The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the sink. By utilizing Intel Berkeley Research Lab (IBRL) dataset, the efficiency of the proposed method was measured. The experimental findings illustrate the benefits of the proposed method as it reduces the overhead on the sensor node level up to 1.25% in remaining data and reduces the energy consumption up to 93% compared to prefix frequency filtering (PFF) and ATP protocols.
In the present investigation two different types of fiber reinforced polymer composites were prepared by hand lay-up method using three different parameters (curing temperature, pressing load and fiber volume fraction). These composites were prepared from the polyester resin as the matrix material reinforced with glass fibers as first group of samples and mat Kevlar fibers as the second group, both with different volume fractions (4%, 8%, and 12%) of fibers. They were then tested by tensile strength and impact strength. The main objective in this study is to use Taguchi method for predicting the better parameters that give the better tensile and impact strength to the composites, and then preparing composites at
... Show MoreThe Estimation Of The Reliability Function Depends On The Accuracy Of The Data Used To Estimate The Parameters Of The Probability distribution, and Because Some Data Suffer from a Skew in their Data to Estimate the Parameters and Calculate the Reliability Function in light of the Presence of Some Skew in the Data, there must be a Distribution that has flexibility in dealing with that Data. As in the data of Diyala Company for Electrical Industries, as it was observed that there was a positive twisting in the data collected from the Power and Machinery Department, which required distribution that deals with those data and searches for methods that accommodate this problem and lead to accurate estimates of the reliability function,
... Show MoreIn recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny
... Show MoreThe treasury bills one internal government debt instruments in iraq that were used to finance temporary deficits in the government budget, There fore the government resorted to issuing for the purpose of providing liquidity, As well as to invest in productive progects, After that it was financing the budget deficit by the monetary authovity of the central baalpennek [the new cash velease] which led to negative effects on the Iraqi econome, Thus we find that the treasury transfers have formed alarge proportion of the gross domestic government debt to finance the deficit ayear ago 2003. &nbs
... Show MoreDeep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d
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