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The extraction of feature points is crucial to computer vision tasks like self-calibration of binocular camera extrinsic parameters, pose estimation and structure from motion (SFM). In the context of autonomous driving, there are numerous unstructured feature points, as well as structured feature points with shapes such as L-type, Y-type, Star-type and centroid. Typically, feature points are extracted without discrimination and used as inputs for feature-based visual algorithms in a generalized manner. However, the influence of the structural characteristics of these feature points on the performance of such algorithms remains largely unexplored. To address this issue, we propose a multi-stream feature point classification network based on circular patches extraction (CPE). CPE uses concentric circles centered on a given feature point to extract the intensity distribution features around that point. Subsequently, a series of circular patches are converted into square patches according to the order of radius and polar angle. Then, we have a multi-stream feature point classification network, where each stream receives a square patch as input to learn the intensity distribution features and classify the feature points into Y-type, centroid and unstructured categories. Finally, the influence of points with structure and without structure on related autonomous driving visual algorithms was verified in the experiment. Experimental results indicate that our proposed network can effectively classify based on the structure of feature points, which can enhance the performance of feature-based vision algorithms.

In this paper, we introduce a novel methodology for calibrating European option pricing within the context of uncertain financial markets. Our approach leverages an artificial neural network, where each input neuron corresponds to the option price function derived from the uncertain stock model. We investigate our method against traditional calibration techniques, including those based on uncertain differential equations and the Black–Scholes model. Numerical experiments demonstrate that the proposed neural network-based strategy significantly enhances the accuracy and performance of option price calibration, yielding improved results for both in-sample and out-of-sample datasets.

Each day a weather forecaster predicts a probability for each type of weather for the next day. After n days, all the predicted probabilities and the real weather data are sent to a test which decides whether to accept the forecaster as having prior knowledge about the distribution of nature. Consider tests that accept with high probability forecasters who know the distribution of nature. Sandroni shows that any such test can be passed with high probability by a forecaster who has no prior knowledge about the distribution of nature, provided that the duration n is revealed to the forecaster in advance [14]. However, Fortnow and Vohra show that Sandroni's result requires forecasters with high computational complexity [6]. Consider the family of forecasters who select a deterministic Turing-machine forecaster according to an arbitrary distribution and then use that machine for all future forecasts. We show that Sandroni's result requires forecasters even more powerful than those in . We also show that Sandroni's result does not apply when the duration n is not revealed to the forecaster in advance.

The notion of complex equiaffine manifolds is an affine generalization of Calabi–Yau manifolds. Similarly as in the Riemannian case the minimality of affine Lagrangian submanifolds in complex equiaffine spaces can be studied via calibrations, phase functions and variational formulas.

We show that if a compact, oriented 4-manifold admits a coassociative($\ast {\varphi }_0$)-free immersion into ${ℝ}^7$, then its Euler characteristic ${\chi }_M$ and signature ${\tau }_M$ vanish. Moreover, in the spin case, the Gauss map is contractible, so that the immersed manifold is parallelizable. The proof makes use of homotopy theory, in particular, obstruction theory. As a further application, we prove a non-existence result for some infinite families of 4-manifolds that have not been addressed previously. We give concrete examples of parallelizable 4-manifolds with complicated non-simply-connected topology.

In this paper, we deal with no-arbitrage pricing problems of a chooser flexible cap written on an underlying LIBOR. The chooser flexible cap allows a right for a buyer to exercise a limited and pre-determined number of the interim period caplets in a multiple-period cap agreement. Assuming a common diffusion short rate dynamics, e.g., Hull–White model, we propose a dynamic programming approach for their risk neutral evaluation. This framework is suited to a calibration from an observed initial yield curve and market price data of discount bonds, caplets, and floorlets.

Quantum Hall devices for primary resistance metrology based on the GaAs/Al${}_x$Ga${}_{1-x}$As heterostructure were fabricated and compared with the devices from international metrology organization International Bureau of Weights and Measures (BIPM) by calibrating the same 100 $\mathrm{\Omega }$ transfer standard resistor with a cryogenic current comparator. Relative deviation between NIM-made device NIM-C1 and BIPM-made device BIPM-1 was −4.49 parts in 10⁹ with an uncertainty of 3.63 parts in 10⁹ which indicated NIM-C1 satisfied the requirement for the primary resistance standard.

Azimuth electromagnetic wave is a new type of electromagnetic prospecting technology. It can detect weak electromagnetic wave signal and realize real-time formation conductivity imaging. For effectively optimizing measurement accuracy of azimuth electromagnetic wave imaging tool, the efficient numerical simulation algorithm is required. In this paper, self-adaptive finite element method (FEM) has been used to investigate the azimuth electromagnetic wave logging tool response by adjusting antenna array system in different geological conditions. Numerical simulation examples show the accuracy and efficiency of the method, and provide physical interpretation of amplitude attenuation and phase shift of electromagnetic wave signal. Meanwhile, the high-accuracy numerical simulation results have great value to azimuth electromagnetic wave imaging tool calibration and data interpretation.

