39 research outputs found

    An Efficient Polyphase Filter Based Resampling Method for Unifying the PRFs in SAR Data

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    Variable and higher pulse repetition frequencies (PRFs) are increasingly being used to meet the stricter requirements and complexities of current airborne and spaceborne synthetic aperture radar (SAR) systems associated with higher resolution and wider area products. POLYPHASE, the proposed resampling scheme, downsamples and unifies variable PRFs within a single look complex (SLC) SAR acquisition and across a repeat pass sequence of acquisitions down to an effective lower PRF. A sparsity condition of the received SAR data ensures that the uniformly resampled data approximates the spectral properties of a decimated densely sampled version of the received SAR data. While experiments conducted with both synthetically generated and real airborne SAR data show that POLYPHASE retains comparable performance to the state-of-the-art BLUI scheme in image quality, a polyphase filter-based implementation of POLYPHASE offers significant computational savings for arbitrary (not necessarily periodic) input PRF variations, thus allowing fully on-board, in-place, and real-time implementation

    Development of Methodologies for Diffusion-weighted Magnetic Resonance Imaging at High Field Strength

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    Diffusion-weighted imaging of small animals at high field strengths is a challenging prospect due to its extreme sensitivity to motion. Periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) was introduced at 9.4T as an imaging method that is robust to motion and distortion. Proton density (PD)-weighted and T2-weighted PROPELLER data were generally superior to that acquired with single-shot, Cartesian and echo planar imaging-based methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio and resistance to artifacts. Simulations and experiments revealed that PROPELLER image quality was dependent on the field strength and echo times specified. In particular, PD-weighted imaging at high field led to artifacts that reduced image contrast. In PROPELLER, data are acquired in progressively rotated blades in k-space and combined on a Cartesian grid. PROPELLER with echo truncation at low spatial frequencies (PETALS) was conceived as a postprocessing method that improved contrast by reducing the overlap of k-space data from different blades with different echo times. Where the addition of diffusion weighting gradients typically leads to catastrophic motion artifacts in multi-shot sequences, diffusion-weighted PROPELLER enabled the acquisition of high quality, motion-robust data. Applications in the healthy mouse brain and abdomen at 9.4T and in stroke patients at 3T are presented. PROPELLER increases the minimum scan time by approximately 50%. Consequently, methods were explored to reduce the acquisition time. Two k-space undersampling regimes were investigated by examining image fidelity as a function of degree of undersampling. Undersampling by acquiring fewer k-space blades was shown to be more robust to motion and artifacts than undersampling by expanding the distance between successive phase encoding steps. To improve the consistency of undersampled data, the non-uniform fast Fourier transform was employed. It was found that acceleration factors of up to two could be used with minimal visual impact on image fidelity. To reduce the number of scans required for isotropic diffusion weighting, the use of rotating diffusion gradients was investigated, exploiting the rotational symmetry of the PROPELLER acquisition. Fixing the diffusion weighting direction to the individual rotating blades yielded geometry and anisotropy-dependent diffusion measurements. However, alternating the orientations of diffusion weighting with successive blades led to more accurate measurements of the apparent diffusion coefficient while halving the overall acquisition time. Optimized strategies are proposed for the use of PROPELLER in rapid high resolution imaging at high field strength

    Applied Harmonic Analysis and Data Processing

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    Massive data sets have their own architecture. Each data source has an inherent structure, which we should attempt to detect in order to utilize it for applications, such as denoising, clustering, anomaly detection, knowledge extraction, or classification. Harmonic analysis revolves around creating new structures for decomposition, rearrangement and reconstruction of operators and functions—in other words inventing and exploring new architectures for information and inference. Two previous very successful workshops on applied harmonic analysis and sparse approximation have taken place in 2012 and in 2015. This workshop was the an evolution and continuation of these workshops and intended to bring together world leading experts in applied harmonic analysis, data analysis, optimization, statistics, and machine learning to report on recent developments, and to foster new developments and collaborations

    Analog Front-End Circuits for Massive Parallel 3-D Neural Microsystems.

