FIR Filter Essay

The following steps are used to design the back propagation neural network algorithm for the proposed research work. The first step is to set the input, output data sets. The second step is to set the number of hidden layer and output activation functions. The third step is to set the training functions and training parameters, finally run the network.

There are many different types of filters that can be used for decimating, including: cascaded comb-integrator (CIC); direct form polyphase finite impulse response (FIR); half-band FIR; symmetric polyphase FIR filters; transposed direct form II infinite impulse response (IIR) and quasi-linear IIR filters amongst others. As well as using these different configurations, hybrid types can be used for more efficient implementations designed either for better magnitude response, lower power consumptions or

The overall learning algorithm now proceed as follows; first, propagate the input forward using equation 3.3 and equation 3.4; next, propagate the sensitivities back using equation 3.15 and equation 3.12; and lastly, update the weights and offset using equation 3.7, equation 3.8, equation 3.10 and equation 3.11. (Murphy,

In the fig1 represents the number of nodes varying with respect to the delay as compared with MILP optimal formulation. It explained our proposed algorithm is better than the MILP formulation.

Apart from the single objective functions considered for this problem, a combined function is also used to perform the multi-objective optimization for the FMS parameters. The function and the variable limits are given using following function. Equal weights are considered for all the responses in this multi-objective optimization problem. Hence W1 and W2 are equal to 0.5.

In speech processing, There are various windowing functions, each with different shapes and different characteristics. The form of the window function is not a critical factor. Usually, some soft window like the Von-Hann, rectangular, hanning or hamming window [24] are used to decrease incoherence of the speech signal at the boundaries of each frame segment.

The proposed filter is an efficient, high-performance and flexible filter. Because of frequency selective nature of BPF, it is used mainly in reception device for electromagnetic interference reduction. Also the nearer frequency channels are separate out by proper filtering at particular frequency

A linear formula idea will be used and the decision variables will be labeled as follow:

Wang Ai-zhen , Ren Guo-feng [8] They determine the wash time by observing the input variables like Turbidity and turbidity change rate. In this paper the values are obtained from , the sensor of the washing machine i.e. Turbidity and turbidity change rate which is then passed to the information processing system , to process, the information was sent them to the controller. The value of input parameters are translated into fuzzy variables by the process of fuzzification, using MCU, accordance with the fuzzy inference rules and, the result is the fuzzy value. After defuzzification the crisp value, the washing time is obtained which we modify by the concept of soft computing neural

Following that, the expected values for decision nodes 6 and 7 should also be calculated. The following results were obtained:

In behavior of an ANN depends in each of the weights and the input-output function (transfer function) that is selected for the units. This function usually located into one of three types[10]:

By Tribuvan Kumar Prakash Bachelor of Engineering in Electronics and Communication Engineering Visveswaraiah Technological University, Karnataka, 2004. August 2007

Abstract — Wireless Body Area Networks (WBANs) are a boon to today’s society. The WBANs find their use in wide variety of fields starting from medical to sports to military. However the main concern for WBANs is their energy efficiency that in turn illustrates the network longevity. Many different algorithms have been employed for this purpose. In this paper we are going to enhance the energy efficiency of our WBAN network by using Neural Network (NN) along with the Genetic algorithm (GA). The neural network will provide the fitness value for our body node coordinator (BNC node that provides the entire data from the sensor nodes deployed on the body to the PC or laptop of the specialist) at a much faster rate and hence this data could be utilized by the Genetic algorithm to generalize the BNC’s location. It could be seen that the optimized placement of BNC leads to the increased energy efficiency of the network which in turn makes the network more dependable and steady. As the energy proficiency upsurges, so does the depletion time which in turn, escalates the network lifetime to a great percentage.

IIR filters require more multiplication and addition when compared to FIR, because FIR is of a higher order in comparison to IIR, which is of lower order

Abstract— Noise in signal records is caused by various factors which increase the difficulty in analyzing the signal and to obtain proper information. To design specific filters to decrease such noises in signal records is necessary. Hence efficient architecture of Delayed LMS (DLMS) algorithm is proposed which supports pipelining and avoid long critical path. A novel partial product generator and a strategy for optimized balanced pipelining across the time-consuming combinational blocks of the structure are used. It also concentrates on minimizing the adaption delay and area. The proposed system is implemented in verilog HDL language using Xilinx 14.2 and simulated using Modelsim simulator.