What is the best way to help humans adapt to a distorted sensory input? Interest in this question is more than academic. The answer may help facilitate auditory learning by people who became deaf after learning language and later received a cochlear implant (a neural prosthesis that restores hearing through direct electrical stimulation of the auditory nerve). There is evidence that some cochlear implants (which provide information that is spectrally degraded to begin with) stimulate neurons with higher characteristic frequency than the acoustic frequency of the original stimulus. In other words, the stimulus is shifted in frequency with respect to what the listener expects to hear. This frequency misalignment may have a negative influence on speech perception by CI users. However, a perfect frequency-place alignment may result in the loss of important low frequency speech information. A trade-off may involve a gradual approach: start with correct frequency-place alignment to allow listeners to adapt to the spectrally degraded signal first, and then gradually increase the frequency shift to allow them to adapt to it over time. We used an acoustic model of a cochlear implant to measure adaptation to a frequency-shifted signal, using either the gradual approach or the “standard” approach (sudden imposition of the frequency shift). Listeners in both groups showed substantial auditory learning, as measured by increases in speech perception scores over the course of fifteen one-hour training sessions. However, the learning process was faster for listeners who were exposed to the gradual approach. These results suggest that gradual rather than sudden exposure may facilitate perceptual learning in the face of a spectrally degraded, frequency-shifted input.

A linear CCD sensor reads temporal data from a CCD array continuously and forms a 2D image profile. Compared to most of the sensors in the current sensor networks that output temporal signals, it delivers more information such as color, shape, and event of a flowing scene. On the other hand, it abstracts passing objects in the profile without heavy computation and transmits much less data than a video. This paper revisits the capabilities of the sensors in data processing, compression, and streaming in the framework of wireless sensor network. We focus on several unsolved issues such as sensor setting, shape analysis, robust object extraction, and real time background adapting to ensure long-term sensing and visual data collection via networks. All the developed algorithms are executed in constant complexity for reducing the sensor and network burden. A sustainable visual sensor network can thus be established in a large area to monitor passing objects and people for surveillance, traffic assessment, invasion alarming, etc.

This work converts the surveillance video to a temporal domain image called temporal profile that is scrollable and scalable for quick searching of long surveillance video by human operators. Such a profile is sampled with linear pixel lines located at critical locations in the video frames. It has precise time stamp on the target passing events through those locations in the field of view, shows target shapes for identification, and facilitates the target search in long videos. In this paper, we first study the projection and shape properties of dynamic scenes in the temporal profile so as to set sampling lines. Then, we design methods to capture target motion and preserve target shapes for target recognition in the temporal profile. It also provides the uniformed resolution of large crowds passing through so that it is powerful in target counting and flow measuring. We also align multiple sampling lines to visualize the spatial information missed in a single line temporal profile. Finally, we achieve real time adaptive background removal and robust target extraction to ensure long-term surveillance. Compared to the original video or the shortened video, this temporal profile reduced data by one dimension while keeping the majority of information for further video investigation. As an intermediate indexing image, the profile image can be transmitted via network much faster than video for online video searching task by multiple operators. Because the temporal profile can abstract passing targets with efficient computation, an even more compact digest of the surveillance video can be created.