Toward the Optimization of the Region-Based P300 Speller

Technology has tremendously contributed to improving communication and facilitating daily activities.Brain-Computer Interface(BCI)study particularly emerged from the need to serve people with disabilities such as Amyotrophic Lateral Sclerosis(ALS).However,with the advancements in cost-effective electronics and computer interface equipment,the BCI study is flourishing,and the exploration of BCI applications for people without disabilities,to enhance normal functioning,is increasing.Particularly,the P300-based spellers are among the most promising applications of the BCI technology.In this context,the region-based paradigm for P300 BCI spellers was introduced in an effort to reduce the crowding effect and adjacency problem that might affect the detection of P300 peak.This study extends this line of research by investigating the effect,in terms of accuracy and usability,of the letters’distribution among the speller’s regions.For this purpose,a clustering algorithm is proposed,and two region-based layouts were generated by redistributing the letters based on their dissimilarity or their similarity.A pilot usability evaluation was also conducted in order to assess the usability of the different layouts in terms of effectiveness,efficiency,and satisfaction.The results indicate that the distribution of the letters has an effect on the classification accuracy as well as the user experience.Particularly,when considering short-term accuracy and cognitive effort,the original region-based layout outperforms other layouts.

A Novel Approach towards Cost Effective Region-Based Group Key Agreement Protocol for Ad Hoc Networks Using Elliptic Curve Cryptography

This paper addresses an interesting security problem in wireless ad hoc networks: the dynamic group key agreement key establishment. For secure group communication in an ad hoc network, a group key shared by all group members is required. This group key should be updated when there are membership changes (when the new member joins or current member leaves) in the group. In this paper, we propose a novel, secure, scalable and efficient region-based group key agreement protocol for ad hoc networks. This is implemented by a two-level structure and a new scheme of group key update. The idea is to divide the group into subgroups, each maintaining its subgroup keys using group elliptic curve diffie-hellman (GECDH) Protocol and links with other subgroups in a tree structure using tree-based group elliptic curve diffie-hellman (TGECDH) protocol. By introducing region-based approach, messages and key updates will be limited within subgroup and outer group;hence computation load is distributed to many hosts. Both theoretical analysis and experimental results show that this Region-based key agreement protocol performs well for the key establishment problem in ad hoc network in terms of memory cost, computation cost and communication cost.

Assessing the Impacts of Land Use and Land Cover Changes on Hydrology of the Mbarali River Sub-Catchment. The Case of Upper Great Ruaha Sub-Basin, Tanzania

Intensification of agricultural land use and population growth from 1990-2017 has caused changes in land cover and land use of the Mbarali River sub-catchment which is located in the Upper Great Ruaha Sub basin, Tanzania. This has affected the magnitude of the surface runoff, total water yield and the groundwater flow. This study assesses the impacts of the land cover and land use changes on the stream flows and hydrological water balance components (surface runoff, water yield, percolation and actual evapotranspiration). The land use and land cover (LULC) maps for three window period snapshots, 1990, 2006 and 2017 were created from Landsat TM and OLI_TIRS with the help of QGIS version 2.6. Supervised classification was used to generate LULC maps using the Maximum Likelihood Algorithm and Kappa statistics for assessment of accuracy. SWAT was set up and run to simulate stream flows and hydrological water balance components. The assessment of the impacts of land use and land cover changes on stream flows and hydrological water balance component was performed by comparing hydrological parameters simulated by SWAT using land use scenarios of 2006 and 2017 against the baseline land use scenario of 1990. Accuracy of LULC classification was good with Kappa statistics ranging between 0.9 and 0.99. There was a drastic increase in areal coverage of cultivated land, for periods 1990-2006 (5.84%) and 2006-2017 (12.05%) compared to other LULC. During 2006 and 2017 surface runoff increased by 4% and 9% respectively;however, water yield increased by only 0.5% compared to 1990 baseline period. This was attributed to increased proportion of cultivated land in the sub-catchment which has a high curve number (59.60) that indicates a higher runoff response and low infiltration rate.

REGION-BASED ACTIVE CONTOUR DRIVEN BY GLOBAL INTENSITY FITTING ENERGY

In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the level set function to be close to a signed distance function. Image global information utilized efficiently makes the proposed model insensitive to noise, and the introduced penalty term can avoid the costly re-initialization for the evolving level set function, which not only speeds up the contour evolvement, but also improves accuracy of the final contour. Comparisons with other classical region-based models, such as Chan-Vese model and Region-Scalable Fitting (RSF) model, show the advantages of our model in terms of efficiency and accuracy. Moreover, the model is robust to noise.

