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Journal articles on the topic 'Threshold-based cognitive'

Author: Grafiati

Published: 6 September 2023

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1

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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2

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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3

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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5

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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6

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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8

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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10

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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11

Li, Yuan, Jia Yin Chen, Xiao Feng Liu, and Ming Chuan Yang. "An Algorithm Based on Double-Threshold Energy Detection in Cognitive Radio." Applied Mechanics and Materials 643 (September 2014): 105–10. http://dx.doi.org/10.4028/www.scientific.net/amm.643.105.

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Aiming at the situation where the double-threshold detection has been widely used without complete mathematical proof and condition of application, this paper proves its correctness under the circumstance of spectrum sensing, and circulates the condition where this method can work. The proof and simulation show that, comparing with traditional energy detection, this method can increase the probability of detection by 27% to 42% at most when the SNR is between-15dB and-2dB, while the probability of false alarm is increased by less than 2%.

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12

Chopra, Khyati, Ranjan Bose, and Anupam Joshi. "Secrecy performance of threshold-based cognitive relay network with diversity combining." Journal of Communications and Networks 20, no. 4 (August 2018): 383–95. http://dx.doi.org/10.1109/jcn.2018.000054.

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13

Joshi, Deepak R., Dimitrie C. Popescu, and Octavia A. Dobre. "Gradient-Based Threshold Adaptation for Energy Detector in Cognitive Radio Systems." IEEE Communications Letters 15, no. 1 (January 2011): 19–21. http://dx.doi.org/10.1109/lcomm.2010.11.100654.

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14

Li, Jiajia, Botao Wang, Guoren Wang, and Yifei Zhang. "Probabilistic threshold query optimization based on threshold classification using ELM for uncertain data." Neurocomputing 174 (January 2016): 211–19. http://dx.doi.org/10.1016/j.neucom.2015.05.122.

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15

Yu, Gui Cai, Cheng Zhi Long, and Man Tian Xiang. "Detection Sensitivity Based on Energy Detection in Cognitive Radio Systems." Advanced Materials Research 462 (February 2012): 500–505. http://dx.doi.org/10.4028/www.scientific.net/amr.462.500.

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Traditional energy detection algorithm is bad in anti-noise. In this paper, the relationship of energy detection performance and detection sensitivity with average noise power fluctuation in short time is investigated. Detection sensitivity drops quickly with the increment of average noise power fluctuation and becomes worse in low signal-to-noise ratio. To the characteristic, a new energy detection algorithm based on dynamic threshold is presented. Theoretic results and simulations show that the proposed scheme removes the falling proportion of performance and detection sensitivity caused by the average noise power fluctuation with a choice threshold, and also improves the antagonism of the average noise power fluctuation in short time and obtains a good performance. Detection sensitivity and performance improves as the dynamic threshold factor increasing.

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S. Sureshkrishna, S. Varalakshmi, K. Senthil Kumar, A. K. Gnanasekar,. "An Effective Adaptive Threshold Based Compressive Spectrum Sensing in Cognitive Radio Networks." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 18, 2021): 1220–24. http://dx.doi.org/10.17762/itii.v9i1.260.

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Spectrum sensing is playing a vital role in Cognitive Radio networks. Wideband spectrum sensing increases the speed of sensing but which in turn requires higher sampling rate and also increases the complexity of hardware and also power consumption. Compression based sensing reduces the sampling rate by using Sub-Nyquist sampling but the compression and the reconstruction problem exists. In compression based spectrum sensing, noise uncertainty is one of the major performance degradation factor. To reduce this degradation, compressive measurements based sensing with adaptive threshold is proposed. In this technique compressed signal is sensed without any reconstruction of the signal. When the nodes are mobile in the low SNR region, the noise uncertainty degrades the performance of spectrum sensing. To conquer this problem, noise variance is estimated using parametric estimation technique and the threshold is varied adaptively. In the low SNR region, this proposed technique reduces the effect of noise and improves the spectrum sensing performance.

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Bagwari, Ashish, and Geetam Singh Tomar. "Multiple Energy detectors Based Cognitive Radio Networks Using Adaptive Double-Threshold scheme." International Journal of Smart Business and Technology 1, no. 1 (December 30, 2013): 17–28. http://dx.doi.org/10.21742/ijsbt.2013.1.1.02.

