9263402
Artificial convolution neural network for medical image pattern recognition
Abstract We have developed several training methods in conjunction with a convolution neural network for general medical image pattern recognition. An unconventional method of using rotation and shift invariance is also proposed to enhance the neural net performance. The structure of the artificial neural network is a simplified network structure of the neocognitron. Two-dimensional local connection as a group is the fundamental architecture for the signal propagation in the convolution neural network. Weighting coefficients of convolution kernels are formed by the neural network through backpropagated training for this artificial neural net. In addition, radiologists' reading procedure was modelled in order to instruct the artificial neural network to recognize the predefined image patterns and those of interest to experts. Our training techniques involve (a) radiologists' rating for each suspected image area, (b) backpropagation of generalized distribution, (c) trainer imposed functions, (d) shift and rotation invariance of diagnosis interpretation, and (e) consistency of clinical input data using appropriate background reduction functions. We have tested these methods for detecting lung nodules on chest radiographs and microcalcications on mammograms. The performance studies have shown the potential use of this technique in a clinical environment. We also used a profile double-matching technique for initial nodule search and used a wavelet high pass filtering technique to enhance subtle clustered microcalcifications. We set searching parameters at a highly sensitive level to identify all potential disease areas. The artificial convolution neural network acts as a final detection classifier to determine whether a disease pattern is shown on the suspected image area.
34280688
Mammography Tomosynthesis System for High Performance 3D Imaging
Tomosynthesis provides a major advance in image quality compared to conventional projection mammography by effectively eliminating the effects of superimposed tissue on anatomical structures of interest. Early tomosynthesis systems focused primarily on feasibility assessment by providing 3-dimensional images to determine performance advantages. However, tomosynthesis image quality depends strongly on three key parameters: 1) detector performance at low dose, 2) angular range and number of projections acquired in the tomosynthesis scan, and 3) reconstruction algorithm processing characteristics used to create slice images from the measured projections. In this work, a new GE mammo-graphy tomosynthesis research system was developed that incorporates key improvements in each of these three areas compared to an early feasibility prototype system in use at Massachusetts General Hospital from 2000 to 2004. The performance gains that can be achieved by these enhancements are cha-racterized, and clinical images acquired with the system at the University of Michigan Cancer and Geriatrics Center are presented. The advanced research system also provides the ability to acquire mechanically co-registered x-ray tomosynthesis and ultrasound images of the breast, and initial dual modality images are also presented.
18562701
Compressed Index for a Dynamic Collection of Texts
Let T be a string with n characters over an alphabet of bounded size. The recent breakthrough on compressed indexing allows us to build an index for T in optimal space (i.e., O(n) bits), while supporting very efficient pattern matching [2,4]. This paper extends the work on optimal-space indexing to a dynamic collection of texts. Precisely, we give a compressed index using O(n) bits where n is the total length of texts, such that searching for a pattern P takes O(|P|log n + occ log2 n) time where occ is the number of occurrences, and inserting or deleting a text T takes O(|T|log n) time.
182657
Key infection: smart trust for smart dust
Future distributed systems may include large self-organizing networks of locally communicating sensor nodes, any small number of which may be subvened by an adversary. Providing security for these sensor networks is important, but the problem is complicated by the fact that managing cryptographic key material is hard: low-cost nodes are neither tamper-proof nor capable of performing public key cryptography efficiently. We show how the key distribution problem can be dealt with in environments with a partially present, passive adversary: a node wishing to communicate securely with other nodes simply generates a symmetric key and sends it in the clear to its neighbours. Despite the apparent insecurity of this primitive, we can use mechanisms for key updating, multipath secrecy amplification and multihop key propagation to build up extremely resilient trust networks where at most a fixed proportion of communications links can be eavesdropped. We discuss applications in which this assumption is sensible. Many systems must perforce cope with principals who are authenticated weakly, if at all; the resulting issues have often been left in the 'too hard' tray. One particular interest of sensor networks is that they present a sufficiently compact and tractable version of this problem. We can perform quantitative analyses and simulations of alternative strategies, some of which we present here. We also hope that This work may start to challenge the common belief that authentication is substantially about bootstrapping trust. We argue that, in distributed systems where the opponent can subvert any small proportion of nodes, it is more economic to invest in resilience than in bootstrapping.
987821
Using Clustering Information for Sensor Network Localization
Sensor network localization continues to be an important research challenge. The goal of localization is to assign geographic coordinates to each node in the sensor network. Localization schemes for sensor network systems should work with inexpensive off-the-shelf hardware, scale to large networks, and also achieve good accuracy in the presence of irregularities and obstacles in the deployment area. 
 
We present a novel approach for localization that can satisfy all of these desired properties. Recent developments in sensor network clustering algorithms have resulted in distributed algorithms that produce highly regular clusters. We propose to make use of this regularity to inform our localization algorithm. The main advantages of our approach are that our protocol requires only three randomly-placed nodes that know their geographic coordinates, and does not require any ranging or positioning equipment (i.e., no signal strength measurement, ultrasound ranging, or directional antennas are needed). So far, only the DV-Hop localization mechanism worked with the same assumptions [1]. We show that our proposed approach may outperform DV-Hop in certain scenarios, in particular when there exist large obstacles in the deployment field, or when the deployment area is free of obstacles but the number of anchors is limited.
11373384
Joint Call Admission Control for Next Generation Wireless Network
Next generation wireless network (NGWN) will be heterogeneous in nature where different radio access technologies (RATs) such as UMTS, WiMax, WLAN, etc, coexist. This coexistence necessitates joint radio resource management (JRRM). This paper focuses on joint call admission control (JCAC) for NGWN. Factors that determine a user's preference for a particular RAT are discussed. We propose a JCAC algorithm which considers the user's preference in making admission decision. A specific case where the user prefers to be served by the RAT which has the least service cost is modeled and evaluated using Markov decision process. The results obtained shows that overall service cost in heterogeneous network can be significantly reduced by using the proposed joint call admission control
15971365
Efficiency of Data Distribution in BitTorrent-Like Systems
BitTorrent (BT) in practice is a very efficient method to share data over a network of clients. In this paper we extend the recent work of Arthur and Panigrahy [1] on modelling the distribution of individual data blocks in BT systems, aiming at a better understanding of why BT can achieve a high degree of parallelism. In particular, we include in our study several new network features that BT systems are using, as well as different local heuristics for routing data blocks in each client. We conduct simulations to figure out to what extent the new network features and routing heuristics would affect the distribution efficiency. Our findings confirm that for the primitive network setting studied in [1], it does require i¾?(blogn) phases for nclients to download bdata blocks. More interestingly, our work suggests that for the more realistic network setting, the heuristics Random and Rarest Block First both allow nclients to download bblocks in b+ O(logn) phases. We believe that the latter bound better reflects the high degree of parallelism of BT observed in reality. It is also worth-mentioning that b+ lognis the smallest possible number of phases needed; it is interesting to see that some simple local routing heuristics have a performance so close to the optimal.