Miracle-dz/UniFAR-FTUs huggingface dataset:
QUANTUM MECHANICS WITH FINAL AS WELL AS INITIAL BOUNDARY CONDITIONS

Modern physics seems to favour the block universe view in which case it seems reasonable to impose final as well as initial boundary conditions on physical systems. If one boundary condition is not merely the time evolution of the other, a consistency condition between the two is required. The necessary consistency condition is derived for the case of non-relativistic quantum mechanics. The double-boundary conditions uniquely determine the states of the quantum system if either their initial or final states do not form superpositions in the history of a quantum system. Macroscopic quantum systems, relevantly measuring instruments, involve many subsystems, at least some of which do not form superpositions of their physical properties between the times when the boundary conditions are imposed and hence measurement results are uniquely determined by the boundary conditions. Since measurement outcomes are determined by the boundary conditions, it is suggested they determine, rather than are determined by, the physical properties of quantum systems and expressions are proposed for assigning a set of (unmeasured) physical properties to a quantum system between measurements. The physical properties assigned in that way avoid the logical inconsistencies implied by the no-go theorems, as shown for the example of Hardy states.
An alternative implementation of time domain adaptive integral method for surface integral equations

This letter presents an alternative implementation of the time domain adaptive integral method (TD‐AIM) for solving combined field integral equation (CFIE) pertinent to electromagnetic surface scattering phenomena. We use the cubic spline as temporal basis function and analytically remove the rot operator from the integrals of far‐zone interactions. Furthermore, we decompose the huge 4D FFT into several smaller ones in time domain. Consequently, the TD‐CFIE can be solved with relatively littler memory and computational resources accurately. Simulation results have been presented and demonstrated the effectiveness of the proposed implementation method. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1819–1822, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23542
Input-output stability of time-varying nonlinear multiloop feedback systems

Sufficient conditions for the input-output stability (BIBO stability) of time-varying nonlinear multiloop feedback systems are established. The present objective is to analyze large-scale systems in terms of their lower order subsystems (subloops) and in terms of their interconnecting structure. Both time-domain and frequency-domain results are presented. In order to demonstrate the usefulness of the present approach, two specific examples are considered.
Probing inputs for subspace identification

There is experimental evidence that the standard subspace methods (e.g. the N4SID method) perform poorly in certain conditions where the past signals (past inputs and past outputs) and future input spaces are nearly parallel. Based on an elementary numerical conditioning analysis, the paper describes a class of (system-dependent) input signals (called probing inputs) which lead to the worst possible conditioning of the identification problem. Numerical results are included demonstrating how these input signals may lead to a substantial deterioration of performance of the algorithms in some experimental conditions.
Metal abundances of RR Lyrae stars in galactic globular clusters

Results are reported for image-tube scanner measurements of Preston's (1959) metal-abundance indicator and metallic-line absorption in the G-band region (4285 to 4317 A) of 47 RR Lyrae stars in 12 galactic globular clusters and 67 RR Lyrae field stars. The relationship (Fe/H) is defined and determined from curve-of-growth analyses of 13 RR Lyrae field stars, while a comparison of metal-abundance vs metallic-line absorption relationships derived for the field and cluster stars indicates that both groups can be calibrated in the same way. Based on this calibration, (Fe/H) values are deduced for the globular-cluster stars and the clusters themselves. It is found that the alpha-rich phenomenon is present in these stars and is confined to calcium and titanium.
Finding Moving Operators in Firefighting Operations Based on Multi-Goal Next-Best-View Exploration

Many recently designed exploration algorithms for search and rescue are based on the expansion of existing exploration algorithms to multiple simultaneously operating agents. These algorithms are quite useful in extensive search and rescue operations but neglect the need for adaptability necessary in small scale environments. Therefore, this paper proposes a novel modular multi-layer approach, which combines conventional Next-Best-View Exploration with predefined boundary conditions to enable a multi-goal driven search for victims and operators. The boundary conditions are mapped on cost maps individually and fused dynamically in a common weighting matrix. Exemplary conditions are the last known operator pose or estimated positions of fire sources. The exploration algorithm compares nearby points of interest in regards to their weight and chooses an appropriate navigation goal. The method is evaluated for usage in context of firefighting operations with teams of humans and robots.
Categorizing the IPv4 Address Space Based on Services Running on IPs