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18

M. Elfatih, Nada, Elmustafa Sayed Ali, Maha Abdelhaq, Raed Alsaqour, and Rashid A. Saeed. "A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks." Computer Systems Science and Engineering 45, no. 1 (2023): 329–42. http://dx.doi.org/10.32604/csse.2023.028528.

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19

Liu, Yulei, Jun Liang, Nan Xiao, Xiaogang Yuan, Zhenhao Zhang, Meng Hu, and Yulong Hu. "Adaptive double threshold energy detection based on Markov model for cognitive radio." PLOS ONE 12, no. 5 (May 16, 2017): e0177625. http://dx.doi.org/10.1371/journal.pone.0177625.

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Chopra, Khyati, Ranjan Bose, and Anupam Joshi. "Secrecy performance of threshold-based decode-and-forward cooperative cognitive radio network." IET Communications 11, no. 9 (June 22, 2017): 1396–406. http://dx.doi.org/10.1049/iet-com.2016.0917.

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21

Charan, Chhagan, and Rajoo Pandey. "Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio." Optik 127, no. 15 (August 2016): 5968–75. http://dx.doi.org/10.1016/j.ijleo.2016.04.049.

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22

Ling, Xiang, Bin Wu, Hong Wen, Pin-Han Ho, Zhiqiang Bao, and Lili Pan. "Adaptive Threshold Control for Energy Detection Based Spectrum Sensing in Cognitive Radios." IEEE Wireless Communications Letters 1, no. 5 (October 2012): 448–51. http://dx.doi.org/10.1109/wcl.2012.062512.120299.

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23

Yu, Shanshan, Ju Liu, Jing Wang, and Inam Ullah. "Adaptive Double-Threshold Cooperative Spectrum Sensing Algorithm Based on History Energy Detection." Wireless Communications and Mobile Computing 2020 (June 30, 2020): 1–12. http://dx.doi.org/10.1155/2020/4794136.

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Spectrum sensing is one of the key technologies in the field of cognitive radio, which has been widely studied. Among all the sensing methods, energy detection is the most popular because of its simplicity and no requirement of any prior knowledge of the signal. In the case of low signal-to-noise ratio (SNR), the traditional double-threshold energy detection method employs fixed thresholds and there is no detection result when the energy is between high and low thresholds, which leads to poor detection performance such as lower detection probability and longer spectrum sensing time. To address these problems, we proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. In each sensing period, we calculate the weighting coefficient of thresholds according to the SNR of all cognitive nodes; thus, the upper and lower thresholds can be adjusted adaptively. Furthermore, in a single cognitive node, once the current energy is within the high and low thresholds, we utilize the average energy of history sensing times to rejudge. To ensure the real-time performance, if the average history energy is still between two thresholds, the single-threshold method will be used for the end decision. Finally, the fusion center aggregates the detection results of each node and obtains the final cooperative conclusion through “or” criteria. Theoretical analysis and simulation results show that the algorithm proposed in this paper improved detection performance significantly compared with the other four different double-threshold algorithms.

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24

RIDGWAY, G., R. OMAR, S. OURSELIN, D. HILL, J. WARREN, and N. FOX. "Issues with threshold masking in voxel-based morphometry of atrophied brains." NeuroImage 44, no. 1 (January 1, 2009): 99–111. http://dx.doi.org/10.1016/j.neuroimage.2008.08.045.

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25

Kim, Sun Mi, Hye Ri Kim, Hyun Jin Min, Kyung Soo Kim, Jae-Chan Jin, and Doug Hyun Han. "A novel olfactory threshold test for screening cognitive decline among elderly people." PLOS ONE 16, no. 7 (July 12, 2021): e0254357. http://dx.doi.org/10.1371/journal.pone.0254357.

