Relevant bibliographies by topics / Threshold-based cognitive

Academic literature on the topic 'Threshold-based cognitive'

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Published: 6 September 2023

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Journal articles on the topic "Threshold-based cognitive"

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Zhang, Deng Yin, Ai Qin Bao, and Ming Xiang Wang. "Dynamic-Dual-Threshold Cooperative Spectrum Sensing Algorithm Based on D-S Evidence Theory." Applied Mechanics and Materials 519-520 (February 2014): 991–94. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.991.

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To further improve the performance of traditional dual-threshold cooperative spectrum sensing, the article proposed a dynamic-dual-threshold cooperative spectrum sensing algorithm based on D-S evidence theory (DDDS). The article is applied the D-S evidence theory in dual-threshold sensing algorithm and set threshold value depending on the channel gain. If the received signal is outside of dual-threshold, the cognitive user will send the judgment result to the base station; otherwise the cognitive user will calculate trust information based on D-S evidence theory, and then transmit them to the cognitive base station. Cognitive base station makes the final decision.
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Chen, Li Na, and Chun Yu Miao. "Spectrum Detection for Cognitive Radio Based on Auto-Adaptive Threshold." Advanced Materials Research 187 (February 2011): 614–20. http://dx.doi.org/10.4028/www.scientific.net/amr.187.614.

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This paper proposed a cooperative spectrum sensing scheme based on auto-adaptive threshold in multi-user cognitive radio network, which can improve detection performance of cognitive user. According to closed-form expressions of detection probability, we compared the noncooperative scheme with the multi-user cooperative scheme, and compared the auto-adaptive threshold with fixed threshold in terms of detection probability through imitation. Result shown that the detection probability of multi-user cooperative detection is improved greatly compared to traditional noncooperative detection, and auto-adaptive scheme is advantage more than fixed threshold detection.
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Chopra, Khyati. "Intercept analysis with threshold-based diversity reception for cognitive network." International Journal of Ad Hoc and Ubiquitous Computing 41, no. 3 (2022): 170. http://dx.doi.org/10.1504/ijahuc.2022.10050033.

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Chopra, Khyati. "Intercept analysis with threshold-based diversity reception for cognitive network." International Journal of Ad Hoc and Ubiquitous Computing 41, no. 3 (2022): 170. http://dx.doi.org/10.1504/ijahuc.2022.126112.

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Ahuja, Bhawna, and Kanchan Sharma. "Adaptive Double Threshold based Spectrum Sensing for Cognitive Radio Networks." International Journal of Energy, Information and Communications 5, no. 6 (December 31, 2014): 1–16. http://dx.doi.org/10.14257/ijeic.2014.5.6.01.

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Ranjeeth, M., and S. Anuradha. "Threshold Based Censoring of Cognitive Radios in Rician Fading Channel." Wireless Personal Communications 93, no. 2 (June 21, 2016): 409–30. http://dx.doi.org/10.1007/s11277-016-3440-4.

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Liu, Xin, Chengwen Zhang, and Xuezhi Tan. "Double-threshold cooperative detection for cognitive radio based on weighing." Wireless Communications and Mobile Computing 14, no. 13 (March 7, 2012): 1231–43. http://dx.doi.org/10.1002/wcm.2219.

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Yu, Gui Cai, Cheng Zhi Long, and Man Tian Xiang. "Research on Dynamic Threshold Based Energy Detection in Cognitive Radio Systems." Advanced Materials Research 462 (February 2012): 506–11. http://dx.doi.org/10.4028/www.scientific.net/amr.462.506.

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In cognitive radio networks, nodes should have the capability to decide whether a signal from a primary transmitter is locally present or not in a certain spectrum within a short detection period. Traditional spectrum sensing schemes based on fixed threshold are sensitive to noise uncertainty, a fractional fluctuate of average noise power in a short time can lead the performance of spectrum detection drop seriously. This paper presents a new spectrum detection algorithm based on dynamic threshold. Theoretical results show that the proposed scheme debate the noise uncertainty, and good detection performance can be gained, if suitable dynamic threshold is chosen. In other words, the proposed scheme can enhance the robustness against noise and improve the capacity of spectrum sensing.
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Wu, Yiping, Zilong Zhao, Fuwei Wu, and Jian Rong. "Information Volume Threshold for Graphical Variable Message Signs Based on Drivers’ Visual Cognition Behavior." Journal of Advanced Transportation 2022 (December 10, 2022): 1–15. http://dx.doi.org/10.1155/2022/7693492.

