A Novel Optimization Algorithm for Notch Bandwidth in Lattice Based Adaptive Filter for the Tracking of Interference in GPS

The weak signal levels experienced at the reception of the messages transmitted by navigation satellites, makes Global Positioning System (GPS) vulnerable to unintentional and intentional interference. This calls for appropriate modelling of GPS signal sources and jammers to assess the anti-jamming and interference mitigation capabilities of algorithms developed to be implemented for GPS receivers. Using a practical simulation model, this work presents an anti-jamming technique based on a novel algorithm. A fully adaptive lattice based notch filter is presented that provides better performance when compared to existing adaptive notch filter based techniques, chosen from the literature, in terms of convergence speed whilst delivering superior performance in the excision of the interference signal. To justify the superiority of the proposed technique, the noise and interference signal power is varied for in a wide dynamic range assessing jamming-to-noise density versus effective carrier-to-noise density performance at the output of the correlator

A FPGA/DSP design for real-time fracture detection using low transient pulse

This work presents the hardware and software architecture for the detection of fractures and edges in materials. While the detection method is based on the novel concept of Low Transient Pulse (LTP), the overall system implementation is based on two digital microelectronics technologies widely used for signal processing: Digital Signal Processor (DSP) and Field Programmable Gate Array (FPGA). Under the proposed architecture, the DSP carries out the analysis of the received baseband signal at a lower rate and hence can be used for large number of signal channels. The FPGA\u27s master clock runs at a higher frequency (62.5MHz) for the generation of LTP signal and to demodulate the passband ultrasonic signals sampled at 1MHz which interrupts the DSP at every 1 [Is. This research elaborates on designing a Quadrature Amplitude Modulator - demodulator (QAM) on the FPGA for the received signal from the ultrasound and edge detection on the DSP processor to detect the presence of edges/fractures on a test Sawbone plate. In this work, the LTP technology is applied to determine the location of the Sawbone plate edges based on the reflected signals to the receivers. This signal is then passed through a QAM to get the maxima (peaks) at the received signal to study the parameters in the DSP. This work successfully demonstrates the feasibility of modular programming approach across the two platforms. The dual time scale platform readily accommodates higher temporal resolution needed for the generation of Low Transient Pulses and the processing of real time baseband signals on the DSP for various test conditions

Differentiable Artificial Reverberation

Artificial reverberation (AR) models play a central role in various audio applications. Therefore, estimating the AR model parameters (ARPs) of a target reverberation is a crucial task. Although a few recent deep-learning-based approaches have shown promising performance, their non-end-to-end training scheme prevents them from fully exploiting the potential of deep neural networks. This motivates to introduce differentiable artificial reverberation (DAR) models which allows loss gradients to be back-propagated end-to-end. However, implementing the AR models with their difference equations "as is" in the deep-learning framework severely bottlenecks the training speed when executed with a parallel processor like GPU due to their infinite impulse response (IIR) components. We tackle this problem by replacing the IIR filters with finite impulse response (FIR) approximations with the frequency-sampling method (FSM). Using the FSM, we implement three DAR models -- differentiable Filtered Velvet Noise (FVN), Advanced Filtered Velvet Noise (AFVN), and Feedback Delay Network (FDN). For each AR model, we train its ARP estimation networks for analysis-synthesis (RIR-to-ARP) and blind estimation (reverberant-speech-to-ARP) task in an end-to-end manner with its DAR model counterpart. Experiment results show that the proposed method achieves consistent performance improvement over the non-end-to-end approaches in both objective metrics and subjective listening test results.Comment: Manuscript submitted to TASL

