An edge detection framework conjoining with IMU data for assisting indoor navigation of visually impaired persons

Smartphone applications based on object detection techniques have recently been proposed to assist visually impaired persons with navigating indoor environments. In the smartphone, digital cameras are installed to detect objects which are important for navigation. Prior to detect the interested objects from images, edges on the objects have to be identified. Object edges are difficult to be detected accurately as the image is contaminated by strong image blur which is caused by camera movement. Although deblurring algorithms can be used to filter blur noise, they are computationally expensive and not suitable for real-time implementation. Also edge detection algorithms are mostly developed for stationary images without serious blur. In this paper, a modified sigmoid function (MSF) framework based on inertial measurement unit (IMU) is proposed to mitigate these problems. The IMU estimates blur levels to adapt the MSF which is computationally simple. When the camera is moving, the topological structure of the MSF is estimated continuously in order to improve effectiveness of edge detections. The performance of the MSF framework is evaluated by detecting object edges on video sequences associated with IMU data. The MSF framework is benchmarked against existing edge detection techniques and results show that it can obtain comparably lower errors. It is further shown that the computation time is significantly decreased compared to using techniques that deploy deblurring algorithms, thus making our proposed technique a strong candidate for reliable real-time navigation.     

Efficient implementation of chaotic image encryption in transform domains

The primary goal of this paper is security management in data image transmission and storage. Because of the increased use of images in industrial operations, it is necessary to protect the secret data of the image against unauthorized access. In this paper, we introduce a novel approach for image encryption based on employing a cyclic shift and the 2-D chaotic Baker map in different transform domains. The Integer Wavelet Transform (IWT), the Discrete Wavelet Transform (DWT), and the Discrete Cosine Transform (DCT) are exploited in the proposed encryption approach. The characteristics of the transform domains are studied and used to carry out the chaotic encryption. A comparison study between the transform-domain encryption approaches in the presence of attacks shows the superiority of encryption in the DWT domain.     

Information fusion in data privacy: A survey

In this paper, we review the role of information fusion in data privacy. To that end, we introduce data privacy, and describe how information and data fusion are used in some fields of data privacy. Our study is focused on the use of aggregation for privacy protections, and record linkage techniques.     

Investigation of a measurement scheme based on IGES

The integration research of measuring machine and CAD information, just CMM/CAD integration technology, is far behind the information integration of mechanical processing and CAD. With the rapid development of information integration industry and in the manufacturing process of the parts with complex curve surface, it is crucial to integrate the measurement and machining procedures based on IGES as that IGES is a general ANSI standard of information exchange for CAD/CAM among different computer systems. The measuring path planning determines the realization of measuring each sampling point and the completion of all sampling points safely and effectively by using measuring equipment. Based on IGES in this paper, the calculation method of normal direction at the location of sample point is studied for different surfaces, so as to improve the measuring precision. Then the calculation strategy for the positioning points and the measuring range is established, and the identification method for the topological relation between surfaces is also researched for improving the measuring efficiency and security. At last, the measuring sequence of the sampling points to be planned reasonably based on TSP is given. It is believed that the research results can provide some useful contributions towards the development of automated inspection task planning and systems, especially for coordinate measuring machines.     

Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise

In practical engineering applications, useful information is often submerged in strong noise and the feature information is difficult to be extracted. Aimed at the detection problem of multi-frequency signal under colored noise background, a novel weak signal detection method based on stochastic resonance (SR) tuning by multi-scale noise is proposed. Firstly, noisy signal is processed by orthogonal wavelet transform to decompose the signal into multi-scale ingredients. According to the orthogonal wavelet transform coefficients characteristics of 1/f distribution, multi-scale noise is constructed so as to make the frequency-band containing the driving frequency be enhanced through SR system. Thus multi-frequency weak signal is detected. The method is effective to detect multi-frequency weak signal under colored noise background. Experiment signal analysis results show that the proposed method is simple for multi-frequency weak signal detection, and has good prospects for engineering applications.     

Memory-efficient real-time map building using octree of planes and points

This paper presents an octree-based map building algorithm for mobile home-service robots. The robot is equipped with a time-of-flight camera, which produces point clouds of the environment surfaces. Given the successive input of point clouds, a 3D map is incrementally computed in real time. The map is accurate and memory-efficient because the octree nodes containing points on a plane are merged and represented simply by an index to the plane. The real-time performance is achieved largely due to the parallel processing capability of the many-core Graphics Processing Unit used for plane extraction.     

