Source-less density measurement using an adaptive neutron-induced gamma correction method

The use of radioactive isotopes,such as Cs-137,to measure formation density is a common practice;however,it poses high risks such as environmental contamination from lost sources.To address these challenges,the use of pulsed neutron sources for density measurements,also known as“source-less density”,has emerged as a promising alternative.By collecting gamma counts at different time gates according to the duty cycle of the pulsed sequence,the inelastic gamma component can be isolated to obtain more accurate density measurements.However,the collection of gamma rays during the neutron burst-on period often contains a proportion of capture gamma rays,which can reduce the accuracy of density measurements.This proportion can vary depending on the formation environment and neutron duty cycle.To address these challenges,an adaptive capture gamma correction method was developed for density measurements.This method distinguishes between“burst-on”and“burst-off”periods based on the gamma time spectra,and derives the capture ratio in the burst-on period by iteratively fitting the capture gamma time spectra,resulting in a more accurate net inelastic gamma.This method identifies the end of the pulse by automatically calculating the differential,and fits the capture gamma time spectra using Gaussian process regression,which considers the differences in formation attenuation caused by different environments.The method was verified through simulations with errors of below 0.025 g/cm3,demonstrating its adaptability and feasibility for use in formation density measurements.Overall,the proposed method has the potential to minimize the risks associated with radioactive isotopes and improve the accuracy of density measurements in various duty cycles and formation environments.

Underwater image enhancement by maximum-likelihood based adaptive color correction and robust scattering removal

Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean engineering.Although extensive research has been conducted into underwater image enhancement,little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes.In this paper,we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters,which effectively removes color casts of a variety of underwater images.A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed,which circumvents the influence of white or bright regions that challenges existing physical model-based methods.To enhance contrast of resultant images,a piece-wise affine transform is applied to the transmission map estimated via background light differential.Finally,with the estimated background light and transmission map,the scene radiance is recovered by addressing an inverse problem of image formation model.Extensive experiments reveal that our results are characterized by natural appearance and genuine color,and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics,which further validates the better robustness and higher generalization ability of our enhancement model.

Multi-scale traffic vehicle detection based on faster ReCNN with NAS optimization and feature enrichment

It well known that vehicle detection is an important component of the field of object detection.However,the environment of vehicle detection is particularly sophisticated in practical processes.It is comparatively difficult to detect vehicles of various scales in traffic scene images,because the vehicles partially obscured by green belts,roadblocks or other vehicles,as well as influence of some low illumination weather.In this paper,we present a model based on Faster ReCNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes.First,we proposed a Retinex-based image adaptive correction algorithm(RIAC)to enhance the traffic images in the dataset to reduce the influence of shadow and illumination,and improve the image quality.Second,in order to improve the feature expression of the backbone network,we conducted Neural Architecture Search(NAS)on the backbone network used for feature extraction of Faster ReCNN to generate the optimal cross-layer connection to extract multi-layer features more effectively.Third,we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets,and improve the robustness of the model for challenging targets such as small scale and severe occlusion.In the implementation of the model,K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model.Our model has been trained and tested on the UN-DETRAC dataset,and the obtained results indicate that our method has art-of-state detection performance.

A new adaptive nonuniformity correction algorithm for infrared line scanner based on neural networks

The striping pattern nonuniformity of the infrared line scanner （IRLS） severely limits the system performance. An adaptive nonuniformity correction （NUC） algorithm for IRLS using neural network is proposed. It uses a one-dimensional median filter to generate ideal output of network and can complete NUC by a single frame with a high correction level. Applications to both simulated and real infrared images show that the algorithm can obtain a satisfactory result with low complexity, no need of scene diversity or global motion between consecutive frames. It has the potential to realize real-time hardware-based applications.

A VHF RFPGA with adaptive phase-correction technique

This paper presents a VHF（30-300 MHz） RF programmable gain amplifier（PGA） with an adaptive phase correction technique.The proposed technique effectively mitigates phase errors over the VHF band,and the RFPGA as a whole satisfies all the specifications of the China mobile multimedia broadcasting VHF band applications.The RFPGA is implemented with a TSMC 0.25μm CMOS process.Measurement results reveal a gain range of around 61 dB,an ⅡP3 of-7 dBm at maximum gain,a power consumption of 10.2 mA at maximum gain,and a phase imbalance of less than 0.3 degrees.