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    Understanding dynamics of the brain has tremendously improved due to the progress in neural recording techniques over the past five decades. The number of simultaneously recorded channels has actually doubled every 7 years, which implies that a recording system with a few thousand channels should be available in the next two decades. Nonetheless, a leap in the number of simultaneous channels has remained an unmet need due to many limitations, especially in the front-end recording integrated circuits (IC). This research has focused on increasing the number of simultaneously recorded channels and providing modular design approaches to improve the integration and expansion of 3-D recording microsystems. Three analog front-ends (AFE) have been developed using extremely low-power and small-area circuit techniques on both the circuit and system levels. The three prototypes have investigated some critical circuit challenges in power, area, interface, and modularity. The first AFE (16-channels) has optimized energy efficiency using techniques such as moderate inversion, minimized asynchronous interface for data acquisition, power-scalable sampling operation, and a wide configuration range of gain and bandwidth. Circuits in this part were designed in a 0.25μm CMOS process using a 0.9-V single supply and feature a power consumption of 4μW/channel and an energy-area efficiency of 7.51x10^15 in units of J^-1Vrms^-1mm^-2. The second AFE (128-channels) provides the next level of scaling using dc-coupled analog compression techniques to reject the electrode offset and reduce the implementation area further. Signal processing techniques were also explored to transfer some computational power outside the brain. Circuits in this part were designed in a 180nm CMOS process using a 0.5-V single supply and feature a power consumption of 2.5μW/channel, and energy-area efficiency of 30.2x10^15 J^-1Vrms^-1mm^-2. The last AFE (128-channels) shows another leap in neural recording using monolithic integration of recording circuits on the shanks of neural probes. Monolithic integration may be the most effective approach to allow simultaneous recording of more than 1,024 channels. The probe and circuits in this part were designed in a 150 nm SOI CMOS process using a 0.5-V single supply and feature a power consumption of only 1.4μW/channel and energy-area efficiency of 36.4x10^15 J^-1Vrms^-1mm^-2.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98070/1/ashmouny_1.pd

    Digital and Mixed Domain Hardware Reduction Algorithms and Implementations for Massive MIMO

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    Emerging 5G and 6G based wireless communications systems largely rely on multiple-input-multiple-output (MIMO) systems to reduce inherently extensive path losses, facilitate high data rates, and high spatial diversity. Massive MIMO systems used in mmWave and sub-THz applications consists of hundreds perhaps thousands of antenna elements at base stations. Digital beamforming techniques provide the highest flexibility and better degrees of freedom for phased antenna arrays as compared to its analog and hybrid alternatives but has the highest hardware complexity. Conventional digital beamformers at the receiver require a dedicated analog to digital converter (ADC) for every antenna element, leading to ADCs for elements. The number of ADCs is the key deterministic factor for the power consumption of an antenna array system. The digital hardware consists of fast Fourier transform (FFT) cores with a multiplier complexity of (N log2N) for an element system to generate multiple beams. It is required to reduce the mixed and digital hardware complexities in MIMO systems to reduce the cost and the power consumption, while maintaining high performance. The well-known concept has been in use for ADCs to achieve reduced complexities. An extension of the architecture to multi-dimensional domain is explored in this dissertation to implement a single port ADC to replace ADCs in an element system, using the correlation of received signals in the spatial domain. This concept has applications in conventional uniform linear arrays (ULAs) as well as in focal plane array (FPA) receivers. Our analysis has shown that sparsity in the spatio-temporal frequency domain can be exploited to reduce the number of ADCs from N to where . By using the limited field of view of practical antennas, multiple sub-arrays are combined without interferences to achieve a factor of K increment in the information carrying capacity of the ADC systems. Applications of this concept include ULAs and rectangular array systems. Experimental verifications were done for a element, 1.8 - 2.1 GHz wideband array system to sample using ADCs. This dissertation proposes that frequency division multiplexing (FDM) receiver outputs at an intermediate frequency (IF) can pack multiple (M) narrowband channels with a guard band to avoid interferences. The combined output is then sampled using a single wideband ADC and baseband channels are retrieved in the digital domain. Measurement results were obtained by employing a element, 28 GHz antenna array system to combine channels together to achieve a 75% reduction of ADC requirement. Implementation of FFT cores in the digital domain is not always exact because of the finite precision. Therefore, this dissertation explores the possibility of approximating the discrete Fourier transform (DFT) matrix to achieve reduced hardware complexities at an allowable cost of accuracy. A point approximate DFT (ADFT) core was implemented on digital hardware using radix-32 to achieve savings in cost, size, weight and power (C-SWaP) and synthesized for ASIC at 45-nm technology

