Miracle-dz/UniFAR-FTUs huggingface dataset:
Diamond NV Centers Based Quantum Sensor Using a VCO Integrated With Filtering Antenna

This article presents a diamond nitrogen-vacancy (NV) center-based magnetic field sensor using a voltage-controlled oscillator (VCO) integrated with a filtering antenna. In existing NV-based quantum sensing systems, microwave source and delivery units are implemented by a set of bulky and costly instruments. This limits its practical applications as well as portability. Here, a VCO integrated with the filtering antenna is designed, providing a more uniform, efficient, broadband and lower phase noise microwave field coupling to the NV centers. As a result, the sensitivity and the dynamic range have been improved over the conventional scheme using separate microwave components, such as antennas, transmission lines, and resonators. Moreover, in this work, the optic filter and photodetector are also integrated using the hybrid microwave integrated circuits (HMICs) technique for the first time. This significantly increases the integration level and reduces the cost of the NV-based magnetometers. The experimental results indicate that our VCO integrated filtering antenna achieves a sensitivity of $8.52~\mu \text{T}$ /Hz $^{\mathrm {1/2}}$ and a dynamic range up to 32.73 dB, which are both greatly improved compared to the VCO with a single resonator. This validates the effectiveness of the approach.
Robotic remote control based on human motion via virtual collaboration system: A survey

This paper offers a review of the latest robotic remote control schemes that are based on human motion through the virtual collaboration system. The robotic remote control schemes have been studied and developed to combine the intelligence of humans with the precision and mechanical strength of robots. They have performed an important role in some dangerous environments by replacing humans in many difficult jobs, but the lack of intuitive control interfaces causes decreased efficiency. To address this problem, various approaches have been recently investigated, including human motion-based control, visual feedback systems, tactile or force feedback, and other various haptic systems. This paper describes the tendencies of these efforts and discusses possibilities and limitations in three parts: remote control based on human motion, multimodal feedback schemes for realistic interaction, and virtual collaboration systems.
FSS bandwidth design using miniaturized elements for angular stability in transmission

Frequency-selective surfaces (FSSs) are commonly employed at microwave frequencies in electromagnetic filtering applications. This paper reports the use of cascaded dual miniaturized elements for radio secure environments where miniaturized elements have been used creating a reduced unit cell (<0.1 wavelengths), and exceptional angular stability. Thin, flexible and optically transparent FSS samples were fabricated by laser engraving elements into a conductive aluminium layer, with a supporting Mylar substrate (relative permittivity 2.7), and thickness of 65 μm. The linearly polarized transmission response was investigated numerically by exploring the scattering parameters and the surface currents at resonance. Miniaturized elements in both single and cascaded FSS structures, have been explored to create both broadband and multiband responses across the L and S frequency bands. The theoretical and experimental results were in good agreement for the fabricated dual element dual layer translated band-stop FSS, displaying a resonant frequency reduction of 70% compared to a square loop with the same dimensions, along with significantly broad operation bandwidths of 118% and 104% for simulated and measured, respectively. These improvements were due to the selection of the individual elements in the array, their geometric arrangement, and the introduction of additional layers with lateral offsets.
Automated and non-invasive UAV-based system for the monitoring and the group size estimation of dolphins

Unmanned Aerial Vehicles (UAVs or drones) have been extensively applied in monitoring studies to address marine and terrestrial wildlife animals. The main advantages of UAV s are their operational flexibility and low cost. In the last few years, the need to safeguard and protect the population of cetaceans in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea) has grown in importance, and the UAVs video-analysis systems meet this need. In particular, dolphins, which can be potentially harmed by human activities in this area, could benefit a lot from the use and the application of automatic systems, in terms of estimation of group size and abundance. The goal of this study is to develop an automated non-invasive system for the analysis of videos acquired by UAV s, devoted to the estimation of cetaceans group size and abundance. An automated system like the proposed one would allow having an immediate estimate of the number of the encountered cetaceans, onboard the vessel, to be compared with the estimation made by the Marine Mammal Observer (MMO) in charge or it would be a useful support system for the expert when he has to analyze the video in the laboratory. In this paper, as a case of study, the UAV s video-analysis system has been applied on videos acquired by a camera mounted on a drone, during surveys in the Gulf of Taranto. Two different models of machine learning were used, and both the trained models are able to solve the task well albeit with some limitations.
Priority-based Energy Management Technique for Integration of Solar PV, Battery, and Fuel Cell Systems in an Autonomous DC Microgrid

