Ensuring reliable data transmission in Network on Chip (NoC) is one of the most challenging tasks, especially in noisy environments. As crosstalk, interference, and radiation were increased with manufacturers' increasing tendency to reduce the area, increase the frequencies, and reduce the voltages. So many Error Control Codes (ECC) were proposed with different error detection and correction capacities and various degrees of complexity. Code with Crosstalk Avoidance and Error Correction (CCAEC) for network-on-chip interconnects uses simple parity check bits as the main technique to get high error correction capacity. Per this work, this coding scheme corrects up to 12 random errors, representing a high correction capacity compared with many other code schemes. This candidate has high correction capability but with a high codeword size. In this work, the CCAEC code is compared to another well-known code scheme called Horizontal-Vertical-Diagonal (HVD) error detecting and correcting code through reliability analysis by deriving a new accurate mathematical model for the probability of residual error Pres for both code schemes and confirming it by simulation results for both schemes. The results showed that the HVD code could correct all single, double, and triple errors and failed to correct only 3.3 % of states of quadric errors. In comparison, the CCAEC code can correct a single error and fails in 1.5%, 7.2%, and 16.4% cases of double, triple, and quadric errors, respectively. As a result, the HVD has better reliability than CCAEC and has lower overhead; making it a promising coding scheme to handle the reliability issues for NoC.
<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol
... Show MoreBackground/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the
... Show MoreThis paper examines the change in planning pattern In Lebanon, which relies on vehicles as a semi-single mode of transport, and directing it towards re-shaping the city and introducing concepts of "smooth or flexible" mobility in its schemes; the concept of a "compact city" with an infrastructure based on a flexible mobility culture. Taking into consideration environmental, economical and health risks of the existing model, the paper focuses on the four foundations of the concepts of "city based on culture flexible mobility, "and provides a SWOT analysis to encourage for a shift in the planning methodology.
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
... Show MoreIn this paper all possible regressions procedure as well as stepwise regression procedure were applied to select the best regression equation that explain the effect of human capital represented by different levels of human cadres on the productivity of the processing industries sector in Iraq by employing the data of a time series consisting of 21 years period. The statistical program SPSS was used to perform the required calculations.
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يعد تقييم اداء العاملين احد اهم الركائز الاساسية التي يتوقف عليها نجاح أي منظمة تسعى بأن تتطور وتتميز بأنشطتها واداءها وبالأخص المنظمات التي لها خصوصية في عملها كالأجهزة الرقابية التي تعتمد في اداء انشطتها ومسؤولياتها على كفاءة مواردها البشرية, ومن هذا المنطلق يهدف هذا البحث الى تصميم انموذج ثلاثي المحاور (المؤهلات والقدرات، الاداء والانجاز، التعاون والالتزام الوظيفي) ثُماني المستويات
... Show MoreWireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s
... Show MoreIn this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.
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This study reported the investigation of the Radio Frequency (RF) signal propagation of Global System for Mobile Communications (GSM) coverage in Emmanuel Alayande College of Education (EACOED), Oyo, Oyo State, Nigeria. The study aims at amplifying the quality of service and augment end users' sensitivity of the wireless services operation. The drive test method is adopted with estimation of coverage level and received signal strength. The Network Cell Info Lite application installed in three INFINIX GSM mobile phones was employed to take the measurement of the signal strength received from the transmitting stations of different mobile networks. The results of the study revealed that MTN has the maximum signal strength with a mean value
... Show MoreThis paper presents the dynamic responses of generators in a multi-machine power system. The fundamental swing equations for a multi-machine stability analysis are revisited. The swing equations are solved to investigate the influence of a three-phase fault on the network largest load bus. The Nigerian 330kV transmission network was used as a test case for the study. The time domain simulation approach was explored to determine if the system could withstand a 3-phase fault. The stability of the transmission network is estimated considering the dynamic behaviour of the system under various contingency conditions. This study identifies Egbin, Benin, Olorunsogo, Akangba, Sakete, Omotosho and Oshogbo as the key buses w
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