There has been an increasing interest in uncovering smuggled nuclear materials associated with the War on Terror. Detection of special nuclear materials hidden in cargo containers is a major challenge in national and international security. We propose a new physics-based method to determine the presence of the spectral signature of one or more nuclides from a poorly resolved spectra with weak signatures. The method is different from traditional methods that rely primarily on peak finding algorithms. The new approach considers each of the signatures in the library to be a linear combination of subspectra. These subspectra are obtained by assuming a signature consisting of just one of the unique gamma rays emitted by the nuclei. We propose a Poisson regression model for deducing which nuclei are present in the observed spectrum. In recognition that a radiation source generally comprises few nuclear materials, the underlying Poisson model is sparse, i.e. most of the regression coefficients are zero (positive coefficients correspond to the presence of nuclear materials). We develop an iterative algorithm for a penalized likelihood estimation that prompts sparsity. We illustrate the efficacy of the proposed method by simulations using a variety of poorly resolved, low signal-to-noise ratio (SNR) situations, which show that the proposed approach enjoys excellent empirical performance even with SNR as low as to -15db. The proposed method is shown to be variable-selection consistent, in the framework of increasing detection time and under mild regularity conditions. We study the problem of testing for shielding, i.e. the presence of intervening materials that attenuate the gamma ray signal. We show that, as detection time increases to infinity, the Lagrange multiplier test, the likelihood ratio test and Wald test are asymptotically equivalent, under the null hypothesis, and their asymptotic null distribution is Chi-square. We also derived the local power of these tests. We also develop a nonparametric approach for detecting spectra indicative of the presence of SNM. This approach characterizes the shape change in a spectrum from background radiation. We do this by proposing a dissimilarity function that characterizes the complete shape change of a spectrum from the background, over all energy channels. We derive the null asymptotic test distributions in terms of functionals of the Brownian bridge. Simulation results show that the proposed approach is very powerful and promising for detecting weak signals. It is able to accurately detect weak signals with SNR as low as -37db.

Abstract not available

<p>The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously."</p><p>The analysis of such a large amount of data on a case by case basis is impossible with the resources available. Therefore a quantitative, data mining procedure has been developed to improve the focus of the clinical signal detection process. The method used, is referred to as the BCPNN (Bayesian Confidence Propagation Neural Network). This not only assists in the early detection of adverse drug reactions (ADRs) but also further analysis of such signals. The method uses Bayesian statistical principles to quantify apparent dependencies in the data set. This quantifies the degree to which a specific drug- ADR combination is different from a background (in this case the WHO database). The measure of disproportionality used, is referred to as the Information Component (IC) because of its' origins in Information Theory. A confidence interval is calculated for the IC of each combination. A neural network approach allows all drug-ADR combinations in the database to be analysed in an automated manner. Evaluations of the effectiveness of the BCPNN in signal detection are described.</p><p>To compare how a drug association compares in unexpectedness to related drugs, which might be used for the same clinical indication, the method is extended to consideration of groups of drugs. The benefits and limitations of this approach are discussed with examples of known group effects (ACE inhibitors - coughing and antihistamines - heart rate and rhythm disorders.) An example of a clinically important, novel signal found using the BCPNN approach is also presented. The signal of antipsychotics linked with heart muscle disorder was detected using the BCPNN and reported.</p><p>The BCPNN is now routinely used in signal detection to search single drug - single ADR combinations. The extension of the BCPNN to discover 'unexpected' complex dependencies between groups of drugs and adverse reactions is described. A recurrent neural network method has been developed for finding complex patterns in incomplete and noisy data sets. The method is demonstrated on an artificial test set. Implementation on real data is demonstrated by examining the pattern of adverse reactions highlighted for the drug haloperidol. Clinically important, complex relationships in this kind of data are previously unexplored.</p><p>The BCPNN method has been shown and tested for use in routine signal detection, refining signals and in finding complex patterns. The usefulness of the output is influenced by the quality of the data in the database. Therefore, this method should be used to detect, rather than evaluate signals. The need for clinical analyses of case series remains crucial.</p>

