This paper presents a method for improving students' conceptual understanding of microarchitecture in a MIPS-based Computer Organization and Architecture course. The main goal of this course is for students to learn the functionality of the MIPS microprocessor, i.e., to understand the MIPS instruction set architecture (ISA) and its different microarchitectures (also known as organizations). In contrast to the traditional rote learning of microarchitecture, the proposed method, called the brick-by-brick approach, adopts jigsaw cooperative learning and modifies it specifically for learning microarchitecture. The proposed approach helps students to achieve significant gains in their understanding of microarchitecture concepts. A microarchitecture concept inventory is developed for this case study to assess students' concepts. The formats of concept inventory questions are very fluid and, as such, allow for easy modifications. Analysis of results and statistical tests demonstrate the benefits of the proposed method.

In higher education, academic departments are always challenged by the need to succeed in operation while maintaining quality practices and outputs. It is common that departments need to adhere to multiple accreditation initiatives and standards at both the institutional and programmatic levels. In this paper, we benefit from the intersection between the improvement cycles of institutional and programmatic accreditations to propose a maximum mutuality model. The suggested model embeds program reviews, as required by ABET accreditation, within a departmental improvement loop. The model aims at being effective, productive, and easy-to-deploy; while responding to the needs for improvement at various levels. The application of the model is shown to be successful in assuring quality and leading to different accreditations. The paper includes thorough analysis, evaluation, and deployment results over six academic years.

This paper investigates incorporating jigsaw cooperative learning and its impact on students' performance in a junior-level engineering course on Signals and Systems. This course is rich in mathematical formulation and analysis of complex processes. To make the material more approachable to students, instructors usually incorporate several problem-solving or tutorial sessions within the course. Much like traditional lectures, the delivery of the material during these sessions is primarily instructor-centered, particularly, due to the complexity of the material. In this paper, we demonstrate that student learning of the course material improves when the traditional instructor-centered lectures are supplemented with jigsaw-based problem-solving sessions. Analysis of assessment data obtained from student scores in exam questions over two years shows improvements in student performance when the material delivery is supplemented with jigsaw-based problem-solving sessions in comparison to the instructor-centered problemsolving sessions. Advantages of the jigsaw technique over more conventional cooperative learning techniques are discussed. Furthermore, application of jigsaw can highlight areas of improvements in the instructor's delivery of the course material.

Accurate and simple assessment frameworks are of essential need in technical higher education. Although accurate results in most cases demand complicated setups, good compromises can lead to the desired assessment with simplicity. In this paper, we propose a unified framework for the assessment of student outcomes based on senior design experiences of undergraduate computer engineering students. Senior design experiences provide unique opportunities for students to demonstrate their abilities, skills, and experiences that are attained throughout a bachelor of engineering program. The proposed framework is built upon capstone design projects and senior design courses. The learning outcomes of senior design courses can be carefully designed to map to all student outcomes. Accordingly, senior design courses can lead to accurate assessments at the program level and within a simple setup of senior courses. The proposed framework allows for sound evaluations of student performance and project qualities. The developed framework comprises criteria, indicators, extensive analytic rubrics, and a summative statistical formulation. The framework is supported by evaluative and comparative analyses of course and student outcome assessments within a pilot study.

A capstone design project is an extensive piece of work that requires creative activity and thinking. It provides a unique opportunity for students to demonstrate their abilities, skills, and experiences that are attained throughout a bachelor of engineering program. The learning outcomes of capstone projects mostly map to all student outcomes at the program level. This paper presents a unified assessment framework for capstone design courses which allows for sound evaluations of student performance and project qualities in addition to assessing student outcomes. The developed framework comprises criteria, indicators, extensive analytic rubrics, and a summative statistical formulation. The presented course and framework are supported by the results, analysis, and evaluation of a pilot study.

This paper proposes methods for combining cepstral and acoustic waveform representations for a front-end of support vector machine (SVM)-based speech recognition systems that are robust to additive noise. The key issue of kernel design and noise adaptation for the acoustic waveform representation is addressed first. Cepstral and acoustic waveform representations are then compared on a phoneme classification task. Experiments show that the cepstral features achieve very good performance in low noise conditions, but suffer severe performance degradation already at moderate noise levels. Classification in the acoustic waveform domain, on the other hand, is less accurate in low noise but exhibits a more robust behavior in high noise conditions. A combination of the cepstral and acoustic waveform representations achieves better classification performance than either of the individual representations over the entire range of noise levels tested, down to$-$18-dB SNR.

In this paper, we investigate the robustness of phoneme classification to additive noise with hybrid features using support vector machines (SVMs). In particular, the cepstral features are combined with short term energy features of acoustic waveform segments to form a hybrid representation. The energy features are then taken into account separately in the SVM kernel, and a simple subtraction method allows them to be adapted effectively in noise. This hybrid representation contributes significantly to the robustness of phoneme classification and narrows the performance gap to the ideal baseline of classifiers trained under matched noise conditions.

A subband acoustic waveform front-end for robust speech recognition using support vector machines (SVMs) is developed. The primary issues of kernel design for subband components of acoustic waveforms and combination of the individual subband classifiers using stacked generalization are addressed. Experiments performed on the TIMIT phoneme classification task demonstrate the benefits of classification in frequency subbands: the subband classifier outperforms the cepstral classifiers in the presence of noise for signal-to-noise ratio (SNR) below 12dB.

This work focuses on the robustness of phoneme classification to additive noise in the acoustic waveform domain using support vector machines (SVMs). We address the issue of designing kernels for acoustic waveforms which imitate the state-of-the-art representations such as PLP and MFCC and are tuned to the physical properties of speech. For comparison, classification results in the PLP representation domain with cepstral mean-and-variance normalization (CMVN) using standard kernels are also reported. It is shown that our custom-designed kernels achieve better classification performance at high noise. Finally, we combine the PLP and acoustic waveform representations to attain better classification than either of the individual representations over the entire range of noise levels tested, from quiet condition up to - 18dB SNR.

The challenge of multichannel equalization for audio applications lies in the physical properties of the underlying multi-input/multi-output (MIMO) linear time-invariant systems which are generally non-minimum phase and exhibit extremely long impulse responses, thereby imposing a considerable computational burden on the equalization task particularly when iterative solutions are sought. In this paper we propose a computationally efficient non-iterative multi-channel equalization algorithm. The proposed algorithm is based on the fast Fourier transform (FFT) and allows for faster and considerably more accurate inversion of MIMO systems compared to traditional deconvolution algorithms and adaptive solutions. We address the accuracy and limitations of the proposed algorithm and present simulation results illustrating its performance.