In the field of computer vision, camera calibration is a hot issue. For the existing coupled problem of calculating distortion center and the distortion factor in the process of camera calibration, this paper presents an iterative-decreasing calibration method based on regional circle, uses the local area of the circle plate to calculate the distortion center coordinates by iterative declining, and then uses the distortion center to calculate the local area calibration factors. Finally, makes distortion center and the distortion factor for the global optimization. The calibration results show that the proposed method has high calibration accuracy.

In the past years, research in eye tracking development and applications has attracted much attention and the possibility of interacting with a computer employing just gaze information is becoming more and more feasible. Efforts in eye tracking cover a broad spectrum of fields, system mathematical modeling being an important aspect in this research. Expressions relating to several elements and variables of the gaze tracker would lead to establish geometric relations and to find out symmetrical behaviors of the human eye when looking at a screen. To this end a deep knowledge of projective geometry as well as eye physiology and kinematics are basic. This paper presents a model for a bright-pupil technique tracker fully based on realistic parameters describing the system elements. The system so modeled is superior to that obtained with generic expressions based on linear or quadratic expressions. Moreover, model symmetry knowledge leads to more effective and simpler calibration strategies, resulting in just two calibration points needed to fit the optical axis and only three points to adjust the visual axis. Reducing considerably the time spent by other systems employing more calibration points renders a more attractive model.

Traditional vision registration technologies require the design of precise markers or rich texture information captured from the video scenes, and the vision-based methods have high computational complexity while the hardware-based registration technologies lack accuracy. Therefore, in this paper, we propose a novel registration method that takes advantages of RGB-D camera to obtain the depth information in real-time, and a binocular system using the Time of Flight (ToF) camera and a commercial color camera is constructed to realize the three-dimensional registration technique. First, we calibrate the binocular system to get their position relationships. The systematic errors are fitted and corrected by the method of B-spline curve. In order to reduce the anomaly and random noise, an elimination algorithm and an improved bilateral filtering algorithm are proposed to optimize the depth map. For the real-time requirement of the system, it is further accelerated by parallel computing with CUDA. Then, the Camshift-based tracking algorithm is applied to capture the real object registered in the video stream. In addition, the position and orientation of the object are tracked according to the correspondence between the color image and the 3D data. Finally, some experiments are implemented and compared using our binocular system. Experimental results are shown to demonstrate the feasibility and effectiveness of our method.

This paper introduces a new calibration method for the mapping camera called Precise Grouped Approach Method (PGAM). The conventional calibration method for the mapping camera is the exact measuring angle method. The accuracy of this method can be reduced by theoretical uncertainties and the number and distribution of observation points. PGAM is able to overcome these disadvantages and improve the accuracy. Firstly, we reduce the theoretical uncertainties by means of a grouped approach method, which rectifies the high-precision rotation stage to zero position. Secondly, a weighted theory is applied to eliminate the effect of the number and distribution of observation points. Finally, the accuracy of PGAM is analyzed. The experiment result shows that the calibration accuracy is significantly improved when using the proposed PGAM algorithm, compared to the conventional one under the identical experimental condition.

In this paper, a new machine vision algorithm for close-range position sensing and bin picking is presented where a Hopfield Neural Network (HNN) is used for the stereo matching process. Stereo Matching is formulated as an energy minimization task and this minimization is accomplished using the HNNs. Various other important aspects of this Vision System are discussed including camera calibration and objects localization using a clustering algorithm.

Monotonic errors cause severe errors and are inherent in several A/D Converter (ADC) architectures. Moreover, several error correcting and ADC output processing methods require a monotonic behavior for a successful operation. Based on the features of asynchronous ADCs, an architecture for the elimination of monotonic errors is presented. This monotonic error correcting module is connected at the output of an ADC and does not require any modification in its internal circuits. It controls an output buffering stage that discards output codes with monotonic errors and this correcting procedure is triggered by changes in specific output bits of the ADC. Simulation results show an improvement by 8 dB or 25% maximum, in the signal-to-noise and distortion ratio (SNDR) of an 8-bit ADC if this monotonic error elimination method is used alone and a further improvement by 1–5 dB if it is combined with a post processing method developed by the authors. Similar improvement can also be achieved in several other architectures like Subrange or Folding ADCs that operate in relatively high oversampling ratio and suffer from monotonic errors with specific features.

