A smart sensor to detect the falls of the elderly

Falls are a major health hazard for the elderly and a major obstacle to independent living. The estimated incidence of falls for both institutionalized and independent persons aged over 75 is at least 30 percent per year. In the SIMBAD (Smart Inactivity Monitor using Array-Based Detectors) project, we've developed an intelligent fall detector based on a low-cost array of infrared detectors. A field trial and user research indicate that SIMBAD could significantly enhance the functionality and effectiveness of existing monitoring systems and community alarm systems.     

Analysis and effects of smart home dataset characteristics for daily life activity recognition

Over the last few years, activity recognition in the smart home has become an active research area due to the wide range of human centric-applications. With the development of machine learning algorithms for activity classification, dataset is significantly important for algorithms testing and validation. Collection of real data is a challenging process due to involved budget, human resources, and annotation cost that’s why mostly researchers prefer to utilize existing datasets for evaluation purposes. However, openly available smart home datasets indicate variation in terms of performed activities, deployed sensors, and environment settings. Unfortunately, the analysis of existing datasets characteristic is a bottleneck for researchers while selecting datasets of their intent. In this paper, we develop a Framework for Smart Homes Dataset Analysis (FSHDA) to reflect their diverse dimensions in predefined format. It analyzes a list of data dimensions that covers the variations in time, activities, sensors, and inhabitants. For validation, we examine the effects of proposed data dimension on state-of-the-art activity recognition techniques. The results show that dataset dimensions highly affect the classifiers’ individual activity label assignments and their overall performances. The outcome of our study is helpful for upcoming researchers to develop a better understanding about the smart home datasets characteristics with classifier’s performance.     

一种低照度视频序列增强算法

为了提高低照度视频的视觉效果,提出一种基于对数图像处理（LIP）的增强算法和改进的双向均衡算法相结合的方法,并运用帧间互相关信息,在保证视觉效果的前提下,减少了处理的帧数,提高了处理的速度。实验结果表明,该方法简单、实用,能够得到较理想的效果。     

A neural network filter to detect small targets in high clutterbackgrounds

The detection of objects in high-resolution aerial imagery has proven to be a difficult task. In the authors' application, the amount of image clutter is extremely high. Under these conditions, detection based on low-level image cues tends to perform poorly. Neural network techniques have been proposed in object detection applications due to proven robust performance characteristics. A neural network filter was designed and trained to detect targets in thermal infrared images. The feature extraction stage was eliminated and raw gray levels were utilized as input to the network. Two fundamentally different approaches were used to design the training sets. In the first approach, actual image data were utilized for training. In the second case, a model-based approach was adopted to design the training set vectors. The training set consisted of object and background data. The neuron transfer function was modified to improve network convergence and speed and the backpropagation training algorithm was used to train the network. The neural network filter was tested extensively on real image data. Receiver operating characteristic (ROC) curves were determined in each case. The detection and false alarm rates were excellent for the neural network filters. Their overall performance was much superior to that of the size-matched contrast-box filter, especially in the images with higher amounts of visual clutter.     

A rank-and-compare algorithm to detect abnormally low bids in procurement auctions

Detecting abnormally low bids in procurement auctions is a recognized problem, since their acceptance could result in the winner not being able to provide the service or work awarded by the auction, which is a significant risk for the auctioneer. A rank-and-compare algorithm is considered to detect such anomalous bids and help auctioneers in achieving an effective rejection decision. Analytical expressions and simulation results are provided for the detection probability, as well as for the false alarm probability. The suggested range of application of the detection algorithm leaves out the cases of many tenderers (more than 20) and quite dispersed bids (coefficient of variation larger than 0.15). An increase in the number of tenderers leads to contrasting effects, since both the false alarm probability and the detection probability are reduced. If the bids are spread over a large range, we have instead a double negative effect, with more false alarms and less detections. The presence of multiple anomalous bids worsens the performance of the algorithm as well. On the other hand, the method is quite robust to the presence of courtesy bids.     

