Nowadays, production and distribution of digital images has become part of our life. Since digital images, which are important carriers of information, are considered as the concrete proofs of facts in many fields and they can be used as evidence in the courts of law, development of techniques to ensure image authenticity is an active research topic. Copy-move forgery is one of the most common manipulation techniques that are implemented on the digital images, and various techniques have been developed for detection of these kinds of forgeries. JPEG format, which presents the ability of making high rate compression without causing remarkable changes in the meaning of the image, is the most commonly used format on digital images. In this study, the topic of detecting free-form copy-move forgeries on digital images is covered. It has been observed that the developed technique is able to detect the professional forgeries in which the copied region is selected in free-form and which are almost impossible to be detected by human eye, with high success rate, and it is able to give successful results even if the image is exposed to postprocesses such as JPEG compression and Gaussian filtering, which make the detection of forgery harder.

Multimedia devices have become increasingly popular due to high quality and low cost products using advanced technology. These devices can capture multimedia files, which can be modified easily by video editing tools. One of the most frequently encountered forgery types in video forensics is the frame duplication (FD) forgery. Many methods have been proposed in the literature to deal with this type of forgery. These methods do not consider frame‐mirroring (FM) attack which copy a sequence of frames and paste its mirrored versions somewhere else on the same video. A new FD/FM detection method is proposed in this work. The method extracts binary features from frames and determines the similarity among features. Peak‐signal‐to‐noise ratio of the candidate frames is used to eliminate some of the large number of candidates to improve the detection of the forged frames. Experimental results show that the proposed method successfully detects FM/FD attacks and also yields better execution time and detection results compared to similar works reported in the literature.

The strict Quality of Service requirements of real-time services in communication networks necessitate the fast recovery from link/node failures by activating alternate paths which avoid the failed component. Multi Topology Routing (MTR) based IP Fast Re-Route (IPFRR) technologies rely on virtual topologies (VTs) to seamlessly forward the IP packets during network failures. In this paper, we implement a new MTR based IPFRR technique for Software Defined Networks (SDN) which uses the Multiple Routing Configurations method to construct VTs. Our approach leads to self-recovering SDN against failures. Out preliminary results show that our approach considerably reduces the failure recovery time compared to the reactive recovery process in SDN.

In this paper, a technique to classify seven different forearm movements using surface electromyography (sEMG) data which were received from 8 able bodied subjects was proposed. A 2-channel sEMG system was used for data acquisition and recording, then this raw electromyography (EMG) signals were applied to the wavelet denoising. In the next step, time-frequency feature is extracted calculating wavelet packet transform (WPT) coefficients for the offline classification. Feature vector of EMG signals were formed using only node energy of the WPT coefficients. In conclusion, seven forearm movements were separated by Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier and 92% success ratios over 500 samples were obtained.

To seamlessly support real-time services such as voice and video over next generation IP networks, routers must continue their forwarding tasks in case of link/node failures by limiting the service disruption time to sub-100 ms. IETF Routing Area Working Group (RTGWG) has been working on standardizing IP Fast Reroute (IPFRR) methods with a complete alternate path coverage. In this paper, a trade-off analysis of Multi Topology Routing (MTR) based IPFRR technologies targeting full coverage, namely Multiple Routing Configurations (MRC) and Maximally Redundant Trees (MRT), are presented. We implemented a comprehensive analysis tool to evaluate the performance of MRC and MRT mechanisms on various synthetic network topologies. The performance results show that MRT's alternative path lengths are not scalable with respect to the network size and density while the alternative path lengths of MRC only slightly change as the network size and density vary. We believe that this is an important scalability result providing a guidance in the selection of MTR-based IPFRR mechanism for improving the availability in ISP networks.

A new fault detection and identification (FDI) scheme for transformer faults are suggested in this paper. The new method is based on fuzzy discrete event, from now FDES, composed from between a transformer's measured outputs and its faults. In the FDES, events and state membership functions take values between zero and one. All events occur at the same time with different membership degrees. The main advantage of the suggested scheme is that different types of incipient or abrupt faults of transformers can correctly be identified. Principal component analysis (PCA) is mainly used for fuzzy event generation purpose. Event based FDES diagnoser involves fuzzy IF-THEN rules created by an artificial neural network (ANN) based on radial basis functions to identify incipient faults in transformers. It shows single or multiple faults and occurring degrees of these faults. The study is concluded by giving some examples about distinguishability of the single or multiple fault types in transformers. The real-time laboratory experiments verify the effectiveness of the suggested method.