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Olfactory impairment is associated with dementia and is a potential early biomarker of cognitive decline. We developed a novel olfactory threshold test called Sniff Bubble using rose odor-containing beads made with 2-phenylethyl alcohol. We aimed to define cut-off scores for this tool to help identify cognitive decline among elderly people. In total, 162 elderly people (mean age ± SD: 73.04 ± 8.73 years) were administered olfactory threshold and neurocognitive tests. For analyses, we divided the participants into two groups based on cognitive functioning, namely cognitive decline (n = 44) and normal cognition (n = 118) groups. The Sniff Bubble and YSK olfactory function test for olfactory threshold and the Structured Clinical Interview for DSM-5 Disorders-Clinician Version and Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease assessment packet for neurocognitive functioning were used. We used K-means cluster analyses and receiver operating characteristic (ROC) analyses to identify the most appropriate cut-off value. We established a positive correlation between the Sniff Bubble and neurocognitive function test scores (r = 0.431, p < 0.001). We defined the cut-off score, using the ROC curve analyses for Sniff Bubble scores, at 3 and higher with an area under the curve of 0.759 (p < 0.001). The Sniff Bubble test can adequately detect cognitive decline in elderly people and may be used clinically as the first step in the screening process.

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Bagwari, Ashish, and Geetam Singh Tomar. "Adaptive double-threshold based energy detector for spectrum sensing in cognitive radio networks." International Journal of Electronics Letters 1, no. 1 (March 2013): 24–32. http://dx.doi.org/10.1080/21681724.2013.773849.

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27

Charan, Chhagan, and Rajoo Pandey. "Intelligent selection of threshold in covariance-based spectrum sensing for cognitive radio networks." Wireless Networks 24, no. 8 (May 29, 2017): 3267–79. http://dx.doi.org/10.1007/s11276-017-1533-y.

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28

Song, Xiaoshi, Changchuan Yin, Danpu Liu, and Rui Zhang. "Spatial Throughput Characterization in Cognitive Radio Networks with Threshold-Based Opportunistic Spectrum Access." IEEE Journal on Selected Areas in Communications 32, no. 11 (November 2014): 2190–204. http://dx.doi.org/10.1109/jsac.2014.1411rp05.

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29

Mosleh, Somayeh, Jamshid Abouei, and Masoud Reza Aghabozorgi. "Distributed Opportunistic Interference Alignment Using Threshold-Based Beamforming in MIMO Overlay Cognitive Radio." IEEE Transactions on Vehicular Technology 63, no. 8 (October 2014): 3783–93. http://dx.doi.org/10.1109/tvt.2014.2305849.

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30

Xu, Gui Sen, and Xue Zhi Tan. "A Cluster Head Rotating Threshold for Cognitive Radio Network." Advanced Materials Research 171-172 (December 2010): 211–14. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.211.

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Cluster-based topology is an energy-efficient topology control method in cognitive radio networks. The cluster head node depletes energy faster than cluster member node and the rotation of cluster head node is needed to balance the energy consumption for the whole network. Aimed at the problem of unbalanced residual energy of each node caused by cluster heads and cluster members in wireless cognitive radio network, a novel algorithm named cluster head rotating threshold (CHRT) is presented. In the proposed algorithm, rotation energy threshold is estimated using cluster head real-time energy load. The simulation results show that comparing with LEACH and EDAC, CHRT can realize the more network lifetime than the others.

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31

Pletnikova, Alexandra, Nicholas S. Reed, Halima Amjad, Sevil Yasar, Milap Nowrangi, Joshua Betz, Frank R. Lin, and Esther S. Oh. "Identification of Hearing Loss in Individuals With Cognitive Impairment Using Portable Tablet Audiometer." Perspectives of the ASHA Special Interest Groups 4, no. 5 (October 31, 2019): 947–53. http://dx.doi.org/10.1044/2019_pers-sig8-2018-0018.

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Purpose The purpose of this study was to determine the feasibility of using a tablet-based portable audiometer to identify hearing loss in a cognitively impaired population. Method In this study, we used a retrospective chart review of an outpatient memory clinic. During a quality initiative, older adult ( M age = 74.6) patients ( n = 91) completed pure-tone hearing testing (0.5–4 kHz) via automated self-administration and technician-administered threshold testing (gold standard) on a tablet-based audiometer. Results Technician-administered auditory threshold testing was successfully conducted in all patients. A total of 54 of the 91 patients (59%) also had hearing thresholds reliably obtained with automated self-administration. In bivariate analyses, older age, lower Mini-Mental State Examination (MMSE) scores, lower education, male sex, and higher degree of hearing impairment were associated with higher odds of having an unreliable self-administered test defined as multiple recorded false positives at 2 or more test frequencies. In multivariable analysis, lower MMSE scores and poorer hearing were significantly associated with higher odds of having an unreliable self-administered test. Conclusion Technician-administered hearing threshold testing using a portable, tablet-based audiometer can be successfully administered to patients with cognitive impairment. Reliability of automated self-administration is limited in patients with lower MMSE scores.

