abstract: In thesis, a test time reduction (a low cost test) methodology for digitally-calibrated pipeline analog-to-digital converters (ADCs) is presented. A long calibration time is required in the final test to validate performance of these designs. To reduce total test time, optimized calibration technique and calibrated effective number of bits (ENOB) prediction from calibration coefficient will be presented. With the prediction technique, failed devices can be identified only without actual calibration. This technique reduces significant amount of time for the total test time. / Dissertation/Thesis / M.S. Electrical Engineering 2013

<p>This report describes both evaluation and implementation of the new coming image compression standard JPEG XR. The intention is to determine if JPEG XR is an appropriate standard for IP based video surveillance purposes. Video surveillance, especially IP based video surveillance, currently has an increasing role in the security market. To be a good standard for surveillance, the video stream generated by the camera is required to be low bit-rate, low latency on the network and at the same time keep a high dynamic display range. The thesis start with a deep insightful study of JPEG XR encoding standard. Since the standard could have different settings,optimized settings are applied to JPEG XR encoder to fit the requirement of network video surveillance. Then, a comparative evaluation of the JPEG XR versusthe JPEG is delivered both in terms of objective and subjective way. Later, part of the JPEG XR encoder is implemented in hardware as an accelerator for further evaluation. SystemVerilog is the coding language. TSMC 40nm process library and Synopsys ASIC tool chain are used for synthesize. The throughput, area, power ofthe encoder are given and analyzed. Finally, the system integration of the JPEGXR hardware encoder to Axis ARTPEC-X SoC platform is discussed.</p>

This report describes both evaluation and implementation of the new coming image compression standard JPEG XR. The intention is to determine if JPEG XR is an appropriate standard for IP based video surveillance purposes. Video surveillance, especially IP based video surveillance, currently has an increasing role in the security market. To be a good standard for surveillance, the video stream generated by the camera is required to be low bit-rate, low latency on the network and at the same time keep a high dynamic display range. The thesis start with a deep insightful study of JPEG XR encoding standard. Since the standard could have different settings,optimized settings are applied to JPEG XR encoder to fit the requirement of network video surveillance. Then, a comparative evaluation of the JPEG XR versusthe JPEG is delivered both in terms of objective and subjective way. Later, part of the JPEG XR encoder is implemented in hardware as an accelerator for further evaluation. SystemVerilog is the coding language. TSMC 40nm process library and Synopsys ASIC tool chain are used for synthesize. The throughput, area, power ofthe encoder are given and analyzed. Finally, the system integration of the JPEGXR hardware encoder to Axis ARTPEC-X SoC platform is discussed.

Cette thèse regroupe nos résultats sur la prédiction de séries temporelles dépendantes. Le document comporte trois chapitres principaux où nous abordons des problèmes différents. Le premier concerne l’agrégation de prédicteurs de décalages de Bernoulli Causales, en adoptant une approche Bayésienne. Le deuxième traite de l’agrégation de prédicteurs de ce que nous définissions comme processus sous-linéaires. Une attention particulaire est portée aux processus autorégressifs localement stationnaires variables dans le temps, nous examinons un schéma de prédiction adaptative pour eux. Dans le dernier chapitre nous étudions le modèle de régression linéaire pour une classe générale de processus localement stationnaires. / This thesis regroups our results on dependent time series prediction. The work is divided into three main chapters where we tackle different problems. The first one is the aggregation of predictors of Causal Bernoulli Shifts using a Bayesian approach. The second one is the aggregation of predictors of what we define as sub-linear processes. Locally stationary time varying autoregressive processes receive a particular attention; we investigate an adaptive prediction scheme for them. In the last main chapter we study the linear regression problem for a general class of locally stationary processes.