Adjustable transmission power in wireless Ad Hoc networks with smart antennas

In this paper, we present a model to analyze the performance of wireless ad hoc networks with smart antennas, i.e. directional antennas with adjustable transmission power. Our results show that smart antennas can improve the network performance by mitigating the effects of interference. We illustrate our model with the NFP (Nearest with Forward Progress) transmission strategy. Our analytical and simulation results show that, for ad hoc networks with smart antennas, NFP yields good throughput and remains stable as the node density varies. © 2008 IEEE.published_or_final_versionThe Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM 2008), New Orleans, LO., USA, 30 November-4 December 2008, p. 1326-133

An integrated visual framework for the human-Web interface

© 2002 IEEE. The design of Web sites has been largely ad hoc, with little concern about the effectiveness of navigation and maintenance. This paper presents a general framework with a human-Web interface that supports Web design through visual programming and reverse Web engineering through visualization. The paper describes the framework in the context of a Web tool, known as HWIT which has been developed for a pilot study

Transmission radius control in wireless Ad Hoc networks with smart antennas

In this paper, we present a model to analyze the performance of three transmission strategies with smart antennas, i.e. directional antennas with adjustable transmission power. Generally, a larger transmission radius contributes a greater progress if a transmission is successful. However, it has a higher probability of collision with other concurrent transmissions. Smart antennas mitigate collisions with sectorized transmission ranges. They also extend the transmission radii. By modelling three transmission strategies, namely, Nearest with Forward Progress (NFP), Most Forward with Fixed Radius (MFR), and Most Forward with Variable Radius (MVR), our analysis illustrates that the use of smart antennas can greatly reduce the possibility of conflicts. The model considers the interference range and computes the interference probability for each transmission strategy. We have analyzed two Medium Access Control (MAC) protocols using our interference model, namely, the slotted ALOHA protocol and the slotted CSMA/CA-like protocol. The result shows that, for slotted ALOHA, NFP yields the best one-hop throughput, whereas MVR provides the best average forward progress. The overall performance is substantially improved with the slotted CSMA/CA-like protocol, and the network becomes more resilient. © 2010 IEEE.published_or_final_versio

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches

Developing an EEG-based on-line closed-loop lapse detection and mitigation system

© 2014 Wang, Huang, Wei, Huang, Ko, Lin, Cheng and Jung. In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age

A timely computer-aided detection system for acute ischemic and hemorrhagic stroke on CT in an emergency environment

Standalone Presentations: no. LL-IN1105BACKGROUND: When a patient is accepted in the emergency room suspected of stroke, time is of the most importance. The infarct brain area suffers irreparable damage as soon as three hours after the onset of stroke symptoms. Non-contrast CT scan is the standard first line of investigation used to identify hemorrhagic stroke cases. However, CT brain images do not show hyperacute ischemia and small hemorrhage clearly and thus may be missed by emergency physicians. We reported a timely computer-aided detection (CAD) system for small hemorrhages on CT that has been successfully developed as an aid to ER physicians to help improve detection for Acute Intracranial Hemorrhage (AIH). This CAD system has been enhanced for diagnosis of acute ischemic stroke in addition to hemorrhagic stroke, which becomes a more complete and clinically useful tool for assisting emergency physicians and radiologists. In the detection algorithm, brain matter is first segmented, realigned, and left-right brain symmetry is evaluated. As in the AIH system, the system confirms hemorrhagic stroke by detecting blood presence with anatomical and medical knowledge-based criteria. For detecting ischemia, signs such as regional hypodensity, blurring of grey and white matter differentiation, effacement of cerebral sulci, and hyperdensity in middle cerebral artery, are evaluated …published_or_final_versio