Iraqi Journal of Information and communication technology

Channel estimation is an essential part of Orthogonal Frequency Division Multiplexing (OFDM)communication systems. In this paper, two Discrete Fourier Transform (DFT) improvement algorithms are proposed and compared, where the first one exploits channel sparsity concept while the other considers significant channel coefficients only. In the proposed algorithms; Enhanced and Sparse DFT (E- DFT and S- DFT), different number of significant channel components is selected either by a threshold determining procedure such as in E- DFT, or through determining channel sparsity level such as in S- DFT. In the presence of Doppler frequency shifts, the Inter Symbol Interference (ISI) effect on channel coefficients is successfully reduced using the proposed estimation algorithms. Vehicular A- ITU channel model is considered with a relatively high vehicle speed up to 68 Km/h in order to test the suitability of the proposed algorithms for mobile systems. E-DFT and S-DFT improves conventional as well as previous suggested works on performance improvement of DFT technique (I- DFT). For 64 sub carriers ,S- DFT outperforms E- DFT and I- DFT by about 3dB at a BER of 0.01 with mobility reaches 45 Km/h, and by about 0.4dB and 2.5 dB at a BER of 0.02 with mobility reaches 68 Km/h

Cloud computing is a fast growing technology used by major corporations these days because of the flexibility framework it provides to consumers. Cloud technology requires large data centers consisting of multiple IT equipment and servers. One main problem with these data centers is the vast amount of power consumed during servers operation. This reduces financial benefit and increases the need to produce more energy to cover the needs of operating the cloud infrastructure. This paper proposes an approach for managing the virtual central processing unit (vCPU) of a virtual machine to improve server power efficiency. A framework is used to study the proposed approach on a server while processing different types of workloads widely found in most general-purpose cloud computing applications. Results indicate an improvement in server power saving

The development of routing protocol to manage energy consumption of wireless sensor devices is one of the active research areas. The main challenge is the resource constraint (e. g. energy). In Internet of Things( IoT) , the devices operate in the presence of these limitations. Thus, an energy- efficient routing protocol is required. Routing Protocol for Low- Power and Lossy Network (RPL) is already introduced to enable IoT over Wireless Sensor Network. In this paper, a modified routing protocol depending on the maximum number of parent objective function ( MNP- OF) is proposed. MNP- OF uses a new node metric called Neighboring-metric ( N-metric). The proposed protocol is called MNP based on Remaining Energy ( MRE) that combines N-metric with the node remaining energy metric for all nodes to the root node to select the preferred path. The results showed that MRE achieved up to 94% energy saving compared to RPL protocol in medium and large scale networks, while a reduction of about 33% in average remaining energy in small scale network. MRE showed considerable reductions in the average delay and the number of dead nodes combined with improvements in throughput, number of control messages, and number of switching parents for large scale network

With the great development in information technology, the commonly used biometrics have become very easy to hack such as face, voice and fingerprint etc. This has led to the discovery of new horizons in this field. One of the safest biometrics of today is finger vein- But this technique faces some unique difficulties, the most commonly used being that the vein pattern is difficult to remove because finger vein images are always low in quality, significantly hampered the feature extraction and classification stages. For this purpose, professional algorithms must be considered with the conventional hardware for capturing finger- vein images using Red Surface Mounted Diode ( SMD) led. For capturing images, Canon 750D camera with micro lens is used. For high quality images the integrated micro lens is used, and with some adjustments it can also obtain finger print. Features extraction was used by a combination of Hierarchical Centroid and Histogram of Gradients. Results were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results displayed improvement as compared to three latest benchmarks in this field that used 6- fold validation ( 5- samples for training and one sample for test) for SDUMLA- HMT. The work novelty is owing to the hardware design of the sensor within the finger- vein recognition system to obtain, simultaneously, highly secured recognition with low computation time, finger vein and finger print at low cost, unlimited users for one device and open source