Novel region-based image compression method based on spiking cortical model

To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.

Farmers’ Perception of Soil Erosion and Adoption of Soil Conservation Technologies at Geshy Sub-Catchment, Gojeb River Catchment, Ethiopia

The study was conducted to investigate farmers’ perception of soil erosion, participation and adoption of soil conservation technologies (SWC) in Geshy sub-catchment of Gojeb river catchment, Omo-Gibe basin, Ethiopia during 2016. The study is based on a detailed survey of 77 households using structured interviews, field observation and focus group discussion. Descriptive and chi-square statistics were applied to analyze factors that affected farmers’ perceived soil erosion severity, participation and adoption options. The results revealed that about 79% of farmers perceived soil erosion problem and its consequences and 97.4% of them believed that it can be controlled. Almost all (97.4%) farmers acknowledged the presence of SWC technologies and about 92.2% of them were participated in conservation activities voluntarily. Thus, 93.5% of them realized decreasing rate of soil erosion and 79.9% of them observed an increasing trend in soil fertility status. Consequently, 94.8% of them confirmed the potential of SWC technologies to halt land degradation and improve land productivity. Furthermore, 98.7% of them were willing to adopt with very good adoption judgment and 94.8% of them were willing to continue maintaining constructed technologies in the future. Principally, farmers’ perception of soil erosion, their genuine participation derived from their conviction, and adoption of induced SWC technologies are the decisive elements for the success of watershed management interventions.

Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.

Modeling Surface Water Availability for Irrigation Development in Mbarali River Sub-Catchment Mbeya, Tanzania

Although Tanzania has a large land suitable for irrigation development, only 4.2% of the arable land which is potential for irrigation has been developed. Mbarali District is characterized by commercial and small-scale irrigation activities for paddy production. Currently, surface water availability for irrigation in Mbarali District is dwindling due to high water demands. Inadequate studies that estimate water availability for irrigation is one of the underlying factors to the lack of irrigation development in many parts of Tanzania including in Mbarali District. This study, therefore, aimed to model surface water availability for irrigation development in Mbarali River sub-catchment Mbeya, Tanzania. The Soil and Water Analysis Tool (SWAT) model and field observations were used to accomplish the study. The model estimates that Mbarali River sub-catchment receives about 631 mm of total mean precipitation annually. About 53% of received precipitation is lost through evapotranspiration, 12% recharged to deep aquifer and the remaining 35% discharged to the stream flow through surface runoff, lateral flow and return flow from unconfined aquifer. Discharge to the steam flow contributes to the total annual means of river discharge ranging from 0 - 10 cubic meters per second at upper catchment to 120 - 140 cubic meters per second at lower catchment. The study recommends that the lower reach of the Mbarali River sub-catchment is potential for irrigation than the upper reach as it has potential river flow that can support irrigation activities. The study also notes the urgent need for water reallocation plan to meet competing water needs in the lower reach of Mbarali River sub-catchment. Moreover, the study addresses the potentiality of irrigation in upper catchment under sustainable water management practices including excavation of small ponds to capture and store surface runoff for dry season use or to supplement irrigation as the rainfall declines.

Institutional Structures and Sustainability of Projects in Nyangores River Sub-Catchment Basin in Bomet County, Kenya