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32

Adhikari, Rammani, and Mazhar Ali. "Performance Comparison of Energy Detection Based non – Cooperative Spectrum Sensing Techniques in Cognitive Radio." Himalayan Journal of Applied Science and Engineering 3, no. 1 (September 8, 2022): 28–38. http://dx.doi.org/10.3126/hijase.v3i1.47898.

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Cognitive radio is a ground-breaking software-defined radio paradigm that offers Dynamic spectrum access, allowing secondary users to use the frequency band allotted to the principal user when it is not in use and vacate when the prime application returns. The ability to sense the spectrum is critical to cognitive radio's efficiency. Energy detection sensing is the simplest and most often used spectrum sensing approach, owing to its ease of implementation in cognitive radio applications. The three-energy detection-based algorithms adopted for different scenarios have been compared in this study. The algorithms include the double-threshold energy detection, adaptive single threshold energy detection, and the adaptive double threshold spectrum sensing algorithm. Since the noise prediction in the practical situation is difficult, the necessity is to find the best algorithm in this condition. The other equally important parameters for efficiently sensing the spectrum are spectrum efficiency and less interference to the primary user. Simulation findings show that the adaptive double threshold approach outperforms the other two algorithms in all respect. The detection probability of the method is typically found to be substantially greater as compared to other two techniques. In addition, the likelihood of a false alarm is significantly reduced. Furthermore, when the signal-to-noise ratio value is low, often below -5dB, the performance of this approach is poor. MATLAB is used to run all of the simulations.

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Devrim, Müge, Tamer Demiralp, Ahmet Ademoglu, and Adnan Kurt. "A model for P300 generation based on responses to near-threshold visual stimuli." Cognitive Brain Research 8, no. 1 (May 1999): 37–43. http://dx.doi.org/10.1016/s0926-6410(99)00007-5.

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34

Ahmad, Hassaan Bin. "Ensemble Classifier Based Spectrum Sensing in Cognitive Radio Networks." Wireless Communications and Mobile Computing 2019 (January 1, 2019): 1–16. http://dx.doi.org/10.1155/2019/9250562.

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Spectrum sensing is one of the most important and challenging tasks in cognitive radio. To develop methods of dynamic spectrum access, robust and efficient spectrum sensors are required. For most of these sensors, the main constraints are the lack of information about the primary user’s (PU) signal, high computational cost, performance limits in low signal-to-noise ratio (SNR) conditions, and difficulty in finding a detection threshold. This paper proposes a machine learning based novel detection method to overcome these limits. To address the first constraint, detection is achieved using cyclostationary features. The constraints of low SNR, finding detection threshold, and computational cost are addressed by proposing an ensemble classifier. First, a dataset is generated containing different orthogonal frequency-division multiplexing signals at different SNRs. Then, cyclostationary features are extracted using FFT accumulation method. Finally, the proposed ensemble classifier has been trained using the extracted features to detect PU’s signal in low SNR conditions. This ensemble classifier is based on decision trees and AdaBoost algorithm. A comparison of the proposed classifier with another machine learning classifier, namely, support vector machine (SVM), is presented, clearly showing that the ensemble classifier outperforms SVM. The results of the simulation also prove the robustness and superior efficiency of the detector proposed in this paper in comparison with a cyclostationary detector without machine learning as well as the classical energy detector.

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35

Bagwari, Ashish, and Geetam Singh Tomar. "Cooperative Spectrum Sensing with Adaptive Double-Threshold Based Energy Detector in Cognitive Radio Networks." Wireless Personal Communications 73, no. 3 (June 11, 2013): 1005–19. http://dx.doi.org/10.1007/s11277-013-1244-3.

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36

Buratti, Laura, Giovanna Viticchi, Lorenzo Falsetti, Clotilde Balucani, Claudia Altamura, Cristina Petrelli, Leandro Provinciali, Fabrizio Vernieri, and Mauro Silvestrini. "Thresholds of impaired cerebral hemodynamics that predict short-term cognitive decline in asymptomatic carotid stenosis." Journal of Cerebral Blood Flow & Metabolism 36, no. 10 (July 22, 2016): 1804–12. http://dx.doi.org/10.1177/0271678x15613526.

