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The controller of claim 1 , wherein the plurality of distinct digital codes are mathematically independent. The controller of claim 1 , wherein the plurality of distinct digital codes is selected from a group consisting of Pseudo-Random codes, Walsh-Hadamard codes, m-sequences, Gold codes, Kasami codes, Barker codes, and delay line multiple tap sequences. The controller of claim 1 , wherein the first one and the second one of the plurality of distinct digital codes define the first modulated signal to be in phase with the second modulated signal during a first time, and the first modulated signal to be out of phase with the second modulated signal during a second time.
The controller of claim 1 , wherein the controller is further configured to determine proximity information for the first input object based on the resultant signals. The controller of claim 1 , wherein the controller is configured to determine a gesture based on the positional information for the first input object.
A sensing device comprising: a plurality of transmitter electrodes;. The sensing device of claim 17 , wherein the plurality of transmitter electrodes and the plurality of receiver electrodes are configured to be non-moveable with respect to each other.
The sensing device of claim 17 , wherein the plurality of transmitter electrodes and the plurality of receiver electrodes are disposed in a non-overlapping arrangement on one side of a substrate. The sensing device of claim 17 , wherein the plurality of transmitter electrodes and the plurality of receiver electrodes are disposed in an overlapping arrangement.
The sensing device of claim 17 , wherein simultaneously applying the first modulated signal and the second modulated signal comprises one of: frequency modulating a carrier signal according to the first one and the second one of the plurality of distinct digital codes; phase modulating a carrier signal according to the first one and the second one of the plurality of distinct digital codes; and amplitude modulating a carrier signal according to the first one and the second one of the plurality of distinct digital codes.
The sensing device of claim 17 , wherein the plurality of distinct digital codes are mathematically independent. The sensing device of claim 17 , further comprising a substantially rigid surface disposed over the plurality of transmitter electrodes and the plurality of receiver electrodes. The sensing device of claim 17 , further comprising a plurality of signal paths configured to individually couple the controller with each of the transmitter electrodes, respectively.
The sensing device of claim 17 , wherein the plurality of transmitter electrodes are disposed on a flexible substrate. A method for capacitive sensing, the method comprising: simultaneously applying a first modulation signal to a first transmitter electrode and a second modulation signal to a second transmitter electrode, wherein the first modulation signal is based on a first one of a plurality of distinct digital codes and the second modulation signal is based on a second one of the plurality of distinct digital codes;.
The method of claim 26 , further comprising: determining positional information for a second input object based on the resultant signals; and. The method of claim 26 , further comprising receiving second resultant signals with the plurality of receiver electrodes and wherein the controller is further configured to adjust a frequency of the first and second modulation signals based on observed noise in the second resultant signals.
The method of claim 26 , further comprising simultaneously applying a first modulation signal to the first transmitter electrode and a second modulation signal to the second transmitter electrode during a first operating mode, and simultaneously applying a third modulation signal to the first transmitter electrode and the second transmitter electrode during a second operating mode.
The method of claim 26 , further comprising determining a gesture based on the positional information for the first input object. Methods and systems for detecting a position-based attribute of an object using digital codes.
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When the measure energy level is above the defined threshold, its considered as occupied. Energy detection method does not require any prior information of the signal. In simple words it does not care about the type of modulation used for transmission of signal, phase or any other parameter of signal. It simply tells if the radio resource is available at any given time instant or not without considering the PU and SU [10].
Where s t is the received signal and n t is the additive white Gaussian noise i. H0 and H1 represent the two outcomes of the energy detection method [4].
The energy detection methods working principal can be explained with the Figure2. A Cyclostationary process is defined as the statistical process which repeats itself cyclically or periodically [6]. Communication signals are Cyclostationary with multiple periodicities.
The equation shows the autocorrelation of the observed signal x t with periodicity T, E represents the expectation of the outcome and represents the cyclic frequency [6]. After autocorrelation Discrete Fourier Transform over resulting correlation is performed to get the desired result in terms frequency components.
The peaks in the acquired data give us the information about the spectrum occupancy. The Cyclostationary detection method requires prior. The implementation of Cyclostationary method is shown in Figure2. In the matched filter detection method a known signal is correlated with an unknown signal captured from the available radio resource to detect the presence of pattern in the unknown signal [6].