Abstract Recent times have witnessed a significant positive trend in adoption of renewable energy sources and DC-based loads, which questions the efficiency and reliability of existing structure of electrical power system. DC microgrids are identified as potential solutions for addressing India's and other developing nations' rural electrification. This paper presents an energy management technique for isolated DC microgrids for academic readers, as well as provides an introduction to not-familiarized readers for understanding the common design challenges for implementing off-grid power systems. The principle of the proposed technique is based on law of energy conservation defined by the energy balance equation and is implemented by synthesizing the instantaneous current reference and hysteresis control with source priority, which was validated by rigors simulation studies in Matlab/Simulink. Furthermore, in order to demonstrate the modularity and scalability of the algorithm, it was tested on a prototype hardware test setup at the Solar Energy Research Centre, VIT University.
Reassessment of a bond correction method for in situ ultrasonic interferometry on elastic wave velocity measurement under high pressure and high temperature

ABSTRACT A new bond correction method for simultaneous elastic wave velocity measurement of in situ synchrotron X-ray technique and ultrasonic interferometry combined with a Kawai type multi-anvil apparatus is developed to measure elastic wave velocity precisely. The new method was validated using data from the literature and new elastic constant data of gold obtained under high pressure and high-temperature conditions. Elastic wave velocities corrected using the new method show lower values than those obtained without bond correction and using the conventional method, which show good agreement with datasets by in situ Brillouin scattering measurement and ab initio calculation. When the sample length is 1 mm under ambient conditions, the corrected V P and V S became 3.7% and 1.6% lower, respectively, than values obtained without bond correction under 12 GPa and 900 K. Results show that correcting the bond effect is extremely important, especially when the sample length is less than 1 mm.
Speeding up Graph Matching by Means of Systematic Graph Reductions Using Centrality Measures

Comparing large graphs using graph edit distance is a time-consuming or even intractable problem. This is mainly due to the exponential time complexity of graph edit distance w.r. t. to the size of the underlying graphs. Nevertheless, considering its great adaptability, graph edit distance is a popular metric in structural pattern recognition and has found widespread applications. Main contribution of the present paper is to substantially speed up the computation of graph edit distance by initially reducing the graphs to the most important substructures. The graph downsizing is based on centrality measures - e.g. Page Rank -. That is, we systematically delete nodes including their edges from our graphs that offer the least level of influence to the overall graph structure. We empirically validate the benefit of our reduction method by classifying reduced graphs from a broad range of applications with a distance-based classifier. The main finding is that we can reduce the computation time by a wide margin while maintaining satisfying classification accuracy.
Broadband Millimeter Wave Notch Filter Based on Metamaterial Structure

In this paper, the results of the research of electromagnetic characteristics of millimeter-wave notch filters based on metasurfaces are presented. The samples of metamaterials were three-layer planar structures based on split-ring resonators made on metallized textolite FR4 using photolithography technology. Topological parameters elementary cells of researched metamaterials are shown. In the CST Studio software package by frequency solver comprehensive modeling of transmission, reflection and absorption coefficients were carried out. Experimental research of metamaterial samples was carried out on two measuring setups (continuous wave and time-domain spectroscopy) in frequency range from 34 GHz to 1 THz. The considered metastructures have a wide band-stop frequency region of the order of 40–50 GHz, which can be shifted in frequency by changing the unit cell parameters of the periodic metasurface. The use of such materials will make it possible to create band-stop filters for various frequency ranges or microwave sensors for electromagnetic parameters of the medium.
Ferroelectric nanocomposites from potassium dihydrogen phosphate with oxidised multi-walled carbon nanotubes: preparation, composition effects, structure, ferroelectricity and electrical properties