Optimal digital filter design for dispersed signal equalization

Any signal a satellite receives from Earth has traveled through the ionosphere. Transmission through the ionosphere results in a frequency dependent time-delay of the signal frequency components. This effect of the medium on the signal is termed dispersion, and it increases the difficulty of pulse detection. A system capable of compensating for the dispersion would be desirable, as pulsed signals would be more readily detected after compression. In this thesis, we investigate the derivation of a digital filter to compensate for the dispersion caused by the ionosphere. A transfer function model for the analysis of the ionosphere as a system is introduced. Based on the signal model, a matched filter response is derived. The problem is formulated as a group delay compensation effort. The Abel-Smith algorithm is employed for the synthesis of a cascaded allpass filter bank with desired group delay characteristics. Extending this work, an optimized allpass filter is then derived using a pole location approach. A mean-square error metric shows that the optimized filter can reproduce, and even improve upon, the results of the Abel-Smith design with a significantly lower order filter. When compared against digital filters produced with the least p-th minimax algorithm, we find that the new method exhibits significantly lower error in the band of interest, as well as lower mean squared error overall. The result is a simple optimized equalization filter that is stable, robust against cascading difficulties, and applicable to arbitrary waveforms. This filter is the cornerstone to a new all-digital electromagnetic pulse detection system

A FPGA/DSP based ultrasound system for tumor detection

This work presents a method of detection of size and location of tumor using ultrasound transmission. The system utilizes Quantitative Ultrasound (QUS) which means sending an ultrasound signal from a transmitter and receiving it at multiple receivers. This received signal is analyzed for echogenic as well as echolucent tumors to differentiate between the two along with non-tumorous sample and also for delay, signal distortion to determine the size/location of the tumor. This analysis is further implemented using Field Programmable Gate Array (FPGA) and Digital Signal Processor (DSP) technologies. The proposed detection system utilizes Low Transient Pulse (LTP) technique. In this co-design architecture, the DSP carries out analysis of received demodulated signal at a lower speed while the FPGA runs at 62.5MHz for the generation of LTP signal and to demodulate bandpass ultrasonic signal sampled at 1MHz which interrupts DSP at every 1µS. This work elaborates the implementation of Quadrature Amplitude Modulation (QAM) receiver on FPGA for received signal from ultrasound detector. LTP is applied to the tumor samples through the transmitter and the received signal at ultrasonic receiver is passed through QAM to get different maxima (peaks) which are then further used for calculation of the location and subsequently, the size of the tumor using DSP. This dual platform co-design demonstrates application of a FPGA/DSP platform for the generation of low transient pulse as well as processing of the received signal

Suprajohtavien kvantti-inferferenssilaitteiden älykäs digitaalinen ohjaus ultramatalan kentän magneettikuvauksessa

Ultra-low-field magnetic resonance imaging (ULF MRI) studies the inner structure of matter by exciting nuclear spins using microtesla-range magnetic fields. The weak spin-induced magnetic signals are received with highly sensitive superconducting-quantum-interference-device-based (SQUID) sensors that act as flux-to-voltage converters. Because of the physical nature of the SQUID, its response to magnetic flux is periodic. To make the measurements easier, the response is linearized with a special feedback scheme. In the measurement setup used in this work, the SQUID feedback is realized with digital signal processors so that the response of the system can be manipulated using computer software. The software is designed for magnetoencephalography, which measures magnetic signals generated by the neuronal currents. These signals are in both amplitude and frequency smaller than those encountered in ULF MRI. In this thesis, new software for the needs of ULF MRI was developed. For example, a method to measure the feedback-to-input response and a new feedback reset algorithm tailored for ULF MRI were designed and implemented. The reset algorithm was designed to reactivate the flux dams in the SQUID input circuits and to reduce the signal transient after the reset. The feedback-to-input response measurements revealed a notable delay in the feedback, which degrades the frequency response of the whole system. It was shown that the frequency response can be improved by an additional digital compensation based on the measured feedback-to-input response.Ultramatalan kentän magneettikuvauksessa tutkitaan aineen rakennetta virittämällä atomiytimien spinejä mikroteslaluokan magneettikentillä. Spinien tuottamat heikot magneettiset signaalit vastaanotetaan erittäin herkillä suprajohtaviin kvantti-interferenssilaitteisiin (SQUID) perustuvilla antureilla, jotka muuntavat magneettivuon jännitteeksi. SQUIDin vaste magneettivuohon on luonnostaan periodinen. Mittausten helpottamiseksi se linearisoidaan kytkemällä mitattu signaali takaisin SQUIDiin. Tässä työssä käytetyssä mittausjärjestelmässä SQUIDien takaisinkytkentä on toteutettu digitaalisten signaaliprosessoreiden avulla, minkä ansiosta systeemin vastetta voidaan muokata tietokoneohjelmistolla. Ohjelmisto on kuitenkin suunniteltu magnetoenkefalografiaa varten. Magnetoenkefalografiassa mitatut signaalit ovat niin taajuudeltaan kuin amplitudiltaan huomattavan pieniä verattuna magneettikuvaukseen. Tämän diplomityön tarkoituksena oli kehittää uutta ohjelmistoa ultramatalan kentän magneettikuvauksen tarpeisiin. Ohjelmistoa kehitettiin esimerkiksi mittaamaan takaisinkytkentävasteita sekä kontrolloimaan vuosignaalia uudella tavalla takaisinkytkennän resetoinnin aikana. Uusi resetointialgoritmi pyrkii ohjaamaan SQUIDien vastaanottopiirien vuopatoja suprajohtavaan tilaan sekä vähentämään signaalitransienttia takaisinkytkennän resetoinnin jälkeen. Takaisinkytkennässä havaittiin viivettä, joka heikentää koko systeemin taajuusvastetta. Taajuusvasteen osoitettiin kohentuvan, kun signaalia kompensoitiin digitaalisesti hyödyntäen tietoa mitatusta takaisinkytkentävasteesta

