumbar simplified model constructed from computed tomography (CT) images. Our experimental findings show that the standard error (STD) and root mean square error (RMSE) of the method proposed in this paper (0.43 $\pm$ 0.506, 0.383 $\pm$ 0.441) are superior to the Cobb method (1.915 $\pm$ 1.237, 1.712 $\pm$ 1.079) and the centroid method (1.789 $\pm$ 1.297, 1.601 $\pm$ 1.134)(p<0.05). Moreover, the correlation coefficient R between the result of the single measurement in our method and the average of multiple measurements in the standard method was relatively high (0.984, 0.978). The results of our comparative analysis showed that the proposed method is more robust and efficient, having the potential for improving the diagnosis and therapeutic monitoring of lumbar diseases.
Two-Stage Unsupervised Classification of Cell Health

Supervised learning for cell classification is one of the most used approaches on different studies. However, due to lack of labelling datasets provided by experts, and the small number of images per dataset, the usage of unsupervised learning would be a better approach. This work reports a study done on a two-stage unsupervised classification of cell health: i) phase one: divide the dataset into two main groups healthy and unhealthy, and ii) phase two: divide healthy group into two smaller clusters and unhealthy group into three different clusters. The groups are defined following the description of the cell health ISO STANDARD for invitro cytotoxicity evaluation. Unsupervised learning is done based on image features and the labelling of the clusters is done after it. K-means algorithm was used for clustering and two different datasets were tested. The second dataset had the best performance and the correct labelling of the clusters.
Ditto: Towards Decentralised Similarity Search for Web3 Services

The Web has become an integral part of life, and over the past decade, it has become increasingly centralised, leading to a number of challenges such as censorship and control, particularly in search engines. Recently, the paradigm of the decentralised Web (DWeb), or Web3, has emerged, which aims to provide decentralised alternatives to current systems with decentralised control, transparency, and openness. In this paper we introduce Ditto, a decentralised search mechanism for DWeb content, based on similarity search. Ditto uses locality sensitive hashing (LSH) to extract similarity signatures and records from content, which are stored on a decentralised index on top of a distributed hash table (DHT). Ditto uniquely supports numerous underlying content networks and types, and supports various use-cases, including keyword-search. Our evaluation shows that our system is feasible and that our search quality, delay, and overhead are comparable to those currently accepted by users of DWeb and search systems.
Broadband Dual-Polarized Magnetoelectric Dipole Antenna for In-Band Full-Duplex Application

A broadband dual-polarized magnetoelectric (ME) dipole antenna is proposed for in-band full-duplex (IBFD) application. The exploited antenna comprises a cross ME dipole, two П-shaped and Г-shaped feedlines, and a ground with a box-shaped baffle. A wideband 180° power divider is connected to the П-shaped feedline and a coaxial line directly to the Г-shaped feedline to excite the cross ME dipole with dual-polarization. Meanwhile, the introduced baffle will stable the radiation pattern and improve the realized gains over a wideband frequency range. As a result, a broadband hybrid decoupling technique is adopted in this design, and the measured antenna prototype realizes an impedance bandwidth of 1.7–2.7 GHz (45.4%) for the 10 dB return loss, high isolation of better than 50 dB, and the realized gains of more than 7 dBi. These characteristics make the proposed antenna a promising candidate for IBFD base station systems.
L-BANCS: A Multi-Phase Tile Design for Nanomagnetic Logic

The CMOS (Complementary Metal Oxide Semiconductor) technology is the industry standard for chip fabrication. Currently, CMOS faces ever-increasing thermal, power, and miniaturization challenges. As a result, researchers are putting efforts into novel alternative technologies to handle these issues, such as nanomagnetic logic (NML), which uses nanomagnets to perform binary logic. This paper presents a novel L-Shaped clocking scheme to synchronize NML circuits. Our proposal is scalable, simple to use, and reduces the number of constraints for placement and routing algorithms for circuit generation. In addition, the L-Shape clocking scheme introduces tiles with a multi-phase design, which allows for a reduced area overhead at the cost of latency, solves feedback path issues, and introduces a model to work with modern NML features. Our results demonstrate a small latency trade-off for a considerable area reduction. Finally, we validate our work with layouts in Topolinano.
MLP Network Prediction for Blast Explosive based Training Algorithm

For many years, researchers have been examining the profile of blast waves resulting from detonations and using experimentation to make predictions based on specific parameters. However, previous studies have mainly focused on the central point of initiation for spherical explosive shapes. The aim of this study is to compare the accuracy of predicting the blast peak overpressure based on various factors, including the type and shape of the explosive and the location of detonation. The experiment involved detonating 500 grams of PE-4 and Emulex at different distances (ranging from 0.5 to 4.0 meters) and creating a prediction model using a Multilayer Perceptron (MLP) network. Bayesian Regularization (BR) proved to be more effective than Backpropagation (BP) when modelling Explosive Blast Prediction. The BR training with Logsig training algorithm shows the best performance with 0.9280 and 0.9658 for MSE and regression, respectively.