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Subjects with asymptomatic carotid stenosis (ACS) may be at risk of cognitive impairment due to cerebral hypoperfusion. In this study, we aimed to detect a threshold of cerebral hemodynamics which is able to identify subjects at risk of cognitive deterioration. In subjects with ACS, cerebral vasomotor reactivity (CVR) was assessed with the breath-holding index (BHI) transcranial Doppler-based method. Cognitive deterioration was defined as a decrease in the MMSE score by ≥2 points after one year. In order to define the threshold of impaired BHI, a ROC curve analysis was performed adopting the binary difference of MMSE score as the outcome and continuous BHI as the testing variable. A total of 548 subjects completed the follow-up. Cognitive deterioration was observed in 119 patients (21.7%). The BHI value ipsilateral to the stenosis was the strongest predictor of cognitive deterioration among the variables tested. The best cut-point to discriminate between normal and abnormal BHI resulted ≤0.89. The post-test probability of cognitive deterioration for an abnormal BHI was 44%, while a normal BHI showed a post-test probability of 9% for the same outcome. The present investigation provides a threshold of reduced CVR that can be useful to identify subjects with ACS at risk of cognitive deterioration.

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Veena, C. N., Rashmitha Vinayak Kamath, and M. Prashanth Kumar. "Effect of abacus training on critical flicker fusion frequency threshold among primary schoolchildren." Indian Journal of Physiology and Pharmacology 65 (August 10, 2021): 115–18. http://dx.doi.org/10.25259/ijpp_410_2020.

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Objectives: The concept of neuronal plasticity has been considerably studied to know how long-lasting changes are associated with brain’s capacity to be shaped or sculptured by experience. Potential cognitive benefits of abacus training have been proved by various studies indicating it to be a promising tool in improving cognitive abilities including arithmetic abilities, visuospatial and working memory. Critical flicker fusion happens when the individual can no longer differentiate between changing visual stimuli. It can be modulated by behavioural or psychophysical techniques. The objectives of the study were to evaluate the effects of abacus training on critical flicker fusion frequency (CFFF) threshold. Materials and Methods: CFFF threshold (CFFFT) was measured in 60 school students studying third and fourth standard (30 trained in abacus and 30 not trained in abacus) using portable software-based apparatus. Results: The mean scores of abacus trained individuals were significantly higher compared to those who were not trained. Conclusion: Since higher CFFFTs can absorb more visual information, increasing their threshold might lead to cognitive improvement and better academic performance. Abacus trained individuals showed significantly higher critical fusion frequency threshold.

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Zhao, Yuan, and Zhiyu Xiang. "A Multichannel Allocation Strategy Based on Preemption Threshold and Preemption Probability in Cognitive Radio Networks." Mobile Information Systems 2021 (September 23, 2021): 1–13. http://dx.doi.org/10.1155/2021/6190872.

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In traditional multichannel cognitive radio networks (CRNs), users are split into two different priorities. Because of the low priority of secondary users (SUs), SU packets’ transmissions are easily interrupted by primary users (PUs). In this paper, two control parameters, called preemption threshold H and preemption probability q, are used to regulate the preemption behavior of PU packets to improve the transmission performance of SU packets. When all channels in the system are occupied, the preemption behavior of PU packets will be adjusted according to the amount of SU packets that are transmitting in the system. If the amount is larger than H, the recently arrived PU packet either preempts a channel with probability q or leaves the system with probability 1 − q . The central controller manages the system’s channel usage right and determines a series of access behaviors of user packets. Considering the possible imperfect sensing, a discrete-time queueing model is developed with the proposed preemption control mechanism. Then we obtain some performance index expressions of PU and SU packets founded on the system’s state transition matrix and make the corresponding performance figures through numerical experiment. Finally, we construct a system utility function and determine the optimal preemption threshold and preemption probability through the seagull optimization algorithm (SOA). Experimental data show that the proposed mechanism by setting preemption threshold and preemption probability can significantly reduce SU packets’ outage rate and improve SU packets’ throughput rate.

