Image Signal Processor (ISP) comprises of various blocks to reconstruct image sensor raw data to final image consumed by human visual system or computer vision applications. Each block typically has many tuning parameters due to the complexity of the operation. These need to be hand tuned by Image Quality (IQ) experts, which takes considerable amount of time. In this paper, we present an automatic IQ tuning using nonlinear optimization and automatic reference generation algorithms. The proposed method can produce high quality IQ in minutes as compared with weeks of hand-tuned results by IQ experts. In addition, the proposed method can work with any algorithms without being aware of their specific implementation. It was found successful on multiple different processing blocks such as noise reduction, demosaic, and sharpening.

Our mobile computing devices connect with other devices over proximity networks on a daily basis. Due to the availability of protocols like DLNA, applications in connected devices can enable media sharing experiences where content flows among devices in the proximity space. Despite the pervasiveness of DLNA and other similar implementations, average users perceive these connections to be intrinsically complex. This perception is not limited to media sharing and affects other types of N-device experiences. In this paper we propose to avoid the inherent complexity problem with the design of intelligent contextual systems that respond proactively to user intent. Users should be capable of expressing intent, and connected devices should gain the intelligence necessary to proactively satisfy user intent. We introduce a software service with an inference engine to determine when the contextual conditions are such that user intent can be satisfied. Media sharing connections, content, and protocols are proactively configured based on contextual inference in anticipation of user needs. The system has been designed as an Android service so that any media-sharing application that uses the service becomes contextually proactive.

"Business Microscope" is our sensornet application in the age of knowledge, which visualizes knowledge workers' interactions by sensing their face-to-face communications. Due to the limitation of energy consumption of sensor nodes and privacy concerns, very short (0.1s) intermittently sensed (10s interval) noise-like signals called siglet is used to for detection task. To detect the speaker from the limited input, "self" vs "others" classification problem is introduced. For this new classification problem, new classifier called AdaBoost LVQ is studied to explore the application of AdaBoost to reduce the error rate of the conventional classifier with strictly limited inputs. As a result, AdaBoost LVQ achieved highest recognition accuracy of 96.45% with 19.86% error rate improvement relative to best conventional classifier.

"Business Microscope" is a tool which provides knowledge workers with a bird-eye view of their daily communication. To meet the problem of the energy consumption of sensor nodes and privacy concerns for wearers and non-wearers, "siglet" sensing is proposed. Siglet sensing is a way to capture very short and noise-like signals by sensors operating on a low duty ratio. To extract the useful information on workers' communication, speech siglet detection is studied. The LBG trained speech and workplace nonspeech models with Mel frequency cepstrum coefficients (MFCCs) as feature vectors are utilized. A hierarchical pruning technique is studied to reduce the calculation cost of the matching process to nearly 25% and refine the classification accuracy. Our approach achieved average speech and nonspeech classification accuracy of 99.96% on 0. Is long test siglets.