Integrated basin management approach has been applied in Nyangores River sub-catchment basin, since the year 2009 but with minimal success. Sub catchment degradation, organizational weakness, the flow and quality of water had started to diminish, creating challenges for local livelihoods, wildlife in the Maasai Mara Game Reserve, and in maintaining biodiversity and healthy ecosystem functioning. Water resources can be successfully managed only if the natural, social, economic and political environments, in which water occurs and used, are taken fully into consideration. The aim of this study is to determine the influence of institutional structures influence on sustainability of projects in Nyagores river sub-catchment basin in Bomet County, Kenya. The research designs used were descriptive survey and correlational research design. Stepwise and purposive sampling formed the sampling procedure. The results are presented descriptively using Tables while for qualitative data, narrative statements were used. Questionnaires, Interview guide and document analysis were used for data collection. The sample size was 371, from a targeted a population of 56,508 household heads and 10 informants, purposively selected from the water concerned institutions and ministries of Water and Agriculture. Total of 371 questionnaires were given out to the respondents and only 321, were duly filled and returned representing (86.5%). The objective was to establish the extent to which institutional structures influence sustainability of projects in Nyangores River sub-catchment Basin. The results indicated that there was a positive correlation r = 0.552, (p is was rejected and concluded that there is a significant relationship between the institutional structures and sustainability of projects in Nyangores river sub-catchment basin. R2 was 0.304;hence, 30.4% of changes in sustainability of projects are explained by institutional structures. Recommendations are;ensure a stringent policy for robust planning and management, and more robust forum for the stakeholders to complement the efforts of WRUA. It is suggested for further research, similar studies are done for the other adjacent river basins and to investigate ways of raising the level of community participation in the basin.

Genetic analysis and candidate gene identification of salt tolerancerelated traits in maize

Soil salinization poses a threat to maize production worldwide,but the genetic mechanism of salt tolerance in maize is not well understood.Therefore,identifying the genetic components underlying salt tolerance in maize is of great importance.In the current study,a teosinte-maize BC2F7 population was used to investigate the genetic basis of 21 salt tolerance-related traits.In total,125 QTLs were detected using a high-density genetic bin map,with one to five QTLs explaining 6.05–32.02%of the phenotypic variation for each trait.The total phenotypic variation explained(PVE)by all detected QTLs ranged from 6.84 to 63.88%for each trait.Of all 125 QTLs,only three were major QTLs distributed in two genomic regions on chromosome 6,which were involved in three salt tolerance-related traits.In addition,10 pairs of epistatic QTLs with additive effects were detected for eight traits,explaining 0.9 to 4.44%of the phenotypic variation.Furthermore,18 QTL hotspots affecting 3–7 traits were identified.In one hotspot(L5),a gene cluster consisting of four genes(ZmNSA1,SAG6,ZmCLCg,and ZmHKT1;2)was found,suggesting the involvement of multiple pleiotropic genes.Finally,two important candidate genes,Zm00001d002090 and Zm00001d002391,were found to be associated with salt tolerance-related traits by a combination of linkage and marker-trait association analyses.Zm00001d002090 encodes a calcium-dependent lipid-binding(CaLB domain)family protein,which may function as a Ca^(2+)sensor for transmitting the salt stress signal downstream,while Zm00001d002391 encodes a ubiquitin-specific protease belonging to the C19-related subfamily.Our findings provide valuable insights into the genetic basis of salt tolerance-related traits in maize and a theoretical foundation for breeders to develop enhanced salt-tolerant maize varieties.

一种面向自动驾驶路况的目标检测算法

为了对常见的行人和车辆进行检测,采用自行标注的数据集,通过基于faster regionbased convolutional neural network(RCNN)框架的算法进行调参与优化.主干网络采用轻量化网络MobileNetv2,在原生锚框的基础上,区域建议网络(RPN)部分增加2个面积尺度,检测部分使用感兴趣区域(ROI)Align结构,减少特征图映射和均分过程中的误差.实验结果表明:使用faster RCNN目标检测网络,可以有效完成行人和车辆的检测任务,整体效果良好.

Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network

Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique system,allowing this system to read computed tomography(CT)images correctly and make diagnosis of pancreatic cancer faster.Methods:The establishment of the artificial intelligence(AI)system for pancreatic cancer diagnosis based on sequential contrastenhanced CT images were composed of two processes:training and verification.During training process,our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set.Additionally,we used VGG16,which was pretrained in ImageNet and contained 13 convolutional layers and three fully connected layers,to initialize the feature extraction network.In the verification experiment,we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network(Faster R-CNN)model that had completed training.Totally,1699 images from 100 pancreatic cancer patients were included for clinical verification.Results:A total of 338 patients with pancreatic cancer were included in the study.The clinical characteristics(sex,age,tumor location,differentiation grade,and tumor-node-metastasis stage)between the two training and verification groups were insignificant.The mean average precision was 0.7664,indicating a good training ejffect of the Faster R-CNN.Sequential contrastenhanced CT images of 100 pancreatic cancer patients were used for clinical verification.The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632.It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image,which is much faster than the time required for diagnosis by an imaging specialist.Conclusions:Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of pancreatic cancer.

