Flash-based in-memory computing for stochastic computing in image edge detection

The“memory wall”of traditional von Neumann computing systems severely restricts the efficiency of data-intensive task execution,while in-memory computing(IMC)architecture is a promising approach to breaking the bottleneck.Although variations and instability in ultra-scaled memory cells seriously degrade the calculation accuracy in IMC architectures,stochastic computing(SC)can compensate for these shortcomings due to its low sensitivity to cell disturbances.Furthermore,massive parallel computing can be processed to improve the speed and efficiency of the system.In this paper,by designing logic functions in NOR flash arrays,SC in IMC for the image edge detection is realized,demonstrating ultra-low computational complexity and power consumption(25.5 fJ/pixel at 2-bit sequence length).More impressively,the noise immunity is 6 times higher than that of the traditional binary method,showing good tolerances to cell variation and reliability degradation when implementing massive parallel computation in the array.

Application of the Algorithm of Fractional Dimension to Extraction Image Edge

The idea of fractional dimension was stated in brief firstly.Then,adopting the fractional statistical similar principle, the method of the least square minimum error was applied to evaluate the fractional dimension of per image pixel depending on the fractional property of image.And the image edge is extracted by magnitude of fractional dimension of image pixel.We presented the algorithm of the local fractional dimension,which made the rule of window size and sentencing the fractional dimension of edge.Although this algorithm was waste time,it is better than the classical ones in extraction edge and anti-jamming.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

Image edge detection based on multi-fractal spectrum analysis

In this paper,an image edge detection method based on multi-fractal spectrum analysis is presented.The coarse grain Holder&&exponent of the image pixels is first computed.then,its multi-fractal spectrum is estimated by the kernel estimation method.Finally,the image edge detection is done by means of different multi-fractal spectrum values.Simulation results show that this method is efficient and has better locality compared with the traditional edge detection methods such as the Sobel method.

Image zooming via surface fitting based on edge constraint

In this paper we present a new image zooming algorithm based on surface fitting with edge constraint. In surface fitting,we consider not only the relationship of corresponding pixels between the original image and the enlarged image, but also the neighbor pixels in the enlarged image according to the local structure of original image. Furthermore, during surface fitting, more interpolation constraints are used in the new algorithm for improving the precision of the super sampling pixels. The experimental results show that the new method outperforms the previous methods which based on surface fitting.

Identification of thermal front dynamics in the northern Malacca Strait using ROMS 3D-model

The thermal front in the oceanic system is believed to have a significant effect on biological activity.During an era of climate change,changes in heat regulation between the atmosphere and oceanic interior can alter the characteristics of this important feature.Using the simulation results of the 3D Regional Ocean Modelling System(ROMS),we identified the location of thermal fronts and determined their dynamic variability in the area between the southern Andaman Sea and northern Malacca Strait.The Single Image Edge Detection(SIED)algorithm was used to detect the thermal front from model-derived temperature.Results show that a thermal front occurred every year from 2002 to 2012 with the temperature gradient at the location of the front was 0.3°C/km.Compared to the years affected by El Ni?o and negative Indian Ocean Dipole(IOD),the normal years(e.g.,May 2003)show the presence of the thermal front at every selected depth(10,25,50,and 75 m),whereas El Ni?o and negative IOD during 2010 show the presence of the thermal front only at depth of 75 m due to greater warming,leading to the thermocline deepening and enhanced stratification.During May 2003,the thermal front was separated by cooler SST in the southern Andaman Sea and warmer SST in the northern Malacca Strait.The higher SST in the northern Malacca Strait was believed due to the besieged Malacca Strait,which trapped the heat and make it difficult to release while higher chlorophyll a in Malacca Strait is due to the freshwater conduit from nearby rivers(Klang,Langat,Perak,and Selangor).Furthermore,compared to the southern Andaman Sea,the chlorophyll a in the northern Malacca Strait is easier to reach the surface area due to the shallower thermocline,which allows nutrients in the area to reach the surface faster.

A ROBUST COLOR EDGE DETECTION ALGORITHM BASED ON THE QUATERNION HARDY FILTER

This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selecting suitable parameters to handle different levels of noise.In particular,the quaternion analytic signal,which is an effective tool in color image processing,can also be produced by quaternion Hardy filtering with specific parameters.Based on the QHF and the improved Di Zenzo gradient operator,a novel color edge detection algorithm is proposed;importantly,it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique.From the experimental results,we conclude that the minimum PSNR improvement rate is 2.3%and the minimum SSIM improvement rate is 30.2%on the CSEE database.The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.

