一种基于三角数分解的可配置2-D卷积器优化方法

多尺寸2-D卷积通过特征提取在检测、分类等计算机视觉任务中发挥着重要作用。然而,目前缺少一种高效的可配置2-D卷积器设计方法,这限制了卷积神经网络(CNN)模型在边缘端的部署和应用。该文基于乘法管理以及奇平方数的三角数分解方法,提出一种高性能、高适应性的卷积核尺寸可配置的2-D卷积器。所提2-D卷积器包含一定数量的处理单元(PE)以及相应的控制单元,前者负责运算任务,后者负责管理乘法运算的组合,二者结合以实现不同尺寸的卷积。具体地,首先根据应用场景确定一个奇数列表,列表中为2-D卷积器所支持的尺寸,并利用三角数分解得到对应的三角数列表;其次,根据三角数列表和计算需求,确定PE的总数量;最后,基于以小凑大的方法,确定PE的互连方式,完成电路设计。该可配置2-D卷积器通过Verilog硬件描述语言(HDL)设计实现,由Vivado 2 022.2在XCZU7EG板卡上进行仿真和分析。实验结果表明,相比同类方法,该文所提可配置2-D卷积器,乘法资源利用率得到显著提升,由20%~50%提升至89%,并以514个逻辑单元实现1 500 MB/s的吞吐率,具有广泛的适用性。

MRI T_2 star mapping、T_1 images与3D DESS融合图在隐匿性膝关节软骨损伤中的应用

目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。

基于离散2-D小波多级分解的电容器外观缺陷视觉检测方法

电容器外观破损、凸起等缺陷直接影响器件的生产质量。目前电容器微小外观缺陷检测难度较大,导致视觉检测效率较低。为此提出了基于离散2-D小波多层分解的电容器外观缺陷视觉检测方法。采用同态滤波处理去除光照对电容器外观视觉检测结果的影响。利用像素点灰度值确定图像边缘点位置,提取电容器外观缺陷区域。应用离散2-D小波分解的方法对其展开多级分解。再差分统计电容器图像的外观缺陷纹理特征。将特征输入Mahalanobis分类器中,完成电容器外观缺陷视觉检测。仿真结果表明,所提方法可以较好检测电容的各种缺陷,召回率最低值是94.9%,误检率最高值为9.8%,漏检率均在10%以内,电容器缺陷检测效果较好。

Novel registration algorithm for 3-D images captured from multiple views of object surface

A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D points can be easily obtained when capturing original 3-D images. The iterative least-mean-squared （LMS） algorithm is applied to optimizing adaptively the transformation matrix parameters. These can effectively improve the registration performance and hurry up the matching process. Experimental results show that it can reach a good subjective impression on aligned 3-D images. Although the algorithm focuses primarily on the human head model, it can also be used for other objects with small modifications.

2型糖尿病Gd-EOB-DTPA增强MRI肝肾影像改变

目的:探讨2型糖尿病(T2D)患者钆塞酸二钠(Gd-EOB-DTPA)增强MRI肝胆期的肝肾影像改变。方法:搜集82例接受Gd-EOB-DTPA增强MRI腹部检查的患者(肝肾功能正常),其中T2D患者34例(观察组),非T2D患者48例(对照组)。两位医师分别计算肝胆期肝脏相对信号强度(RL)、肾皮质和髓质相对信号强度(Rrc、Rrm)、肾皮髓质信号强度的相对差异(Rc-m)。采用独立样本t检验或Mann-Whitney U检验比较观察组与对照组的相关参数差异。选择组间差异有统计学意义的变量,使用受试者工作特征(ROC)曲线分析其鉴别T2D的效能;采用组内相关系数(ICC)评估其可重复性。结果:观察组的RL、Rc-m均较对照组降低,差异有统计学意义(P<0.05),两组的Rrc、Rrm差异均无统计学意义(P>0.05)。RL和Rc-m鉴别T2D的ROC曲线下面积分别为0.78和0.72,诊断敏感度分别为77.10%和64.60%,特异度分别为70.60%和79.40%。RL和Rc-m的观察者间一致性均较好,RL和Rc-m的ICC分别为0.91和0.89。结论:T2D患者Gd-EOB-DTPA增强MRI肝胆期肝肾RL和Rc-m降低。RL和Rc-m在一定条件下可能有助于鉴别T2D,其测量可重复性较好。

Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

Evaluation of Ventricular-vascular Coupling in Patients with Type 2 Diabetes Mellitus Using 2-Dimensional Speckle Tracking Imaging