The use of matched filter detection is very limited as it requires the prior information about the unknown signal. For example in case of GSM, the information about the preamble is required to detect the spectrum through matched filter detection method. To detect the wideband signals, wavelet detection method offers advantage over the rest of the methods in terms of both simplicity and flexibility.
It can be used for dynamic spectrum access. To identify the white spaces or spectrum holes in the available radio resource, the entire spectrum is treated as the sequence of frequency sub-bands. Each sub-band of frequency has smooth power characteristics within the sub-band but changes abruptly on the edge of next sub-band.
By using the wavelet detection method the spectrum holes can be found at a given instance of time by finding the singularities in the attained result [10]. The Figure 2. Qualitative analysis of spectrum sensing techniques All of the above mentioned spectrum sensing techniques have certain advantages and disadvantages. Some of the techniques are suitable for sensing of licensed spectrum whereas others are suitable for unlicensed spectrum.
The qualitative analysis of above mentioned spectrum sensing techniques are presented in Table1. Spectrum sensing techniques comparison Sensing technique Advantages Does not require prior Energy Detection information, Efficient, less complex Cyclostationary Works perfectly in low SNR areas, robust against interference Low computation cost, accurate detection Works efficiently for wideband signal detection.
It can clearly be seen from table1 that energy detection is the most suitable technique for unlicencessed spectrum bands as it does not require any prior information about the PU and SU. Radio spectrum overview Radio spectrum comprises of electromagnetic frequencies ranging lower than 30GHz or having wavelength larger than 1milimeter mm. Various parts of the radio spectrum are allocated for different kinds of communication application varying from microphones to satellite communication.
Today, in most of the countries radio spectrum is government regulated i. There are several different frequency bands defined inside the radio spectrum on the basis of wavelength and frequency f. Generically radio spectrum can be classified into two categories as licensed spectrum and unlicensed spectrum. Licensed spectrum comprises of frequency bands governed by government regulated agencies. It is illegal to use licensed frequency spectrum without taking permission from the regulatory bodies.
Unlicensed frequency bands can be used by anyone for any scientific or industrial research. It comprises of several different frequency bands. Most commonly used ISM bands are 2. Though these ISM bands were reserved for the purpose of research but currently these bands are used for different wireless communication standards.
The research carried out in this thesis is performed using the 2. Each channel is 5MHz apart from each other and all the adjacent channel overlap with each other as each channel is 22MHz wide as shown in Figure 2.
Most of the ongoing debate is concerned about finding the right spectrum sensing techniques for CR, channel allocation and transmission power handing for the Media Access Control MAC and Physical PHY layer implementation [5]. Underutilized bandwidth detection is key element of any spectrum sensing technique [9].
There are some other technique that could be considered as alternative to spectrum sensing. One of these techniques is cognition enabling pilot channel CPC [6]. According to CPC, a database of licensed users can be created which will monitor the use of spectrum by creating another channel and by advertising the spectrum opportunities in timely manner.
But this will restult in additional infrastructure and use of another channel known as CPC. It is not the best approach to overcome spectrum scarcity as it will result in extra overhead in terms of radio resource. It is widely used in the wireless communication research and real time implementation of software radio systems [16]. GNU Radio applications are mainly written and developed by using Python programming language.
Python provides a user friendly frontend environment to the developer to write routines in a rapid way. Very high speed integrated circuits hardware description language VHDL is a hardware descriptive language. GNU radio applications can be developed using both Object Oriented Approach and Procedural Approach depending upon the complexity of the problem under consideration.
Any GNU Radio application can be presented as a collection of flow graphs as in graph theory. The nodes of such flow graphs are called processing blocks.
These processing blocks are tied together through flow graphs or lines connecting blocks. Data flows from one block to another through these flow graphs. Each block connecting one end of flow graph performs one signal processing operation for example encoding, decoding,.
Every flow graph in GNU Radio requires at least one source or sink. Source and Sinks can be explained with the example of spectrum sensing scenario explained later in this chapter. In case of spectrum sensing our command line interface acts as sink whereas USRP2 acts as a source. Receive and transmit path of typical software radio is shown in Figure3.
Its the upgraded version of its earlier release USRP. The main idea behind the design of USRP is to perform all the signal processing tasks for example modulation and demodulation, filtering at the host computer. The Figure 3. The main features of USRP2 are given in table 3. Figure 3. The main task for the FPGA is the down conversion of remaining frequency and data rate conversion. After processing, FPGA transfers the results to gigabit Ethernet controller which passes it over to the host computer where the rest of the signal processing tasks are performed.
