This paper summarizes recent works done in NAU in the field of development of icing detection algorithm, based on remote sensing of clouds and precipitation. Conditions of aircraft icing are described. Backscattering ...
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This paper summarizes recent works done in NAU in the field of development of icing detection algorithm, based on remote sensing of clouds and precipitation. Conditions of aircraft icing are described. Backscattering of microwaves on water droplets and ice crystals of different shapes is considered as function of polarization, antenna elevation, size distribution of scatterers and other factors. Polarization parameters of scattered signal are calculated for hazardous and non-hazardous cases. Icing detection algorithm is proposed.
Failure detectors are a fundamental part of safe fault-tolerant distributed systems. Many failure detectors use heartbeats to draw conclusions about the state of nodes within a distributed environment. The contributio...
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ISBN:
(纸本)9780769531021
Failure detectors are a fundamental part of safe fault-tolerant distributed systems. Many failure detectors use heartbeats to draw conclusions about the state of nodes within a distributed environment. The contribution of this paper is an approach whose benefits are twofold. On the one hand it reduces the network overhead produced by heartbeat-style failure detectors. On the other hand it improves the quality of these failure detectors by providing them with richer information about the current network condition. We call this approach lazy monitoring since the active sending of heartbeats is avoided if possible. As it is independent of the actual failure detection algorithm it can be used in many domains. For evaluation purposes we applied our approach to the Smart Doorplate Project. In this testbed the proposed technique reduced the traffic to 1.2% while providing much more information about the environment to the failure detectors.
The global health, threatened by emerging infectious diseases, pandemic influenza, and biological warfare, is becoming increasingly dependent on the rapid acquisition, processing, integration and interpretation of mas...
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ISBN:
(纸本)9781424427086
The global health, threatened by emerging infectious diseases, pandemic influenza, and biological warfare, is becoming increasingly dependent on the rapid acquisition, processing, integration and interpretation of massive amounts of data. In response to these pressing needs, new information infrastructures are needed to support active, real time surveillance. detection algorithms may have a high computational cost in both the time and space domains. High performance computing platforms may be the best approach for efficiently computing these algorithms. Unfortunately, these platforms are unavailable to many health care agencies. Our work focuses on efficient parallelization of outbreak detection algorithms within the context of cloud computing as a high throughput computing platform. Cloud computing is investigated as an approach to meet real time constraints and reduce or eliminate costs associated with real time disease surveillance systems.
Event detection algorithms cover enormous relevance when their execution environment has reduced capabilities as the CPU computing power, memory budget and overall energy capacity. It is the case for embedded wireless...
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Event detection algorithms cover enormous relevance when their execution environment has reduced capabilities as the CPU computing power, memory budget and overall energy capacity. It is the case for embedded wireless sensor nodes or motes that promise an actual and massive ubiquity on future real world applications. We have implemented a "soft" acoustic event detector of human activities, particularly speech signal. The algorithm has been adapted from the wireless multi-channel IEEE 802.11 QoS VoIP field. The events are detected on the base of the change of rate of the sum of the current input signal energy average plus the signal energy standard deviation values. The detector performance is presented in terms of false and positive detections, for three input signals with different SNR levels.
In high-noise environment, the background noises have greatly bad influence on the speech recognition and its transmission quality. The speech extraction and the noise reduction are long-term questions which the commu...
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In high-noise environment, the background noises have greatly bad influence on the speech recognition and its transmission quality. The speech extraction and the noise reduction are long-term questions which the communication system has not solved effectively all the while. In this paper, we propose an improved system solution based on the combination of the bone-conduction sensor, a digital signal processor contained denoising algorithms and an active noise reduction part. At the beginning, the bone-conduction sensor is used to gather the voice. Next, the collected noisy speech signal is enhanced with spectral subtraction algorithm. Then the enhanced signal is detected with the improved short-time energy voice activity detection algorithm based on the Mel frequency. Finally we employ the ANR technology in the receiver. We have designed a prototype system based on the wireless platform. The results of tests show that it achieves better progress in anti-noise performance.
