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Table 3. Summary of the main experimental results. Although the analysis is strictly related to a specific technology (i.e., Berkeley motes) we nevertheless think that the results obtained still provides general useful information. We found that the transmission range of mica2/mica2dot sensor nodes significantly decreases in the presence of fog or rain. In addition, we found that there is a minimum distance from the ground at which sensor nodes should be set. These aspects need to be taken into account for a correct deployment of sensor nodes. Based on our experimental results, we also derived a channel model for the CSMA/CA MAC protocol used in motes. This model is very similar to the IEEE 802.11 channel model. Since

in Performance measurements of motes sensor networks
by G. Anastasi, M. Conti, A. Falchi, E. Gregori, A. Passarella 2004
Cited by 21

Table 1: FSC Models Representing Lognormal Fading Channels

in Adaptive Rate Allocation In A Joint Source/channel Coding Framework For Wireless Channels
by Hamid Jafarkhani, Paschalis Ligdas, Nariman Farvardin 1996
"... In PAGE 3: ... The state probabilities of the FSC were so chosen that the average BER for each state of the model matches that of the corresponding range of the fade depth of the original channel. The resulting FSC models are shown in Table1 . Here, we are not concerned with the state transition probabilities since we have assumed su ciently slow fading.... In PAGE 3: ... In all cases investigated, the model selects the optimal (rs; rc) pair. Table1 shows the di erent FSC models representing lognormal fading channels. The FSC apos;s for the Rayleigh channel were very similar to those for the lognormal chan- nels and therefore we chose to present results for the lat- ter case only.... In PAGE 3: ... This is also the most relevant case for the problem at hand since Rayleigh fading is usually faster than lognormal and does not lend itself easily to adaptive transmission. For each case in Table1 , we investigate the performance of the system for memoryless Gaussian sources as well as Gauss-Markov sources with correlation coe cient = 0:9. We consider di erent dimensions for the TSVQ and di erent transmission rates for the chan- nel as summarized in Tables 2-5.... ..."
Cited by 15

Table 1: FSC Models Representing Lognormal Fading Channels

in Adaptive Rate Allocation In A Joint Source/Channel Coding Framework For Wireless Channels
by Hamid Jafarkhani, Paschalis Ligdas, Nariman Farvardin 1996
"... In PAGE 3: ... The state probabilities of the FSC were so chosen that the average BER for each state of the model matches that of the corresponding range of the fade depth of the original channel. The resulting FSC models are shown in Table1 . Here, we are not concerned with the state transition probabilities since we have assumed su ciently slow fading.... In PAGE 3: ... In all cases investigated, the model selects the optimal (rs; rc) pair. Table1 shows the di erent FSC models representing lognormal fading channels. The FSC apos;s for the Rayleigh channel were very similar to those for the lognormal chan- nels and therefore we chose to present results for the lat- ter case only.... In PAGE 3: ... This is also the most relevant case for the problem at hand since Rayleigh fading is usually faster than lognormal and does not lend itself easily to adaptive transmission. For each case in Table1 , we investigate the performance of the system for memoryless Gaussian sources as well as Gauss-Markov sources with correlation coe cient = 0:9. We consider di erent dimensions for the TSVQ and di erent transmission rates for the chan- nel as summarized in Tables 2-5.... ..."
Cited by 15

Table 9. Application Data

in Project Da CaPo++, Volume II: Implementation Documentation TIK-Report No. 29
by Burkhard Stiller, Germano Caronni, Christina Class, Christian Conrad, Bernhard Plattner, Marcel Waldvogel 1997
"... In PAGE 32: ... There are three different lists: for the application, session and flow data. The contents of the application data is given in Table9 . Similarly, the contents of the session data in Table 9, and finally, the contents of the flow data in Table 10 and Table 11.... ..."
Cited by 1

Table 9. Application Data

in Project Da CaPo++, Volume II: Implementation Documentation TIK-Report No. 29
by Burkhard Stiller Germano, Burkhard Stiller, Germano Caronni, Christina Class, Christian Conrad, Bernhard Plattner, Marcel Waldvogel 1997
"... In PAGE 32: ... There are three different lists: for the application, session and flow data. The contents of the application data is given in Table9 . Similarly, the contents of the session data in Table 9, and finally, the contents of the flow data in Table 10 and Table 11.... ..."
Cited by 1

Table II. Channel model

in unknown title
by unknown authors

Table 1: The channels in the saliency model.

in Abstract A GPU based Saliency Map for High-Fidelity Selective Rendering
by Peter Longhurst
"... In PAGE 3: ... Figure 2 shows an overview of our model. In total seven channels are combined to give the nal map, these are described in Table1 . The image space components taken on their own can be used as a GPU implementation of a more traditional saliency map that takes an image as input.... ..."

Table 1. Channel Model Parameters

in Extending the Scope of 802.11 WLAN through LMMSE CDMA Receiver Structures
by Ian Oppermann Cwc, Cwc Oulu

TABLE II CHANNEL MODELS [3]

in High Performance Local Area Network Type 2
by Celal Esli, Student Member, Baris Ozgul, Student Member, Hakan Delic, Senior Member

Table 2. U.S. Meat Demand Models: Thep-values for Equation-by-Equation System Misspe- cification Tests a

in System Misspecification Testing and Structural Change in the Demand for Meats
by Anya Mcguirk, Paul Driscoll, Jeffrey Alwang, Huilin Huang
"... In PAGE 12: ... Table2 . Continued Model C Model D Item Beef Pork Chicken Beef Pork Chicken Individual Tests Normality Functional Form: RESET2 KG2 Heteroskedasticity:b Static Beef RESET2 Pork Chicken Static Beef WHITE Pork Chicken Dynamic Beef Pork Chicken Autocorrelation Parameter Stability: Variance Mean Joint Tests Overall Mean Test Parameter Stability Functional Form Autocorrelation Overall Variance Test:b Beef Pork Chicken Parameter Stability: Beef Pork Chicken Static Heteroskedasticity: Beef Pork Chicken Dynamic Heteroskedasticity: Beef Pork Chicken 0.... ..."
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