Visualization on colour based flow vector of thermal image for movement detection during interactive session

Recently thermal imaging is exploited in applications such as motion and face detection. It has drawn attention many researchers to build such technology to improve lifestyle. This work proposed a technique to detect and identify a motion in sequence images for the application in security monitoring system or outdoor surveillance. Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. The results obtained could be observed in flow vector parameter and the segmentation colour based image for the time frame from 1 to 10 seconds. The visualization of both the parameters clarified the movement and changes of pixel intensity between two frames by the supportive colour segmentation, either in smooth or rough motion. Thus, this technique may contribute to others application such as biometrics, military system, and surveillance machine

Spectrally Modulated Spectrally Encoded Framework Based Cognitive Radio in Mobile Environment

Radio spectrum has become a precious resource, and it has long been the dream of wireless communication engineers to maximize the utilization of the radio spectrum. Dynamic Spectrum Access (DSA) and Cognitive Radio (CR) have been considered promising to enhance the efficiency and utilization of the spectrum. Since some of the spectrum bands are occupied by primary users (PUs), the available spectrum for secondary users (SUs) are non-contiguous, and multi-carrier transmission technologies become the natural solution to occupy those non-contiguous bands. Non-contiguous multi-carrier based modulations, such as NC-OFDM (non-contiguous Orthogonal Frequency Division Multiplexing), NC-MC-CDMA (non-contiguous multi-carrier code division multiple access) and NC-SC-OFDM (non-contiguous single carrier OFDM), allow the SUs to utilize the available spectrum. Spectrally Modulated Spectrally Encoded (SMSE) framework offers a general framework to generate multi-carrier based waveform for CR. However, it is well known that all multi-carrier transmission technologies suffer significant performance degradation resulting from inter-carrier interference (ICI) in high mobility environments. Current research work in cognitive radio has not sufficiently considered and addressed this issue yet. Hence, it is highly desired to study the effect of mobility on CR communication systems and how to improve the performance through affordable low-complexity signal processing techniques. In this dissertation, we analyze the inter-carrier interference for SMSE based multi-carrier transmissions in CR, and propose multiple ICI mitigation techniques and carrier frequency offset (CFO) estimator. Specifically, (1) an ICI self-cancellation algorithm is adapted to the MC-CDMA system by designing new spreading codes to enable the system with the capability to reduce the ICI; (2) a blind ICI cancellation technique named Total ICI Cancellation is proposed to perfectly remove the ICI effect for OFDM and MC-CDMA systems and provide the performance approximately identical to that of the systems without ICI; (3) a novel modulation scheme, called Magnitude Keyed Modulation (MKM), is proposed to combine with SC-OFDM system and provide ICI immunity feature so that the system performance is not affected by the mobility or carrier frequency offset; (4) a blind carrier frequency offset estimation algorithm is proposed to accurately estimate the CFO; (5) finally, compared to traditional ICI analysis and cancellation techniques with assumption of constant carrier frequency offset among all the subcarriers, subcarrier varying CFO scenario is considered for the wideband multi-carrier transmission and non-contiguous multi-carrier transmission for CR, and an ICI total cancellation algorithm is proposed for the multi-carrier system with subcarrier varying CFOs to entirely remove the ICI

Spectrally Modulated Spectrally Encoded Framework Based Cognitive Radio in Mobile Environment

Radio spectrum has become a precious resource, and it has long been the dream of wireless communication engineers to maximize the utilization of the radio spectrum. Dynamic Spectrum Access (DSA) and Cognitive Radio (CR) have been considered promising to enhance the efficiency and utilization of the spectrum. Since some of the spectrum bands are occupied by primary users (PUs), the available spectrum for secondary users (SUs) are non-contiguous, and multi-carrier transmission technologies become the natural solution to occupy those non-contiguous bands. Non-contiguous multi-carrier based modulations, such as NC-OFDM (non-contiguous Orthogonal Frequency Division Multiplexing), NC-MC-CDMA (non-contiguous multi-carrier code division multiple access) and NC-SC-OFDM (non-contiguous single carrier OFDM), allow the SUs to utilize the available spectrum. Spectrally Modulated Spectrally Encoded (SMSE) framework offers a general framework to generate multi-carrier based waveform for CR. However, it is well known that all multi-carrier transmission technologies suffer significant performance degradation resulting from inter-carrier interference (ICI) in high mobility environments. Current research work in cognitive radio has not sufficiently considered and addressed this issue yet. Hence, it is highly desired to study the effect of mobility on CR communication systems and how to improve the performance through affordable low-complexity signal processing techniques. In this dissertation, we analyze the inter-carrier interference for SMSE based multi-carrier transmissions in CR, and propose multiple ICI mitigation techniques and carrier frequency offset (CFO) estimator. Specifically, (1) an ICI self-cancellation algorithm is adapted to the MC-CDMA system by designing new spreading codes to enable the system with the capability to reduce the ICI; (2) a blind ICI cancellation technique named Total ICI Cancellation is proposed to perfectly remove the ICI effect for OFDM and MC-CDMA systems and provide the performance approximately identical to that of the systems without ICI; (3) a novel modulation scheme, called Magnitude Keyed Modulation (MKM), is proposed to combine with SC-OFDM system and provide ICI immunity feature so that the system performance is not affected by the mobility or carrier frequency offset; (4) a blind carrier frequency offset estimation algorithm is proposed to accurately estimate the CFO; (5) finally, compared to traditional ICI analysis and cancellation techniques with assumption of constant carrier frequency offset among all the subcarriers, subcarrier varying CFO scenario is considered for the wideband multi-carrier transmission and non-contiguous multi-carrier transmission for CR, and an ICI total cancellation algorithm is proposed for the multi-carrier system with subcarrier varying CFOs to entirely remove the ICI

