Evolving PCB visual inspection programs using genetic programming

Automated optical inspection (AOI) is desirable in printed circuit board (PCB) manufacturing as inspecting manually is time-consuming and error-prone. This paper presents a study on evolving an AOI program with Genenetic- Programming (GP), an evolution-inspired technique. Using a GP-based approach, domain knowledge such as board design and lighting conditions are not required. Conventional feature extraction processes can also be avoided. The result demonstrates the evolved program capability to detect flaws under varied scenarios. Furthermore, it can be readily applied on different types of images without calibration or re-training

Student and supervisor perceptions of writing competencies for a Computer Science PhD

A PhD in any discipline requires a student to produce a substantial written document, which is then assessed by a group of experts in the specific discipline. In the discipline of computer science, it has often been noted anecdotally that many students struggle with the English writing skill needed to produce a thesis (and other documents, such as scientific papers). English writing skill issues seem particularly acute for students for whom English is not their first language, especially as undergraduate degrees in computer science generally do not require students to undertake significant amounts of English writing. In this project, we investigated the level of competence in written English that is appropriate for Australian PhD students enrolled in Computer Science. In particular, we sought to determine the appropriate level of writing skill required, how the level of skill may change during the students' candidature, and the reasons for this change, as perceived by both students and supervisors. We approached these questions by surveying both students and PhD supervisors from a variety of Australian universities, to determine both their perceptions of the writing skill requirements that are appropriate, difficulties encountered, and support services, in the context of the English language learning background of all participants. We also analysed the performance of students on a given writing task, which was assessed by experienced PhD Computer Science supervisors, English for Academic Purposes support staff and by an IELTS examiner. We found insufficient awareness of the writing supports available, a need for writing support targeted at technical writing, and an average supervisor expectation of IELTS 6.5 for writing at PhD commencement

Music Genre Classification Using MIDI and Audio Features

We report our findings on using MIDI files and audio features from MIDI, separately and combined together, for MIDI music genre classification. We use McKay and Fujinaga's 3-root and 9-leaf genre data set. In order to compute distances between MIDI pieces, we use normalized compression distance (NCD). NCD uses the compressed length of a string as an approximation to its Kolmogorov complexity and has previously been used for music genre and composer clustering. We convert the MIDI pieces to audio and then use the audio features to train different classifiers. MIDI and audio from MIDI classifiers alone achieve much smaller accuracies than those reported by McKay and Fujinaga who used not NCD but a number of domain-based MIDI features for their classification. Combining MIDI and audio from MIDI classifiers improves accuracy and gets closer to, but still worse, accuracies than McKay and Fujinaga's. The best root genre accuracies achieved using MIDI, audio, and combination of them are 0.75, 0.86, and 0.93, respectively, compared to 0.98 of McKay and Fujinaga. Successful classifier combination requires diversity of the base classifiers. We achieve diversity through using certain number of seconds of the MIDI file, different sample rates and sizes for the audio file, and different classification algorithms