This study aims to categorize the service types of IP addresses based on running services. The service types can be categorized into Proxy, VPN, Cloud, CDN, and Hosting. The reason for this classification is, that by using these insights one can build better intrusion detection systems. For example, if the IP addresses of the daily users of the websites are mostly VPNs and proxies then owners can be aWare of the traffic and IP addresses of the visitors. Another advantage is one can build better IP blacklists and understand the nature of the ASNs(Some ASNs contain more VPN IPs, and some contain more cloud IPs likewise). The data gathering was done using many methods such as Internet-wide scanning, ASN data collection, and Whois data collection. By using these techniques millions of IP addresses were collected. Many well-known cloud vendors have officially published their IP addresses and this study takes these published IP ranges as the ground truth. About 130,000 IP addresses were collected as the ground truth. By merging these ground truth IPs, with the datasets which are collected by various techniques, the final ground truth IP dataset was constructed. By using this dataset the most important features were identified and trained in various machine learning models. From these trained machine learning models Random Forest Classifier was chosen as the best classmer for this classification since it achieves 94.71% of accuracy and a 95% F1 score.
Orchestration of Reconfigurable Intelligent Surfaces for Positioning, Navigation, and Timing

Positioning, Navigation, and Timing (PNT) services are exploited by critical infrastructures which are strategic for the functioning of the modern society, such as telecom, energy, finance, and transportation. Though the most popular PNT services' provider is the Global Positioning System (GPS), its performance is often impacted by adverse conditions and different varieties of interference, either intentional or unintentional. In this paper, we exploit the efficient and effective orchestration of Reconfigurable Intelligence Surfaces (RISs) as a means of offering an alternative PNT model, improving accuracy and availability. In particular, we initially introduce a low-complexity reinforcement learning-based approach to enable the various targets under consideration to select the most appropriate set of RISs that, acting complementary to available anchor nodes, will minimize the error in the targets' positioning and timing calculation. Subsequently, the optimal phase shifts of the reflected signals on the selected RISs are determined, in order to further improve the proposed PNT model's accuracy. Finally, an iterative least square (ILS) algorithm determines the targets' positioning and timing in a fully distributed manner. The performance of the proposed PNT model is achieved via modeling and simulation, and indicative numerical results are presented demonstrating its benefits and tradeoffs.
FurtherRemarksonStrictInput-to-State StableLyapunovFunctionsforTime-VaryingSystems ?

We study the stability properties of a class of time-varying nonlinear systems. We assume that non-strict input-to-state stable (ISS) Lyapunov functions for our systems are given and posit a mild persistency of excitation condition on our given Lyapunov functions which guarantee the existence of strict ISS Lyapunov functions for our systems. Next, we provide simple direct constructions of explicit strict ISS Lyapunov functions for our systems by applying an integral smoothing method. We illustrate our constructions using a tracking problem for a rotating rigid body.
On Path Regression with Extreme Learning and the Linear Configuration Space

This paper studies the path regression problem, that is learning motion planning functions that render trajectories from initial to end robot configurations in a single forward pass. To this end, we have studied the path regression problem using the linear transition in the configuration space and shallow neural schemes based on Extreme Learning Machines. Our computational experiments involving a relevant and diverse set of 6-DOF robot trajectories have shown path regression’s feasibility and practical efficiency with attractive generalization performance in out-of-sample observations. In particular, we show that it is possible to learn neural policies for path regression in about 10 ms. - 31 ms. and achieving 10−3 – 10−6 Mean Squared Error on unseen out-of-sample scenarios. We believe our approach has the potential to explore efficient algorithms for learning-based motion planning.
Realizing Power-efficient Encryption Communication for Computational RFID Tags

To achieve encryption communication, existing CR-FID tags usually adopt software-based encryption and software-defined ciphertext communication. However, these solutions are power-consuming and limit the communication range of CRFID tags. In this paper, we propose a power-efficient encryption communication for CRFID tags. We propose a low-power AES hardware solution feasible in practical deployment, and a hardware-based RFID communication module for low-power ciphertext transmission. With these designs, the communication range of the proposed tag reaches 5m, making an improvement of $2.5\times$ compared to classic CRFID tags which adopt software encryption and software-defined communication.
Lane Changing Trajectory Planning and Tracking Controller Design for Intelligent Vehicle Running on Curved Road