The impact of Rayleigh fading channel effects on the RF-DNA fingerprinting process

The Internet of Things (IoT) consists of many electronic and electromechanical devices connected to the Internet. It is estimated that the number of connected IoT devices will be between 20 and 50 billion by the year 2020. The need for mechanisms to secure IoT networks will increase dramatically as 70% of the edge devices have no encryption. Previous research has proposed RF-DNA fingerprinting to provide wireless network access security through the exploitation of PHY layer features. RF-DNA fingerprinting takes advantage of unique and distinct characteristics that unintentionally occur within a given radio’s transmit chain during waveform generation. In this work, the application of RF-DNA fingerprinting is extended by developing a Nelder-Mead-based algorithm that estimates the coefficients of an indoor Rayleigh fading channel. The performance of the Nelder-Mead estimator is compared to the Least Square estimator and is assessed with degrading signal-to-noise ratio. The Rayleigh channel coefficients set estimated by the Nelder-Mead estimator is used to remove the multipath channel effects from the radio signal. The resulting channel-compensated signal is the region where the RF-DNA fingerprints are generated and classified. For a signal-to-noise ratio greater than 21 decibels, an average percent correct classification of more than 95% was achieved in a two-reflector channel

Frequency estimation in multipath rayleigh-sparse-fading channels

Maximum-likelihood (ML) data-aided frequency estimation in multipath Rayleigh-fading channels with sparse impulse responses is investigated. We solve this problem under the assumption that the autocorrelation matrix of the pilot signal can be approximated by a diagonal matrix, the fading of different path amplitudes are independent from each other, and the additive noise is white and Gaussian. The ML frequency estimator is shown to be based on combining nonlinearly transformed path periodograms. We have derived the nonlinear function for the two cases: known and unknown fading variances. The new frequency estimators lead, in particular cases, to known ML frequency estimators for nonsparse multipath fading channels. The use of a priori information about the mean number of paths in the channel allows a significant improvement of the accuracy performance. Exploiting the sparseness of the channel impulse response is shown to significantly reduce the threshold signal-to-noise ratio at which the frequency error departs from the Cramer-Rao lower bound. However, precise knowledge of the channel sparseness is not required in order to realize this improvement

SYNCHRONIZATION AND RESOURCE ALLOCATION IN DOWNLINK OFDM SYSTEMS

The next generation (4G) wireless systems are expected to provide universal personal and multimedia communications with seamless connection and very high rate transmissions and without regard to the users’ mobility and location. OFDM technique is recognized as one of the leading candidates to provide the wireless signalling for 4G systems. The major challenges in downlink multiuser OFDM based 4G systems include the wireless channel, the synchronization and radio resource management. Thus algorithms are required to achieve accurate timing and frequency offset estimation and the efficient utilization of radio resources such as subcarrier, bit and power allocation. The objectives of the thesis are of two fields. Firstly, we presented the frequency offset estimation algorithms for OFDM systems. Building our work upon the classic single user OFDM architecture, we proposed two FFT-based frequency offset estimation algorithms with low computational complexity. The computer simulation results and comparisons show that the proposed algorithms provide smaller error variance than previous well-known algorithm. Secondly, we presented the resource allocation algorithms for OFDM systems. Building our work upon the downlink multiuser OFDM architecture, we aimed to minimize the total transmit power by exploiting the system diversity through the management of subcarrier allocation, adaptive modulation and power allocation. Particularly, we focused on the dynamic resource allocation algorithms for multiuser OFDM system and multiuser MIMO-OFDM system. For the multiuser OFDM system, we proposed a lowiv complexity channel gain difference based subcarrier allocation algorithm. For the multiuser MIMO-OFDM system, we proposed a unit-power based subcarrier allocation algorithm. These proposed algorithms are all combined with the optimal bit allocation algorithm to achieve the minimal total transmit power. The numerical results and comparisons with various conventional nonadaptive and adaptive algorithmic approaches are provided to show that the proposed resource allocation algorithms improve the system efficiencies and performance given that the Quality of Service (QoS) for each user is guaranteed. The simulation work of this project is based on hand written codes in the platform of the MATLAB R2007b

Channel Estimation in Uplink of Long Term Evolution

Long Term Evolution is considered to be the fastest spreading communication standard in the world.To live up to the increasing demands of higher data rates day by day and higher multimedia services,the existing UMTS system was further upgraded to LTE.To meet their requirements novel technologies are employed in the downlink as well as uplink like Orthogonal Frequency Division Multiple Access (OFDMA) and Single Carrier- Frequency Division Multiple Access (SC-FDMA).For the receiver to perform properly it should be able to recover athe transmittedadata accurately and this is done through channel estimation.Channel Estimation in LTE engages Coherent Detection where a prior knowledge of the channel is required,often known as Channel State Information (CSI).This thesis aims at studying the channel estimation methods used in LTE and evaluate their performance in various multipath models specified by ITU like Pedestrian and Vehicular.The most commonly used channel estimation algorithms are Least Squarea(LS) and Minimum MeanaSquare error (MMSE) algorithms.The performance of these estimators are evaluated in both uplink as well as Downlink in terms of the Bit Error Rate (BER).It was evaluated for OFDMA and then for SC-FDMA,further the performance was assessed in SC-FDMA at first without subcarrier Mapping and after that with subcarrier mapping schemes like Interleaved SC-FDMA (IFDMA) and Localized SC-FDMA (lFDMA).It was found from the results that the MMSE estimator performs better than the LS estimator in both the environments.And the IFDMA has a lower PAPR than LFDMA but LFDMA has a better BER performance