Image interpolation algorithm on a triangular grid

A new method for constructing a fitting surface on a triangular grid is presented. Assuming images are obtained by sampling from the original scene. Conventional polynomial interpolation methods generally construct the fitting surface on a square grid. Different from existing methods, the new method constructs the fitting surface on a triangular grid which can divide the original surface more detailed and improve approximation accuracy. As the quality of the image edges plays a key role in visual effects of image, the new method uses image edges as constraints to get a triangle grid. The new method constructs a cubic polynomial patch locally using image data to approximate the original surface. Experimental comparison results of the new method with other methods show that the new method can produce high-quality images and remove the zigzagging artifact.

Novel welding image processing method based on fractal theory

Computer vision has come into used in the fields of welding process control and automation. In order to improve precision and rapidity of welding image processing, a novel method based on fractal theory has been put forward in this paper. Compared with traditional methods, the image is preliminarily processed in the macroscopic regions then thoroughly analyzed in the microscopic regions in the new method. With which, an image is divided up to some regions according to the different fractal characters of image edge, and the fuzzy regions including image edges are detected out, then image edges are identified with Sobel operator and curved by LSM (Lease Square Method). Since the data to be processed have been decreased and the noise of image has been reduced, it has been testified through experiments that edges of weld seam or weld pool could be recognized correctly and quickly.

Application of improved equivalent edge currents in synthetic aperture radar imaging

Target modeling and scattering function calculating are important prerequisites and groundwork for the synthetic aperture radar（SAR） imaging simulation.According to the difficult problems that normal methods cannot calculate the scattering function of electrically large object under the condition to wideband,an effective method of improved equivalent edge currents is presented and applied to SAR imaging simulation for the first time.This method improves calculating velocity and has relatively high precision.The concrete steps of applying the method are given.By way of the simulation experiment,the effectiveness of the method is verified.

Sub-Pixel Edge Location Algorithm for Bilevel Images

Accurate edge localization of bilevel images is of primary importance in barcode decoding.In the sub-pixel edge location algorithm for bilevel images,the bilevel image（barcode） imaging process is modeled as a square wave convoluted with a Gaussian kernel and then sampled discretely by pixel arrays.Based on the gray levels of the pixels,assumed sub-pixel edge locations are set and adjusted so that the discrepancy of the theoretical gray level of pixels and the actual gray level of pixels reaches the minimum and then the best approximation of the actual sub-pixel edges of the bilevel image is obtained.Examples are presented to illustrate the techniques of the algorithm which can solve the problems of edge location or signal recovery of bilevel images in the case of the two features：one is that the support of the Gaussian kernel is comparable to the distance of the adjacent edges;the other is that the distance between the adjacent edges is comparable to the distance of the adjacent pixels.

An edge detection algorithm for imaging ladar

In this paper, the morphological filter based on parametric edge detection is presented and applied to imaging ladar image with speckle noise. This algorithm and Laplacian of Gaussian (LOG) operator are compared on edge detection. The experimental results indicate the superior performance of this kind of the edge detection.

Revealing the residual stress distribution in laser welded Eurofer97 steel by neutron diffraction and Bragg edge imaging

Eurofer97 steel is a primary structural material for applications in fusion reactors. Laser welding is a promising technique to join Eurofer97 plasma-facing components and overcome remote handling and maintenance challenges. The interaction of the induced residual stress and the heterogeneous microstructure degrades the mechanical performance of such fusion components. The present study investigates the distribution of residual stress in as-welded and post-heat treated Eurofer97 joints. The mechanistic connections between microstructure, material properties, and residual stress are also studied. Neutron diffraction is used to study the through-thickness residual stress distribution in three directions,and neutron Bragg edge imaging(NBEI) is applied to study the residual strain in high spatial resolution.The microstructures and micro-hardness are characterised by electron backscatter diffraction and nanoindentation, respectively. The M-shaped residual stress distribution through the thickness of the as-welded weldment is observed by neutron diffraction line scans over a region of 1.41 × 10 mm^(2). These profiles are cross-validated over a larger area(∼56 × 40 mm^(2)) with the higher spatial resolution by NBEI. The micro-hardness value in the fusion zone of the as-welded sample almost doubles from 2.75 ± 0.09 GPa to 5.06 ± 0.29 GPa due to a combination of residual stress and cooling-induced martensite. Conventional post weld heat treatment(PWHT) is shown to release ∼90% of the residual stress but not fully restore the microstructure. By comparing its hardness with that of stress-free samples, it is found that the microstructure is the primary contribution to the hardening. This study provides insight into the prediction of structural integrity for critical structural components of fusion reactors.