Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutilized crossbar regions and supports rapid on-chip training within two clock cycles. This research also leverages plasticity mechanisms such as neurogenesis and homeostatic intrinsic plasticity to strengthen the robustness and performance of the SP. The proposed design is benchmarked for image recognition tasks using Modified National Institute of Standards and Technology (MNIST) and Yale faces datasets, and is evaluated using different metrics including entropy, sparseness, and noise robustness. Detailed power analysis at different stages of the SP operations is performed to demonstrate the suitability for mobile platforms.
In the present work, a D.C. magnetron sputtering system was
designed and fabricated. This chamber of this system includes two
coaxial cylinders made from copper .the inner one used as a cathode
while the outer one used as a node. The magnetic coils located on
the outer cylinder (anode) .The profile of magnetic field for various
coil current (from 2Amp to 14Amp) are shown. The effect of
different magnetic field on the Cu thin films thickness at constant
pressure of 7x10-5mbar is investigated. The result shown that, the
electrical behavior of the discharge strongly depends on the values
of the magnetic field and shows an optimum value at which the
power absorbed by the plasma is maximum. Furthermore, the
pl
ABSTRACT Porous silicon has been produced in this work by photochemical etching process (PC). The irradiation has been achieved using ordinary light source (150250 W) power and (875 nm) wavelength. The influence of various irradiation times and HF concentration on porosity of PSi material was investigated by depending on gravimetric measurements. The I-V and C-V characteristics for CdS/PSi structure have been investigated in this work too.
In the present work, a d.c. magnetron sputtering system was designed and fabricated. The chamber of this system was includes from two copper coaxial cylinders where the inner one used as a cathode (target) while the outer one used as the anode with Solenoid magnetic coil located on the outer cylinder (anode). The axial profile of magnetic field for various coil current (from 2A to 14 A) are shown. The plasma characteristics in the normal glow discharge region are diagnostics by the 2.2mm diameter Langmuir probe with different length along the cathode and located at different radial positions 1cm and 2cm from the cathode surface. The result of this work shows that, the electron energy distributions at different radial positions along the
... Show MoreThe research investigates the political effect and its directions on the architectural thoughts and its achievements and how can this political system affect all fields of life in communities including architectural urban design. The problem of the research lies in the ambiguity effects of the ideological national directions of the Nazi Party on the architecture and urban design of the city of Berlin, then determining the aims of the research to discuss the concepts of politics and architecture and their relation to the way of thinking that plays a role in the process of design that works on property and achieving the suitable urban environments for those communities. After that, the Nazi's party's thought would be studied and analyzed,
... Show MoreA three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an
... Show MoreText categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th
... Show MoreDeep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to
... Show MoreThe huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed a great competence of the proposed WELM compared to the ELM.
AO Dr. Ali Jihad, Journal of Physical Education, 2021
Learning Disabilities are described as a hidden and puzzling disability. Children with these difficulties have the potential to hide weaknesses in their performance because they are a homogenous group of disorders that consist of obvious difficulties in acquiring and using reading, writing, Mathematical inference. Thus, the research aims to identify the disabilities of academic learning in (reading, writing, mathematics), identify the problems of behavior (general, motor, social). Identify the relationship among behaviour problems. The research also aims to identify the counseling needs to reduce the behavioral problems. The researcher adopted the analytical descriptive method by preparing two main tools for measuring learning disabiliti
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