ABSTRACT A novel ferroelectric nanocomposite based on potassium dihydrogen phosphate (KDP) combined with the oxidised form of multiwalled carbon nanotubes (OMWCNT) was prepared and utilised for investigating the influence of OMWCNT on structure, phase transition, ferroelectric switching and electrical properties. The results pointed out the expansion of the ferroelectric phase and the increase in dielectric constant in comparison with pure KDP. Besides, a weaker ferroelectric switching accompanied by the deformation of hysteresis loops has been found with a higher OMWCNT content. Finally, the impedance and conductivity measurements demonstrated the electrode effects. The interaction of KDP with OMWCNT, and the high electrical conductivity of OMWCNT were supposed to be related to the described anomalies.
Complex structure reconstruction using segmented random sampling and combined dictionary

The reconstruction of data is a critical preliminary work in the seismic data processing. Compressed sensing (CS) has been well applied in the field of reconstruction. The key point of CS is random sampling, which converts the mutual interference alias caused by regular undersampling into lower-amplitude outside noise. But traditional sampling methods lack constraints on sampling points, emerging too much alias. Segmented random sampling (SRS) effectively controls the distance between sampling points. On the other hand, a single mathematical transformation will lead to incomplete sparse expression and bad restoration effects. Morphological component analysis (MCA) decomposes a signal into several components with outstanding morphological features to approximate the complex internal structure of data. In this paper, we found a new dictionary combination (shearlet + DCT) under the MCA framework and used the block coordinate relaxation algorithm to get the optimal solution to obtain reconstruction results. Tests of 2D data and 3D data have proved that the proposed method can get a better effect when reconstructing the SRS data.
Spherical DG-functors

For two DG-categories A and B we define the notion of a spherical Morita quasi-functor A -> B. We construct its associated autoequivalences: the twist T of D(B) and the co-twist F of D(A). We give powerful sufficiency criteria for a quasi-functor to be spherical and for the twists associated to a collection of spherical quasi-functors to braid. Using the framework of DG-enhanced triangulated categories, we translate all of the above to Fourier-Mukai transforms between the derived categories of algebraic varieties. This is a broad generalisation of the results on spherical objects in [ST01] and on spherical functors in [Ann07]. In fact, this paper replaces [Ann07], which has a fatal gap in the proof of its main theorem. Though conceptually correct, the proof was impossible to fix within the framework of triangulated categories.
Remote Sensing Change Detection via Temporal Feature Interaction and Guided Refinement

Remote sensing change detection (RSCD), which identifies the changed and unchanged pixels from a registered pair of remote sensing images, has enjoyed remarkable success recently. However, locating changed objects with fine structural details is still a challenging problem in RSCD. In this article, we propose a novel RSCD network via temporal feature interaction and guided refinement (TFI-GR) to solve this issue. Specifically, unlike previous methods, which just employ one single concatenation or subtraction operation for bi-temporal feature fusion, we design a temporal feature interaction module (TFIM) to enhance interaction between bi-temporal features and capture temporal difference information at diverse feature levels. Afterward, a guided refinement modules (GRMs), which aggregates both low- and high-level temporal difference representations to polish the location information of high-level features and filter the background clutters of low-level features, is repeatedly performed. Finally, the multilevel temporal difference features are progressively fused to generate change maps for change detection. To demonstrate the effectiveness of the proposed TFI-GR, comprehensive experiments are performed on three high spatial resolution RSCD datasets. Experimental results indicate that the proposed method is superior to other state-of-the-art change detection methods. The demo code of this work is publicly available at https://github.com/guanyuezhen/TFI-GR.
CMOS-Driven VCSEL-Based Photonic Links: an Exploration of the Power-Sensitivity Trade-Off