The elastic and functional coupling of heart and vessels makes the stroke work （SW） of the heart optimal. Speckle tracking imaging （STI） can evaluate the myocardial strain and function. We studied ventricular-vascular coupling in 80 diabetic patients with different systolic function using STI. The patients were divided into two groups according to ejection fraction （EF）： the diabetes mel- litus with normal EF （DMN） group and the diabetes mellitus with abnormal EF （DMA） group. Forty-two volunteers served as control group. The relative wall thickness （RWT）, left ventricular mass index （LVMI）, stroke volume （SV）, SW, rate-pressure product （RPP）, systemic vascular resis- tance index （SVRI）, left ventricular end-systolic elastance （Ees）, effective arterial elasticity （Ea） and ventricular-vascular coupling index （VVI） were measured and calculated by conventional echocardi- ography. The longitudinal strain （LS） at basement （LSBA）, papillary muscle （LSvM） and cardiac apex （LSAv） was assessed with STI. It was found： （A） compared with control group, in DMN and DMA groups, LSBA, LSvM and LSAp decreased, and they were lower in DMA group. （B） VVI, RPP and SVRI increased, and they were higher in DMN group; Ees decreased, and it was lower in DMA group. （C） LSBA, LSpM, and LSAv had negative correlation with VVI. LSAp, RWT, LVMI and SW were independent predictors for VVI. The area under the receiver operating characteristic （ROC） curves was used for identification of DMA and DMN with LSBA, LSpM, and LSAp, and the area under the ROC of LSAp was the largest. This study supports that myocardial LS could reflect the ventricu- lar-vascular coupling. Different segments had an order to “respond to” the state of the coupling, and the cardiac apex might be the earliest.

Analysis of Two-Dimensional Gel Electrophoresis Images of Protein from Posterior Silk Gland of Silkworm (Bombyx mori) on Day 1 and Day 4 in the 5th Instar Stage

The posterior silk gland （PSG） of silkworm is an important organ where fibroin is synthesized and secreted exclusively. Because fibroin constitutes 75-80% of the silk filament, the mechanism governing fibroin secretion, quality and yield of cocoon can be elucidated by the study on the PSG. Using two-dimensional gel electrophoresis （2-DE） and image analysis system, the changes in the protein composition in the PSG cell were investigated on the day 1 （D1） and day 4 （D4） in the 5th instar stage from five different strains of silkworm （Bombyx mori）. While differences at protein level between days and strains were far less than those observed at the gene level using EST analysis. The change trends in protein composition from D1 to D4 were diverse among the different strains. The results suggest that the secretion of fibroin is regulated by multiple proteins. The site of regulation and the proteins responsible for the regulation vary with the strain, which leads to differences between strains in the capacity of fibroin secretion in the PSG cell.

2-D mini mumfuzzy entropy method of image thresholding based on genetic algorithm

A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images

AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.

An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

A color image encryption scheme based on a 2D coupled chaotic system and diagonal scrambling algorithm

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.

Algorithm Development of Cloud Removal from Solar Images Based on Pix2Pix Network

Sky clouds affect solar observations significantly.Their shadows obscure the details of solar features in observed images.Cloud-covered solar images are difficult to be used for further research without pre-processing.In this paper,the solar image cloud removing problem is converted to an image-to-image translation problem,with a used algorithm of the Pixel to Pixel Network(Pix2Pix),which generates a cloudless solar image without relying on the physical scattering model.Pix2Pix is consists of a generator and a discriminator.The generator is a well-designed U-Net.The discriminator uses PatchGAN structure to improve the details of the generated solar image,which guides the generator to create a pseudo realistic solar image.The image generation model and the training process are optimized,and the generator is jointly trained with the discriminator.So the generation model which can stably generate cloudless solar image is obtained.Extensive experiment results on Huairou Solar Observing Station,National Astronomical Observatories,and Chinese Academy of Sciences(HSOS,NAOC and CAS)datasets show that Pix2Pix is superior to the traditional methods based on physical prior knowledge in peak signal-to-noise ratio,structural similarity,perceptual index,and subjective visual effect.The result of the PSNR,SSIM and PI are 27.2121 dB,0.8601 and 3.3341.

