Mammogram Classification with HanmanNets Using Hanman Transform Classifier

Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.

基于协同重排序的手势识别方法

手势识别是计算机视觉的一个非常具有挑战性的问题,可运用于人机交互、手语识别、虚拟角色控制等众多领域.然而,由于手本身具有极高的自由度,通过样本直接估计所有手势参数相当困难.为此,提出一种可分解手势参数的手势估计方法——协同重排序.首先将手根据指骨的关节角度划分为多个局部观测单元,并建立离线的局部估计数据库;然后利用此数据库,使用k-最邻近(k-NN)搜索算法对从深度图中获得的局部观测单元进行姿态估计;最后依据当前观测单元的k-NN搜索结果对姿态估计结果重新排序,收敛后得到最终估计结果.除了手势局部参数估计方法之外,还提出一种手的全局姿态估计的方法,使得整个方法可更好地适用于多种任务场景.对合成图像和真实深度图像数据集验证文中方法的性能:不用GPU加速的情况下,该方法可以在30 ms内完成手势识别(其中局部姿态估计17 ms,全局姿态估计12 ms),最大平均估计误差小于10°,具有很高的效率和有效性.

Review of light field technologies

Light fields are vector functions that map the geometry of light rays to the corresponding plenoptic attributes.They describe the holographic information of scenes by representing the amount of light flowing in every direction through every point in space.The physical concept of light fields was first proposed in 1936,and light fields are becoming increasingly important in the field of computer graphics,especially with the fast growth of computing capacity as well as network bandwidth.In this article,light field imaging is reviewed from the following aspects with an emphasis on the achievements of the past five years:(1)depth estimation,(2)content editing,(3)image quality,(4)scene reconstruction and view synthesis,and(5)industrial products because the technologies of lights fields also intersect with industrial applications.State-of-the-art research has focused on light field acquisition,manipulation,and display.In addition,the research has extended from the laboratory to industry.According to these achievements and challenges,in the near future,the applications of light fields could offer more portability,accessibility,compatibility,and ability to visualize the world.

Adaptive recurrent neural network for uncertainties estimation in feedback control system

In this paper,a recurrent neural network(RNN)is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems.The neural network approximates the uncertainties related to unmodeled dynamics,parametric variations,and external disturbances.The RNN has a single hidden layer and uses the tracking error and the output as feedback to estimate the disturbance.The RNN weights are online adapted,and the adaptation laws are developed from the stability analysis of the controlled system with the RNN estimation.The used activation function,at the hidden layer,has an expression that simplifies the adaptation laws from the stability analysis.It is found that the adaptive RNN enhances the tracking performance of the feedback controller at the transient and steady state responses.The proposed RNN based feedback control is applied to a DC–DC converter for current regulation.Simulation and experimental results are provided to show its effectiveness.Compared to the feedforward neural network and the conventional feedback control,the RNN based feedback control provides good tracking performance.

Proposal for a cross layer scheme for real-time wireless video

This paper focuses on the design of the cross layer between the video application layer and the MIMO physical layer. MIMO physical layer research has promised an enormous increase in the capacity of wireless communication systems. Also MIMO wireless systems operate under fading conditions where the channel faces arbitrary fluctuations. Since the wireless channel changes over each coherence period, the capacity of the wireless channel, given the power constraints, changes. Hence to make efficient use of the available capacity one needs to adapt the video bit rate. However it is impossible to adapt at the application layer as changing the parameters of the video takes more time than the coherence period of the channel. In this paper we address this problem through a novel solution and also investigate its performance through a simulation study.

Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

一种新的Web链接提取模型

以搜索引擎链接提取模块所要求的容错性、正确性、全面性、高效性和可扩展性为目标,提出了一种新的链接提取模型的设计思路。该模型将链接提取过程划分为信息提取、信息加工、信息分析和信息储存。信息的获取是通过HTM L(hypertex t m arkup language)文法分析方法从文档中得到初始统一资源地址(un iform resourceiden tifier,UR I)数据;信息加工阶段通过运用UR I解析算法对初始数据进行精练;然后在信息分析过程中进一步加以筛选和过滤;最后将结果存储在一个灵活的数据结构中。通过对比测试证实这种新的链接提取模式比传统方法在各项指标上均具有明显优势。