High-speed digital filtering: Structures and finite wordlength effects

This paper is a study of high-throughput filter structures such as block structures and their behavior in finite precision environments. Block structures achieve high throughput rates by using a large number of processors working in parallel. It has been believed that block structures which are relatively robust to round-off noise must also be robust to coefficient quantization errors. However, our research has shown that block structures, in fact, have high coefficient sensitivity. A potential problem that arises as a result of coefficient quantization is a periodically time-varying behavior exhibited by the realized filter. We will demonstrate how finite wordlength errors can change a nominally time-invariant filter into a time-varying system. We will identify the block structures that have low coefficient sensitivity, and develop high-speed structures that are immune to the time-varying problems caused by coefficient quantization.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41355/1/11265_2004_Article_BF00930646.pd

A Hypertuned Pipeline Vector Using Meta Classifier Technique for Feature Selection in Multi Disease Prediction

Automation of health sector plays a very important role especially during this pandemic due to the side effects of either vaccination or attack of the COVID. Most of the researchers designed a system to predict whether a person suffers from a particular disease or not. Few researchers worked on prediction variants of a single disease based on symptoms but due to this COVID-19, different people are getting attacked with different diseases as a side effect. This proposed system aims to identify the multiple diseases that a person may suffer from based on the symptoms. In this paper, the dataset obtained from the open access repository “Kaggle” contains 17 symptoms combinations to identify the one of the 41 types of diseases as class label. All the symptoms may not be important for identification, so in this model, the important features are identified using the pipeline vector of different Machine Learning approaches are passed as base line classifier and decision tree classifier as meta line to the elimination function. The model has got “99.48%” accuracy for selecting the essential features using bagging and boosting algorithms

Efficient Execution of Sequential Instructions Streams by Physical Machines

Any computational model which relies on a physical system is likely to be subject to the fact that information density and speed have intrinsic, ultimate limits. The RAM model, and in particular the underlying assumption that memory accesses can be carried out in time independent from memory size itself, is not physically implementable. This work has developed in the field of limiting technology machines, in which it is somewhat provocatively assumed that technology has achieved the physical limits. The ultimate goal for this is to tackle the problem of the intrinsic latencies of physical systems by encouraging scalable organizations for processors and memories. An algorithmic study is presented, which depicts the implementation of high concurrency programs for SP and SPE, sequential machine models able to compute direct-flow programs in optimal time. Then, a novel pieplined, hierarchical memory organization is presented, with optimal latency and bandwidth for a physical system. In order to both take full advantage of the memory capabilities and exploit the available instruction level parallelism of the code to be executed, a novel processor model is developed. Particular care is put in devising an efficient information flow within the processor itself. Both designs are extremely scalable, as they are based on fixed capacity and fixed size nodes, which are connected as a multidimensional array. Performance analysis on the resulting machine design has led to the discovery that latencies internal to the processor can be the dominating source of complexity in instruction flow execution, which adds to the effects of processor-memory interaction. A characterization of instruction flows is then developed, which is based on the topology induced by instruction dependences

Adding Data Parallelism to Streaming Pipelines for Throughput Optimization

The streaming model is a popular model for writing high-throughput parallel applications. A streaming application is represented by a graph of computation stages that communicate with each other via FIFO channels. In this report, we consider the problem of mapping streaming pipelines — streaming applications where the graph is a linear chain — in order to maximize throughput. In a parallel setting, subsets of stages, called components can be mapped onto different computing resources. The through-put of an application is determined by the throughput of the slowest component. Therefore, if some stage is much slower than others, then it may be useful to replicate the stage’s code and divide its workload among two or more replicas in order to increase throughput. However, pipelines may consist of some replicable and some non-replicable stages. In this paper, we address the problem of mapping these partially replicable streaming pipelines on both homogeneous and heterogeneous platforms so as to maximize throughput. We consider two types of platforms, homogeneous platforms — where all resources are identical, and heterogeneous platforms — where resources may have different speeds. In both cases, we consider two network topologies — unidirectional chain and clique. We provide polynomial-time algorithms for mapping partially replicable pipelines onto unidirectional chains for both homogeneous and heterogeneous platforms. For homogeneous platforms, the algorithm for unidirectional chains generalizes to clique topologies. However, for heterogeneous platforms, mapping these pipelines onto clique topologies is NP-complete. We provide heuristics to generate solutions for cliques by applying our chain algorithms to a series of chains sampled from the clique. Our empirical results show that these heuristics rapidly converge to near-optimal solutions