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Variable message signs (VMS) are widely employed to offer drivers dynamic traffic information. However, it is still lacking practical guidance about the information volume displayed on a graphical VMS. Building on the result of the subjective questionnaire survey, a static cognitive experiment was conducted to analyze the influence of volume information (i.e., elements and displaying the number of roads) of graphical VMS on drivers’ visual cognition characteristics and then determine the threshold number of roads displayed on VMS. Forty-five drivers participated in the static cognitive experiment. Five indicators, including visual cognition time, cognition accuracy, comprehension accuracy, general assessment, and information acceptance, were used to estimate the influences of graphical VMS. Study results by descriptive statistics and statistical hypothesis testing indicated that drivers also preferred auxiliary elements (i.e., distance or time information) besides basic design elements (i.e., driving direction, current position, and road name) displayed on graphical VMS. With the increase in information volume, driver visual cognition time increased while other companion indexes (i.e., visual cognition accuracy and comprehension accuracy) generally worsened. Combining the data of drivers’ objective behavior and subjective scoring, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method revealed that the number of roads shown on the graphical VMS should be no greater than five. The study results were verified by dynamic simulation experiments. This finding provides a supplement for the design standards and usage specifications for VMS.
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Zhang Xue-Jun, Lu You, Tian Feng, Sun Zhi-Xin, and Cheng Xie-Feng. "Double-threshold cooperative spectrum sensing for cognitive radio based on trust." Acta Physica Sinica 63, no. 7 (2014): 078401. http://dx.doi.org/10.7498/aps.63.078401.

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Dissertations / Theses on the topic "Threshold-based cognitive"

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Hedner, Margareta. "Olfactory Function : The Influence of Demographic, Cognitive, and Genetic Factors." Doctoral thesis, Stockholms universitet, Psykologiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-85907.

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Olfactory function is affected by demographic, cognitive, and genetic factors. In the present thesis, three empirical studies investigated individual differences in olfactory ability. Study I explored demographic and cognitive correlates in common olfactory tasks; odor detection, odor discrimination, and odor identification. The results indicated that old age influenced performance negatively in all tasks, and that semantic memory proficiency and executive functioning were related to odor discrimination and odor identification performance. No cognitive influence was observed for measurements of olfactory threshold. Using population-based data, Study II investigated a potential influence of the ApoE gene on olfactory identification after controlling for health status, semantic memory, and preclinical and clinical dementia. The main finding was that the ApoE- ɛ4 allele interacted with age, such that older ɛ4-carriers had an impaired odor identification performance relative to older non-carriers. Importantly, the negative ApoE- ɛ4 effect on olfactory proficiency was independent of clinical dementia conversion within five years. Study III investigated the effects of the BDNF val66met polymorphism on olfactory change over a five-year interval, in a community dwelling sample of young and old age cohorts. The results showed that age-related decline in olfactory identification was influenced by the BDNF val66met. In middle-aged subjects, no effect of BDNF val66met was observed although older val homozygote carriers showed a selectively larger olfactory decline than the older met carriers. Overall, results suggest that the relative influence of demographic and cognitive factors vary across different olfactory tasks and that two genes (ApoE and BDNF) impact age-related deficits in odor identification. Potential theoretical and practical implications of the findings are discussed as well as potential limitations of association studies in genomics research.
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Song, Yao. "Threshold Based Opportunistic Scheduling of Secondary Users in Underlay Cognitive Radio Networks." Thesis, 2011. http://hdl.handle.net/10754/205811.