In this paper, a 12-bit current-steering digital-to-analog converter (DAC) with high static and dynamic linearity is proposed. Compared to traditional intrinsic-accuracy DACs, the static linearity is obtained by a series of subsidiary DACs which can shorten the calibration cycle with smaller additional circuits. The presented DAC is based on the segmented architecture and layout has been carefully designed so that better synchronization among the current sources can be achieved. The DAC is implemented in a standard 0.18-μm CMOS technology and the current source block occupies less than 0.5 mm². The measured differential nonlinearity (DNL) and integral nonlinearity (INL) performance is ± 0.3 LSB and ± 0.5 LSB, respectively, and the spurious free dynamic range (SFDR) is 75 dB at 1 MHz signal frequency and 200 MHz sampling frequency.

We present the integration and accuracy and power improved hearing aid system that proposes the triple oscillation-based loops (TOL) technique. Compared with the conventional techniques, the technique replaces the interface capacitors, A/D and D/A convertors with the oscillation-based closed loop and the calibration/operation feedback paths, which are able to enhance the integration of the system and improve the system's accuracy and power performance. Specifically, the technique achieves the only once of the second-order delta-sigma modulation with an integrator and an oscillator. To realize the architecture, the current mode blocks with calibration and successive subtraction are presented on the circuit design. Simulated with a 0.13 μm standard CMOS process in Cadence, with the 16 ohm loudspeaker impedance under the 1 V supply voltage, the input referred DC offset voltage with interface capacitor free is achieved below 2.5 mV; and the signal-to-noise and distortion rate (SNDR) achieves 50 dB@400 mV_p-p output voltage. Moreover, the power consumption with no load is maintained within 350 μW.

This paper presents, a novel digital foreground calibration technique in order to reduce the effects of timing-skew in time-interleaved analog-to-digital converters (ADCs). The proposed technique implementation is simple and helps to achieve very low power consumption. This technique is based on the using of a simple reference comparator which is synchronized by one of sub-channels in each cycle of calibration. Also the detection and correction are implemented by a simple LMS loop that guarantee the convergence of algorithm. Finally, simulation results show that the new approach method can effectively correct timing errors for a specified input signal, and achieves a low power consumption, low computational complexity and high convergence speed and also verify theoretical equations for it.

Mismatch and parasitic effects of bridge capacitors in successive-approximation-register analog-to-digital converter’s (SAR-ADC) split capacitor digital-to-analog conversion (DAC) cause a significant performance deterioration. This paper presents a nonlinearity analysis based on an analytical model, and a modified calibration method utilizing a pre-bias bridge capacitor is accordingly proposed. The proposed method, which uses three-segment split capacitor DAC structure, can effectively eliminate over-calibration error caused by conventional structure. To verify the technique, a 14-bit SAR-ADC has been designed in 0.35-$\mu$m 2P4M CMOS process with the PIP capacitor, and the simulation results show the method can further improve ADC performance.

A PLL-based clock generator with an auto-calibration circuit is presented. The auto-calibration circuit employs an oscillator-based time-to-digital converter (TDC) to achieve a constant loop bandwidth and fast lock time. The TDC measures the operating frequency of $M$-stage ring-VCO with a resolution of $f_{\mathrm{\text{REF}}}∕(k\cdot 2M)$ in a time period of $k\cdot T_{\mathrm{\text{REF}}}$. The measured frequency is utilized to calibrate loop bandwidth and VCO frequency. The clock generator is designed in 40nm CMOS process and operates from 1.2GHz to 3.6GHz with 8-phase outputs. The total lock time is less than 3$\mu$s including calibration and PLL closed-loop locking processes. Operating at 3.2GHz, the in-band phase noise is better than $-99.4$dBc/Hz and root-mean square (RMS) jitter integrated from 10KHz to 100MHz is 2 ps. In the entire operating range, the RMS jitter and reference spur are better than 5.5ps and $-68.5$dBc/Hz, respectively. The clock generator consumes only 3mW from 1.1V supply at high-frequency end and 1.6mW at low-frequency end. The active area is only 0.04mm² including on-chip loop filter and auto-calibration circuits.

Time-interleaving has been a popular choice for multi-GHz analog-to-digital converters (ADCs) with a resolution of 8–14 bits. Unfortunately, inherent defects such as offset, gain, timing-skew mismatches among sub-ADCs degrade overall performance seriously. At present, the method for eliminating offset and gain mismatch is fairly straightforward; however, calibration for timing-skew is still in a state of exploration. A systematic overview of various calibration methods for timing-skew in time-interleaved ADCs (TI-ADCs) has been provided in this paper. Meanwhile, current state-of-the-art TI-ADCs recently are reported and several noteworthy trends can be observed from the statistical results.