In case of transmission the same procedure is repeated in reverse order. USRP2 device was used to physically receive the signal from real time environment. It is a graphical user interface GUI application which returns the frequency range for the transceiver board and its gain in milliWatt-decibel dBm.
Figure 4. The experimental setup is shown in Figure 4. The routine works as a software spectrum analyzer and is flexible enough to monitor any RF spectrum. It can only monitor a maximum of 8MHz bandwidth at any. Hence we modified the routine to make it work with the USRP2. There are some other routines written in python available in GNU Radio but none of these routines are flexible enough to monitor the whole spectrum for a long interval of time.
Secondly it is a GUI application and it can only be executed using a low decimation rate, otherwise system specs should be very high. The application works as a broad band receiver, it fetches the signal from the external source environment and dumps the Digital down converter output in form of I and Q data directly into the appended file. The Figure show the utilization of RF spectrum at 2.
Spectrum sensing Algorithm implementation Implementation of spectrum sensing algorithm can be explained with the help of flow graph presented in Figure 4.
Flow chart shows the flow of data from source i. Source i. USRP2 fetches the signal of the desired frequency passed to it as a tuning parameter. After passing through USRP2 the raw data bit streams are converted to vectors or arrays of data. FFT is performed on the received raw data with the help of signal processing blocks and it is passed through the Blackman-Harris window to overcome the spectral leakage effect. When the FFT routine is implemented on a non periodic data, it results into spectral leakage i.
Window functions helps out in reducing the effect of spectral leakage. After performing the FFT magnitude, decimation of the data is performed. Decimation is the inverse of interpolation. Decimation reduces the sample rate of data by performing down sampling to the desired rate and send to USPR2 as a tuning parameter. After performing decimation the obtained data is appended into a file or it can be plotted on the go through a GUI tool.
The data collected is in the form of FFT magnitude bins. By plotting these magnitude bins against the given frequency range the frequency envelop of the signal can be monitored to find the white spaces or spectrum holes. By taking. The minimum number of bins is 3 and the maximum is bins. The FFT magnitude bins received at a certain frequency consist of 2 parts. First half of the bins i. The gain parameter sets the gain of tuner card. The time delay and dual delay parameters depends upon the decimation rate and length of FFT.
By default decimation rate is set to 16 with FFT bins and with time delay of 1ms. Time delay is a key parameter without setting the correct time delay the results could be altered or false. The reason for it is time delay displaying the amount of FFT frames from the source to sink should be.
Time delay for a given decimation rate and FFT size can be calculated as follows: No. Without FFT overlapping we were getting white spaces at the end of every sweep. In some cases we even choose overlapping of 0.
Decimation rate of 8 means that USRP2 processed The gain at any given center frequency can be calculated by summing the values of all the bins and by taking square root of the result. Most of the results were collected during the same project room except a few instances where the results were collected in the cafeteria while turning on the microwave to check the performance of the algorithm in presence of high noise.
Project Limitations Project limitations can be classified into two categories i. Hardware limitations Energy Detection Algorithm limitations. The results were attained using decimation rate of 8. More precise results can be obtained by using lower decimation value. The receiver sensitivity can be increased by using a high gain antenna with the tuner card. Energy detection Algorithm limitations As previously mentioned energy detection based algorithms cannot differentiate between PU and SU [11].
The results are solely based on the threshold level received, or the energy level measured from the environment. Energy detection method cannot be used in a low noise setup. The implemented routine can sense large bandwidth but not at the same time as it steps across the RF spectrum with the change in time domain.
All the results in this chapter are plotted using Matlab and Sigview. The sample raw data extracted in the. The results obtained show the usage of 2. The results are collected using both 2.
The 5. We have used three different types of plots to verify our results. These are frequency, magnitude and time, frequency, gain 3dimensional 3D plots and time, frequency spectrograms. As it can be observed from the graph, it is hard to find the exact channel utilization of The advantage of time axis is that we can monitor the results at any given instance of time.
The results obtained using a smaller step of 10MHz is shown in Figure 5. The spikes around 2. Frequency and magnitude plots provide us gain at a given frequency but still its not good enough to find the threshold level or to decide which part of spectrum is free because it does not has the time-axis.