Small objects detection in UAV aerial images based on improved Faster R-CNN

In order to solve the problem of small objects detection in unmanned aerial vehicle(UAV)aerial images with complex background,a general detection method for multi-scale small objects based on Faster region-based convolutional neural network(Faster R-CNN)is proposed.The bird’s nest on the high-voltage tower is taken as the research object.Firstly,we use the improved convolutional neural network ResNet101 to extract object features,and then use multi-scale sliding windows to obtain the object region proposals on the convolution feature maps with different resolutions.Finally,a deconvolution operation is added to further enhance the selected feature map with higher resolution,and then it taken as a feature mapping layer of the region proposals passing to the object detection sub-network.The detection results of the bird’s nest in UAV aerial images show that the proposed method can precisely detect small objects in aerial images.

Weakly- and Semi-Supervised Fast Region-Based CNN for Object Detection

Learning an effective object detector with little supervision is an essential but challenging problem in computer vision applications. In this paper, we consider the problem of learning a deep convolutional neural network (CNN) based object detector using weakly-supervised and semi-supervised information in the framework of fast region-based CNN (Fast R-CNN). The target is to obtain an object detector as accurate as the fully-supervised Fast R-CNN, but it requires less image annotation effort. To solve this problem, we use weakly-supervised training images (i.e., only the image-level annotation is given) and a few proportions of fully-supervised training images (i.e., the bounding box level annotation is given), that is a weakly-and semi-supervised (WASS) object detection setting. The proposed solution is termed as WASS R-CNN, in which there are two main components. At first, a weakly-supervised R-CNN is firstly trained;after that semi-supervised data are used for finetuning the weakly-supervised detector. We perform object detection experiments on the PASCAL VOC 2007 dataset. The proposed WASS R-CNN achieves more than 85% of a fully-supervised Fast R-CNN's performance (measured using mean average precision) with only 10%of fully-supervised annotations together with weak supervision for all training images. The results show that the proposed learning framework can significantly reduce the labeling efforts for obtaining reliable object detectors.

Object detection of artifact threaded hole based on Faster R-CNN

In order to improve the accuracy of threaded hole object detection,combining a dual camera vision system with the Hough transform circle detection,we propose an object detection method of artifact threaded hole based on Faster region-ased convolutional neural network(Faster R-CNN).First,a dual camera image acquisition system is established.One industrial camera placed at a high position is responsible for collecting the whole image of the workpiece,and the suspected screw hole position on the workpiece can be preliminarily selected by Hough transform detection algorithm.Then,the other industrial camera is responsible for collecting the local images of the suspected screw holes that have been detected by Hough transform one by one.After that,ResNet50-based Faster R-CNN object detection model is trained on the self-built screw hole data set.Finally,the local image of the threaded hole is input into the trained Faster R-CNN object detection model for further identification and location.The experimental results show that the proposed method can effectively avoid small object detection of threaded holes,and compared with the method that only uses Hough transform or Faster RCNN object detection alone,it has high recognition and positioning accuracy.

Integrating Color and Spatial Feature for Content-Based Image Retrieval

In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact that only a relatively low number of distinct values of a particular visual feature is present in most images. To extract color feature and build indices into our image database we take into consideration factors such as human color perception and perceptual range, and the image is partitioned into a set of regions by using a simple classifying scheme. The compact color feature vector and the spatial color histogram, which are extracted from the seqmented image region, are used for representing the color and spatial information in the image. We have also developed the region-based distance measures to compare the similarity of two images. Extensive tests on a large image collection were conducted to demonstrate the effectiveness of the proposed approach.

Lithological and tectonic response on catchment characteristics of Rishi Khola, Sikkim, India

