Methods for Model Complexity Reduction for the Nonlinear Calibration of Amplifiers Using Volterra Kernels

Volterra models allow modeling nonlinear dynamical systems, even though they require the estimation of a large number of parameters and have, consequently, potentially large computational costs. The pruning of Volterra models is thus of fundamental importance to reduce the computational costs of nonlinear calibration, and improve stability and speed, while preserving accuracy. Several techniques (LASSO, DOMP and OBS) and their variants (WLASSO and OBD) are compared in this paper for the experimental calibration of an IF amplifier. The results show that Volterra models can be simplified, yielding models that are 4–5 times sparser, with a limited impact on accuracy. About 6 dB of improved Error Vector Magnitude (EVM) is obtained, improving the dynamic range of the amplifiers. The Symbol Error Rate (SER) is greatly reduced by calibration at a large input power, and pruning reduces the model complexity without hindering SER. Hence, pruning allows improving the dynamic range of the amplifier, with almost an order of magnitude reduction in model complexity. We propose the OBS technique, used in the neural network field, in conjunction with the better known DOMP technique, to prune the model with the best accuracy. The simulations show, in fact, that the OBS and DOMP techniques outperform the others, and OBD, LASSO and WLASSO are, in turn, less efficient. A methodology for pruning in the complex domain is described, based on the Frisch–Waugh–Lovell (FWL) theorem, to separate the linear and nonlinear sections of the model. This is essential because linear models are used for equalization and cannot be pruned to preserve model generality vis-a-vis channel variations, whereas nonlinear models must be pruned as much as possible to minimize the computational overhead. This methodology can be extended to models other than the Volterra one, as the only conditions we impose on the nonlinear model are that it is feedforward and linear in the parameters

Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture

Real-time asset tracking in indoor mass production manufacturing environments can reduce losses associated with pausing a production line to locate an asset. Complemented by monitored contextual information, e.g. machine power usage, it can provide smart information, such as which components have been machined by a worn or damaged tool. Although sensor based Internet of Things (IoT) positioning has been developed, there are still key challenges when benchmarked approaches concentrate on precision, using computationally expensive filtering and iterative statistical or heuristic algorithms, as a trade-off for timeliness and scalability. Precise but high-cost hardware systems and invasive infrastructures of wired devices also pose implementation issues in the Industrial IoT (IIoT). Wireless, selfpowered sensors are integrated in this paper, using a novel, communication-economical RSSI/ToF ranging method in a proposed semantic IIoT architecture. Annotated data collection ensures accessibility, scalable knowledge discovery and flexibility to changes in consumer and business requirements. Deployed at a working indoor industrial facility the system demonstrated comparable RMS ranging accuracy (ToF 6m and RSSI 5.1m with 40m range) to existing systems tested in non-industrial environments and a 12.6-13.8m mean positioning accuracy

REAL-TIME ADAPTIVE PULSE COMPRESSION ON RECONFIGURABLE, SYSTEM-ON-CHIP (SOC) PLATFORMS

New radar applications need to perform complex algorithms and process a large quantity of data to generate useful information for the users. This situation has motivated the search for better processing solutions that include low-power high-performance processors, efficient algorithms, and high-speed interfaces. In this work, hardware implementation of adaptive pulse compression algorithms for real-time transceiver optimization is presented, and is based on a System-on-Chip architecture for reconfigurable hardware devices. This study also evaluates the performance of dedicated coprocessors as hardware accelerator units to speed up and improve the computation of computing-intensive tasks such matrix multiplication and matrix inversion, which are essential units to solve the covariance matrix. The tradeoffs between latency and hardware utilization are also presented. Moreover, the system architecture takes advantage of the embedded processor, which is interconnected with the logic resources through high-performance buses, to perform floating-point operations, control the processing blocks, and communicate with an external PC through a customized software interface. The overall system functionality is demonstrated and tested for real-time operations using a Ku-band testbed together with a low-cost channel emulator for different types of waveforms

Multi-modal probabilistic indoor localization on a smartphone

The satellite-based Global Positioning System (GPS) provides robust localization on smartphones outdoors. In indoor environments, however, no system is close to achieving a similar level of ubiquity, with existing solutions offering different trade-offs in terms of accuracy, robustness and cost. In this paper, we develop a multi-modal positioning system, targeted at smartphones, which aims to get the best out of each of its constituent modalities. More precisely, we combine Bluetooth low energy (BLE) beacons, round-trip-time (RTT) enabled WiFi access points and the smartphone’s inertial measurement unit (IMU) to provide a cheap robust localization system that, unlike fingerprinting methods, requires no pre-training. To do this, we use a probabilistic algorithm based on a conditional random field (CRF). We show how to incorporate sparse visual information to improve the accuracy of our system, using pose estimation from pre-scanned visual landmarks, to calibrate the system online. Our method achieves an accuracy of around 2 meters on two realistic datasets, outperforming other distance-based localization approaches. We also compare our approach with an ultra-wideband (UWB) system. While we do not match the performance of UWB, our system is cheap, smartphone compatible and provides satisfactory performance for many applications

