Ink-Jet Printing of Color Optical Filters for LCD Applications

At the age of multi-media, portable electronic devices such as mobile phones, personal digital assistant and handheld gaming systems have increased the demand for high performance displays with low cost production. Inkjet printing color optical filters (COF) for LCD applications seem to be an interesting alternative to decrease the production costs. The advantage of inkjet printing technology is to be fast, accurate, easy to run and cheaper than other technologies. In this master thesis work, we used various disciplines such as optical microscopy, rheology, inkjet printing, profilometering and colorimetry. The specific aim of the thesis was to investigate the feasibility of using company-A pigment formulation in inkjet production of COF for active matrix LCD applications. Ideal viscosity parameters were determined from 10 to 20mPa·s for easy inkjet printing at room temperature. The red pigments used are fully dispersed into the solvent and present an excellent homogenous repartition after printing. Thickness investigations revealed that the printed COF were equal or slightly thicker than typically manufactured ones. The colorimetry investigations demonstrated color coordinates very close to the NTSC red standard. LED backlighting seems to be a valuable solution to combine with the printed COF regarding to the spectrum and color analysis. The results on this thesis will increase the understanding of inkjet printing company-A pigments to produce COF for LCD applications.

Assessment of PD Speech Anomalies @ Home

Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.