This article explores the power-sensitivity trade-off in optical receivers aiming to improve the energy-efficiency of the overall link. Optical receivers with field-effect transistor (FET) front-ends (FEs) are usually designed for optimal noise performance by matching the circuit’s input capacitance <inline-formula> <tex-math notation="LaTeX">$\left ({C_{I} }\right)$ </tex-math></inline-formula> to the total input parasitic capacitance <inline-formula> <tex-math notation="LaTeX">$\left ({C_{D} }\right)$ </tex-math></inline-formula>. However, the receiver’s power dissipation is also proportional to the input capacitance <inline-formula> <tex-math notation="LaTeX">$C_{I}$ </tex-math></inline-formula>. Therefore, this paper studies the feasibility of the capacitive matching rule in the context of minimizing the power dissipation of the overall link. For that purpose, design trade-offs for the receiver, transmitter, and the overall link are presented. Comparisons are made to study how much the receiver can be downsized, sacrificing optimal noise performance, before its power reduction is offset by the transmitter’s increase in power. Simulation results show that energy-efficient links require low-power receivers with input capacitance much smaller than that required for noise-optimum performance. As an example, for a 25 Gb/s operation, an optical loss budget of 12.6 dB, and a receiver designed in 65 nm CMOS technology with <inline-formula> <tex-math notation="LaTeX">$C_{D}$ </tex-math></inline-formula> of 200 fF, the overall link dissipates 2.55 pJ/bit when the receiver’s noise is minimized, leading to a receiver with <inline-formula> <tex-math notation="LaTeX">$C_{I}/C_{D}=1.29$ </tex-math></inline-formula>. When optimized for overall link efficiency, the receiver size is significantly reduced to <inline-formula> <tex-math notation="LaTeX">$C_{I}/C_{D}=0.38$ </tex-math></inline-formula> and the link’s energy-efficiency also improves to 1.41 pJ/bit. If the link budget or knowledge of the transmitter side is incomplete, our analysis indicates that maximizing gain with value of <inline-formula> <tex-math notation="LaTeX">$C_{I}/C_{D}=0.5$ </tex-math></inline-formula> is a reasonable choice.
Spectral and Cepstral Analysis of Colombian Birdsongs using Multidimensional Scaling

Birds population monitoring is a widely used approach for assessing the state of biodiversity. Since a birdsong contains features specific to distinct species, acoustic signals recording and processing are used for addressing the birds population characterization. This work deals with the analysis of information contained in recordings of birds from the Colombian ecosystem. For this purpose, the signals are processed using the Fourier transform and a novel cepstral representation based on the wavelet transform. The spectral and cepstral metrics are analysed using the multidimensional scaling (MDS) method, that yields a two- or three-dimensional visualization of the data. The interpretation of the results is based on the patterns of points in the MDS chart and the metadata of the birdsongs recordings. The results evince that the spectral and the proposed wavelet cepstral representations characterize different aspects of the birdsongs dataset. Emerging patterns are related with the bird species, the taxonomic order and the altitude above the sea level. Indeed, the MDS approach is an attractive option for unveiling characteristics embedded in the birdsongs.
A 1D Spring-Slider Model with a Simple Poly-Linear Failure Law Produces Rich Variations in Slip Behavior

