Provenance analysis for instagram photos

As a feasible device fingerprint, sensor pattern noise (SPN) has been proven to be effective in the provenance analysis of digital images. However, with the rise of social media, millions of images are being uploaded to and shared through social media sites every day. An image downloaded from social networks may have gone through a series of unknown image manipulations. Consequently, the trustworthiness of SPN has been challenged in the provenance analysis of the images downloaded from social media platforms. In this paper, we intend to investigate the effects of the pre-defined Instagram images filters on the SPN-based image provenance analysis. We identify two groups of filters that affect the SPN in quite different ways, with Group I consisting of the filters that severely attenuate the SPN and Group II consisting of the filters that well preserve the SPN in the images. We further propose a CNN-based classifier to perform filter-oriented image categorization, aiming to exclude the images manipulated by the filters in Group I and thus improve the reliability of the SPN-based provenance analysis. The results on about 20, 000 images and 18 filters are very promising, with an accuracy higher than 96% in differentiating the filters in Group I and Group II

Designing materials for electrochemical carbon dioxide recycling

Electrochemical carbon dioxide recycling provides an attractive approach to synthesizing fuels and chemical feedstocks using renewable energy. On the path to deploying this technology, basic and applied scientific hurdles remain. Integrating catalytic design with mechanistic understanding yields scientific insights and progresses the technology towards industrial relevance. Catalysts must be able to generate valuable carbon-based products with better selectivity, lower overpotentials and improved current densities with extended operation. Here, we describe progress and identify mechanistic questions and performance metrics for catalysts that can enable carbon-neutral renewable energy storage and utilization

Soft-Boosted Self-Constructing Neural Fuzzy Inference Network

© 2013 IEEE. This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets

Multi-view Vehicle Detection based on Part Model with Active Learning

© 2018 IEEE. Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: Background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods

A method to enhance the deep learning in an aerial image

© 2017 IEEE. In this paper, we propose a kind of pre-processing method which can be applied to the depth learning method for the characteristics of aerial image. This method combines the color and spatial information to do the quick background filtering. In addition to increase execution speed, but also to reduce the rate of false positives

Chaotic motions in the real fuzzy electronic circuits

Fuzzy electronic circuit (FEC) is firstly introduced, which is implementing Takagi-Sugeno (T-S) fuzzy chaotic systems on electronic circuit. In the research field of secure communications, the original source should be blended with other complex signals. Chaotic signals are one of the good sources to be applied to encrypt high confidential signals, because of its high complexity, sensitiveness of initial conditions, and unpredictability. Consequently, generating chaotic signals on electronic circuit to produce real electrical signals applied to secure communications is an exceedingly important issue. However, nonlinear systems are always composed of many complex equations and are hard to realize on electronic circuits. Takagi-Sugeno (T-S) fuzzy model is a powerful tool, which is described by fuzzy IF-THEN rules to express the local dynamics of each fuzzy rule by a linear system model. Accordingly, in this paper, we produce the chaotic signals via electronic circuits through T-S fuzzy model and the numerical simulation results provided by MATLAB are also proposed for comparison. T-S fuzzy chaotic Lorenz and Chen-Lee systems are used for examples and are given to demonstrate the effectiveness of the proposed electronic circuit. © 2013 Shih-Yu Li et al

Automatic age estimation system for face images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al

Demonstration project on epilepsy in Brazil - Outcome assessment

Purpose: To assess the outcome of patients with epilepsy treated at primary care health units under the framework of the demonstration project on epilepsy in Brazil, part of the WHO/ILAE/IBE Global Campaign Against Epilepsy. Method. We assessed the outcome of patients treated at four primary health units. The staff of the health units underwent information training in epilepsy. The outcome assessment was based on: 1) reduction of seizure frequency, 2) subjective perception from the patient's and the physician's point of view, 3) reduction of absenteeism, 4) social integration (school and work), and 5) sense of independence. Results: A total of 181 patients (93 women - 51%) with a mean age of 38 (range from 2 to 86) years were studied. The mean follow-up was 26 months (range from 1 to 38 months, 11 patients had follow-up of less than 12 months). Seizure frequency was assessed based on a score system, ranging from 0 (no seizure in the previous 24 months) to 7 (> 10 seizure/day). The baseline median seizure-frequency score was 3 (one to three seizures per month). At the end of the study the median seizure-frequency score was 1 (one to three seizures per year). The patients' and relatives' opinions were that in the majority (59%) the health status had improved a lot, some (19%) had improved a little, 20% experienced no change and in 2% the health status was worse. With regard to absenteeism, social integration and sense of independence, there were some modest improvements only. Discussion: The development of a model of epilepsy treatment at primary health level based on the existing health system, with strategic measures centred on the health care providers and the community, has proved to be effective providing important reductions in seizure frequency, as well as in general well being. This model can be applied nationwide, as the key elements already exist provided that strategic measures are put forward in accordance with local health providers and managers

Designing Mamdani-Type Fuzzy Reasoning for Visualizing Prediction Problems Based on Collaborative Fuzzy Clustering

In this paper a collaborative fuzzy c-means (CFCM) is used to generate fuzzy rules for fuzzy inference systems to evaluate the time series model. CFCM helps system to integrate two or more different datasets having similar features which are collected at the different environment with the different time period and it integrates these datasets together in order to visualize some common patterns among the datasets. In order to do any mode of integration between datasets, there is a necessity to define the common features between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling

Sulfur-Modulated Tin Sites Enable Highly Selective Electrochemical Reduction of CO2 to Formate

Electrochemical reduction of carbon dioxide (CO2RR) to formate provides an avenue to the synthesis of value-added carbon-based fuels and feedstocks powered using renewable electricity. Here, we hypothesized that the presence of sulfur atoms in the catalyst surface could promote undercoordinated sites, and thereby improve the electrochemical reduction of CO2 to formate. We explored, using density functional theory, how the incorporation of sulfur into tin may favor formate generation. We used atomic layer deposition of SnSx followed by a reduction process to synthesize sulfur-modulated tin (Sn(S)) catalysts. X-ray absorption near-edge structure (XANES) studies reveal higher oxidation states in Sn(S) compared with that of tin in Sn nanoparticles. Sn(S)/Au accelerates CO2RR at geometric current densities of 55 mA cm−2 at −0.75 V versus reversible hydrogen electrode with a Faradaic efficiency of 93%. Furthermore, Sn(S) catalysts show excellent stability without deactivation (<2% productivity change) following more than 40 hours of operation. With rapid advances in the efficient and cost-effective conversion of sunlight to electrical power, the development of storage technologies for renewable energy is even more urgent. Using renewable electricity to convert CO2 into formate simultaneously addresses the need for storage of intermittent renewable energy sources and the need to reduce greenhouse gas emissions. We report an increase of greater than 4-fold in the current density (hence the rate of reaction) in formate electrosynthesis compared with relevant controls. Our catalysts also show excellent stability without deactivation (<2% productivity change) following more than 40 hours of operation. The electrochemical reduction of carbon dioxide (CO2RR) offers a compelling route to energy storage and high-value chemical manufacture. The presence of sulfur atoms in catalyst surfaces promotes undercoordinated sites, thereby improving the electrochemical reduction of CO2 to formate. The resulting sulfur-modulated tin catalysts accelerate CO2RR at geometric current densities of 55 mA cm−2 at −0.75 V versus RHE with a Faradaic efficiency of 93%