RHINO software-defined radio processing blocks

This MSc project focuses on the design and implementation of a library of parameterizable, modular and reusable Digital IP blocks designed around use in Software-Defined Radio (SDR) applications and compatibility with the RHINO platform. The RHINO platform has commonalities with the better known ROACH platform, but it is a significantly cut-down and lowercost alternative which has similarities in the interfacing and FPGA/Processor interconnects of ROACH. The purpose of the library and design framework presented in this work aims to alleviate some of the commercial, high cost and static structure concerns about IP cores provided by FPGA manufactures and third-party IP vendors. It will also work around the lack of parameters and bus compatibility issues often encountered when using the freely available open resources. The RHINO hardware platform will be used for running practical applications and testing of the blocks. The HDL library that is being constructed is targeted towards both novice and experienced low-level HDL developers who can download and use it for free, and it will provide them experience of using IP Cores that support open bus interfaces in order to exploit SoC design without commercial, parameter and bus compatibility limitations. The provided modules will be of particularly benefit to the novice developers in providing ready-made examples of processing blocks, as well as parameterization settings for the interfacing blocks and associated RF receiver side configuration settings; all together these examples will help new developers establish effective ways to build their own SDR prototypes using RHINO

Single-ensemble-based eigen-processing methods for color flow imaging-Part I. the Hankel-SVD filter

Because of their adaptability to the slow-time signal contents, eigen-based filters have shown potential in improving the flow detection performance of color flow images. This paper proposes a new eigen-based filter called the Hankel-SVD filter that is intended to process each slow- time ensemble individually. The new filter is derived using the notion of principal Hankel component analysis, and it achieves clutter suppression by retaining only the principal components whose order is greater than the clutter eigen- space dimension estimated from a frequency-based analysis algorithm. To assess its efficacy, the Hankel-SVD filter was first applied to synthetic slow-time data (ensemble size: 10) simulated from two different sets of flow parameters that model: (1) arterial imaging (blood velocity: 0 to 38.5 cm/s, tissue motion: up to 2 mm/s, transmit frequency: 5 MHz, pulse repetition period: 0.4 ms) and 2) deep vessel imaging (blood velocity: 0 to 19.2 cm/s, tissue motion: up to 2 cm/s, transmit frequency: 2 MHz, pulse repetition period: 2.0 ms). In the simulation analysis, the post-filter clutter- to-blood signal ratio (CBR) was computed as a function of blood velocity. Results show that for the same effective stopband size (50 Hz), the Hankel-SVD filter has a narrower transition region in the post-filter CBR curve than that of another type of adaptive filter called the clutter- downmixing filter. The practical efficacy of the proposed filter was tested by application to in vivo color flow data obtained from the human carotid arteries (transmit frequency: 4 MHz, pulse repetition period: 0.333 ms, ensemble size: 10). The resulting power images show that the Hankel-SVD filter can better distinguish between blood and moving- tissue regions (about 9 dB separation in power) than the clutter-downmixing filter and a fixed-rank multi-ensemble- based eigen-filter (which showed a 2 to 3 dB separation). © 2006 IEEE.published_or_final_versio