In cognitive radio networks, there are two important probabilities; the first probability is important to primary users called probability of detection as it indicates their protection level from secondary users, and the second probability is important to the secondary users called probability of false alarm which is used for determining their using of unoccupied channel. Cooperation sensing can improve the probabilities of detection and false alarm. A new approach of determine optimal value for these probabilities, is supposed and considered to face multi secondary users through discovering an optimal threshold value for each unique detection curve then jointly find the optimal thresholds. To get the aggregated throughput over transmission

Industrial effluents loaded with heavy metals are a cause of hazards to the humans and other forms of life. Conventional approaches, such as electroplating, ion exchange, and membrane processes, are used for removal of copper, cadmium, and lead and are often cost prohibitive with low efficiency at low metal ion concentration. Biosorption can be considered as an option which has been proven as more efficient and economical for removing the mentioned metal ions. Biosorbents used are fungi, yeasts, oil palm shells, coir pith carbon, peanut husks, and olive pulp. Recently, low cost and natural products have also been researched as biosorbent. This paper presents an attempt of the potential use of Iraqi date pits and Al-Khriet (i.e. substances l

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

The planning, designing, construction of excavations and foundations in soft to very soft clay soils are always difficult. They are problematic soil that caused trouble for the structures built on them because of the low shear strength, high water content, and high compressibility. This work investigates the geotechnical behavior of soft clay by using tyre ash material burnt in air. The investigation contains the following tests: physical tests, chemical tests, consolidation test, Compaction tests, shear test, California Bearing Ratio test CBR, and model tests. These tests were done on soil samples prepared from soft clay soil; tyre ash was used in four percentages (2, 4, 6, and 8%). The results of the tests were; The soil samples which

            In this paper, new method have been investigated using evolving algorithms (EA's) to cryptanalysis one of the nonlinear stream cipher cryptosystems which depends on the Linear Feedback Shift Register (LFSR) unit by using cipher text-only attack. Genetic Algorithm (GA) and Ant Colony Optimization (ACO) which are used for attacking one of the nonlinear cryptosystems called "shrinking generator" using different lengths of cipher text and different lengths of combined LFSRs. GA and ACO proved their good performance in finding the initial values of the combined LFSRs. This work can be considered as a warning for a stream cipher designer to avoid the weak points, which may be f

The traditional technique of generating MPSK signals is basically to use IQ modulator that involves analog processing like multiplication and addition where inaccuracies may exist and would lead to imbalance problems that affects the output modulated signal and hence the overall performance of the system. In this paper, a simple method is presented for generating the MPSK using logic circuits that basically generated M-carrier signals each carrier of different equally spaced phase shift. Then these carriers are time multiplexed, according to the data symbols, into the output modulated signal.

Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time.  In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D

The area of character recognition has received a considerable attention by researchers all over the world during the last three decades. However, this research explores best sets of feature extraction techniques and studies the accuracy of well-known classifiers for Arabic numeral using the Statistical styles in two methods and making comparison study between them. First method Linear Discriminant function that is yield results with accuracy as high as 90% of original grouped cases correctly classified. In the second method, we proposed algorithm, The results show the efficiency of the proposed algorithms, where it is found to achieve recognition accuracy of 92.9% and 91.4%. This is providing efficiency more than the first method.