Control of hysteresis in smart actuators with application to micro-positioning

Hysteresis in smart material actuators makes the effective use of these actuators quite challenging. The Preisach operator has been widely used to model smart material hysteresis. Motivated by positioning applications of smart actuators, this paper addresses the value inversion problem for a class of discretized Preisach operators, i.e., to find an optimal input trajectory given a desired output value. This problem is solved through optimal state transition of a finite state machine (FSM) that corresponds to the discretized Preisach operator. A state-space reduction scheme for the FSM is developed, which significantly saves the memory and the computation time. Experimental results on micro-positioning control of a magnetostrictive actuator are presented to demonstrate the effectiveness of the proposed approach.     

一种高噪声低照度图像的隐藏特征修复算法

当前的图像隐藏特征修复算法的特征融合过程为单次迭代,且为根据光照情况建立暗适应函数,导致低照度图像隐藏特征修复结果存在失真问题,图像噪声也偏高。为此,提出一种基于交替优化的低照度图像隐藏特征修复算法。模拟低光照对图像环境的自动应变能力,并根据光照情况设计暗适应函数,对隐藏特征像素点实现边缘拉伸及中值滤波操作,提取处理后的隐藏特征分量数值,建立非线性映射函数,交替优化融合特征信息,实现低照度图像隐藏特征的修复。仿真结果证明,所提方法可以有效提高色彩饱和度,并且不易出现失真、特征丢失以及噪声现象,在最大程度上保证原始图像的自身特征属性,实现合理有效的隐藏特征修复。     

A photographic negative imaging inspired method for low illumination night-time image enhancement

Multimedia Tools and Applications - Images captured in low illumination conditions usually suffer from a poor visibility which has an important affect on the performance of computer vision systems....     

A fast connected components labeling algorithm and its application to real-time pupil detection

We describe a fast connected components labeling algorithm using a region coloring approach. It computes region attributes such as size, moments, and bounding boxes in a single pass through the image. Working in the context of real-time pupil detection for an eye tracking system, we compare the time performance of our algorithm with a contour tracing-based labeling approach and a region coloring method developed for a hardware eye detection system. We find that region attribute extraction performance exceeds that of these comparison methods. Further, labeling each pixel, which requires a second pass through the image, has comparable performance.     

A new approach to prediction of radiotherapy of bladder cancer cells in small dataset analysis

Bladder cancer is a common urologic cancer. Radiotherapy plays an increasingly important role in treatment bladder cancer due to radiotherapy preserves normal bladder function. However, the five-year survival rate after radiotherapy for bladder cancer patients is 30–50%. Some biological proteins influence the outcome of radiotherapy. One or two specific proteins may not be sufficient to predict the effect of radiotherapy, analyzing multiple oncoproteins and tumor suppressor proteins may help the prediction. At present, no effective technique has been used to predict the outcome of radiotherapy by multiple protein expression file from a very limited number of patients. The bootstrap technique provides a new approach to improve the accuracy of prediction the outcome of radiotherapy in small dataset analysis. In this study, 13 proteins in each cell line from individual patient were measured and then cell viability was determined after cells irradiated with 5, 10, 20, or 30 Gy of cobalt-60. The modeling results showed that when the number of training data increased, the learning accuracy of the prediction the outcome of radiotherapy was enhanced stably, from 55% to 85%. Using this technique to analyze the outcome of radiotherapy related to protein expression profile of individual cell line provides an example to help patients choosing radiotherapy for treatment.     

