3D Sound Synthesis using the Head Related Transfer Function

Three-dimensional (3D) sound is a significant component of virtual reality. 3D sound systems or directional sound systems are designed to animate the sound space produced by real sound sources. In this thesis, basic concepts of 3D sound are introduced. The Head Related Transfer Functions (HRTFs) are analyzed in both the time and frequency domain. A 3D sound system is implemented using practical, measured HRTF data

An Effective Approach to Nonparametric Quickest Detection and Its Decentralized Realization

This dissertation focuses on the study of nonparametric quickest detection and its decentralized implementation in a distributed environment. Quickest detection schemes are geared toward detecting a change in the state of a data stream or a real-time process. Classical quickest detection schemes invariably assume knowledge of the pre-change and post-change distributions that may not be available in many applications. A distribution free nonparametric quickest detection procedure is presented based on a novel distance measure, referred to as the Q-Q distance calculated from the Quantile-Quantile plot. Theoretical analysis of the distance measure and detection procedure is presented to justify the proposed algorithm and provide performance guarantees. The Q-Q distance based detection procedure presents comparable performance compared to classical parametric detection procedure and better performance than other nonparametric procedures. The proposed procedure is most effective when detecting small changes. As the technology advances, distributed sensing and detection become feasible. Existing decentralized detection approaches are largely parametric. The decentralized realization of Q-Q distance based nonparametric quickest detection scheme is further studied, where data streams are simultaneously collected from multiple channels located distributively to jointly reach a detection decision. Two implementation schemes, binary quickest detection and local decision fusion, are described. Experimental results show that the proposed method has a comparable performance to the benchmark parametric cumulative sum (CUSUM) test in binary detection. Finally the dissertation concludes with a summary of the contributions to the state of the art

An Exploration Study of Mixed-initiative Query Reformulation in Conversational Passage Retrieval

In this paper, we report our methods and experiments for the TREC Conversational Assistance Track (CAsT) 2022. In this work, we aim to reproduce multi-stage retrieval pipelines and explore one of the potential benefits of involving mixed-initiative interaction in conversational passage retrieval scenarios: reformulating raw queries. Before the first ranking stage of a multi-stage retrieval pipeline, we propose a mixed-initiative query reformulation module, which achieves query reformulation based on the mixed-initiative interaction between the users and the system, as the replacement for the neural reformulation method. Specifically, we design an algorithm to generate appropriate questions related to the ambiguities in raw queries, and another algorithm to reformulate raw queries by parsing users' feedback and incorporating it into the raw query. For the first ranking stage of our multi-stage pipelines, we adopt a sparse ranking function: BM25, and a dense retrieval method: TCT-ColBERT. For the second-ranking step, we adopt a pointwise reranker: MonoT5, and a pairwise reranker: DuoT5. Experiments on both TREC CAsT 2021 and TREC CAsT 2022 datasets show the effectiveness of our mixed-initiative-based query reformulation method on improving retrieval performance compared with two popular reformulators: a neural reformulator: CANARD-T5 and a rule-based reformulator: historical query reformulator(HQE).Comment: The Thirty-First Text REtrieval Conference (TREC 2022) Proceeding

Zero-shot Query Reformulation for Conversational Search

As the popularity of voice assistants continues to surge, conversational search has gained increased attention in Information Retrieval. However, data sparsity issues in conversational search significantly hinder the progress of supervised conversational search methods. Consequently, researchers are focusing more on zero-shot conversational search approaches. Nevertheless, existing zero-shot methods face three primary limitations: they are not universally applicable to all retrievers, their effectiveness lacks sufficient explainability, and they struggle to resolve common conversational ambiguities caused by omission. To address these limitations, we introduce a novel Zero-shot Query Reformulation (ZeQR) framework that reformulates queries based on previous dialogue contexts without requiring supervision from conversational search data. Specifically, our framework utilizes language models designed for machine reading comprehension tasks to explicitly resolve two common ambiguities: coreference and omission, in raw queries. In comparison to existing zero-shot methods, our approach is universally applicable to any retriever without additional adaptation or indexing. It also provides greater explainability and effectively enhances query intent understanding because ambiguities are explicitly and proactively resolved. Through extensive experiments on four TREC conversational datasets, we demonstrate the effectiveness of our method, which consistently outperforms state-of-the-art baselines.Comment: Accepted by the 9th ACM SIGIR International Conference on the Theory of Information Retrieva

Micro Deep Drawing of C1100 Conical-cylindrical Cups

AbstractMicro deep drawing was prompted by the rapid development of micro electro mechanical systems, electron industries, new energy, and biomedical in recent years because of its mass production, high efficiency, high precision, low cost and no pollution. However, most researches concentrated on micro cylindrical cups, few studies were reported on other shaped parts. Micro deep drawing of micro conical-cylindrical cups was investigated in this study by using a micro blanking-deep drawing multiple operation mould. The specimen material was pure copper C1100 with a thickness of 50μm which was thermally treated in vacuum condition at 723K for 1h. Micro deep drawing experiments were carried out at room temperature on a universal testing machine at a drawing velocity of 0.05mm/s with the lubrication of polyethylene (PE) film. The results showed that micro conical-cylindrical cups with internal conical bottom diameter of only 0.4mm were well formed. The drawing force and limiting drawing ratio (LDR) micro conical-cylindrical cups were also discussed at the end of this paper

Built-In Self-Test for Automatic Analog Frequency Response Measurement

Abstract-We present a Built-In Self-Test (BIST) approach based on direct digital synthesizer (DDS) for functional test of analog circuitry in mixed-signal systems. DDS with delta-sigma noise shaping is used to generate test signals with different frequencies and phases. The DDS-based BIST hardware implementation can sweep the frequencies through the interested bands and thus measure the frequency response of the analog circuit. The proposed BIST approach has been implemented in Verilog and synthesized into a Field Programmable Gate Array (FPGA). The actual device under test (DUT) was implemented using a Field Programmable Analog Array (FPAA) to form a complete BIST testbed for analog functional tests