Multiple-input multiple-output (MIMO) antennas can be exploited to provide high data rate using a limited bandwidth through multiplexing gain. MIMO combined with orthogonal frequency division multiplexing (OFDM) could potentially provide high data rate and high spectral efficiency in frequency-selective fading channels. MIMO-OFDM technology has been widely employed in modern communication systems, such as Wireless Local Area Network (WLAN), Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). However, most of the conventional schemes either are computationally prohibitive or underutilize the full performance gain provided by the inherent merits of MIMO and OFDM techniques. In the first part of this dissertation, we firstly study the channel matrix inversion which is commonly required in various MIMO detection schemes. An algorithm that exploits second-order extrapolation in the time domain is proposed to efficiently reduce the computational complexity. This algorithm can be applied to both linear detection and non-linear detection such as ordered successive interference cancellation (OSIC) while maintaining the system performance. Secondly, we study the complexity reduction for Lattice Reduction Aided Detection (LRAD) of MIMO-OFDM systems. We propose an algorithm that exploits the inherent feature of unimodular transformation matrix that remains the same for relatively highly correlated frequency components. This algorithm effectively eliminates the redundant brute-force lattice reduction iterations among adjacent subcarriers. Thirdly, we analyze the impact of channel coherence bandwidth on two LRAD algorithms. Analytical and simulation results demonstrate that carefully setting the initial calculation interval according to the coherence bandwidth is essential for both algorithms. The second part of this dissertation focuses on efficient multi-user (MU) scheduling and coordination for the uplink of WLAN that uses MIMO-OFDM techniques. On one hand, conventional MU-MIMO medium access control (MAC) protocols require large overhead, which lowers the performance gain of concurrent transmissions rendered by the multi-packet reception (MPR) capability of MIMO systems. Therefore, an efficient MU-MIMO uplink MAC scheduling scheme is proposed for future WLAN. On the other hand, single-user (SU) MIMO achieves multiplexing gain in the physical (PHY) layer and MU-MIMO achieves multiplexing gain in the MAC layer. In addition, the average throughput of the system varies depending on the number of antennas and users, average payload sizes, and signal-to-noise-ratios (SNRs). A comparison on the performance between SU-MIMO and MU-MIMO schemes for WLAN uplink is hence conducted. Simulation results indicate that a dynamic switch between the SU-MIMO and MU-MIMO is of significance for higher network throughput of WLAN uplink. / Graduation date: 2013

This research concerns analysis of system capacity, development of adaptive transmission schemes with known channel state information at the transmitter (CSIT) and design of new signal detection and channel estimation schemes with low complexity in some multiple antenna systems. We first analyze the sum-rate capacity of the downlink of a cellular system with multiple transmit antennas and multiple receive antennas assuming perfect CSIT. We evaluate the ergodic sum-rate capacity and show how the sum-rate capacity increases as the number of users and the number of receive antennas increases. We develop upper and lower bounds on the sum-rate capacity and study various adaptive MIMO schemes to achieve, or approach, the sum-rate capacity. Next, we study the minimum outage probability transmission schemes in a multiple-input-single-output (MISO) flat fading channel assuming partial CSIT. Considering two special cases: the mean feedback and the covariance feedback, we derive the optimum spatial transmission directions and show that the associated optimum power allocation scheme, which minimizes the outage probability, is closely related to the target rate and the accuracy of the CSIT. Since CSIT is obtained at the cost of feedback bandwidth, we also consider optimal allocation of bandwidth between the data channel and the feedback channel in order to maximize the average throughput of the data channel in MISO, flat fading, frequency division duplex (FDD) systems. We show that beamforming based on feedback CSI can achieve an average rate larger than the capacity without CSIT under a wide range of mobility conditions. We next study a SAGE-aided List-BLAST detection scheme for MIMO systems which can achieve performance close to that of the maximum-likelihood detector with low complexity. Finally, we apply the EM and SAGE algorithms in channel estimation for OFDM systems with multiple transmit antennas and compare them with a recently proposed least-squares based estimation algorithm. The EM and SAGE algorithms partition the problem of estimating a multi-input channel into independent channel estimation for each transmit-receive antenna pair, therefore avoiding the matrix inversion encountered in the joint least-squares estimation.

The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously." The analysis of such a large amount of data on a case by case basis is impossible with the resources available. Therefore a quantitative, data mining procedure has been developed to improve the focus of the clinical signal detection process. The method used, is referred to as the BCPNN (Bayesian Confidence Propagation Neural Network). This not only assists in the early detection of adverse drug reactions (ADRs) but also further analysis of such signals. The method uses Bayesian statistical principles to quantify apparent dependencies in the data set. This quantifies the degree to which a specific drug- ADR combination is different from a background (in this case the WHO database). The measure of disproportionality used, is referred to as the Information Component (IC) because of its' origins in Information Theory. A confidence interval is calculated for the IC of each combination. A neural network approach allows all drug-ADR combinations in the database to be analysed in an automated manner. Evaluations of the effectiveness of the BCPNN in signal detection are described. To compare how a drug association compares in unexpectedness to related drugs, which might be used for the same clinical indication, the method is extended to consideration of groups of drugs. The benefits and limitations of this approach are discussed with examples of known group effects (ACE inhibitors - coughing and antihistamines - heart rate and rhythm disorders.) An example of a clinically important, novel signal found using the BCPNN approach is also presented. The signal of antipsychotics linked with heart muscle disorder was detected using the BCPNN and reported. The BCPNN is now routinely used in signal detection to search single drug - single ADR combinations. The extension of the BCPNN to discover 'unexpected' complex dependencies between groups of drugs and adverse reactions is described. A recurrent neural network method has been developed for finding complex patterns in incomplete and noisy data sets. The method is demonstrated on an artificial test set. Implementation on real data is demonstrated by examining the pattern of adverse reactions highlighted for the drug haloperidol. Clinically important, complex relationships in this kind of data are previously unexplored. The BCPNN method has been shown and tested for use in routine signal detection, refining signals and in finding complex patterns. The usefulness of the output is influenced by the quality of the data in the database. Therefore, this method should be used to detect, rather than evaluate signals. The need for clinical analyses of case series remains crucial.