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Chopra, Khyati, Ranjan Bose, and Anupam Joshi. "Secrecy Outage Performance of Cognitive Radio Network with Selection Combining at Eavesdropper." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 987–98. http://dx.doi.org/10.2174/2213275912666190807122631.

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Background:: Based on the idea of cooperative communication, recently a lot of attention has been drawn to cooperative spectrum access for the secure information transmission in a Cognitive Radio Network (CRN). Security is one of the most important aspects of these networks, as due to their open and dynamic nature, they are extremely vulnerable to malicious behavior. Cooperative cognitive radio has emerged as a dynamic spectrum access technique, where an unlicensed (secondary) user is allowed to simultaneously access the licensed channels dedicated to a Primary User (PU), as long as the Quality of Service (QoS) of primary communication is not affected. Method:: This paper investigates the secrecy outage performance of threshold-based cognitive decode-andforward relay network, with interference constraints from primary licensed user. Threshold-based relaying is considered where; the source message is successfully decoded by the relay, only if the received SNR satisfies the particular threshold. Outage probability expressions have been derived for the worst-case scenario, where only the eavesdropper can achieve the advantage of diversity. The Selection Combining (SC) diversity scheme is employed only at the secondary eavesdropper. Results:: The system secrecy performance is better for SC diversity scheme at the eavesdropper than Maximal Ratio Combining (MRC) diversity scheme, as MRC has better diversity performance than SC. We have shown that the improvement in desired secrecy rate, predetermined threshold, eavesdropper channel quality and interference constraints affect the secrecy performance of the cognitive radio system. The outage probability decreases accordingly with an increase in the maximum tolerable interference level at primary destination. The outage probability of Optimal relay Selection (OS) scheme is derived for a multi-relay system, when either the Instantaneous Channel State Information (ICSI) or the Statistical Channel State Information (SCSI) is available. We have shown that the secrecy performance of the OS with ICSI of the system is better than with SCSI. Also, the OS improves the performance of the multi-relay system, when the number of relays is increased. Conclusion:: The secrecy outage probability of threshold-based DF underlay cognitive relay network is evaluated. Both interference and maximum transmit power constraints are considered at secondary source and secondary relay. Also, the relay can successfully decode the message, only if it meets the pre-defined threshold. We have investigated the performance of MRC and SC diversity schemes at the secondary eavesdropper and have shown that the system secrecy performance is better for SC than MRC, as MRC has better diversity performance than SC. We have shown that the system secrecy performance is significantly affected by the required secrecy rate, pre-defined threshold, interference constraints and choice of diversity scheme (MRC/SC) at the eavesdropper. The outage probability of OS scheme is derived for a multi-relay system, when either the ICSI or the SCSI is available. We have shown that the secrecy performance of the OS with ICSI of the system is better than with SCSI. Also, the OS improves the performance of the multi-relay system, when the number of relays is increased.

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Caudle, Brad, Mark M. Espeland, Stephen R. Rapp, Sally A. Shumaker, Debbie Pleasants, and Laura D. Baker. "TELEPHONE-BASED COGNITIVE ASSESSMENTS IN A LARGE, MULTISITE RCT: THE COSMOS-MIND STUDY." Innovation in Aging 3, Supplement_1 (November 2019): S117. http://dx.doi.org/10.1093/geroni/igz038.432.

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Abstract Identifying safe, affordable, and well-tolerated interventions that prevent or delay cognitive decline in older adults is of critical importance. There is growing evidence from basic science and small randomized trials that cocoa flavanols may provide protection against this decline. Funded by the NIA, COSMOS-Mind is an ancillary study of COSMOS and was designed to add cognitive outcomes to the parent study, a 2x2 factorial randomized controlled trial testing the effects of cocoa flavanols (600 mg/d) and a multivitamin with matching placebo on cardiovascular disease and cancer endpoints. A validated telephone-based protocol conducted at baseline and then annually for three years measures attention, memory, language, executive function, and global cognitive functioning in 2,262 women and men, ages 65 and older without insulin-dependent diabetes. Cases of mild cognitive impairment and Alzheimer’s and related dementias will be centrally adjudicated. For participants who score below a pre-specified threshold on a test of global cognition, a study partner is interviewed to obtain additional information regarding cognitive and functional status. With >5,000 interviews completed, this presentation will describe the cognitive battery, operational procedures used to ensure high data fidelity, and strategies employed that have maintained retention at >90%. Our experiences in COSMOS-Mind can inform the design and implementation of other large, multi-site RCTs and epidemiological studies. Telephone-based assessments of cognitive function are a cost-efficient method for assessing cognitive function.

