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

A time-domain control signal detection technique for OFDM

Transmission of system-critical control information plays a key role in efficient management of limited wireless network resources and successful reception of payload data information. This paper uses an orthogonal frequency division multiplexing (OFDM) architecture to investigate the detection performance of a time-domain approach used to detect deterministic control signalling information. It considers a type of control information chosen from a finite set of information, which is known at both transmitting and receiving wireless terminals. Unlike the maximum likelihood (ML) estimation method, which is often used, the time-domain detection technique requires no channel estimation and no pilots as it uses a form of time-domain correlation as the means of detection. Results show that when compared with the ML method, the time-domain approach improves detection performance even in the presence of synchronisation error caused by carrier frequency offset

EM-Based iterative channel estimation and sequence detection for space-time coded modulation

Reliable detection of signals transmitted over a wireless communication channel requires knowledge of the channel estimate. In this work, the application of expectationmaximization (EM) algorithm to estimation of unknown channel and detection of space-time coded modulation (STCM) signals is investigated. An STCM communication system is presented which includes symbol interleaving at the transmitter and iterative EM-based soft-output decoding at the receiver. The channel and signal model are introduced with a quasi-static and time-varying Rayleigh fading channels considered to evaluate the performance of the communication system. Performance of the system employing Kalman filter with per-survivor processing to do the channel estimation and Viterbi algorithm for sequence detection is used as a reference. The first approach to apply the EM algorithm to channel estimation presents a design of an online receiver with sliding data window. Next, a block-processing EM-based iterative receiver is presented which utilizes soft values of a posteriori probabilities (APP) with maximum a posteriori probability (MAP) as the criterion of optimality in both: detection and channel estimation stages (APP-EM receiver). In addition, a symbol interleaver is introduced at the transmitter which has a great desirable impact on system performance. First, it eliminates error propagation between the detection and channel estimation stages in the receiver EM loop. Second, the interleaver increases the diversity advantage to combat deep fades of a fast fading channel. In the first basic version of the APP-EM iterative receiver, it is assumed that noise variance at the receiver input is known. Then a modified version of the receiver is presented where such assumption is not made. In addition to sequence detection and channel estimation, the EM iteration loop includes the estimation of unknown additive white Gaussian noise variance. Finally, different properties of the APP-EM iterative receiver are investigated including the effects of training sequence length on system performance, interleaver and channel correlation length effects and the performance of the system at different Rayleigh channel fading rates

A Real Time Radio Spectrum Scanning Technique Based On The Bayesian Model And Its Comparison With The Frequentist Technique

The proliferation of mobile devices led to an exponential demand for wireless radio spectrum resources. The current fixed spectrum assignment has caused some portions of the radio spectrum to be heavily used whereas others to be scarcely used. This has resulted in underutilization of spectrum resources, and, hence has demanded the need for solutions to address the spectrum scarcity problem. Cognitive radio was proposed as one of the solutions. One of the techniques involved in cognitive radio is the dynamic spectrum access technique. This technique requires the identification of free channels in order to allow secondary users to exploit the spectrum resources. The process of identification of free channels is known as radio spectrum scanning, which is performed by sensing a particular channel in the radio spectrum to determine the presence or absence of a signal. In most of existing studies, the frequentist technique using energy detection with fixed threshold was used to scan the radio spectrum. However, this method comes with a major drawbacks. First, energy detection is unable to distinguish between signals and noise and suffer for high false detection rates. Second, energy detection has high false alarm probability. Finally, frequentist techniques are subject to uncertainty and do not provide real time monitoring/sensing. Therefore, the goal of this thesis is to develop a more efficient scanning technique that deals with uncertainty and scans the radio spectrum in real time and determines its occupancy levels. An enhanced spectrum scanning approach is developed using an efficient spectrum sensing technique: an uncertainty handling Bayesian model along with a Bayesian inferential approach. Two Bayesian models are developed: 1) a simplified model, and 2) an improved model to incorporate the Bayesian inferential approach to estimate the spectrum occupancy level. The performance evaluation of the proposed technique has been done using simulations as well as real experiments. For this purpose, two metrics were used: probability of detection and probability of false alarm. Furthermore, the efficiency of the proposed technique was compared to the efficiency of the frequentist technique, which uses only a spectrum sensing technique to identify the occupancy of the spectrum channels. As expected significant improvements in the spectrum occupancy measurements have been observed with the proposed Bayesian inference method

Advanced methods in automatic modulation classification for emerging technologies