To enhance the active safety and realize the autonomy of intelligent vehicle on highway curved road, a lane changing trajectory is planned and tracked for lane changing maneuver on curved road. The kinematics model of the intelligent vehicle with nonholonomic constraint feature and the tracking error model are established firstly. The longitudinal and lateral coupling and the difference of curvature radius between the outside and inside lane are taken into account, which is helpful to enhance the authenticity of desired lane changing trajectory on curved road. Then the trajectory tracking controller of closed-loop control structure is derived using integral backstepping method to construct a new virtual variable. The Lyapunov theory is applied to analyze the stability of the proposed tracking controller. Simulation results demonstrate that this controller can guarantee the convergences of both the relative position tracking errors and the position tracking synchronization.
Voltage Sensorless Sliding Mode Control for Five Level PUC Single Phase Inverter Used in PV System

This paper presents a voltage sensor-less first order sliding mode control applied to a standalone 5-level packed U -cell topology. A voltage sensor-less control modulation strategy is used to minimize the complexity of the control system and to ensure fast self-voltage balancing of the capacitor voltage. Furthermore, a suggested controller has been designed to improve the performance and robustness of the system by imposing a desired current dynamic to maintain the reference load current while keeping the voltage of the capacitor at half of the main DC source. It is noted that this control strategy has achieved fast self-balancing of the capacitor voltage value, better stability performances and improved system responsiveness. The results of this work have been demonstrated through a simulation that highlights the nuances of this technique in details.
A new per-field classification method using mixture discriminant analysis

In this study, a new per-field classification method is proposed for supervised classification of remotely sensed multispectral image data of an agricultural area using Gaussian mixture discriminant analysis (MDA). For the proposed per-field classification method, multivariate Gaussian mixture models constructed for control and test fields can have fixed or different number of components and each component can have different or common covariance matrix structure. The discrimination function and the decision rule of this method are established according to the average Bhattacharyya distance and the minimum values of the average Bhattacharyya distances, respectively. The proposed per-field classification method is analyzed for different structures of a covariance matrix with fixed and different number of components. Also, we classify the remotely sensed multispectral image data using the per-pixel classification method based on Gaussian MDA.
Theoretical optimization of self-pulsating 650-nm-wavelength AlGaInP laser diodes

Self-pulsating laser diodes operating at a wavelength of 650 nm are attractive for high-density optical storage. The main candidate for such a device is an AlGaInP laser diode including an epitaxially integrated saturable absorber. The characteristic self-pulsation occurs due to the interplay between gain in the active region and the absorption within the structure. In the paper, we calculate the dynamics of self-pulsation in this type of AlGaInP laser diode, including a detailed description of gain and absorption within the relative sections. In particular, we identify how, by modifying the structure of the epitaxial absorber layers, we can alter the operating characteristics of these laser diodes.
A Study of a Grid-Forming Photovoltaic Plant for Fast Frequency Response

Massive integration of inverter-based generation (IBG) has begun to displace conventional generation units. This type of generation does not provide support for frequency deviations, causing stability problems in weak power systems. In-verter control methods have emerged to extend IBG integration. Grid-forming control is a promising emerging technology that generates its own voltage signal and can regulate the frequency and voltage at the point of common coupling. The transition to a grid with very low or no inertia makes frequency regulation a challenge; nevertheless, grid forming technology makes it possible, besides being able to restore a power system from blackstart. Currently, conventional generation in hydroelectric and thermal power plants maintains a certain system stability; however, understanding what happens when moving towards an IBG-dominated grid is crucial in order to develop technologies that mitigate the shortcomings and enable a smooth transition. This changing grid landscape creates a wide range of challenges for system modeling, planning, stability and control. As an emerging technology, there is a lack of documentation and well-established generic models that can be used for testing. This paper presents the main theory and detailed mathematical modeling of a three-phase inverter, with emphasis on the control design and the capability to provide frequency support.
On the Limits of Learning a Discrete Memoryless Communication Channel

This paper explores bounds on the minimum number of channel probes required to learn sufficient information to establish reliable communication at a given communication rate (below channel capacity) for a discrete memoryless channel. Given a set of discrete channel input-output sample pairs (where for each discrete input value, the associated aggregate set of discrete output values observations is multinomially distributed), we leverage a non-asymptotic probably approximately correct (PAC) bound on the mutual information (channel capacity) between the label (discrete) random variable (RV) and the observation RV to establish a convergence rate for the worst case channel. Previous bounds (such as those based on Sanov's Theorem) provide high probability (i.e. PAC) bounds on the true mutual information that converge with rate $O(\log(N)/\sqrt{N})$, where N is the number of independent and identically distributed (i.i.d.) samples used to compute the empirical probability mass functions. Using an improved PAC sublevel-set bound, we sharpen the rate of convergence to $O(\sqrt{\log(\log(N))\log(N)}/N)$.
Modified U-Net Architeture based Ischemic Stroke Lesions Segmentation