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In underlay cognitive radio networks, secondary users can share the spectrum with primary users as long as the interference caused by the secondary users to primary users is below a certain predetermined threshold. It is reasonable to assume that there is always a large pool of secondary users trying to access the channel, which can be occupied by only one secondary user at a given time. As a result, a multi-user scheduling problem arises among the secondary users. In this thesis, by manipulating basic schemes based on selective multi-user diversity, normalized thresholding, transmission power control, and opportunistic round robin, we propose and analyze eight scheduling schemes of secondary users in an underlay cognitive radio set-up. The system performance of these schemes is quantified by using various performance metrics such as the average system capacity, normalized average feedback load, scheduling outage probability, and system fairness of access. In our proposed schemes, the best user out of all the secondary users in the system is picked to transmit at each given time slot in order to maximize the average system capacity. Two thresholds are used in the two rounds of the selection process to determine the best user. The first threshold is raised by the power constraint from the primary user. The second threshold, which can be adjusted by us, is introduced to reduce the feedback load. The overall system performance is therefore dependent on the choice of these two thresholds and the number of users in the system given the channel conditions for all the users. In this thesis, by deriving analytical formulas and presenting numerical examples, we try to provide insights of the relationship between the performance metrics and the involved parameters including two selection thresholds and the number of active users in the system, in an effort to maximize the average system capacity as well as satisfy the requirements of scheduling outage probability and feedback load.
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Book chapters on the topic "Threshold-based cognitive"

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Liu, Sha, Runqi Li, and Jiaxin Dai. "Research on Visual Sensing Capability Difference Between Designers and Non-designers Based on Difference Threshold Measurement." In Advances in Neuroergonomics and Cognitive Engineering, 161–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94866-9_16.

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Kiselev, Mikhail, Alexander Ivanitsky, and Andrey Lavrentyev. "Comparison of Memory Mechanisms Based on Adaptive Threshold Potential and Short-Term Synaptic Plasticity." In Advances in Neural Computation, Machine Learning, and Cognitive Research V, 334–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91581-0_44.

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Ranjeeth, M., and S. Anuradha. "Threshold Based Censoring of Cognitive Radios in Rician Fading Channel with Perfect Channel Estimation." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 220–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40352-6_18.

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Sreenivasan, Rajagopal, G. V. K. Sasirekha, and Jyotsna Bapat. "Adaptive Threshold Based on Group Decisions for Distributed Spectrum Sensing in Cognitive Adhoc Networks." In Communications in Computer and Information Science, 633–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17878-8_64.

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Peng, Chongxiao, Yifei Wei, Bo Gu, Ligang Ren, and Mei Song. "A Dynamic Double Threshold Based Cooperative Spectrum Sensing Strategy in Heterogeneous Cognitive Radio Networks." In Human Centered Computing, 376–81. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74521-3_40.

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Charan, Chhagan, and Rajoo Pandey. "Cooperative Spectrum Sensing Using Eigenvalue-Based Double-Threshold Detection Scheme for Cognitive Radio Networks." In Advances in Intelligent Systems and Computing, 189–99. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1822-1_18.

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Salhab, Anas M., Fawaz Al-Qahtani, Salam A. Zummo, and Hussein Alnuweiri. "An Efficient Switching Threshold-Based Scheduling Protocol for Multiuser Cognitive AF Relay Networks with Primary Users Using Orthogonal Spectrums." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 135–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24540-9_11.

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Gotthardt, Marie, Julian Striegl, Claudia Loitsch, and Gerhard Weber. "Voice Assistant-Based CBT for Depression in Students: Effects of Empathy-Driven Dialog Management." In Lecture Notes in Computer Science, 451–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08648-9_52.

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AbstractWith a rising number of students with depression, new low-threshold solutions have to be found to strengthen the resilience against and help those affected by mental disorders. One approach lies in the usage of chatbots (CBs) to provide tools based in cognitive behavioral therapy (CBT) that can be used independently in order to reduce symptoms of depression. To ensure the adherence to such systems, a good usability and acceptance is important. Conversational agents (CAs) that provide CBT-based content should further be sensitive to the users emotional state, as empathy is one central aspect of therapy. While promising research has been going on in the field of CB-based empathy-driven CBT, voice assistant-based (VA-based) solutions have thus far not been investigated deeply. Therefore, we propose a VA-based, empathy-driven system, capable of delivering selected methods from CBT to students with depression.To assess the effects of empathy-driven dialog management on perceived usability and acceptance, we conducted a single blind randomized controlled A/B testing experiment with 10 participants. While the application of empathetical dialog management shows no benefits to the usability and acceptance, results overall indicate a good usability and acceptance of the system in the target group.
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Zhang, Q., J. Guo, Z. Yu, and G. Liu. "Dynamic threshold Energy Detection based on spectrum prediction for cognitive radio." In Energy Science and Applied Technology, 469–72. CRC Press, 2015. http://dx.doi.org/10.1201/b19779-107.

