The headway of modern technology and facility to use processing software leads to tamper and implicate of digital images. This tampering is being performed without leaving any a clear effect noted with the naked eye. The discrimination between different authentic and forged images can be based on its Probability Density Functions (PDFs). This paper introduces a new model for digital image forgery detection. This framework has two main phases; training and testing. In the training phase, the peak is calculated for the derivatives histogram of the illumination components by using homomorphic filter to separate the illumination components on each image. Firstly, the derivative of illumination histogram for authentic and forged images is calculated then the PDFs are estimated for authentic and forged images, finally the threshold is determined. In the testing phase, the determined threshold is tested with realistic dataset followed by using the selected bins for feature calculation in the prediction process. In the final prediction step, a detection and decision process is performed to obtain performance of the new model. This new model is provided a very effective performance. Different color image contrast systems RGB and HIS are studied and utilized for testing our model and compare between each channel for two systems to estimate performance and obtain more sensitive channel.

Innovations in nanotechnology are making a revolution in manufacturing and production, creating new materials and products through novel processes for commercial applications. New products based on nanotechnology with novel characteristics are continued to grow and benefit the society. Being general purpose technology, nanotechnology is expected to support all fields of the society, but some fields like medicine, energy, environmental remediation, robotics, manufacturing, commerce, and space exploration are expected to undergo explosive developments. In this paper, we made a detailed analysis of advantages, benefits, constraints, and disadvantages of progress in nanotechnology for the society and discussed the reason for the failure of estimated timeline for realizing various generations of progress and proposed a modified timeframe for anticipated nanotechnology innovations. Based on the business perspective, we have developed a nanotechnology products/services commercialization model and discussed the opportunities for nanotechnology commercialization. The paper also contains a detailed analysis of common commercialization challenges faced by nanotechnology firms including time lag, the valley of death, lack of infrastructure, lack of a standard for evaluation, bureaucratic delays, the dearth of funding, and lack of trained professionals, brand image, and public support. Finally, we have mentioned how the various corporate business strategies can be used in the successful business of nanotechnology products and services.

This work presents an enhancement in accuracy for brain disorder detection using optimal unification. The strategy for detection of segments and brain regions causing medical conditions are described. This work demonstrates the application of multilateral filter and applied watershed method with EM-GM method. The most popular existing techniques of brain tumor detection are not optimal compared to this combination of Watershed and EM-GM technique with the proposed optimal unification technique. The result is optimally unified and achieved high accuracy. The multilateral filter enhances the image edges for better segmentation using signal amplitude moderation of the pixel. In the unification process, the optimal sets of segments are divided and finest merged results are considered with the brain regions detected with anomalies. Henceforth the number of possible medical investigations will be reduced.

Image denoising plays extremely important role in digital image processing. The primary objective of this paper is to explore highlighted challenges of the image filtering techniques. The comprehensive study has evidently shown that the one of most challenging issue in image filtering is edge preserving while removing the noise. Because edges deliver the most important information to the human visual system. This paper has compared different recent image filtering methods based upon certain factors. The comparisons have shown that the noise reduction using wavelet coefficients based on OMP has quite effective improvements over available methods. Challenging issue in image filtering technique is removing the multiplicative noise and high density of noises is still found.

Virtualization technology play a vital role in cloud computing. In virtualization environment multiple applications can run at a same time. VM migration is one of the important features of the virtualization, which allows application to be transparently migrated along with their execution environments across physical machines. VM migration consists of four steps. These steps are source PM selection, VM selection from the selected PM in first step, target PM selection for placing the selected VM and selecting method for transfer VM data. In our proposed approach we are focusing on the last step i.e., choosing method for transferring VM data.
In this paper proposed approach is divided into two phases. First phase filter all pages which is modified in the last iteration and unmodified in the current iteration. In the second phase, page is divided into two types i.e., high dirty page and normal based on the modification in the last few iterations. For all filtered pages in the first phase now we check the number of times when the page is modified from the history record. If the page is modified more times the page will not be send in the current iteration otherwise the page will be send to the destination VM.
To evaluate the performance of the proposed approach it is implemented in CloudSim simulator and compare with the existing time series based pre-copy approach in term of total migration time and down time. Experiment result shows that proposed approach gives better result as compare to the base approach.

Open Educational Resources (OER) are those teaching and learning materials that are available either in the public domain or under an open license. The focus of this research work is to propose a conceptual framework for design and implementation of TVET autonomic cloud-based OERs for it integration into classroom teaching and learning strategies towards sustainable development. The beauty of this proposed system is its autonomous and self-managed features. The system will have capability of including the following: laboratory activities; syllabi, homework and assignment; assessments (CBT- computer-based test), lecture notes; audio visual lectures; simulation; lesson plan; and textbooks etc. while the system will be own and maintained by Yaba college of technology Nigeria, it's services will be available for usage by any individual with interest in TVET across the globe.