In this paper, we propose a lensless compressive imaging architecture. The architecture consists of two components, an aperture assembly and a sensor. No lens is used. The aperture assembly consists of a two dimensional array of aperture elements. The transmittance of each aperture element is independently controllable. The sensor is a single detection element. A compressive sensing matrix is implemented by adjusting the transmittance of the individual aperture elements according to the values of the sensing matrix. The proposed architecture is simple and reliable because no lens is used. The architecture can be used for capturing images of visible and other spectra such as infrared, or millimeter waves, in surveillance applications for detecting anomalies or extracting features such as speed of moving objects. Multiple sensors may be used with a single aperture assembly to capture multiview images simultaneously. A prototype was built by using a LCD panel and a photoelectric sensor for capturing images of visible spectrum.

A hierarchical modulation scheme is proposed to upgrade an existing digital broadcast system, such as satellite TV, or satellite radio, by adding more data in its transmission. The hierarchical modulation consists of a basic constellation, which is the same as in the original system, and a secondary constellation, which carries the additional data for the upgraded system. The upgraded system with the hierarchical modulation is backward compatible in the sense that receivers that have been deployed in the original system can continue receiving data in the basic constellation. New receivers can be designed to receive data carried in the secondary constellation, as well as those in the basic constellation. Analysis will be performed to show the tradeoff between bit rate of the data in secondary constellation and the penalty to the performance of receiving the basic constellation.

the client has ample computational resources and a high resolution display, there is no way to get a better viewing experience which is commensurate with the available resources. This phenomenon is known as the "cliff effect"-no video is available unless some threshold constraints are met and no improvement is achieved when the constraints are exceeded. Scalable video coding (SVC) [18] encodes video into ordered layers, where each higher layer provides a refinement to the encoding of the lower layers. Since decoding of lower layers is a prerequisite for the decoding of higher ones, the lower layers need a higher level of protection in transmission. In broadcast applications, each layer may be transmitted as a separate stream, with different protection levels, e.g., by using hierarchical modulation [9]. In point-to-point applications, feedback from the client usually indicates how many layers are to be transmitted. In these situations, providing different protection to each layer is usually difficult-all layers receive the same protection, which severely limits the scalability. The practical limitation of SVC is its relatively small number of layers (due to

A stochastic conjugate gradient method for approximation of a function is proposed. The proposed method avoids computing and storing the covariance matrix in the normal equations for the least squares solution. In addition, the method performs the conjugate gradient steps by using an inner product that is based stochastic sampling. Theoretical analysis shows that the method is convergent in probability. The method has applications in such fields as predistortion for the linearization of power amplifiers.

Multi-view images are acquired by a lensless compressive imaging architecture, which consists of an aperture assembly and multiple sensors. The aperture assembly consists of a two dimensional array of aperture elements whose transmittance can be individually controlled to implement a compressive sensing matrix. For each transmittance pattern of the aperture assembly, each of the sensors takes a measurement. The measurement vectors from the multiple sensors represent multi-view images of the same scene. We present theoretical framework for multi-view reconstruction and experimental results for enhancing quality of image using multi-view.