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Lal, Sangeeta, Neetu Sardana, and Ashish Sureka. "Improving Logging Prediction on Imbalanced Datasets." In Cognitive Analytics, 740–72. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch039.

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Logging is an important yet tough decision for OSS developers. Machine-learning models are useful in improving several steps of OSS development, including logging. Several recent studies propose machine-learning models to predict logged code construct. The prediction performances of these models are limited due to the class-imbalance problem since the number of logged code constructs is small as compared to non-logged code constructs. No previous study analyzes the class-imbalance problem for logged code construct prediction. The authors first analyze the performances of J48, RF, and SVM classifiers for catch-blocks and if-blocks logged code constructs prediction on imbalanced datasets. Second, the authors propose LogIm, an ensemble and threshold-based machine-learning model. Third, the authors evaluate the performance of LogIm on three open-source projects. On average, LogIm model improves the performance of baseline classifiers, J48, RF, and SVM, by 7.38%, 9.24%, and 4.6% for catch-blocks, and 12.11%, 14.95%, and 19.13% for if-blocks logging prediction.
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Conference papers on the topic "Threshold-based cognitive"

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Moorthy, Yamuna K., and Sakuntala S. Pillai. "Adaptive threshold based energy detection for cognitive radios." In 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342799.

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Maity, Priyanka, and Siddharth Deshmukh. "Optimization of Majority Rule Threshold in Double Threshold Based Cooperative Cognitive Radio Network." In 2018 Twenty Fourth National Conference on Communications (NCC). IEEE, 2018. http://dx.doi.org/10.1109/ncc.2018.8599992.

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Singh, Ajay, Manav R. Bhatnagar, and Ranjan K. Mallik. "Threshold optimization of finite sample based cognitive radio network." In 2012 National Conference on Communications (NCC). IEEE, 2012. http://dx.doi.org/10.1109/ncc.2012.6176816.

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Yu, Gui-cai, Yu-bin Shao, Hua Long, and Guang-xin Yue. "Dynamic Threshold Based Spectrum Detection in Cognitive Radio Systems." In 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2009. http://dx.doi.org/10.1109/wicom.2009.5301508.

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Gupta, Manish, Gaurav Verma, and Rahul Kumar Dubey. "Cooperative Spectrum Sensing for Cognitive Radio Based on Adaptive Threshold." In 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT). IEEE, 2016. http://dx.doi.org/10.1109/cict.2016.94.

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Dubey, Rahul Kumar, and Gaurav Verma. "Improved Spectrum Sensing for Cognitive Radio Based on Adaptive Threshold." In 2015 Second International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE, 2015. http://dx.doi.org/10.1109/icacce.2015.70.

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Xin Liu, Chengwen Zhang, and Xuezhi Tan. "Double-threshold cooperative detection for cognitive radio based on weighing." In 2011 6th International ICST Conference on Communications and Networking in China (CHINACOM). IEEE, 2011. http://dx.doi.org/10.1109/chinacom.2011.6158149.

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Imtiaz, Junaid, and Dongwoo Kim. "Threshold based reporting for improving sensing efficiency in cognitive radio systems with multiple cognitive users." In 2015 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2015. http://dx.doi.org/10.1109/ictc.2015.7354641.

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Penna, Federico, Roberto Garello, Davide Figlioli, and Maurizio A. Spirito. "Exact non-asymptotic threshold for eigenvalue-based spectrum sensing." In 2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM). IEEE, 2009. http://dx.doi.org/10.1109/crowncom.2009.5189008.

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Zhang, Yi, Lingling Zhang, and Chengkai Tang. "Dual-Threshold Cooperative Sensing Based on Coherent MAC in Cognitive Radio." In 2010 International Conference on Management and Service Science (MASS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmss.2010.5576475.

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