    Design of energy efficient high speed I/O interfaces

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    Energy efficiency has become a key performance metric for wireline high speed I/O interfaces. Consequently, design of low power I/O interfaces has garnered large interest that has mostly been focused on active power reduction techniques at peak data rate. In practice, most systems exhibit a wide range of data transfer patterns. As a result, low energy per bit operation at peak data rate does not necessarily translate to overall low energy operation. Therefore, I/O interfaces that can scale their power consumption with data rate requirement are desirable. Rapid on-off I/O interfaces have a potential to scale power with data rate requirements without severely affecting either latency or the throughput of the I/O interface. In this work, we explore circuit techniques for designing rapid on-off high speed wireline I/O interfaces and digital fractional-N PLLs. A burst-mode transmitter suitable for rapid on-off I/O interfaces is presented that achieves 6 ns turn-on time by utilizing a fast frequency settling ring oscillator in digital multiplying delay-locked loop and a rapid on-off biasing scheme for current mode output driver. Fabricated in 90 nm CMOS process, the prototype achieves 2.29 mW/Gb/s energy efficiency at peak data rate of 8 Gb/s. A 125X (8 Gb/s to 64 Mb/s) change in effective data rate results in 67X (18.29 mW to 0.27 mW) change in transmitter power consumption corresponding to only 2X (2.29 mW/Gb/s to 4.24 mW/Gb/s) degradation in energy efficiency for 32-byte long data bursts. We also present an analytical bit error rate (BER) computation technique for this transmitter under rapid on-off operation, which uses MDLL settling measurement data in conjunction with always-on transmitter measurements. This technique indicates that the BER bathtub width for 10^(−12) BER is 0.65 UI and 0.72 UI during rapid on-off operation and always-on operation, respectively. Next, a pulse response estimation-based technique is proposed enabling burst-mode operation for baud-rate sampling receivers that operate over high loss channels. Such receivers typically employ discrete time equalization to combat inter-symbol interference. Implementation details are provided for a receiver chip, fabricated in 65nm CMOS technology, that demonstrates efficacy of the proposed technique. A low complexity pulse response estimation technique is also presented for low power receivers that do not employ discrete time equalizers. We also present techniques for implementation of highly digital fractional-N PLL employing a phase interpolator based fractional divider to improve the quantization noise shaping properties of a 1-bit ∆Σ frequency-to-digital converter. Fabricated in 65nm CMOS process, the prototype calibration-free fractional-N Type-II PLL employs the proposed frequency-to-digital converter in place of a high resolution time-to-digital converter and achieves 848 fs rms integrated jitter (1 kHz-30 MHz) and -101 dBc/Hz in-band phase noise while generating 5.054 GHz output from 31.25 MHz input

    Safety of Simultaneous Scalp and Intracranial Electroencephalography Functional Magnetic Resonance Imaging