Multiflash stereopsis: depth-edge-preserving stereo with small baseline illumination

Traditional stereo matching algorithms are limited in their ability to produce accurate results near depth discontinuities, due to partial occlusions and violation of smoothness constraints. In this paper, we use small baseline multi-flash illumination to produce a rich set of feature maps that enable acquisition of discontinuity preserving point correspondences. First, from a single multi-flash camera, we formulate a qualitative depth map using a gradient domain method that encodes object relative distances. Then, in a multiview setup, we exploit shadows created by light sources to compute an occlusion map. Finally, we demonstrate the usefulness of these feature maps by incorporating them into two different dense stereo correspondence algorithms, the first based on local search and the second based on belief propagation. Experimental results show that our enhanced stereo algorithms are able to extract high quality, discontinuity preserving correspondence maps from scenes that are extremely challenging for conventional stereo methods. We also demonstrate that small baseline illumination can be useful to handle specular reflections in stereo imagery. Different from most existing active illumination techniques, our method is simple, inexpensive, compact, and requires no calibration of light sources.     

Sensitive deep convolutional neural network for face recognition at large standoffs with small dataset

In this paper, we propose a sensitive convolutional neural network which incorporates sensitivity term in the cost function of Convolutional Neural Network (CNN) to emphasize on the slight variations and high frequency components in highly blurred input image samples. The proposed cost function in CNN has a sensitivity part in which the conventional error is divided by the derivative of the activation function, and subsequently the total error is minimized by the gradient descent method during the learning process. Due to the proposed sensitivity term, the data samples at the decision boundaries appear more on the middle band or the high gradient part of the activation function. This highlights the slight changes in the highly blurred input images enabling better feature extraction resulting in better generalization and improved classification performance in the highly blurred images. To study the effect of the proposed sensitivity term, experiments were performed for the face recognition task on small dataset of facial images at different long standoffs in both night-time and day-time modalities.     

Body landmark detection with an extremely small dataset using transfer learning

Pattern Analysis and Applications - We present a new landmark detection problem on the upper body of a clothed person for tailoring purposes. This is a landmark detection problem unknown in the...     

Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset

Neural Computing and Applications - The prediction of hospital patients and outpatients with suspected arboviral infection individuals in research-limited settings of the urban areas is defined as...     

A study of application platform for smart contract visualization based blockchain

The Journal of Supercomputing - In recent years, blockchain, which is the base technology of the 4th industrial revolution, is rapidly emerging as an alternative to the centralized data management...     

A new combination active contour model for segmenting texture image with low contrast and high illumination variations

Multimedia Tools and Applications - This paper presents a novel combined energy functional based on edge and region information for active contour model, which can be applied to segment textured...     

Prediction of domestic power peak demand and consumption using supervised machine learning with smart meter dataset

Multimedia Tools and Applications - The prediction of electricity consumption is a vital foundation for smart energy management. Since the consumption of power varies with different appliances,...     

Noisy practical facial super-resolution method via deformable constrained model with small dataset

Multimedia Tools and Applications - Face Super-Resolution (FSR) is to infer high resolution facial image(s) from given low resolution one(s). But when large-scale training samples are absent, FSR...     

Fuzzy inductive reasoning forecasting strategies able to cope with missing data: A smart grid application

Dealing with missing data is of great practical and theoretical interest in forecasting applications. In this study, we deal with the problem of forecasting with missing data in smart grid and BEMS applications, where the information from home area sensors and/or smart meters is sometimes missing, which may hinder or even prevent the forecasting of the next hours and days. In concrete, we focus in a Soft Computing technique called Fuzzy Inductive Reasoning (FIR) and its improved version that can cope with missing information in the forecasting process: flexible FIR. In this article eight different strategies for flexible FIR forecasting are defined and studied taking into account: causal relevance of input variables, consistency of predictions, inertia criterion and K-Nearest Neighbours. Furthermore, we evaluate the implications of prediction accuracy and number of predictions, when the number of Missing Values (MVs) in the training dataset is increased progressively. To this end, a real smart grid forecasting application, i.e. electricity load forecasting, has been chosen in this study. The results show that all eight strategies proposed are able to cope with MVs and take advantage of the inherent information in the data, with better results in those strategies making use of causal relevance. In addition, the robustness of flexible FIR and its eight strategies are proved taking into account that the percentage of electricity load predictions from the test dataset is on average 96.15% when the %MVs in training dataset was around 73%.