Gradient

2.1 Introduction In last few years, remote sensing image scene classification has got a remarkable attention due to its importance. Researchers are trying hard to classify remote sensing images correctly. For this reason, we have studied different topics related to this research. Rest of this chapter will describe some important Hand-Engineered feature extraction methods along with deep learning technique. In section 2.2, we will discuss some hand-engineered feature extraction methods, in section

Feature plays a very important role in the area of image processing. Different feature extraction techniques are applied on different types of images to get features that will be useful in classifying and recognition of images. Features describes the important information of images that helps to classify images correctly and remarkably reduce the dimension of the images. In pattern recognition and image processing, feature extraction is a special form of dimensionality reduction. The main goal

to test the effect of the concentration gradient on the diffusion rate. It was hypothesized that the greater the stronger the concentration gradient, the faster the rate of diffusion would be. To test this, dialysis tubes were submerged in different concentration fructose solutions. We weighed the tubes at specific time intervals to measure the rate of diffusion of water in each different solution. The results illustrated that increased concentration gradient increases the rate of diffusion of water

widely used activation functions. The ReLU activation function is defined as: f(x) = max(x,0). Effectively, ReLU is a linear function that prunes the negative part to zero and retains the positive part as is. Intuitively, ReLU avoids the vanishing gradient problem by setting the positive part to identity. (Krizhevsky et al., 2012) showed that that deep networks can be efficiently trained using ReLU even without pre-training. Compared to tanh and sigmoid neurons, that involve expensive operations, the

1. A. Passive transport is when molecule travels from a higher concentration to a lower concentration down a concentration gradient whereas active transport is when molecule travels from a region of lower concentration to a region of higher concentration against the concentration gradient. Passive transport does not require energy whereas active transport requires energy in order for substances to pass through it. (Marieb & Hoehn, 2007). B. Active Transport: Active Diffusion: Passive

is known as vanishing gradient problem ??. Since network parameters of an RNN are shared over time, at any time step error derivative not only depends on the current input of network but also on the previous state of the network. So there is a multiplicative term of error derivatives back to time step 0 to calculate error derivative of current time. This is a modification of back propagation algorithm and known as back propagation through time (BPTT) ??. If individual gradients are close to zero this

phosphorylation occurs as the electron transport chain establishes a proton gradient by moving electrons from NADH and FADH2 to molecular oxygen. The four protein complexes labeled I through IV along with associated electron carriers move electrons from one component to the next quickly until the end of this cycle. The beginning of this cycle starts when two electrons on NADH are carried to the complex I. The hydrogen ion gradient is established by Complex I pumping four hydrogen ions across the membrane

region of low concentration (OpenStax college, 2013). Osmosis is the process by which water pass through a semi-permeable membrane. Facilitated transport, also known as facilitated diffusion is the movement of particular molecules down a concentrated gradient. Active transport is the process in which

-Abstract: The aim of the experiment was to determine the gravity (g) on the period (T) of oscillation pendulum has a simple harmonic motion (SHM). A stopwatch was used to measure the time taken for 10 complete cycles, which called oscillations. The length of the pendulum was varied for fixed mass and recorded in table 1. The output result for this experiment was agreed with the theory. The graph plotted and got R2=0.99, which was close to the linearity 1. -Introduction: The motion repeats itself

The purpose of this experiment was to test the hypothesis that the crayfish resting membrane potential is primarily dependent on the potassium ion concentration gradient. Our approach to testing this hypothesis involves taking intracellular recordings using an IWX/214 interface, iWorx and LabScribe software, and a Model 3100 electrometer complete with a head stage, ground electrode, glass microelectrodes, and micromanipulator. The study organism used for this experiment involved the abdominal extensor