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Perić, Dragana, and Miroslav Perić. "Low Power Consumption Digital Clock Recovery Circuit Based on Threshold Crossing." Journal of Electrical Engineering 67, no. 6 (December 1, 2016): 433–38. http://dx.doi.org/10.1515/jee-2016-0063.

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Abstract In this paper a new structure of digital clock recovery — DCR circuit is presented. The main features of this DCR are: low complexity design, low power consumption and a single system clock operation. Thus, multiple instantiation of this type of DCR on a single chip is not complex. Due to this, such DCR can target application in energy-efficient cognitive radio systems with carrier aggregation. For performance evaluation, we have derived Markov chain based mathematical model for peak-to-peak and root mean square jitter performance analysis. The stability problem of this model, rising from the fact that some phase error states have several orders of magnitude lower probabilities than the others, is solved using mathematical apparatus for symbolic analysis. The mathematical model validity is examined by laboratorial measurements of proposed DCR for 4-PAM signal. The measurement methodology and results are described in details.

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Abzhandadze, Tamar, Erik Lundström, Dongni Buvarp, Marie Eriksson, Terence J. Quinn, and Katharina S. Sunnerhagen. "Development of a Swedish short version of the Montreal Cognitive Assessment for cognitive screening in patients with stroke." Journal of Rehabilitation Medicine 55 (June 13, 2023): jrm4442. http://dx.doi.org/10.2340/jrm.v55.4442.

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Objective: The primary objective was to develop a Swedish short version of the Montreal Cognitive Assessment (s-MoCA-SWE) for use with patients with stroke. Secondary objectives were to identify an optimal cut-off value for the s-MoCA-SWE to screen for cognitive impairment and to compare its sensitivity with that of previously developed short forms of the Montreal Cognitive Assessment.Design: Cross-sectional study.Subjects/patients: Patients admitted to stroke and rehabilitation units in hospitals across Sweden.Methods: Cognition was screened using the Montreal Cognitive Assessment. Working versions of the s-MoCA-SWE were developed using supervised and unsupervised algorithms.Results: Data from 3,276 patients were analysed (40% female, mean age 71.5 years, 56% minor stroke at admission). The suggested s-MoCA-SWE comprised delayed recall, visuospatial/executive function, serial 7, fluency, and abstraction. The aggregated scores ranged from 0 to 16. A threshold for impaired cognition ≤ 12 had a sensitivity of 97.41 (95% confidence interval, 96.64–98.03) and positive predictive value of 90.30 (95% confidence interval 89.23–91.27). The s-MoCA-SWE had a higher absolute sensitivity than that of other short forms.Conclusion: The s-MoCA-SWE (threshold ≤ 12) can detect post-stroke cognitive issues. The high sensitivity makes it a potentially useful “rule-out” tool that may eliminate severe cognitive impairment in people with stoke. LAY ABSTRACTStroke survivors have an increased risk of developing cognitive impairment, a common consequence of stroke. Therefore, many international guidelines recommend cognitive screening for all patients admitted to hospital with stroke. The Montreal Cognitive Assessment (MoCA) has been recommended as an appropriate cognitive test to be applied in stroke units. Although the administration of MoCA takes approximately 15 min, the screening can take longer in patients with acute stroke. Therefore, this study aimed to develop a Swedish short version of the Montreal Cognitive Assessment (s-MoCA-SWE) based on data from a large Swedish sample of acute and early subacute stroke survivors. The current study analysed data from 3,276 patients and suggest an s-MoCA-SWE that comprised the following tasks: delayed recall, visuospatial/executive function, serial 7, fluency, and abstraction. The s-MoCA-SWE could identify cognitive impairment in 97% of patients. In conclusion, s-MoCA-SWE has the potential to rule out severe cognitive impairment.

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Xie, Gang, Xincheng Zhou, and Jinchun Gao. "Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing." Sensors 23, no. 10 (May 14, 2023): 4751. http://dx.doi.org/10.3390/s23104751.

