Numerous studies have indicated that ATM available bit rate (ABR) service can provide low-delay, fairness, and high throughput, and can handle congestion effectively inside the ATM network. However, network congestion is not really eliminated but rather it is pushed out to the edge of the ATM network, packets from TCP sources competing for the available ATM bandwidth are buffered in the routers or switches at the network edges, causing severe congestion, degraded throughput, and unfairness. This poor performance is mainly due to the uncoordinated interaction between the congestion control mechanism of TCP and ATM. It is well accepted that some form of cooperation at edge device would help to control TCP traffic flow over ATM more effectively. We have previously proposed the fair intelligent explicit window adaptation (FIEWA) scheme and fair intelligent ACK bucket control (FIABC) scheme. The key idea is to combine the feedback information from the receiver, from the underlying ATM network, and from the local information at the edge device intelligently to explicitly/implicitly control the TCP rate. We present a comparative simulation study on our schemes with other established schemes; to identify the characteristics of each different scheme; and to indicate the requirement for a fairer, simpler and more robust coherent approach at the edge device.
We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events in cell video sequences. Cells in images that are captured from various biomedical applications usually have different shapes and motility, which pose difficulties for the automated event detection in cell videos. Current methods to detect cellular events are based on supervised machine learning and rely on tedious manual annotation from investigators with specific expertise. So that our LSTM network could be trained in an unsupervised manner, we designed it with a branched structure where one branch learns the frequent, regular appearance and movements of objects and the second learns the stochastic events, which occur rarely and without warning in a cell video sequence. We tested our network on a publicly available dataset of densely packed stem cell phase-contrast microscopy images undergoing cell division. This dataset is considered to be more challenging that a dataset with sparse cells. We compared our method to several published supervised methods evaluated on the same dataset and to a supervised LSTM method with a similar design and configuration to our unsupervised method. We used an F1-score, which is a balanced measure for both precision and recall. Our results show that our unsupervised method has a higher or similar F1-score when compared to two fully supervised methods that are based on Hidden Conditional Random Fields (HCRF), and has comparable accuracy with the current best supervised HCRF-based method. Our method was generalizable as after being trained on one video it could be applied to videos where the cells were in different conditions. The accuracy of our unsupervised method approached that of its supervised counterpart.
Function imaging has been playing an important role in modern biomedical research and clinical diagnosis, which provides human internal biochemical information previously not available. However, for a routine dynamic study with a typical medical function imaging system, such as positron emission tomography (PET), it is easily to acquire nearly 1000 images for just one patient in one study. Such a large number of images has given a considerable burden for computer image storage space, data processing and transmission time. In this paper, we present the theory and principles for the minimization of image frames in dynamic biomedical function imaging. We show that the minimum number of image frames required is just equal to the model identifiable parameters and that the quality of the physiological parameter estimation, based on these minimum number of image frames, can be controlled at a comparable level. As a result of our study, the image storage space required can be reduced by more than 80 percent.
A novel content-adaptive audio watermarking technique is proposed. To optimally balance in-audibility and robustness when embedding and extracting watermarks, the embedding scheme is high related to audio content by making use of the properties of human auditory system and multiple-bit hopping technique. The experimental results in robustness are provided to support all the novel features in our watermarking scheme.
The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most challenging tasks in automated cervix cytological analysis due to specifically the presence of overlapping cells. This paper introduces a multi-pass fast watershed-based method (MPFW) to segment both nucleus and cytoplasm from large cell masses of overlapping cervical cells in three watershed passes. The first pass locates the nuclei with barrier-based watershed on the gradient-based edge map of a pre-processed image. The next pass segments the isolated, touching, and partially overlapping cells with a watershed transform adapted to the cell shape and location. The final pass introduces mutual iterative watersheds separately applied to each nucleus in the largely overlapping clusters to estimate the cell shape. In MPFW, the line-shaped contours of the watershed cells are deformed with ellipse fitting and contour adjustment to give a better representation of cell shapes. The performance of the proposed method has been evaluated using synthetic, real extended depth-of-field, and multi-layers cervical cytology images provided by the first and second overlapping cervical cytology image segmentation challenges in ISBI 2014 and ISBI 2015. The experimental results demonstrate superior performance of the proposed MPFW in terms of segmentation accuracy, detection rate, and time complexity, compared with recent peer methods.
Image reconstruction of fluorescent molecular tomography (FMT) often involves repeatedly solving large-dimensional matrix equations, which are computationally expensive, especially for the case where there are large deviations in the optical properties between the target and the reference medium. In this paper, a wavelet-based multiresolution reconstruction approach is proposed for the FMT reconstruction in combination with a parallel forward computing strategy, in which both the forward and the inverse problems of FMT are solved in the wavelet domain. Simulation results demonstrate that the proposed approach can significantly speed up the reconstruction process and improve the image quality of FMT.
Maximum a posteriori (MAP) reconstruction makes use of an anatomical prior from CT or MRI imaging to enforce smoothness of reconstructed PET images while preserving anatomical edges. The tendency of this technique to smooth parts of the image between anatomical boundaries may reduce the detectability of functional lesions if, as is commonly the case, the edges of these lesions do not conform to anatomical boundaries. We have investigated the use of a functional prior in addition to an anatomical prior to improve the detection and quantification of lesions in PET imaging. We introduce a new parameter, Q, which controls the weight, β, of the functional prior on a spatially-variant basis, to enable a reduction of the smoothing effect in regions containing lesions. Such regions constitute the functional prior. They can be defined, for example, by applying a threshold to a preliminary reconstructed PET image. They are quarantined from the smoothing of the standard MAP algorithm, and subjected to a lesser degree of smoothing as determined by the combined effects of Q and β. We call this dual-prior technique quarantine MAP reconstruction (QMAP). Thus the method alters the degree of smoothing in specific parts of the image with the aim of enhancing lesion detectability. We have compared the QMAP algorithm in computer simulations with standard One-Step-Late (OSL) MAP reconstruction and OSL-MAP with CT prior information. QMAP provided better lesion contrast than the other algorithms, without altering the properties of other parts of the image.
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