Quantitative cross impact analysis with latent semantic indexing

Cross impact analysis (CIA) consists of a set of related methodologies that predict the occurrence probability of a specific event and that also predict the conditional probability of a first event given a second event. The conditional probability can be interpreted as the impact of the second event on the first. Most of the CIA methodologies are qualitative that means the occurrence and conditional probabilities are calculated based on estimations of human experts. In recent years, an increased number of quantitative methodologies can be seen that use a large number of data from databases and the internet. Nearly 80% of all data available in the internet are textual information and thus, knowledge structure based approaches on textual information for calculating the conditional probabilities are proposed in literature. In contrast to related methodologies, this work proposes a new quantitative CIA methodology to predict the conditional probability based on the semantic structure of given textual information. Latent semantic indexing is used to identify the hidden semantic patterns standing behind an event and to calculate the impact of the patterns on other semantic textual patterns representing a different event. This enables to calculate the conditional probabilities semantically. A case study shows that this semantic approach can be used to predict the conditional probability of a technology on a different technology.     

Hyperspectral image coding and transmission scheme based on wavelet transform and distributed source coding

Multimedia Tools and Applications - This paper presents a novel scheme for satellite hyperspectral images broadcasting over wireless channels. First, a simple pre-processing is performed. Then, a...     

Aligning Real Process Executions and Prescriptive Process Models through Automated Planning

Modern organizations execute processes to deliver product and services, whose enactment needs to adhere to laws, regulations and standards. Conformance checking is the problem of pinpointing where deviations are observed. This paper shows how instances of the conformance checking problem can be represented as planning problems in PDDL (Planning Domain Definition Language) for which planners can find a correct solution in a finite amount of time. If conformance checking problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with evident advantages in term of versatility and customization. The paper also reports on results of experiments conducted on two real-life case studies and on eight larger synthetic ones, mainly using the Fast-downward planner framework to solve the planning problems due to its performances. Some experiments were also repeated though other planners to concretely showcase the versatility of our approach. The results show that, when process models and event logs are of considerable size, our approach outperforms existing ones even by several orders of magnitude. Even more remarkably, when process models are extremely large and event log traces very long, the existing approaches are unable to terminate because they run out of memory, while our approach is able to properly complete the alignment task.     

Blind signal modulation recognition through clustering analysis of constellation signature

Automatic recognition of digital modulation schemes is becoming an active research area in many covert operations. It has many military applications where surveillance and electronic warfare requires a type of modulation in intercepted signal to prepare jamming signals. Most of the approaches are based on modulated signal's component, but the modulation type can be best identified with the use of constellation diagram. The proposed technique is able to recognize M-QAM, M-ASK, and M-PSK modulation scheme in Additive White Gaussian Noise (AWGN) environment. As the constellation points form clusters in the I-Q plane, the order of the modulation can be obtained by estimating the correct number of clusters, which is calculated by OPTICS algorithm. The least square error has been calculated using linear regression from the obtained constellation points, to identify either ASK or PSK and QAM. The error is least for ASK which differentiates ASK from PSK and QAM. To identify between the PSK and QAM, k-means clustering is employed to find the number of centroids equal to order of modulation estimated by OPTICS. With the difference in maximum and minimum absolute value of the centroids, PSK or QAM is recognized. The proposed method shows an improvement in the classification accuracy which reaches 100% using 1024 symbols at 20 dB compared to 98.89%, 98.05%, and 98% when using more complex classifiers like Support Vector Machine, Naive Bayes Classifier, KNN respectively. The method used is unsupervised whereas most of the methods in the literature require training phase to set the thresholds or weights for final model to detect modulation type. This algorithm is also implemented in LabVIEW, and tested on real-time signals. An intelligent system is made which does not require any knowledge of symbol rate, carrier frequency, and any training phase to set thresholds, and detects the type of modulation blindly in real time. Modulated RF signals are generated by NI PXIe-5673 (RF transmitter). NI PXI 5600 is used to downconvert RF signal and NI PXI-5142 (100 MS/s OSP digitizer) is used to sample the downverted signal.