For this purpose we have used spectrograms. The color bar presented at the right side of the plot shows the different level of energy or gain values. To find the spectrum holes or availability at the defined threshold at any instant, we can compare the color with time and frequency axis. The color red in this. Spectrogram shows the spectrum holes or underutilized bandwidth at a given instance of time and Frequency. Due to large frequency step it is hard to differentiate between the colors and it is hard to find the white spaces at the exact location.
To gather even more accurate results we repeated the experiment with 5 MHz frequency step and sweep across the spectrum again. The results obtained are shown in Figure 5.
Here we can easily observe the use of channel 1,6,11 in the campus WLAN environment by looking at Figure 5. To check the limitations of our project we conducted another experiment by placing the USRP2 and host PC near microwave ovens to check if energy detection method is susceptible to the high signal strength environment or not.
The results attained are shown in Figure 5. The results clearly show increase in gain in less than 1 second. We received gain values as high as 50dBm and energy detection algorithm didnt identify any other WiFi channel though the university caf is a hotspot and it has a number of wireless routers placed in the vicinity.
The spectrogram in Figure 5. Since 5. It can be observed by looking at the Figure 5. A few colored spots in the spectrogram are due to the thermal noise or other hardware noise figure. Different experiments were performed to find out the spectrum holes in the occupied 2.
The qualitative analysis of different spectrum sensing methods shows that energy detection is the most reliable and authentic method for the spectrum sensing. The raw data collected in the form of FFT bins is the most efficient way of collecting data for spectrum sensing as it requires just a few signal processing operations. Rest of the work is performed inside the USRP2, This is closest one can get to the hardware for precise results.
The results obtained proved that it is possible to find the underutilized bandwidth in a spectrum without having prior knowledge of PU and SU. Wavelet methods though often used in astronomy and acoustics can be used to identify the spectrum holes as shown in the result by plotting spectrograms.
Future work Cognitive radio is relatively new area of research as compared to the rest of communication theory. There are several other methods of spectrum sensing which need to be explored. This can be done by connecting multiple USRP2 together and sensing the spectrum for a large area.
Another way to sense the spectrum is by using cooperative communication and by taking antenna diversity and other factors under consideration.
This study could be extended by repeating the same experiment for licensed frequency bands and the results obtained can be compared with ISM band results to get the better understanding of the area of research. Bibliography [1] J. Universal software radio peripheral. Yucek and H. Sarijari, A. Conference Paper, published, Cabric, A. Tkachenko, R. Brodersen, Experimental study of spectrum sensing based on energy detection and network cooperation, Proc.
Penna, C. Pastrone, M. Spirito, and R. ICC, Jun. Urkowitz, Energy detection of unknown deterministic signals, Proc. Tandra, A. Zeng and Y. Mitola and G. Arshad and K. The modified part of the code is in italics. There might be some indentation errors in the routine which can easily be fixed by copying the code in any standard python editor e. This file is part of GNU Radio. Default is eth0" parser.
Default is auto-select" parser. The values in the parenthesis are the FFT magnitudes at the center frequency mentioned at beginning of brackets. This thesis is submitted to the School of Engineering at Blekinge Institute of Technology in partial fulllment of the requirements for the degree of Master of Science in Electrical Engineering. Author: Adeel Ashfaq and Umer Bilal email: ashfaq. Examiner: Dr. Arlos bth. Abstract Web browsing activites have increased to huge volumes in the last decade, causing more interest in the analysis of web trac to extract user behaviour.
TCP, the transport layer protocol and HTTP, the application layer protocol plays a major role in web browsing activities. An experimental setup has been devised in order to observe the relationship between the TCP ows and user behaviour. We created a simple client server architecture based setup for investigating the TCP connection packets. We categorized the web pages on the bases of their data type i.
We selected the four widely used browsers based on the stats provided by dierent websites. Similarly, the selection of operating system for client and server ends were made on the basis of the stats.
Each type of web page is loaded on the server one by one and then accessed by a specic browser ten times. A total of repetitions are performed to get reliable results. A network packet analyzer is used on both ends to analyze the traces and extract the causes of resets. Acknowledgement First of all we would like to thanks Almighty GOD who gave us strength and wisdom and made us capable of doing this work. We show our bundle of thanks to Mr. Junaid shaikh for his guidance, feedback and support throughout our thesis work.
We really appreciate his friendly and encouraging attitude.
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