Rishi Khola,a sub-watershed of Teesta river,traverses along the Main Central Thrust(MCT)with a multitude of litho units and structural entities.This study examines the impact of tectonic and lithologic controls in configuring the catchment characteristics of Rishi Khola,Sikkim,India.Corrected SRTM 30m DEM and Landsat 8 satellite image have been used for extracting the river network,preparing the sub-catchments,the longitudinal profile and thereby calculating the morphotectonic indices.An aggregated tectonic index(ATI)has been prepared to map the intensity of tectonic perturbations in the fluvial environment using the entropy weightage method(EWM)and Weighted Linear Combination(WLC).The undulating nature of the longitudinal profile with prominent knick points confirms the presence of tectonic disturbances and lithological variations.From all the computed morphotectonic indices and the ATI,it has been evident that the region has experienced surface deformations.When viewed at the entire catchment,the morphotectonic indices suggest ample responses to the tectonic perturbations due to the dominance of lithology-controlled hill slope processes and fluvial erosion.The spatiality of the tectonic sensitiveness is rather concentrated into certain pockets of differential stress field formed due to fault thrusting of the Himalayas.The study chiefly focuses on the peculiarity of the watershed which displays a complex response of tectonic and rock structure;wherein the proposed methodology has been successful in excavating such complex responses around the Himalayan thrusts.

Real-time detection of moving objects in video sequences

An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame difference and adjusted background subtraction. An adaptive threshold technique is employed to automatically choose the threshold value to segment the moving objects from the still background. And experiment results show that the algorithm is effective and efficient in practical situations. Furthermore, the algorithm is robust to the effects of the changing of lighting condition and can be applied for video surveillance system.

Land Cover Change and Its Socio-Economic Impact on the Residents of the Mara River, Kenya

Anthropogenic activities are increasingly catalyzing natural climatic factors that drive land cover change at different spatial scales. Available land cover data of the Mara River basin however give a broader picture of the entire basin regardless of the heterogeneity that exists at the sub-catchment level. This study sought to establish sub-catchment specific information on land cover changes through examination of satellite images of four Mara River sub-catchments (Amala, Nyangores, Talek and Sand River) for the period 1987-2017. The relationship between temperature, rainfall and land cover was also computed. In addition, a household survey and focus group discussions were conducted in each sub-catchments to establish the socio-economic impacts of land cover change on the community’s wellbeing. Forest cover was dominant in Amala (39.8%) and Nyangores (43.7%) sub-catchments in 1987 but by 2017 crop lands had surpassed forest cover in the two sub-catchments, accounting for 53.2% and 45.7%, respectively. However, in Talek (52.8%) and Sand River (47.4%) sub-catchments, grassland was the dominant land cover type in 1987 and after the 30 year period, grasslands remained dominant in Sand River, while shrub land became dominant in Talek sub-catchment. A weak positive correlation was observed between rainfall and forest cover, shrub land and cropland, while a negative correlation was observed between rainfall and bare land. Average temperature showed a positive moderate correlation with bare land and built up areas. Analysis of survey data revealed that livestock keeping, temperature increase, type of trees, education level of household head and weak environmental laws were the main drivers of land cover change (P −0.587), beans (r = −0.5459), sorghum (r = −0.351), cow peas (r = −0.544), and pigeon peas (r = −0.337). Focus group discussions participants were supportive of environmental protective measures to reverse negative land cover changes, while planting drought resistant trees, crop diversification and awareness creation among community members were recommended as the most ideal environmental management strategies.

Subtle Impacts of Temperature and Rainfall Patterns on Land Cover Change Overtime and Future Projections in the Mara River Basin, Kenya

The interactive and cumulative effect of temperature and rainfall on land cover change is a priority at global, regional and local scale. This study examined changes in six land cover categories (forestland, grasslands, shrub land, bare land, built-up areas and agricultural lands) in four sub-catchments (Amala, Nyangores, Talek and Sand River), of the Mara River basin over a 30-year period (1987-2017) and made predictions of future land cover change patterns. Landsat Imageries of 90 m resolution were retrieved and analyzed using ArcGIS 10.0 software. Relationship between NDVI, temperature and precipitation was determined using Pearson’s correlation coefficient, while Markov chains analyses were performed on different land cover categories to project future trends. Results showed low to moderate (R2 = 0.002 to 0.6) trends of change in NDVI of different land cover categories across all sub-catchments. The greatest change (R2 0.34 to 0.5) was recorded in bare land in three of the four sub-catchments studied. Precipitation showed a strong positive correlation with built-up areas, forestlands, croplands, bare land, grasslands and shrub lands, while temperature correlated strongly but negatively with the same land cover categories. The change detection matrix projected significant but varying changes in land cover categories across the four sub-catchments by 2027. This study underscores the impact of changing climatic factors on various land cover categories in the Mara River basin sub-catchments, with different land cover categories exhibiting strong positive sensitivity to high precipitation and low temperature and vice-versa.