基于二萘并[2,3-B∶2′,3′-D]呋喃基团的高效窄发射蓝光OLED器件

高效率窄光谱蓝色有机电致发光器件(OLED)是柔性显示领域的研究重点之一。本文以二萘并[2,3-B∶2′,3′-D]呋喃为弱电子受体、N-(4-联苯基)-1-萘胺作为电子给体设计合成了一种D-A-D型蓝光分子DPF-NA,其在正己烷溶液中的发射峰位于441 nm。理论计算与光物理测试结果显示DPF-NA具有杂化局域电荷转移激发态(HLCT)特性,兼具局域态(LE)高发光效率与电荷转移态(CT)高激子利用率特征,在二氯甲烷溶液中的光致发光量子效率(PLQY)为81.2%。基于质量分数3%DPF-NA掺杂浓度的OLED器件电致发光(EL)峰位于455 nm,半峰宽(FWHM)仅为26 nm,CIE(x,y)坐标为(0.14,0.08),最大外量子效率(EQEmax)为6.76%。

Three-dimensional time-of-flight magnetic resonance angiography combined with high resolution T2-weighted imaging in preoperative evaluation of microvascular decompression

BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and HFS caused by NVC. The judgement of NVC is a critical step in the preoperative evaluation of MVD, which is related to the effect of MVD treatment. Magnetic resonance imaging(MRI) technology has been used to detect NVC prior to MVD for several years. Among many MRI sequences, three-dimensional time-of-flight magnetic resonance angiography(3D TOF MRA) is the most widely used. However, 3D TOF MRA has some shortcomings in detecting NVC. Therefore, 3D TOF MRA combined with high resolution T2-weighted imaging(HR T2WI) is considered to be a more effective method to detect NVC.AIM To determine the value of 3D TOF MRA combined with HR T2WI in the judgment of NVC, and thus to assess its value in the preoperative evaluation of MVD.METHODS Related studies published from inception to September 2022 based on PubMed, Embase, Web of Science, and the Cochrane Library were retrieved. Studies that investigated 3D TOF MRA combined with HR T2WI to judge NVC in patients with TN or HFS were included according to the inclusion criteria. Studies without complete data or not relevant to the research topics were excluded. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. The publication bias of the included literature was examined by Deeks’ test. An exact binomial rendition of the bivariate mixed-effects regression model was used to synthesize data. Data analysis was performed using the MIDAS module of statistical software Stata 16.0. Two independent investigators extracted patient and study characteristics, and discrepancies were resolved by consensus. Individual and pooled sensitivities and specificities were calculated. The I_(2) statistic and Q test were used to test heterogeneity. The study was registered on the website of PROSERO(registration No. CRD42022357158).RESULTS Our search identified 595 articles, of which 12(including 855 patients) fulfilled the inclusion criteria. Bivariate analysis showed that the pooled sensitivity and specificity of 3D TOF MRA combined with HR T2WI for detecting NVC were 0.96 [95% confidence interval(CI): 0.92-0.98] and 0.92(95%CI: 0.74-0.98), respectively. The pooled positive likelihood ratio was 12.4(95%CI: 3.2-47.8), pooled negative likelihood ratio was 0.04(95%CI: 0.02-0.09), and pooled diagnostic odds ratio was 283(95%CI: 50-1620). The area under the receiver operating characteristic curve was 0.98(95%CI: 0.97-0.99). The studies showed no substantial heterogeneity(I2 = 0, Q = 0.001 P = 0.50).CONCLUSION Our results suggest that 3D TOF MRA combined with HR T2WI has excellent sensitivity and specificity for judging NVC in patients with TN or HFS. This method can be used as an effective tool for preoperative evaluation of MVD.

Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

Groundwater Exploration Using 2-D Geoelectrical Resistivity Imaging Technique at Sungai. Udang, Melaka

Electrical resistivity imaging surveys have been conducted in order to locate, delineate subsurface water resource and estimate its reserve. The resistivity imaging surveys carried out basically measure and map the resistivity of subsurface materials. Electrical imaging is an appropriate survey technique for areas with complex geology where the use of resistivity sounding and other techniques are unsuitable to provide detailed subsurface information. The purpose of electrical surveys is to determine the subsurface resistivity distribution by making measurements on the ground surface. The resistivity imaging measurement employing Wenner electrode configuration was carried out using an ABEM SAS 1000 terrameter and electrode selector system ES464. The field survey was conducted along four profiles which provide a continuous coverage of the resistivity imaging below surface. The surface soil material is mainly clayey silt. The results showed that the layers associated with the low resistivities （Ωm） are located at depth ranging from 2 m to 28 m. This low resistivity values are associated with zone of water saturated weathered layer and fractures. The results showed that the thickness of residual soil is about 0.5-2.55 m. Borehole data indicated that the depth of bedrock is about 10 m and the groundwater level is ranging from 8.73 m to 8.54 m.