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    Understanding the brain and its activity is one of the great challenges of modern science. Normal brain activity (cognitive processes, etc.) has been extensively studied using electroencephalography (EEG) since the 1930’s, in the form of spontaneous fluctuations in rhythms, and patterns, and in a more experimentally-driven approach in the form of event-related potentials allowing us to relate scalp voltage waveforms to brain states and behaviour. The use of EEG recorded during functional magnetic resonance imaging (EEG-fMRI) is a more recent development that has become an important tool in clinical neuroscience, for example, for the study of epileptic activity. The primary aim of this thesis is to devise a protocol in order to minimise the health risks that are associated with simultaneous scalp and intracranial EEG during fMRI (S- icEEG-fMRI). The advances in this technique will be helpful in presenting a new imaging method that will allow the measurement of brain activity with unprecedented sensitivity and coverage. However, this cannot be achieved without assessing the safety implications of such a technique. Therefore, five experiments were performed to fulfil the primary aim. First, the safety of icEEG- fMRI using body transmit RF coil was investigated to improve the results of previous attempts using a head transmit coil at 1.5T. The results of heating increases during a high-SAR sequence were in the range of 0.2-2.4 °C at the contacts with leads positioned along the central axis inside the MRI bore. These findings suggest the need for careful lead placement. Second, also for the body transmit coil we compared the heating in the vicinity of icEEG electrodes placed inside a realistically-shaped head phantom following the addition of scalp EEG electrodes. The peak temperature change was +2.7 °C at the most superior icEEG electrode contact without scalp electrodes, and +2.1 °C at the same contact and the peak increase in the vicinity of a scalp electrode contact was +0.6 °C (location FP2). These findings show that the S-icEEG-fMRI technique is feasible if our protocol is followed carefully. Third, the heating of a realistic 3D model of icEEG electrode during MRI using EM computational simulation was investigated. The resulting peak 10 g averaged SAR was 20% higher than without icEEG. Moreover, the superior icEEG placed perpendicular to B0 showed significant local SAR increase. These results were in line with previous studies. Fourth, the possibility of simplifying a complete 8-contact with 8 wires depth icEEG electrode model into an electrode with 1-contact and 1 wire using EM simulations was addressed. The results showed similar patterns of averaged SAR values around the electrode tip during phantom and electrode position along Z for the Complete and Simplified models, except an average maximum at Z = ~2.5 W/kg for the former. The SAR values during insertion depth for the Simplified model were double those for the Complete model. The effect of extension cable length is in agreement with previous experiments. Fifth, further simulations were implemented using two more simplified models: 8-contact with 1 wire shared with all contact and 8-contact 1 wire connected to each contact at a time as well as the previously modelled simplified 1-contact 1 wire. Two sets of simulations were performed: with a single electrode and with multiple electrodes. For the single electrode, three scenarios were tested: the first simplified model used only, the second simplified models used only and the third model positioned in different 13 locations. The results of these simulations showed about 11.4-20.5-fold lower SAR for the first model than the second and 0.29-5.82-fold lower SAR for the first model than the complete model. The results also showed increased SAR for the electrode close to the head coil than the ones away from it. For the multiple electrodes, three scenarios were tested: two 1-contact and wire electrodes in different separations, multiple electrodes with their wires separated and multiple electrodes with their wires shorted. The results showed interaction between the two tested electrodes. The results of the multiple electrodes presented 2 to ~10 times higher SAR for the separated setup than the shorted. The comparison between the 1-contact with 1 wire model and the complete model is still unknown and more tests are required to show it. From the findings of this PhD research, we conclude that a body RF coil can be utilized for icEEG-fMRI at 1.5 T; however, the safety protocol has to be implemented. In addition, scalp EEG can be used in conjunction with icEEG electrodes inside the body RF coil at 1.5 T and the safety protocol has to be followed. Finally, it is feasible to perform EM computational simulations using realistic icEEG electrodes on a human model. However, simplifying the realistic icEEG electrode model might result in overestimations of the heating, although it is possible that the simplification of the model can help to simulate more complex implantations such as the implantation of multiple electrodes with their leads open circuited or short circuited, which can provide more information about the safety of implanted patients inside the MRI

    Learning-Based Hardware Design for Data Acquisition Systems

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    This multidisciplinary research work aims to investigate the optimized information extraction from signals or data volumes and to develop tailored hardware implementations that trade-off the complexity of data acquisition with that of data processing, conceptually allowing radically new device designs. The mathematical results in classical Compressive Sampling (CS) support the paradigm of Analog-to-Information Conversion (AIC) as a replacement for conventional ADC technologies. The AICs simultaneously perform data acquisition and compression, seeking to directly sample signals for achieving specific tasks as opposed to acquiring a full signal only at the Nyquist rate to throw most of it away via compression. Our contention is that in order for CS to live up its name, both theory and practice must leverage concepts from learning. This work demonstrates our contention in hardware prototypes, with key trade-offs, for two different fields of application as edge and big-data computing. In the framework of edge-data computing, such as wearable and implantable ecosystems, the power budget is defined by the battery capacity, which generally limits the device performance and usability. This is more evident in very challenging field, such as medical monitoring, where high performance requirements are necessary for the device to process the information with high accuracy. Furthermore, in applications like implantable medical monitoring, the system performances have to merge the small area as well as the low-power requirements, in order to facilitate the implant bio-compatibility, avoiding the rejection from the human body. Based on our new mathematical foundations, we built different prototypes to get a neural signal acquisition chip that not only rigorously trades off its area, energy consumption, and the quality of its signal output, but also significantly outperforms the state-of-the-art in all aspects. In the framework of big-data and high-performance computation, such as in high-end servers application, the RF circuits meant to transmit data from chip-to-chip or chip-to-memory are defined by low power requirements, since the heat generated by the integrated circuits is partially distributed by the chip package. Hence, the overall system power budget is defined by its affordable cooling capacity. For this reason, application specific architectures and innovative techniques are used for low-power implementation. In this work, we have developed a single-ended multi-lane receiver for high speed I/O link in servers application. The receiver operates at 7 Gbps by learning inter-symbol interference and electromagnetic coupling noise in chip-to-chip communication systems. A learning-based approach allows a versatile receiver circuit which not only copes with large channel attenuation but also implements novel crosstalk reduction techniques, to allow single-ended multiple lines transmission, without sacrificing its overall bandwidth for a given area within the interconnect's data-path