Modulation classification (MC) is of large importance in both military and commercial communication applications. It is a challenging problem, especially in non-cooperative wireless environments, where channel fading and no prior knowledge on the incoming signal are major factors that deteriorate the reception performance. Although the average likelihood ratio test method can provide an optimal solution to the MC problem with unknown parameters, it suffers from high computational complexity and in some cases mathematical intractability. Instead, in this research, an array-based quasi-hybrid likelihood ratio test (qHLRT) algorithm is proposed, which depicts two major advantages. First, it is simple yet accurate enough parameter estimation with reduced complexity. Second the incorporation of antenna arrays offers an effective ability to combat fading. Furthermore, a practical array-based qHLRT classifier scheme is implemented, which applies maximal ratio combining (MRC) to increase the accuracy of both carrier frequency offset (CFO) estimation and likelihood function calculation in channel fading. In fact, double CFO estimations are executed in this classifier. With the first the unknown CFO, phase offsets and amplitudes are estimated as prerequisite for MRC operation. Then, MRC is performed using these estimates, followed by a second CFO estimator. Since the input of the second CFO estimator is the output of the MRC, fading effects on the incoming signals are removed significantly and signal-to-noise ratio (SNR) is augmented. As a result, a more accurate CFO estimate is obtained. Consequently, the overall classification performance is improved, especially in low SNR environment. Recently, many state-of-the-arts communication technologies, such as orthogonal frequency division multiplexing (OFDM) modulations, have been emerging. The need for distinguishing OFDM signal from single carrier has become obvious. Besides, some vital parameters of OFDM signals should be extracted for further processing. In comparison to the research on MC for single carrier single antenna transmission, much less attention has been paid to the MC for emerging modulation methods. A comprehensive classification system is proposed for recognizing the OFDM signal and extracting its parameters. An automatic OFDM modulation classifier is proposed, which is based on the goodness-of-fittest. Since OFDM signal is Gaussian, Cramer-von Mises technique, working on the empirical distribution function, has been applied to test the presence of the normality. Numerical results show that such approach can successfully identify OFDM signals from single carrier modulations over a wide SNR range. Moreover, the proposed scheme can provide the acceptable performance when frequency-selective fading is present. Correlation test is then applied to estimate OFDM cyclic prefix duration. A two-phase searching scheme, which is based on Fast Fourier Transform (FFT) as well as Gaussianity test, is devised to detect the number of subcarriers. In the first phase, a coarse search is carried out iteratively. The exact number of subcarriers is determined by the fine tune in the second phase. Both analytical work and numerical results are presented to verify the efficiency of the proposed scheme

Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems

In this paper, we propose a MIMO receiver algorithm that exploits {\em channel hardening} that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the ${\bf H}^H{\bf H}$ matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix ${\bf H}$ increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes ${\bf H}^T{\bf H}{\bf x}$, where ${\bf x}$ is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the ${\bf H}^T{\bf H}$ matrix. We also propose a simple estimation scheme which directly obtains an estimate of ${\bf H}^T{\bf H}$ (instead of an estimate of ${\bf H}$), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the {\em channel hardening-exploiting message passing (CHEMP)} receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of ${\bf H}$. We also present a convergence analysis of the proposed MPD algorithm. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes

UWB communication systems acquisition at symbol rate sampling for IEEE standard channel models

For ultra-wideband (UWB) communications, acquisition is challenging. The reason is from the ultra short pulse shape and ultra dense multipath interference. Ultra short pulse indicates the acquisition region is very narrow. Sampling is another challenge for UWB design due to the need for ultra high speed analog-to digital converter.A sub-optimum and under-sampling scheme using pilot codes as transmitted reference is proposed here for acquisition. The sampling rate for the receiver is at the symbol rate. A new architecture, the reference aided matched filter is studied in this project. The reference aided matched filter method avoids using complex rake receiver to estimate channel parameters and high sampling rate for interpolation. A limited number of matched filters are used as a filter bank to search for the strongest path. Timing offset for acquisition is then estimated and passed to an advanced verification algorithm. For optimum performance of acquisition, the adaptive post detection integration is proposed to solve the problem from dense inter-symbol interference during the acquisition. A low-complex early-late gate tracking loop is one element of the adaptive post detection integration. This tracking scheme assists in improving acquisition accuracy. The proposed scheme is evaluated using Matlab Simulink simulations in term of mean acquisition time, system performance and false alarm. Simulation results show proposed algorithm is very effective in ultra dense multipath channels. This research proves reference aided acquisition with tracking loop is promising in UWB application

A selective control information detection scheme for OFDM receivers

In wireless communications, both control information and payload (user-data) are concurrently transmitted and required to be successfully recovered. This paper focuses on block-level detection, which is applicable for detecting transmitted control information, particularly when this information is selected or chosen from a finite set of information that are known at both transmitting and receiving devices. Using an orthogonal frequency division multiplexing architecture, this paper investigates and evaluates the performance of a time-domain decision criterion in comparison with a form of Maximum Likelihood (ML) estimation method. Unlike the ML method, the proposed time-domain detection technique requires no channel estimation as it uses the correlation (in the time-domain) that exists between the received and the transmitted selective information as a means of detection. In comparison with the ML method, results show that the proposed method offers improved detection performance, particularly when the control information consists of at least 16. However, the implementation of the proposed method requires a slightly increased number of mathematical computations