A Study of Environment Noise in Ultra-Wideband Indoor Position Tracking

This work is motivated by the problem of improving the accuracy of indoor ultra-wideband (UWB) position tracking through the study of the environment noise that affects such a system. Current systems can provide accuracy in the range of 30-100 cm in a small building, suitable for applications that require rough room-level precision such as asset tracking and surveillance. Our long-term goal is to improve the accuracy to 1 cm or better, expanding potential applications to telepresence, augmented reality, training and entertainment. This work investigates the possibility of systematically observing the measurement noise of an UWB position tracking system and building a map of it throughout a facility. In order to understand the effect of environment noise on UWB indoor positioning and in turn filter out the effects of this noise, it is important to have an idea of what this measurement noise looks like in a real world scenario. In this work, an understanding of the measurement noise is gained by taking many measurements using a commercially-available UWB positioning system installed in a real world scenario and analyzing these measurements in various ways. To the author\u27s knowledge, no one has used such an exhaustive approach to analyze measurement noise in UWB indoor positioning. The results of this work show that the measurement noise that affects a UWB indoor position tracking system can be effectively modeled using a weighted sum of Gaussians, is stable over time and is locally similar. Furthermore, a particle filter augmented with a measurement noise map is proposed to improve position tracking accuracy. Finally, a metric is proposed that can be used to quantify expected system performance based on sensor location, sensor orientation and facility floorplan. Using this metric, a procedure is developed to determine the parameters, i.e. sensor position, sensor orientation and potentially others, of the physical installation of the UWB tracking system that will produce minimum measurement error based on sensor geometry and physical facility constraints

mm-Wave Data Transmission and Measurement Techniques: A Holistic Approach

The ever-increasing demand on data services places unprecedented technical requirements on networks capacity. With wireless systems having significant roles in broadband delivery, innovative approaches to their development are imperative. By leveraging new spectral resources available at millimeter-wave (mm-wave) frequencies, future systems can utilize new signal structures and new system architectures in order to achieve long-term sustainable solutions.This thesis proposes the holistic development of efficient and cost-effective techniques and systems which make high-speed data transmission at mm-wave feasible. In this paradigm, system designs, signal processing, and measurement techniques work toward a single goal; to achieve satisfactory system level key performance indicators (KPIs). Two intimately-related objectives are simultaneously addressed: the realization of efficient mm-wave data transmission and the development of measurement techniques to enable and assist the design and evaluation of mm-wave circuits.The standard approach to increase spectral efficiency is to increase the modulation order at the cost of higher transmission power. To improve upon this, a signal structure called spectrally efficient frequency division multiplexing (SEFDM) is utilized. SEFDM adds an additional dimension of continuously tunable spectral efficiency enhancement. Two new variants of SEFDM are implemented and experimentally demonstrated, where both variants are shown to outperform standard signals.A low-cost low-complexity mm-wave transmitter architecture is proposed and experimentally demonstrated. A simple phase retarder predistorter and a frequency multiplier are utilized to successfully generate spectrally efficient mm-wave signals while simultaneously mitigating various issues found in conventional mm-wave systems.A measurement technique to characterize circuits and components under antenna array mutual coupling effects is proposed and demonstrated. With minimal setup requirement, the technique effectively and conveniently maps prescribed transmission scenarios to the measurement environment and offers evaluations of the components in terms of relevant KPIs in addition to conventional metrics.Finally, a technique to estimate transmission and reflection coefficients is proposed and demonstrated. In one variant, the technique enables the coefficients to be estimated using wideband modulated signals, suitable for implementation in measurements performed under real usage scenarios. In another variant, the technique enhances the precision of noisy S-parameter measurements, suitable for characterizations of wideband mm-wave components

Advanced image reconstruction in parallel magnetic resonance imaging : constraints and solutions.

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references.Imaging speed is a crucial consideration for magnetic resonance imaging (MRI). The speed of conventional MRI is limited by hardware performance and physiological safety measures. "Parallel" MRI is a new technique that circumvents these limitations by utilizing arrays of radiofrequency detector coils to acquire data in parallel, thereby enabling still higher imaging speeds. In parallel MRI, coil arrays are used to accomplish part of the spatial encoding that was traditionally performed by magnetic field gradients alone. MR signal data acquired with coil arrays are spatially encoded with the distinct reception patterns of the individual coil elements. T[he quality of parallel MR images is dictated by the accuracy and efficiency of an image reconstruction (decoding) strategy. This thesis formulates the spatial encoding and decoding of parallel MRI as a generalized linear inverse problem. Under this linear algebraic framework, theoretical and empirical limits on the performance of parallel MR image reconstructions are characterized, and solutions are proposed to facilitate routine clinical and research applications. Each research study presented in this thesis addresses one or more elements in the inverse problem, and the studies are collectively arranged to reflect three progressive stages in solving the inverse problem: 1) determining the encoding matrix, 2) computing a matrix inverse, 3) characterizing the error involved. First, a self-calibrating strategy is proposed which uses non-Cartesian trajectories to automatically determine coil sensitivities without the need of an external scan or modification of data acquisition, guaranteeing an accurate formulation of the encoding matrix.(cont.) Second, two matrix inversion strategies are presented which, respectively, exploit physical properties of coil encoding and the phase information of the magnetization. While the former allows stable and distributable matrix inversion using the k-space locality principle, the latter integrates parallel image reconstruction with conjugate symmetry. Third, a numerical strategy is presented for computing noise statistics of parallel MRI techniques which involve magnitude image combination, enabling quantitative image comparison. In addition, fundamental limits on the performance of parallel image reconstruction are derived using the Cramer-Rao bounds. Lastly, the practical applications of techniques developed in this thesis are demonstrated by a case study in improved coronary angiography.Ph.D