The multi-channel Hybrid Fiber Coaxial (HFC) network is essentially a shared medium with multi-channels. Its operation requires the use of a scheduling algorithm to manage the data transmission within each channel. The Data-Over-Cable Service Interface Specification (DOCSIS) protocol is an important standard for HFC networks. Since this protocol does not explicitly specify the scheduling algorithm to be used, many alternative algorithms have been proposed. However, none of these algorithms are applicable to the scheduling of non-Unsolicited Grant Service (UGS) data in multi-channel HFC networks. Accordingly, the present study develops a multi-channel scheduling algorithm which optimizes the scheduling delay time of each transmitted non-UGS request. This algorithm manages the amount of data transmission in each upstream channel according to the overall network load and the bandwidth available in each channel. This study constructs a mathematical model of the algorithm and then uses this model as the basis for a series of simulations in which the performance of the scheduling algorithm is evaluated.

An Off-line mammography screening system is used in pre-screening mammograms to separate high-risk mammograms from most normal cases. Off-line system can run before radiologist's review and is particularly useful in the national breast cancer screening program which usually consists of high percentage of normal cases. Until now, the shortcomings of on-line detection of clustered microcalcifications from a mammogram remain in the necessity of manual selection of regions of interest. The developed technique focuses on detection of microcalcifications within a region of interest indicated by the radiologist. Therefore, this kind of system is not efficient enough to process hundreds of mammograms in a short time without a large number of radiologists. In this paper, based on a "hierarchically-coarse-to-fine" approach, an off-line mammography screening system for the detection and segmentation of clustered microcalcifications is presented. A serial off-line procedures without any human intervention should consider the complexity of organization of mammograms. In practice, it is impossible to use one technique to obtain clustered microcalcifications without consideration of background text and noises from image acquisition, the position of breast area and regions of interest. "Hierarchically-coarse-to-fine" approach is a serial procedures without any manual operations to reduce the potential areas of clustered microcalcifications from a mammogram until clustered microcalcifications are found. The reduction of potential areas starts with a mammogram, through identification of the breast area, identification of the suspicious areas of clustered microcalcifications, and finally segmentation of clustered microcalcifications. It is achieved hierarchically from coarse level to fine level. In detail, the proposed system includes breast area separation, enhancement, detection and localization of suspicious areas, segmentation of microcalcifications, and target selection of microcalcifications. The system separates its functions into hierarchical steps and follows the rule of thumb "coarse detection followed by fine segmentation" in performing each step of processing. The decomposed hierarchical steps are as follows: The system first extracts the breast region from which suspicious areas are detected. Then precise clustered microcalcification regions are segmented from the suspicious areas. For each step of operation, techniques for rough detection are first applied followed by a fine segmentation to accurately detect the boundaries of the target regions. With this "hierarchically-coarse-to-fine" approach, a complicated work such as the detection of clustered microcalcifications can be divided and conquered. The effectiveness of the system is evaluated by three experienced radiologists using two mammogram databases from the Nijmegen University Hospital and the Taichung Veterans General Hospital. Results indicate that the system can precisely extract the clustered microcalcifications without human intervention, and its performance is competitive with that of experienced radiologists, showing the system as a promising asset to radiologists.