• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.713-719
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• 2010

Collaborative spectrum sensing has been found to be an effective means for detecting the activity of primary users in a fading environment. Most previous works on collaborative spectrum sensing are based on the assumption that the local spectrum sensing decisions of secondary users are statistically independent. However, it may not hold in some practical situations. In this paper, we consider a cognitive radio network where the local spectrum sensing decisions of secondary users are statistically correlated with the same level of correlation if they are next to each other in location and statistically independent, otherwise. Then, for the system, we analyzed the performance of the collaborative spectrum sensing with the AND and the OR fusion rules and found that the scheme with the AND fusion rule performs better than the one with OR fusion rule when the degree of correlation is significant.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.195-203
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• 2012

This paper proposes ground target classification algorithm based on decision fusion and feature extraction method using multi-sensor images. The decisions obtained from the individual classifiers are fused by applying a weighted voting method to improve target recognition rate. For classifying the targets belong to the individual sensors images, features robust to scale and rotation are extracted using the difference of brightness of CM images obtained from CCD image and the boundary similarity and the width ratio between the vehicle body and turret of target in FLIR image. Finally, we verity the performance of proposed ground target classification algorithm and feature extraction method by the experimentation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3302-3320
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• 2014

An allocation of sensing and reporting times is proposed to improve the sensing performance by scheduling them in an efficient way for cognitive radio networks with cluster-based cooperative spectrum sensing. In the conventional cooperative sensing scheme, all secondary users (SUs) detect the primary user (PU) signal to check the availability of the spectrum during a fixed sensing time slot. The sensing results from the SUs are reported to cluster heads (CHs) during the reporting time slots of the SUs and the CHs forward them to a fusion center (FC) during the reporting time slots of the CHs through the common control channels for the global decision, respectively. However, the delivery of the local decision from SUs and CHs to a CH and FC requires a time which does not contribute to the performance of spectrum sensing and system throughput. In this paper, a super-allocation technique, which merges reporting time slots of SUs and CHs to sensing time slots of SUs by re-scheduling the reporting time slots, has been proposed to sense the spectrum more accurately. In this regard, SUs in each cluster can obtain a longer sensing duration depending on their reporting order and their clusters except for the first SU belonged to the first cluster. The proposed scheme, therefore, can achieve better sensing performance under -28 dB to -10 dB environments and will thus reduce reporting overhead.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.8-25
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• 2017

This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1A
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• pp.87-94
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• 2010

It is widely known that cooperative spectrum sensing in which secondary users scattered in some region collaborate to detect primary users can significantly reduce the performance degradation due to the fading phenomenon. Most of previous works on cooperative spectrum sensing are based on the assumption that the local spectrum sensing decisions of secondary users are statistically independent. However, there can be practically some statistical correlation between the local decisions of any two secondary users in close proximity, which is caused by shadowing effect. In order to evaluate the effect of this correlation on the performance of collaborative spectrum sensing, we assumed that, for the case that a primary user are active in the spectrum of interest, any two local decisions are statistically correlated to each other with some level of constant correlation and independent otherwise, and analyzed the achievable throughput with the degree of correlation varying. The results showed that, as the degree of correlation gets higher, the throughput increases for the case of the AND fusion rule and decreases for the OR fusion rule.

• Neurospine
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• v.15 no.4
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.37-45
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• 2013

Context dependent fusion (CDF) is a fusion algorithm that combines multiple outputs from different classifiers to achieve better performance. CDF tries to divide the problem context into several homogeneous sub-contexts and to fuse data locally with respect to each sub-context. CDF showed better performance than existing methods, however, it is sensitive to noise due to the large number of parameters optimized and the innate linearity limits the application of CDF. In this paper, a variant of CDF using support vector machines (SVMs) for fusion and kernel principal component analysis (K-PCA) for context extraction is proposed to solve the problems in CDF, named CDF-SVM. Kernel PCA can shape irregular clusters including elliptical ones through the non-linear kernel transformation and SVM can draw a non-linear decision boundary. Regularization terms is also included in the objective function of CDF-SVM to mitigate the noise sensitivity in CDF. CDF-SVM showed better performance than CDF and its variants, which is demonstrated through the experiments with a landmine data set.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5A
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• pp.429-435
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• 2009

Cooperative spectrum sensing allows secondary users of a cognitive radio(CR) network to collaborate to determine whether a primary user occupies the spectrum of interest or not. It usually performs spectrum sensing by combining the individual decisions of each second user into a final one and the k-out-of-n fusion rule is a general approach for decision fusion. This rule declares that the spectrum is occupied only when the decisions from more than k-1 secondary users indicate the presence of a primary user. In this paper, we analyze a cooperative spectrum sensing scheme with the fusion rule under the constraint that its detection probability is maintained to be no less than a given level and its numerical results for the case of a CR network with 10 secondary users.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.777-784
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• 2013

Synthesis process from the data produced by different types of sensor into a single information is being studied and used in a variety of platforms in terms of multi-sensor data fusion. Heterogeneous sensors has been integrated into various aircraft and modern avionic systems manage them. As the performance of sensors in aircraft is getting higher, the integration of sensor information is required from the viewpoint of avionics gradually. Information fusion is not studied widely in the view of software that provide a pilot with fused information from data produced by the sensor in the form of symbology on a display device. The purpose of information fusion is to assist pilots to make a decision in order to perform mission by providing the correct combat situation from avionics of the aircraft and to minimize their workload consequently. In the aircraft avionics equipped with different types of sensors, the software architecture that produce a comprehensive information using the sensor data through multi-sensor data fusion process to the user is shown in this paper.