The failure law prescribed along the fault surface and the elastic stiffness of the surrounding medium play important roles in determining the characteristics of earthquakes. Here we use a 1D spring-slider model that includes inertia, along with a simple poly-linear failure law composed of multiple linear segments to provide insight into earthquake initiation and growth. The poly-linear failure law, which parameterizes shear resistance as a function of slip, allows analytical solutions describing the system for each failure law segment. Analytical solutions facilitate investigation of the effects of the slopes of the different failure law segments in relation to the slope of the elastic loading curve determined by the spring stiffness. Depending on the relation between the slope of the failure law segment and the elastic loading slope, there are three stability regimes in the system: harmonic oscillations, exponential growth, and cubic growth. By combining the different solution regimes within one earthquake cycle, we observe a wide range of behaviors of this simple system: interseismic oscillatory creep, precursory signals before the main event, a shorter or a much longer acceleration phase before the onset of instability, and varying durations of the preseismic and coseismic phases. These results provide a potential explanation for some seismic observations, including increased levels of “seismic noise” prior to an earthquake, precursory events, tremor and low-frequency earthquakes.
Modeling and control of an antagonistically actuated tendon driven anthropomorphic hand

Abstract: One of the major limitations of object manipulation with a robotic hand is the fragility of the hardware. This is one of the motivations for developing the new anthropomorphic and extremely robust Hand Arm System at the robotics and mecatronics center of DLR. The system is unique in terms of complexity, with 52 motors and more than 200 sensors, and also in terms of dynamics. Indeed, the system is mechanically compliant, thus offers the possibility to store and release energy, thereby providing two essential functions: The impacts are filtered and the dynamics are enhanced. This thesis focuses on the hand. It has 19 degrees of freedom, each being actuated by two flexible antagonistic tendons. Because the stiffnes of the tendons is not linear, it is possible to adjust the mechanical stiffness of the joints, similar to the co-contraction of human muscles, in order to adapt to a task. However, the stiffness adjustability rises new challenges in modeling and control. The state of the art usually focuses on the problems of tendon-driven systems or flexible joint robots but seldomly both simultaneously. In the first part, the modeling of the hand and the wrist is conducted. Several problems specific to tendon-driven systems are presented, such as the coupling matrices and the joint position estimation based on the tendon displacement. The second part focuses on the control of a single joint actuated by two flexible tendons. The distribution of the tendon forces and the correction of the effective stiffness are reported. Linear and nonlinear approaches are used and multiple experiments are realised to compare them. The major result is that the backstepping, a nonlinear control method,
COVID-19 Vaccines Related User's Response Categorization Using Machine Learning Techniques

Respiratory viruses known as coronaviruses infect people and cause death. The multiple crown-like spikes on the virus’s surface give them the name “corona”. The pandemic has resulted in a global health crisis and it is expected that every year we will have to fight against different COVID-19 variants. In this critical situation, the existence of COVID-19 vaccinations provides hope for mankind. Despite severe vaccination campaigns and recommendations from health experts and the government, people have perceptions regarding vaccination risks and share their views and experiences on social media platforms. Social attitudes to these types of vaccinations are influenced by their positive and negative effects. The analysis of such opinions can help to determine social trends and formulate policies to increase vaccination acceptance. This study presents a methodology for sentiment analysis of the global perceptions and perspectives related to COVID-19 vaccinations. The research is performed on five vaccinations that include Sinopharm, Pfizer, Moderna, AstraZeneca, and Sinovac on the Twitter platform extracted using Twitter crawling. To effectively perform this research, tweets datasets are categorized into three groups, i.e., positive, negative and natural. For sentiment classification, different machine learning classifiers are used such as Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). It should be noted that the Decision tree classifier achieves the highest classification performance in all datasets as compared to the other machine learning algorithms. For COVID-19 Vaccine Tweets with Sentiment Annotation (CVSA), the highest accuracy obtained is 93.0%, for the AstraZeneca vaccine dataset 90.94%, for the Pfizer vaccine dataset 91.07%, 88.01% accuracy for the Moderna vaccine dataset, for the Sinovac vaccine dataset 92.8% accuracy, and 93.87% accuracy for the Sinopharm vaccine dataset, respectively. The quantitative comparisons demonstrate that the proposed research achieves better accuracy as compared to state-of-the-art research.