Improving the performance of visual computing systems is achieved by removing unwanted reflections from a picture captured in front of a glass. Reflection and transmission layers are superimposed in a linear form at the reflected photographs. Decomposing an image into these layers is often a difficult task. Plentiful classical separation methods are available in the literature which either works on a single image or requires multiple images. The major step in reflection removal is the detection of reflection and background edges. Separation of the background and reflection layers is depended on edge categorization results. In this paper a wavelet transform is used as a prior estimation of background edges to sepa

Steganography is the art of secret communication. Its purpose is to hide the presence of information, using, for example, images as covers. The frequency domain is well suited for embedding in image, since hiding in this frequency domain coefficients is robust to many attacks. This paper proposed hiding a secret image of size equal to quarter of the cover one. Set Partitioning in Hierarchal Trees (SPIHT) codec is used to code the secret image to achieve security. The proposed method applies Discrete Multiwavelet Transform (DMWT) for cover image. The coded bit stream of the secret image is embedded in the high frequency subbands of the transformed cover one. A scaling factors ? and ? in frequency domain control the quality of the stego

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

 This paper adapted the neural network for the estimating of the direction of arrival (DOA). It uses an unsupervised adaptive neural network with GHA algorithm to extract the principal components that in turn, are used by Capon method to estimate the DOA, where by the PCA neural network we take signal subspace only and use it in Capon (i.e. we will ignore the noise subspace, and take the signal subspace only).

Steganography is a mean of hiding information within a more obvious form of
communication. It exploits the use of host data to hide a piece of information in such a way
that it is imperceptible to human observer. The major goals of effective Steganography are
High Embedding Capacity, Imperceptibility and Robustness. This paper introduces a scheme
for hiding secret images that could be as much as 25% of the host image data. The proposed
algorithm uses orthogonal discrete cosine transform for host image. A scaling factor (a) in
frequency domain controls the quality of the stego images. Experimented results of secret
image recovery after applying JPEG coding to the stego-images are included.

The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. In this work, we implement an interaction between the feedforward neural network and the stream cipher, so the secret message will be encrypted by unsupervised neural network method in addition to the first encryption process which is performed by the stream cipher method. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding le

The research deals with Environmental Management and how to develop its programs with the use of Knowledge Management, the environmental programs that integrate with processes can add strategic value to business through improving rates of resource utilization , efficiencies , reduce waste, use risk management, cut costs, avoid fines and reduce insurance. All these activities and processes can improve it through knowledge management, the optimal usage for all organizations information , employ it in high value and share it among all organizations members who involves in modify its strategy . Choosing suitable environmental management information system, develop it and modify it with organization processes, can greatly serve the en

Due to the difficulties that Iraqi students face when writing in the English language, this preliminary study aimed to improve students' writing skills by using online platforms remotely. Sixty first-year students from Al-Furat Al–Awsat Technical University participated in this study. Through these platforms, the researchers relied on stimuli, such as images, icons, and short titles to allow for deeper and more accurate participations. Data were collected through corrections, observations, and feedback from the researchers and peers. In addition, two pre and post-tests were conducted. The quantitative data were analysed by SPSS statistical Editor, whereas the qualitative data were analyzed using the Piot table, an Excel sheet. The resu

This paper presents a hierarchical model of localized company effective when is used in a university campus or site. To highlight the standard criteria for each layer of the model and to prove the positive aspects of this model is the best in use and make the Dell Network as case of study. Through the case of study it has been shown that the expansion of the on-site network does not affect services or bandwidth.

In this paper we investigate the automatic recognition of emotion in text. We propose a new method for emotion recognition based on the PPM (PPM is short for Prediction by Partial Matching) character-based text compression scheme in order to recognize Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method is very effective when compared with traditional word-based text classification methods. We have also found that our method works best if the sizes of text in all classes used for training are similar, and that performance significantly improves with increased data.