Wireless Multimedia Sensor Networks (WMSNs) are networks of wirelessly interconnected sensor nodes equipped with multimedia devices, such as cameras and microphones. Thus a WMSN will have the capability to transmit multimedia data, such as video and audio streams, still images, and scalar data from the environment. Most applications of WMSNs require the delivery of multimedia information with a certain level of Quality of Service (QoS). This is a challenging task because multimedia applications typically produce huge volumes of data requiring high transmission rates and extensive processing; the high data transmission rate of WMSNs usually leads to congestion, which in turn reduces the Quality of Service (QoS) of multimedia applications. To address this challenge, This paper proposes the Neural Control Exponential Weight of Priority Based Rate Control (NEWPBRC) algorithm for adjusting the node transmission rate and facilitate the problem of congestion occur in WMSNs. The proposed algorithm combines Neural Network Controller (NC) with the Exponential Weight of Priority Based Rate Control (EWPBRC) algorithms. The NC controller can calculate the appropriate weight parameter λ in the Exponential Weight (EW) algorithm for estimating the output transmission rate of the sink node, and then, on the basis of the priority of each child node, an appropriate transmission rate is assigned. The proposed algorithm can support four different traffic classes namely, Real Time traffic class (RT class); High priority, Non Real-Time traffic class (NRT1 class); Medium priority, Non Real-Time traffic class (NRT2 class); and Low priority,
tock markets changed up and down during time. Some companies’ affect others due to dependency on each other . In this work, the network model of the stock market is discribed as a complete weighted graph. This paper aims to investigate the Iraqi stock markets using graph theory tools. The vertices of this graph correspond to the Iraqi markets companies, and the weights of the edges are set ulrametric distance of minimum spanning tree.
Entrepreneurial events are understood to be imperious in accelerating the economic development of nations owing to a large number of jobs it creates. Thus, both developed and developing countries understand the importance of entrepreneurship education to instil student interest in entrepreneurial action. This study investigates the moderating effect of entrepreneurship education (EEP) on the relationship between attitude (ATT), subjective norms (SNMS), and perceived behavioural control (PBC) towards entrepreneurship intention (EINT) of university undergraduate students. The study population covered 794 students from all the four faculties of Northwest University Kano, that were taught a compulsory entrepreneurship education course in their
... Show MoreThis study was undertaken to introduce a fast, accurate, selective, simple and environment-friendly colorimetric method to determine iron (II) concentration in different lipstick brands imported or manufactured locally in Baghdad, Iraq. The samples were collected from 500-Iraqi dinars stores to establish routine tests using the spectrophotometric method and compared with a new microfluidic paper-based analytical device (µPAD) platform as an alternative to cost-effective conventional instrumentation such as Atomic Absorption Spectroscopy (AAS). This method depends on the reaction between iron (II) with iron(II) selective chelator 1, 10-phenanthroline(phen) in the presence of reducing agent hydroxylamine (HOA) and sodium acetate (NaOAc) b
... Show MoreThe temperature control process of electric heating furnace (EHF) systems is a quite difficult and changeable task owing to non-linearity, time delay, time-varying parameters, and the harsh environment of the furnace. In this paper, a robust temperature control scheme for an EHF system is developed using an adaptive active disturbance rejection control (AADRC) technique with a continuous sliding-mode based component. First, a comprehensive dynamic model is established by using convection laws, in which the EHF systems can be characterized as an uncertain second order system. Second, an adaptive extended state observer (AESO) is utilized to estimate the states of the EHF system and total disturbances, in which the observer gains are updated
... Show MoreThis paper deals with finding an approximate solution to the index-2 time-varying linear differential algebraic control system based on the theory of variational formulation. The solution of index-2 time-varying differential algebraic equations (DAEs) is the critical point of the equivalent variational formulation. In addition, the variational problem is transformed from the indirect into direct method by using a generalized Ritz bases approach. The approximate solution is found by solving an explicit linear algebraic equation, which makes the proposed technique reliable and efficient for many physical problems. From the numerical results, it can be implied that very good efficiency, accuracy, and simplicity of the pre
... Show MoreSemantic segmentation is effective in numerous object classification tasks such as autonomous vehicles and scene understanding. With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation. Most of the algorithms gain the required accuracy while compromising on their storage and computational requirements. The work showcases the implementation of Convolutional Neural Network (CNN) using Discrete Cosine Transform (DCT), where DCT exhibit exceptional energy compaction properties. The proposed Adaptive Weight Wiener Filter (AWWF) rearranges the DCT coefficients by truncating the high frequency coefficients. AWWF-DCT model reinstate the convolutional l
... Show MoreEarly detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med
... Show MoreA graphene-based supercapacitors (SC) were manufactured. The main objective of this research was to use as possible as environmentally, clean and natural materials for the SC electrodes, electrolytes and the separators. The SC consisted of a multi-layer graphene (MLG); as the electrode material, prepared by mixing graphene powder with water/acetone mixture, then the solution deposited on metal foils (aluminum and copper) by chemical spray technique, which is a simple and inexpensive technique to prepare the MLG films. The spraying time was (2 and 4 minutes) for making two MLG films with different thicknesses. The electrolytes were used is (lemon juice, table salt dissolved in water, and distillated water). The separators were a commercia
... Show MoreThe video steganography is a technique to hide information inside video file.Whereas video Steganography is a very important task in real life where the users want to keep data, so the steganography process used for the secure data transmission from the sender to receiver through the internet. Least significant bit (LSB) insertion technique operates on LSB bit of the media file to hide the information bit. In this paper steganography technique used to hide the information inside compressed video as development of a standard method in order to benefit from the advantages of the compression process, which added to the video, these features are reduce storage size of video, and reduce bandwidth to transfer data in faster way with save time
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