To reduce the false positive hits, a new method for the computer aided detection of microcalcification clusters is proposed in the paper by joint analysis of two views of the same breast. The novelty of the scheme inc...
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To reduce the false positive hits, a new method for the computer aided detection of microcalcification clusters is proposed in the paper by joint analysis of two views of the same breast. The novelty of the scheme includes a consequent two steps of matching processes: spatial and feature matching. The former links a suspicious cluster located on the MLO view with a corresponding location on the CC view using their spatial information to form a paired cluster, and then in the latter stage, each cluster candidates are characterized by its single-view features such as size, shape and intensity. Finally a similarity function is calculated between the pair to determine if they were true microcalcification clusters. The experiments show that the proposed method has advantages of lower FP rate compared to the one on a single view.
Ant colony optimization (ACO) is a metaheuristic approach for solving hard optimization problem. It has been applied to solve various image processing problems such as image segmentation, classification, image analysi...
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ISBN:
(纸本)9780769533599
Ant colony optimization (ACO) is a metaheuristic approach for solving hard optimization problem. It has been applied to solve various image processing problems such as image segmentation, classification, image analysis and edge detection. In this paper, we present an Improved Canny edges (ICE-ACO) algorithm which uses ACO to solve the problem of linking disjointed edges produced by Canny edge detector.
Anomaly detection is to find aggregate which is different from the most aggregates. Aiming at the limitation of the anomaly detection algorithm of shifted wavelet tree (SWT) in data streams, we propose the improved al...
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ISBN:
(纸本)9781424439027
Anomaly detection is to find aggregate which is different from the most aggregates. Aiming at the limitation of the anomaly detection algorithm of shifted wavelet tree (SWT) in data streams, we propose the improved algorithm which constructs the monotonic search space for binary search after removing the disturbance of bumps to increase the efficiency of detection, and uses the real-time incremental update algorithm for meeting the requirement of the online processing of the data streams. The simulation experiments using two data sets of the Gamma Ray and the Power Quality Disturbance (PQD) verify the high effectiveness and accuracy of our algorithm.
We develop an analytical framework to evaluate the probability-of-miss and probability-of-false-alarm performance of an impulse noise (IN) detection algorithm for orthogonal frequency-domain multiplexing (OFDM) symbol...
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We develop an analytical framework to evaluate the probability-of-miss and probability-of-false-alarm performance of an impulse noise (IN) detection algorithm for orthogonal frequency-domain multiplexing (OFDM) symbols based on monitoring the post-frequency-domain-equalizer (FEQ) instantaneous error at the output of the QAM constellation demapper over a range of tones (sub-carriers). Using the VDSL system as a reference, we further analyze the impact of impulse-noise detection error events at the scale of the OFDM symbol (miss and false alarm) on the bit-error-rate (BER) performance of interleaved Reed-Solomon (RS) codes with erasure-decoding. The analysis culminates with a sequence of steps to design the parameters of the frequency-domain IN detector based on the design BER of the communication system and practical transceiver features such as noise-margin and seamless-rate-adaptation (SRA). We conclude that a tone-error based frequency-domain IN detector monitoring 100 tones is capable of providing IN detection performance that does not cause a deterioration of the average BER below the VDSL system design BER of 10 -7 when interleaved RS coding with erasure-decoding is employed.
Normally, voice activity detection (VAD) refers to speech processing algorithms for detecting the presence or absence of human speech in segments of audio signals. In this paper, however, we focus on speech detection ...
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Normally, voice activity detection (VAD) refers to speech processing algorithms for detecting the presence or absence of human speech in segments of audio signals. In this paper, however, we focus on speech detection algorithms that take VoIP traffic instead of audio signals as input. We call this category of algorithms network-level VAD. Traditional VAD usually plays a fundamental role in speech processing systems because of its ability to delimit speech segments. Network-level VAD, on the other hand, can be quite helpful in network management, which is the motivation for our study. We propose the first real-time network-level VAD algorithm that can extract voice activity from encrypted and non-silence-suppressed Skype traffic. We evaluate the speech detection accuracy of the proposed algorithm with extensive real-life traces. The results show that our scheme achieve reasonably good performance even high degree of randomness has been injected into the network traffic.
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