Image reconstruction based on inverse problems has the potential for providing good image quality in ultrasound imaging. In this paper, two Inverse Problems-Based (IPB) methods, i.e., Regularized Least Squares (RLS) a...
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Image reconstruction based on inverse problems has the potential for providing good image quality in ultrasound imaging. In this paper, two Inverse Problems-Based (IPB) methods, i.e., Regularized Least Squares (RLS) and the iterative Conjugated Gradient (CG), are evaluated in comparison to the traditional Delay-And-Sum (DAS) Beamrfoming method. It is shown an improvement from 0:36mm to 0:2mm for the axial resolution when comparing the traditional DAS method with the IPB methods, showing the high potential of the method for improving image quality. Similarly, an improvement from 0:5mm to 0:2mm in lateral resolution was found. Further evaluation need to be carried out using data from actual ultrasound systems when noise and speckle is expected.
Error-correction coding is essentially a signal processing technique that is used to improve the reliability of digital communication systems. In the Information Set (IS) decoding a collection of information sets are ...
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Error-correction coding is essentially a signal processing technique that is used to improve the reliability of digital communication systems. In the Information Set (IS) decoding a collection of information sets are used to generate candidate codewords. The procedure then selects as the decoded codeword the one which is closest to the received sequence. This decoding approach can reduces the complexity and time processing in comparison to maximum-likelihood decoding (MLD), and may presents the same decoding level. The performance of the IS algorithm depends on the number of error patterns that the collection of information sets can cover. There are no known constructive procedures for finding optimum collection of information sets. This paper presents an approach for obtaining optimum collections of information sets using genetic algorithms. Using this approach we found, in short time, collections of information sets with high covering capacity. Results from computer simulation show that the performance of the IS algorithm with optimized collections is nearly identical to the maximum-likelihood decoding.
Western blotting readily identifies specific proteins amidst complex biological backgrounds [1, 2]. Nevertheless, immunob-lotting suffers from tremendous labor-intensive and time-intensive requirements [3]. The slab-g...
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ISBN:
(纸本)9781618395955
Western blotting readily identifies specific proteins amidst complex biological backgrounds [1, 2]. Nevertheless, immunob-lotting suffers from tremendous labor-intensive and time-intensive requirements [3]. The slab-gel assays require 1-2 days for completion with multiple hands-on "blotting" steps and yield semi-quantitative information. Recently, our group has introduced new approaches for completing Western blotting. The microfluidic integration strategies introduced and used allow rapid results reporting, full assay automation, and limited sample consumption (1-10 uL). Our integration strategies use spatial, temporal, and spatiotemporal modulation of separation mechanisms in fully electrophoretic systems. The present study reports on recapitulation of immunoaffinity in previously sized proteins, using novel in-transit electrophoretic removal of SDS from SDS-protein complexes. Early results show both the length- and timescales for protein 'renaturation' are compatible with on-chip operation. Further, substantial binding affinity is recapitulated using this streamlined and promising approach.
This paper consists in a novel transport protocol description for Delay-Tolerant Networks and Disruption Tolerant Networks (both DTN). This protocol was designed to offer a better information delivery rate in DTNs sce...
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This paper consists in a novel transport protocol description for Delay-Tolerant Networks and Disruption Tolerant Networks (both DTN). This protocol was designed to offer a better information delivery rate in DTNs scenarios. Fountain codes techniques are used to achieve our needs. The performance results were tested taking into account the hosts buffer size, the TTL (Time To Live) of the messages and the amount of redundant information generated into the network. A better delivery rate and performance were achieved using the proposed protocol.
Background: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable prom...
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Background: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. Results: We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, *** and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. Conclusions: We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the applic
This paper proposes a new prediction-based routing protocol for Delay Tolerant Networks (DTNs) called Greedy Ant (GrAnt). GrAnt uses a greedy transition rule of the Ant Colony Optimization (ACO) metaheuristic to provi...
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This paper proposes a new prediction-based routing protocol for Delay Tolerant Networks (DTNs) called Greedy Ant (GrAnt). GrAnt uses a greedy transition rule of the Ant Colony Optimization (ACO) metaheuristic to provide the exploitation of good previous solutions, when available, and to forward the messages to the most promising node(s). By making use of pheromone concentration and heuristic function, GrAnt protocol provides three modules: scheduling, routing, and buffer management. Simulation results show that in a community-based movement model GrAnt is able to achieve higher successfully message delivery and lower bytes overhead than Epidemic and PROPHET. In a buffer size of 5 MB, for example, GrAnt delivers almost 79 percent of the messages against 39 percent of messages delivered by PROPHET and 45 percent of messages delivered by Epidemic.
Passivation of porous silicon sensors is a critical issue for achieving high sensitivity and selectivity toward biomolecule targets that may be detected in complex physiologic solution. Without appropriate passivation...
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Enhanced rates of oxide growth have been observed on silicon when exposed to high-energy x-ray irradiation. This observed effect could potentially be utilized for remote total ionizing dose-sensing applications.
Enhanced rates of oxide growth have been observed on silicon when exposed to high-energy x-ray irradiation. This observed effect could potentially be utilized for remote total ionizing dose-sensing applications.
Wireless sensor networks can be used in habitat monitoring for detecting fire, in disaster for helping rescue teams and in agriculture for sensing humidity. Node localization is essential for some important sensor net...
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Wireless sensor networks can be used in habitat monitoring for detecting fire, in disaster for helping rescue teams and in agriculture for sensing humidity. Node localization is essential for some important sensor network applications. Despite the high relative accuracy of some localization algorithms, node localization is still an opened research area, due to the physical phenomena such as attenuation, reflection, diffraction, scattering and so forth. The current developed algorithms have different accuracy when are tested under dissimilar environments. We propose to use Smart Beacon Nodes (SBNs) to infer the Obstruction Level Indicator over an occupied area, then, use this indicator for estimating the distance among nodes. In our experimental simulation, SBNs decrease the node localization error of Triangular Centroid Localization and Weighted Centroid Localization up to 18%.
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