C In Depth By Srivastava Ebook 14
This text is very thorough in its coverage of child and adolescent development. Important theories and frameworks in developmental psychology are discussed in appropriate depth. There is no glossary of terms at the end of the text, but I do not...read more
c in depth by srivastava ebook 14
This text is very thorough in its coverage of child and adolescent development. Important theories and frameworks in developmental psychology are discussed in appropriate depth. There is no glossary of terms at the end of the text, but I do not think this really hurts its comprehensiveness.
The second effect is a consequence of Snell's law. The propagation speed of an acoustic signal underwater is, on average, approximately1,500ms; however, the actual value depends on the salinity, temperature and pressure of the medium, among other factors. A complete nine-term equation for calculating propagation speed is defined in , as shown in Equation (4), where T denotes the temperature in degrees Celsius, D, the depth in meters, and S, the salinity in parts per thousand of the water.
Figure 2 shows an example of how sound speed propagation changes along a water column placed in the Pacific ocean. As can be seen in subfigure (a), the speed rapidly decreases until approximately a 600-m depth, remaining nearly constant around 4 in deep oceans waters. In subfigure (b), where the individual contributions of temperature, salinity and pressure to sound speed are displayed, shows that deep water sound speed variation mainly depends on the increasing pressure.
Combination of reflection and refraction can lead to some remarkable effects, as explained for the sonar propagation in :Positive temperature gradient combined with surface reflection. Due to positive gradient, vertical rays are deflected upwards, as shown in Figure 3a. Both deflected and direct rays are reflected on the water surface. Reflected rays are once again deflected, and so on. As a result, acoustic waves barely achieve depth areas under these conditions.
As a conclusion, the impulse response of an acoustic channel is strongly correlated to the deployment scenario: holography, depth, particular environmental conditions, final nodes placement and even the current time slot have a severe impact on the channel response.
The most simple propagation model is the empirical Thorp's formula presented in Equation (2). Although the simulation can be very fast, attenuation formula accuracy is very poor, as long as it is only based on the frequency and the distance from the nodes, ignoring other important physical criteria, such as ocean waves, depth, holography, etc.
The next model in complexity is the Monterrey Miami Parabolic Equation (MMPE) , implemented in OPNETin a simulator called Xie Gibson . Its main equation is shown in Equation (8) This model offers a better description of the attenuation calculation by including the effects of ocean waves, the depth of the nodes and the seafloor multipath. However, several parameters need to be computed before running any simulation .
In depth-based routing (DBR)for underwater sensor networks , each sensor calculates the forwarding action, taking into account its depth and the depth of the previous node. In this greedy algorithm, when a node has data to send, it broadcasts the message. Depth calculation and comparison are executed by neighbor nodes. In this line, nodes with shallower depths than the sender accept messages while dropping the other ones. The main disadvantage of DBR is that all nodes need a depth sensor, increasing the consumption and cost. The required broadcasting is another disadvantage. Finally, it is important to show the significant difference in performance when the node density changes.
A similar approach is the one given by HydroCast , which is intended as an alternative to geographic routing, by using anycast routing and the pressure level with the goal of forwarding the data messages up to the surface. In this manner, is not necessary to implement an expensive distributed localization mechanism. HydroCast takes routing decisions after a depth information comparison, forwarding packets in a greedy manner towards a node with minor pressure using its neighbors. HydroCast assures that each local maximum node maintains a recovery path towards a neighbor with shallower depth. In this manner, a data packet can be routed out of the void region being switched back to the greedy mode. Simulation results show that HydroCast provides elevated delivery ratios with reduced delays.
Another approach that utilizes clusters is the location-based clustering algorithm for data gathering (LCAD) . Its main goal is to improve the behavior of sensor nodes near the sink which rapidly consume their battery power. It is a protocol for 3D UWSNs where sensor nodes are deployed at fixed relative depths, being organized in clusters. An algorithm which takes into account the node location is used for cluster head choice. In LCAD, each cluster will have multiple cluster heads. In order to implement intra-cluster communication, nodes transmit through the horizontal links.
In this sub-section, the routing protocols which use special physical mechanisms or devices in order to operate in an efficient manner are discussed. These special physical mechanisms can include, for example, mechanical modules that regulate sensor depth [122,123], low-power wake-up systems , or autonomous underwater vehicles (AUVs) which have to harvest data from actual sensor nodes .
Mansehra is one of the floristically rich and most varied districts of Pakistan, gifted with plentiful pteridophytes diversity. Various ethnic communities residing in district Mansehra and its adjacent localities exhibits unique tradition, dialect and culture. They collect pteridophytes from the wild especially for medicinal purposes, general healthcare, food uses and to meet daily life requirements. An inventory survey was conducted in order to assess the traditional uses of pteridophytes by the local inhabitants of the study area. Ethno botanical information and Ornamental potential of the taxa of wild pteridophytes was documented through field trips during 2013-2014. First-hand Information and data was collected through structured questionnaire and in-depth interviews were conducted from the natives in the hilly regions. A univariate level of analysis of the collected data such as percentage and frequency distribution was performed. 60 taxa are traditionally used, distributed in 16 families, and 26 genera. This figure meets about 32 % of the total known pteridophytes taxa of Pakistan. 56 taxa (93.34%) are widely used as medicines while 55 taxa having ornamental potential and may be cultivated for commercial purpose. 15 taxa are of great economic values i.e. a good source of vegetables and bio fertilizers. Our study concluded that, elders of the area have more knowledge than youngers in the population, an ethno medicinal practice of pteridophyte species by various indigenous people for treating various diseases and food use is prominent and may be considered as potential source for pharmaceutical industries to prepare new drugs to fight against various diseases. 350c69d7ab