    Minimizing the Adverse Effects of Electric Fields in Magnetic Resonance Imaging using Optimized Gradient Encoding and Peripheral Nerve Models

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    Magnetic Resonance Imaging (MRI) is an important imaging modality in both the clinic and in research. MRI technology has been trending toward increasing field strengths to improve the signal-to-noise ratio of the MR signal and fast excitation/encoding strategies to more flexible target anatomical regions during excitation to reduce the total imaging time. While largely successful, both strategies rely on the application of increasingly strong and rapidly switched magnetic fields: the radio frequency (RF) field for excitation and the gradient field for encoding. The technology for generating these fields (and rapidly switching them) has advanced to the point that we are limited by biological responses to the switching fields. For the gradient field, the electric field generated in the tissue causes peripheral nerve stimulation (PNS) causing mild but bothersome sensations at low levels, up to pain or cardiac malfunction at higher levels. The electric fields created by the much faster time-varying RF cause heat deposition, ultimately denaturing proteins and causing tissue damage. In this thesis, methods are presented to characterize and minimize these two problems associated with the switched magnetic fields in MRI. The deposited RF energy (Specific Absorption Rate, SAR) incurred during shaped excitations can be significantly reduced by optimizing gradient and RF waveforms for inner-volume excitations that allow imaging of a sub-volume of the body without wrapping artifacts. The adverse effects of the switching gradient fields are addressed by designing time-optimal gradient encoding waveforms and by developing a method to predict and characterize PNS using field simulations and a full-body nerve model allowing these critical effects to be addressed at the gradient coil design stage. In the first part, time-optimal gradient trajectories are demonstrated that use the gradient hardware at the maximum available performance. The skeleton of the trajectory is defined by a set of k-space control points. The method optimizes gradient waveforms that traverse the k-space control points in the minimum possible amount of time. By using an analytic representation of the gradients (piece-wise linear), the design process is fast and numerically robust. The resulting trajectories sample k-space efficiently while using the gradient system at maximum performance. Compared to the leading Optimal Control method, the proposed method generates gradient waveforms that are 9.2% shorter. The computation process is ∼100x faster and does not suffer from numerical instabilities such as oscillations. In the second part, a method is developed that jointly optimizes parallel transmission RF and gradient waveforms for fast and robust 3-D inner-volume excitation of the MRI signal in minimal time and with minimal energy deposition. The optimization of the k-space trajectories is based on a small number of shape parameters that are well-suited for joint optimization with the RF waveforms. Within each iteration of the trajectory optimization, a small tip-angle least-squares RF pulse design problem is solved. Using optimized 3-D cross (shells) trajectories, a cube shape (brain shape) region was excited with 3.4% (6.2%) NRMSE in less than 5 ms using a 7 T scanner with 8 Tx channels and a clinical gradient system (Gmax = 40 mT/m, Smax = 150 T/m/s). Incorporation of off-resonance robustness in the pulse design significantly altered the k-space trajectory solutions and improved the practical performance of the pulses. In the final part, a framework is presented that simulates PNS thresholds for realistic gradient coil geometries and thus allows, for the first time, to directly address PNS in the coil design process. The PNS framework consists of an accurate body model for simulation of the induced electric fields, an atlas of peripheral nerves, and a neurodynamic model to predict the nerve responses to imposed electric fields. With this model, measured PNS thresholds of two leg/arm solenoid coils and three commercial actively-shielded MR gradient coils could be reproduced with good accuracy. The proposed method can be used to assess the PNS capability of gradient coils during the design phase, without building expensive prototype coils
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