Software-hardware analysis of signal feature classification algorithms

Over the last few decades, signal feature analysis has been significantly used in a wide variety of fields. While several techniques have been proposed in the area of signal feature extraction and classification, all of these techniques are achieved by using modern computers, which rely on softwares, such as MATLAB. However, in real-time applications or portable devices, software implementation is not enough by itself, and a hardware-software co-design or fully hardware implementation needs to be considered. The selection of the right signal feature analysis tool for an application depends not only on the software performance, but also on the hardware efficiency of a method. However, there is not enough studies in existence to provide comparison of these signal feature extraction methods from the hardware implentation aspect. Therefore, the objective of this thesis is to investigate both the hardware and algorithmic perspectives of three commonly used signal feature extraction techniques: Autoregressive (AR), pole modeling, and Mel-frequency Cepstral coefficients (MFCCs). To fulfill this objective, first, the hardware analysis of AR, pole modeling, and MFCC feature extraction methods is performed by calculating the computational complexity of the mathematical equations of each method. Second the FPGA area usage of each feature extraction methods is estimated. Third, algorithmic evaluation of these three methods is performed for audio scene analysis. Once the results are obtained from the above stages, the overall performance of each feature extraction method is compared in terms of both the hardware analysis and algorithmic performances. Finally, based on the performed comparison, pole modeling feature extraction approach is proposed as the suitable method for the audio scene analysis application. The suitable method (pole modeling feature extraction) + linear discriminant analysis (LDA) classifier are implemented in Altera DE2 Board using Altera Nios II soft-core processor. The audio classification accuracy obtained using this implementation is achieved to be equal to the MATLAB implementation. The classification time for one audio sample is determined to be 0.1s, which is fast enough to be considered as a real-time system for audio scene analysis application.

Software-hardware analysis of signal feature classification algorithms

Over the last few decades, signal feature analysis has been significantly used in a wide variety of fields. While several techniques have been proposed in the area of signal feature extraction and classification, all of these techniques are achieved by using modern computers, which rely on softwares, such as MATLAB. However, in real-time applications or portable devices, software implementation is not enough by itself, and a hardware-software co-design or fully hardware implementation needs to be considered. The selection of the right signal feature analysis tool for an application depends not only on the software performance, but also on the hardware efficiency of a method. However, there is not enough studies in existence to provide comparison of these signal feature extraction methods from the hardware implentation aspect. Therefore, the objective of this thesis is to investigate both the hardware and algorithmic perspectives of three commonly used signal feature extraction techniques: Autoregressive (AR), pole modeling, and Mel-frequency Cepstral coefficients (MFCCs). To fulfill this objective, first, the hardware analysis of AR, pole modeling, and MFCC feature extraction methods is performed by calculating the computational complexity of the mathematical equations of each method. Second the FPGA area usage of each feature extraction methods is estimated. Third, algorithmic evaluation of these three methods is performed for audio scene analysis. Once the results are obtained from the above stages, the overall performance of each feature extraction method is compared in terms of both the hardware analysis and algorithmic performances. Finally, based on the performed comparison, pole modeling feature extraction approach is proposed as the suitable method for the audio scene analysis application. The suitable method (pole modeling feature extraction) + linear discriminant analysis (LDA) classifier are implemented in Altera DE2 Board using Altera Nios II soft-core processor. The audio classification accuracy obtained using this implementation is achieved to be equal to the MATLAB implementation. The classification time for one audio sample is determined to be 0.1s, which is fast enough to be considered as a real-time system for audio scene analysis application.

A Tale of Two Bases: Progressive Taxation of Capital and Labor Income

Macroeconomic models routinely abstract simultaneously from two features of the US federal tax code: the joint taxation of ordinary capital and labor income and the special taxation of preferential capital income. In this article, we argue that this abstraction omits a “portfolio-effect” mechanism where endogenous changes to the ordinary-preferential composition of households’ capital income influence individuals’ optimal labor and saving decisions through its impact on their effective marginal tax rates. We demonstrate the quantitative importance of this tax detail by simulating provisions from the recently enacted “Tax Cuts and Jobs Act” using a heterogeneous-agent overlapping generations framework calibrated to the US economy. Our findings imply that accounting for the detailed taxation of labor and capital income should be considered an important modeling feature for tax policy analysis.

Deep Field-Aware Interaction Machine for Click-Through Rate Prediction

Modeling feature interactions is of crucial importance to predict click-through rate (CTR) in industrial recommender systems. Because of great performance and efficiency, the factorization machine (FM) has been a popular approach to learn feature interaction. Recently, several variants of FM are proposed to improve its performance, and they have proven the field information to play an important role. However, feature-length in a field is usually small; we observe that when there are multiple nonzero features within a field, the interaction between fields is not enough to represent the feature interaction between different fields due to the problem of short feature-length. In this work, we propose a novel neural CTR model named DeepFIM by introducing Field-aware Interaction Machine (FIM), which provides a layered structure form to describe intrafield and interfield feature interaction, to solve the short-expression problem caused by the short feature-length in the field. Experiments show that our model achieves comparable and even materially better results than the state-of-the-art methods.

Validation of Solutions for Dynamic Analysis of Pipeline Systems by Comparing the Results of Calculations Obtained in the Russian CAE system APM StructFEM and in ANSYS as Applied to Seismic Resistance Problems

Modeling pipelines using special tubular finite elements is a convenient tool for the analysis of pipeline systems. Such a formulation significantly reduces the dimension of the problem being solved and significantly accelerates the time required to perform calculation procedures. These advantages can be explained by a modeling feature that involves the use of a pipe model in the form of a rod having an annular cross section. This paper presents solutions to the problem of dynamic analysis of pipeline systems, including analysis of the pipeline response to seismic effects. The work was performed to assess the reliability of the dynamic solutions obtained using the Russian CAE system APM StructFEM.

Better Paracoherent Answer Sets with Less Resources

Answer Set Programming (ASP) is a well-established formalism for logic programming. Problem solving in ASP requires to write an ASP program whose answers sets correspond to solutions. Albeit the non-existence of answer sets for some ASP programs can be considered as a modeling feature, it turns out to be a weakness in many other cases, and especially for query answering. Paracoherent answer set semantics extend the classical semantics of ASP to draw meaningful conclusions also from incoherent programs, with the result of increasing the range of applications of ASP. State of the art implementations of paracoherent ASP adopt the semi-equilibrium semantics, but cannot be lifted straightforwardly to compute efficiently the (better) split semi-equilibrium semantics that discards undesirable semi-equilibrium models. In this paper an efficient evaluation technique for computing a split semi-equilibrium model is presented. An experiment on hard benchmarks shows that better paracoherent answer sets can be computed consuming less computational resources than existing methods.