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

Acceleration of MCMC-based algorithms using reconfigurable logic

Monte Carlo (MC) methods such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) have emerged as popular tools to sample from high dimensional probability distributions. Because these algorithms can draw samples effectively from arbitrary distributions in Bayesian inference problems, they have been widely used in a range of statistical applications. However, they are often too time consuming due to the prohibitive costly likelihood evaluations, thus they cannot be practically applied to complex models with large-scale datasets. Currently, the lack of sufficiently fast MCMC methods limits their applicability in many modern applications such as genetics and machine learning, and this situation is bound to get worse given the increasing adoption of big data in many fields. The objective of this dissertation is to develop, design and build efficient hardware architectures for MCMC-based algorithms on Field Programmable Gate Arrays (FPGAs), and thereby bring them closer to practical applications. The contributions of this work include: 1) Novel parallel FPGA architectures of the state-of-the-art resampling algorithms for SMC methods. The proposed architectures allow for parallel implementations and thus improve the processing speed. 2) A novel mixed precision MCMC algorithm, along with a tailored FPGA architecture. The proposed design allows for more parallelism and achieves low latency for a given set of hardware resources, while still guaranteeing unbiased estimates. 3) A new variant of subsampling MCMC method based on unequal probability sampling, along with a highly optimized FPGA architecture. The proposed method significantly reduces off-chip memory access and achieves high accuracy in estimates for a given time budget. This work has resulted in the development of hardware accelerators of MCMC and SMC for very large-scale Bayesian tasks by applying the above techniques. Notable speed improvements compared to the respective state-of-the-art CPU and GPU implementations have been achieved in this work.Open Acces

Algorithm Libraries for Multi-Core Processors

By providing parallelized versions of established algorithm libraries, we ease the exploitation of the multiple cores on modern processors for the programmer. The Multi-Core STL provides basic algorithms for internal memory, while the parallelized STXXL enables multi-core acceleration for algorithms on large data sets stored on disk. Some parallelized geometric algorithms are introduced into CGAL. Further, we design and implement sorting algorithms for huge data in distributed external memory

Adaptive Interference Mitigation in GPS Receivers

Satellite navigation systems (GNSS) are among the most complex radio-navigation systems, providing positioning, navigation, and timing (PNT) information. A growing number of public sector and commercial applications rely on the GNSS PNT service to support business growth, technical development, and the day-to-day operation of technology and socioeconomic systems. As GNSS signals have inherent limitations, they are highly vulnerable to intentional and unintentional interference. GNSS signals have spectral power densities far below ambient thermal noise. Consequently, GNSS receivers must meet high standards of reliability and integrity to be used within a broad spectrum of applications. GNSS receivers must employ effective interference mitigation techniques to ensure robust, accurate, and reliable PNT service. This research aims to evaluate the effectiveness of the Adaptive Notch Filter (ANF), a precorrelation mitigation technique that can be used to excise Continuous Wave Interference (CWI), hop-frequency and chirp-type interferences from GPS L1 signals. To mitigate unwanted interference, state-of-the-art ANFs typically adjust a single parameter, the notch centre frequency, and zeros are constrained extremely close to unity. Because of this, the notch centre frequency converges slowly to the target frequency. During this slow converge period, interference leaks into the acquisition block, thus sabotaging the operation of the acquisition block. Furthermore, if the CWI continuously hops within the GPS L1 in-band region, the subsequent interference frequency is locked onto after a delay, which means constant interference occurs in the receiver throughout the delay period. This research contributes to the field of interference mitigation at GNSS's receiver end using adaptive signal processing, predominately for GPS. This research can be divided into three stages. I first designed, modelled and developed a Simulink-based GPS L1 signal simulator, providing a homogenous test signal for existing and proposed interference mitigation algorithms. Simulink-based GPS L1 signal simulator provided great flexibility to change various parameters to generate GPS L1 signal under different conditions, e.g. Doppler Shift, code phase delay and amount of propagation degradation. Furthermore, I modelled three acquisition schemes for GPS signals and tested GPS L1 signals acquisition via coherent and non-coherent integration methods. As a next step, I modelled different types of interference signals precisely and implemented and evaluated existing adaptive notch filters in MATLAB in terms of Carrier to Noise Density (\u1d436/\u1d4410), Signal to Noise Ratio (SNR), Peak Degradation Metric, and Mean Square Error (MSE) at the output of the acquisition module in order to create benchmarks. Finally, I designed, developed and implemented a novel algorithm that simultaneously adapts both coefficients in lattice-based ANF. Mathematically, I derived the full-gradient term for the notch's bandwidth parameter adaptation and developed a framework for simultaneously adapting both coefficients of a lattice-based adaptive notch filter. I evaluated the performance of existing and proposed interference mitigation techniques under different types of interference signals. Moreover, I critically analysed different internal signals within the ANF structure in order to develop a new threshold parameter that resets the notch bandwidth at the start of each subsequent interference frequency. As a result, I further reduce the complexity of the structural implementation of lattice-based ANF, allowing for efficient hardware realisation and lower computational costs. It is concluded from extensive simulation results that the proposed fully adaptive lattice-based provides better interference mitigation performance and superior convergence properties to target frequency compared to traditional ANF algorithms. It is demonstrated that by employing the proposed algorithm, a receiver is able to operate with a higher dynamic range of JNR than is possible with existing methods. This research also presents the design and MATLAB implementation of a parameterisable Complex Adaptive Notch Filer (CANF). Present analysis on higher order CANF for detecting and mitigating various types of interference for complex baseband GPS L1 signals. In the end, further research was conducted to suppress interference in the GPS L1 signal by exploiting autocorrelation properties and discarding some portion of the main lobe of the GPS L1 signal. It is shown that by removing 30% spectrum of the main lobe, either from left, right, or centre, the GPS L1 signal is still acquirable

Estimation and control of non-linear and hybrid systems with applications to air-to-air guidance

Issued as Progress report, and Final report, Project no. E-21-67