Empathy and memory are two central aspects that make us human. In the following work, I combined these two areas with genetics and asked how they would interrelate against the background of age. At study, 28 younger and 32 older adults went through an item recognition/source memory paradigm with neutral and emotional (i.e., angry) faces. Dispositional empathy was measured using the Interpersonal Reactivity Index (IRI) and the Empathy Quotient (EQ). Further, 13 single-nucleotide polymorphisms (SNPs) from mainly oxytocin receptors (OXTR) were extracted. Results revealed that older adults had a lower score on the Fantasy dimension of the IRI. Younger and older adults did not differ in hit rate, but older adults showed a higher false alarm rate for neutral source memory. For emotional item recognition, older adults showed a higher liberal response bias whereas, for neutral source memory, younger adults showed a higher conservative response bias. For both memory and empathy, main effects and age interactions were found for OXTR rs237887, rs237897, rs2254298, rs4564970, and rs4686302. These findings illustrated the close interconnectivity of memory, empathy, and genetics over the human life span.

A new algorithm is proposed which provides a sub-optimum near-far resistant pattern for correlation with a known signal in a spread-spectrum multiple access environment with additive white gaussian noise (AWGN). Only the patterns and respective delays of the K-1 interfering users are required. The technique does not require the inversion of a cross-correlation matrix. The technique can be easily extended to as many users as desired using a simple recursion equation. The computational complexity is O(K²) for each user to be decoded. It is shown that this method provides the same results as the "one-shot" method proposed by Verdu and Lupas. Also shown is a new array architecture for implementing this new solution using delta-sigma modulation and a correlator for non-binary patterns that takes advantage of the digitized Al: signals. Simulation results are presented which show the algorithm and correlator to be implementable in VLSI technology. This approach allows processing of the received signal in real-time with a delay of O(.K) bit periods per user. A modification of the algorithm is examined which allows further reduction of complexity at the expense of reduced performance. / Graduation date: 1992

The objective of the proposed research is to develop a new algorithm for range and Doppler ambiguity resolution in radar detection data using L1 minimization methods for sparse signals and to investigate the properties of such techniques. This novel approach to ambiguity resolution makes use of the sparse measurement structure of the post-detection data in multiple pulse repetition frequency radars and the resulting equivalence of the computationally intractable L0 minimization and the surrogate L1 minimization methods. The ambiguity resolution problem is cast as a linear system of equations which is then solved for the unique sparse solution in the absence of errors. It is shown that the new technique successfully resolves range and Doppler ambiguities and the recovery is exact in the ideal case of no errors in the system. The behavior of the technique is then investigated in the presence of real world data errors encountered in radar measurement and detection process. Examples of such errors include blind zone effects, collisions, false alarms and missed detections. It is shown that the mathematical model consisting of a linear system of equations developed for the ideal case can be adjusted to account for data errors. Empirical results show that the L1 minimization approach also works well in the presence of errors with minor extensions to the algorithm. Several examples are presented to demonstrate the successful implementation of the new technique for range and Doppler ambiguity resolution in pulse Doppler radars.

Active integrated antennas (AIAs) are divided into oscillator type AIAs, amplifier type AIAs and frequency-conversion type AIAs. The AIAs designed in this master thesis are oscillator type. Instead of using lumped component like inductors and capacitors, I use a half-wavelength antenna as resonator. In this design, antenna is also treat as a radiated loading. According to reciprocity, antenna receives the reflection signal affected by human body movement and vital sign at the same time. This behavior is regarded as a self-injection locking oscillator. In this master thesis, active antenna is used in monitoring and contacting measurement. In monitoring measurement, active antenna and subject keep their distance. Subject random body movement affects the measured result. Contacting measurement means active antenna pastes on the subject, thus there is no relative displacement between active antenna and subject. Random body movement affect iscancelled in theory. In contacting measurement design some different body motions to test the tolerance of this measurement structure, and use correlation to cancel random body movement. The sensitivity of active antenna structure is enough to detect the vocal vibration in contacting measurement.