An ischemic stroke transpires when part of the brain tissue is unable to get oxygen and nutrients due to the interruption or the reduction of blood supply. A stroke is a medical emergency that can lead to serious complications if left untreated. Early action can reduce potential brain damage and other consequences. Therefore, quantitative assessment of ischemic stroke lesions using magnetic resonance imaging (MRI) is paramount for an accurate and decisive diagnosis. However, a diagnosis of hundreds of MRI-generated slices by trained specialists is time-consuming and requires painstaking attention to details. Automating this process using advanced image segmentation methods allows an exact localization of the tissue presenting lesions and provide an approximation of its volume. Automatic detection of lesions provides faster answers and better assessments to assist medical practitioners. In this paper, we propose a novel method for ischemic stroke lesions detection based on a modified UNet architecture. We use the data from 2015 Ischemic Stroke Lesion Segmentation Challenge (ISLES 2015) for performance evaluation of our proposed method. Experiments have shown that the latter outperforms the state-of-the-art methods.
Generation of soft X-rays and extreme ultraviolet (EUV) using a laser-irradiated gas puff target

The paper gives a review of investigations on generation of soft X-rays and EUV using a gas puff target irradiated with high-power laser pulses performed during last few years. In the studies a newly developed double-stream gas puff target approach was used. The gas puff targets were irradiated with various lasers, including Nd:glass, Kr:F, Nd:YAG, and iodine lasers. The aim of the studies was to develop efficient and debris-free laser plasma X-ray and EUV sources for applications in microscopy, lithography, micro- and nanotechnology.
Road Boundary Detection using Camera and mmwave Radar

Road boundary detection has been an active research area for autonomous driving to support full autonomy in all weather conditions. It also helps human drivers to drive safely in bad weather conditions when vehicles ahead and road boundaries are obscured. For example, knowing the road boundaries enables snow plow vehicles to clean the road more precisely, thereby increasing the amount of drivable area available during the winter. The majority of current road boundary detection techniques use camera and lidar sensors. The camera excels in clear daylight conditions but struggles in low visibility light. While lidar sensors perform well in low light, they struggle in inclement weather conditions such as rain or fog. The high attenuation power of automotive radar makes it extremely effective in all types of weather conditions. However, due to the low resolution of the radar, it is currently limited to object detection for cruise control applications. This paper proposes a method for detecting road boundaries in all weather conditions by combining a camera and mmwave radar. We present radar sensor filters that will aid researchers in making more efficient use of millimeter-wave radars. We demonstrate that our approach performs 20% better than the pure vision-based approach. We showcase that in inclement weather conditions when a camera can barely see our approach can precisely detect road boundaries. The proposed method has been validated by mounting an experimental setup on a test vehicle and driving it in a variety of different conditions and on a variety of different types of roads.
CNN-based Feature Extraction for Robotic Laser Scanning of Weld Grooves in Tubular T-joints

This paper presents an algorithm for feature point extraction from scanning data of large tubular T-joints (a subtype of a TKY joint). Extracting such feature points is a vital step for robot path generation in robotic welding. Therefore, fast and reliable feature point extraction is necessary for developing adaptive robotic welding solutions. The algorithm is based on a Convolutional Neural Network (CNN) for detecting feature points in a scanned weld groove, where the scans are done using a laser profile scanner. To facilitate fast and efficient training, we propose a methodology for generating synthetic training data in the computer graphics software Blender using realistic physical properties of objects. Further, an iterative feature point correction procedure is implemented to improve initial feature point results. The algorithm’s performance was validated using a real-world dataset acquired from a large tubular T-joint.
Vanishing Point Detection Algorithm Based on Clustering Method

Camera self-calibration is a key step in photo based reconstruction works. Vanishing point detection is a very important method in single photo based camera self-calibration. We research current vanishing point detection method and put forward a detection algorithm based on a new clustering idea: J-Linkage. First, we construct a similar concept space from straight lines from images by edge detection and segmentation method. Then, we cluster the similar concept space to decide several main directions. At last, we can easily estimate vanishing points from the clustered categories. Experiment prove that, the method has a high performance efficiency and with good accuracy.