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No description available.

This study examined the bias and precision of four residualized variable validity estimates (C0, C1, C2, C3) across a number of study conditions. Validity estimates that considered measurement error, correlations among error scores, and correlations between error scores and true scores (C3) performed the best, yielding no estimates that were practically significantly different than their respective population parameters, across study conditions. Validity estimates that considered measurement error and correlations among error scores (C2) did a good job in yielding unbiased, valid, and precise results. Only in a select number of study conditions were C2 estimates unable to be computed or produced results that had sufficient variance to affect interpretation of results. Validity estimates based on observed scores (C0) fared well in producing valid, precise, and unbiased results. Validity estimates based on observed scores that were only corrected for measurement error (C1) performed the worst. Not only did they not reliably produce estimates even when the level of modeled correlated error was low, C1 produced values higher than the theoretical limit of 1.0 across a number of study conditions. Estimates based on C1 also produced the greatest number of conditions that were practically significantly different than their population parameters.

Recently, the difficulty of placing multiple antennas onto a mobile terminal to exploit more diversity has been solved by using the cooperative communication technique, in which several relay nodes with a single antenna partner with each other to serve as virtual multiple antennas for providing the spatial diversity. Many existing researches in cooperative communication focuses on designing relay strategies to achieve better communication performance. However, most of their designs require the channel state information (CSI) being perfectly known. Unfortunately, CSI is generally unknown in practice. Therefore, before getting benefits brought by the relay-assisted network, it is necessary to obtain accurate channel state information (CSI) at the destination or relays. In this thesis, we also consider the training design for channel estimation in the AF relay network. The involvement of multiple relay nodes to exploit space diversity in cooperative communications requires sophisticated and complicated protocols, which poses a difficulty in avoiding all possible misbehaving relay nodes. Therefore, the channel estimation scheme in cooperative communication network needs to be robust against the possible relay misbehaviors. However, most prior works focused on developing channel estimation schemes by assuming perfect relayassisted communication protocol. By contrast, this work focuses on designing robust channel estimation schemes to combat the possible presence of the relay misbehaviors. Besides considering the robust design against relay misbehaviors, this work also considers more general channel model when designing the training sequence and channel estimation scheme. Specifically, in contrast to assume independent channels across relays, this thesis considers the correlated channels in both phases and the correlated noises in the first phase. Overall, the main problem of this work is to design robust channel estimation and training sequences against relay misbehaviors when the communication channels within the cooperative network are not restricted to be independent.

In multi-sensor data fusion target tracking system, using information filtering can implement distributed location with uncorrelated measurement noises, but the measurement noises of different sensors are often correlated. If measurement noises are correlated, the covariance matrix of measurement noises is not a diagonal matrix. We can not use information filtering to implement distributed location with correlated measurement noises. By using the matrix theory, the covariance matrix of measurement noises can be transformed to a diagonal matrix. The observation models are transformed to new observation models, and the multi-sensor measurements with correlated measurement noises are transformed to equivalent pseudo ones with uncorrelated measurement noises. There are many methods in the matrix theory, we use Cholesky fatorization in this thesis. Cholesky fatorization is from Gaussian elimination, and there are many advantages in the computation process.However, the observation models need to be transformed to new observation models, and the measurement datas for the approach need to be separated and recombined. For measurement datas being separated and recombined, every sensor must communicate with each other. In practice, one sensor does not directly communicate with other sensors except its direct neighbors. By formulating the Cholesky factorization process, we present architectures which are applied in wireless distributed location. Distributed architectures with clustered nodes are proposed to achieve measurement exchange and information sharing for wireless location and target tracking. With limited times of data exchanges between clustered nodes, the correlated noise components in the measurements are transformed into uncorrelated ones through the Cholesky process, and the resultant information can be directly shared and processed by the derived extended information filters at the nodes in the distributed system. Hybrid TDOA/AOA wireless location systems with the NLOS error effects are used as examples in investigating the distributed information architecture. Simulation results show that the proposed distributed information processing and data fusion architecture effectively achieve improved location and tracking accuracy.

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No description available.

While digital coding provides us with numerous benefits, it does have some shortcomings, namely the threshold and the leveling-off effects. These problems do not occur with the use of analog coding, however. Hybrid digital-analog coding systems use both digital and analog coding at the same time to take advantage of the strengths of both coding schemes, while avoiding the problems associated with each. In this work, we propose an HDA system for coding correlates sources. We find the optimal power allocation between the digital and analog parts, and verify the advantages of the suggested system by comparing it to its digital and analog counterparts under different scenarios. / October 2016

Fluorescence lifetime sensing using time correlated single photon counting (TCSPC) is a key analytical tool for molecular and cell biology research, medical diagnosis and pharmacological development. However, commercially available TCSPC equipment is bulky, expensive and power hungry, typically requiring iterative software post-processing to calculate the fluorescence lifetime. Furthermore, the technique is restrictively slow due to a low photon throughput limit which is necessary to avoid distortions caused by TCSPC pile-up. An investigation into CMOS compatible multimodule architectures to miniaturise the standard TCSPC set up, allow an increase in photon throughput by overcoming the TCSPC pile-up limit, and provide fluorescence lifetime calculations in real-time is presented. The investigation verifies the operation of the architectures and leads to the selection of optimal parameters for the number of detectors and timing channels required to overcome the TCSPC pile-up limit by at least an order of magnitude. The parameters are used to implement a low power miniaturised sensor in a 130 nm CMOS process, combining single photon detection, multiple channel timing and embedded pre-processing of the fluorescence lifetime, all within a silicon area of < 2 mm2. Single photon detection is achieved using an array of single photon avalanche diodes (SPADs) arranged in a digital silicon photomultiplier (SiPM) architecture with a 10 % fill-factor and a compressed 250 ps output pulse, which provides a photon throughput of > 700 MHz. An array of time-interleaved time-to-digital converters (TI-TDCs) with 50 ps resolution and no processing dead-time records up to eight photon events during each excitation period, significantly reducing the effect of TCSPC pile-up. The TCSPC data is then processed using an embedded centre-of-mass method (CMM) pre-calculation to produce single exponential fluorescence lifetime estimations in real-time. The combination of high photon throughput and real-time calculation enables advances in applications such as fluorescence lifetime imaging microscopy (FLIM) and time domain fluorescence lifetime activated cell sorting. To demonstrate this, the device is validated in practical bulk sample fluorescence lifetime, FLIM and simulated flow based experiments. Photon throughputs in excess of the excitation frequency are demonstrated for a range of organic and inorganic fluorophores for minimal error in lifetime calculation by CMM (< 5 %).

Since the rediscovery of Mendel's law in 1900, there have been many studies of inheritance in wheat. Of these rather numerous investigations, however, only a few have consisted of correlated studies of various characters on the same plant. Inheritance of awns and of spike density have received some attention but the studies have hardly more than indicated the problem. Both have been thought by some to be rather simple in their inheritance, and in some crosses without doubt such is the case, but lately there has come a recognition of considerable complexity. Density. As here used, "density" refers to compactness of the spike. Compact spikes have short rachis internodes and the spikelets are close together. Such