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In cognitive radio systems, cooperative spectrum sensing (CSS) can effectively improve the sensing performance of the system. At the same time, it also provides opportunities for malicious users (MUs) to launch spectrum-sensing data falsification (SSDF) attacks. This paper proposes an adaptive trust threshold model based on a reinforcement learning (ATTR) algorithm for ordinary SSDF attacks and intelligent SSDF attacks. By learning the attack strategies of different malicious users, different trust thresholds are set for honest and malicious users collaborating within a network. The simulation results show that our ATTR algorithm can filter out a set of trusted users, eliminate the influence of malicious users, and improve the detection performance of the system.

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Piera-Jiménez, Jordi, Anne Etzelmueller, Spyros Kolovos, Frans Folkvord, and Francisco Lupiáñez-Villanueva. "Guided Internet-Based Cognitive Behavioral Therapy for Depression: Implementation Cost-Effectiveness Study." Journal of Medical Internet Research 23, no. 5 (May 11, 2021): e27410. http://dx.doi.org/10.2196/27410.

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Background Major depressive disorder is a chronic condition; its prevalence is expected to grow with the aging trend of high-income countries. Internet-based cognitive-behavioral therapy has proven efficacy in treating major depressive disorder. Objective The objective of this study was to assess the cost-effectiveness of implementing a community internet-based cognitive behavioral therapy intervention (Super@, the Spanish program for the MasterMind project) for treating major depressive disorder. Methods The cost-effectiveness of the Super@ program was assessed with the Monitoring and Assessment Framework for the European Innovation Partnership on Active and Healthy Ageing tool, using a 3-state Markov model. Data from the cost and effectiveness of the intervention were prospectively collected from the implementation of the program by a health care provider in Badalona, Spain; the corresponding data for usual care were gathered from the literature. The health states, transition probabilities, and utilities were computed using Patient Health Questionnaire–9 scores. Results The analysis was performed using data from 229 participants using the Super@ program. Results showed that the intervention was more costly than usual care; the discounted (3%) and nondiscounted incremental cost-effectiveness ratios were €29,367 and €26,484 per quality-adjusted life-year, respectively (approximately US $35,299 and $31,833, respectively). The intervention was cost-effective based on the €30,000 willingness-to-pay threshold typically applied in Spain (equivalent to approximately $36,060). According to the deterministic sensitivity analyses, the potential reduction of costs associated with intervention scale-up would reduce the incremental cost-effectiveness ratio of the intervention, although it remained more costly than usual care. A discount in the incremental effects up to 5% exceeded the willingness-to-pay threshold of €30,000. Conclusions The Super@ program, an internet-based cognitive behavioral therapy intervention for treating major depressive disorder, cost more than treatment as usual. Nevertheless, its implementation in Spain would be cost-effective from health care and societal perspectives, given the willingness-to-pay threshold of €30,000 compared with treatment as usual.

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Yang, Mingchuan, Guanchang Xue, Botao Liu, and Yupu Yang. "Dual Threshold Cooperative Sensing Based Dynamic Spectrum Sharing Algorithm for Integrated Satellite and Terrestrial System." Remote Sensing 14, no. 23 (November 29, 2022): 6061. http://dx.doi.org/10.3390/rs14236061.

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In this paper, cognitive technology is introduced into the integrated satellite terrestrial system to realize the dynamic spectrum sharing of the system and improve the utilization rate of spectrum resources. To overcome the effects of low signal-to-noise ratio (SNR) and noise uncertainty in the channel, a dual-threshold cooperative sensing strategy based on energy detection is introduced. Spectrum sensing is considered as a binary hypothesis problem, but the uncertainty of noise interference in the integrated satellite terrestrial cognitive system will cause the perception to appear ambiguous. Moreover, the noise power varies with time and relative position within a certain range. In the fuzzy state, the perception technology adopts the equal-gain merging algorithm, and derives the voting optimization algorithm to improve the accuracy of decision-making. In addition, taking the minimum error probability as the optimization goal, the optimal adjustment of the adaptive double threshold is realized based on the equal-gain combining algorithm. The simulation results show that the spectrum detection accuracy under low SNR is improved, and the opportunity for terrestrial networks to share spectrum resources is increased.

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Charan, Chhagan, and Rajoo Pandey. "An Adaptive Spectrum-Sensing Algorithm for Cognitive Radio Networks based on the Sample Covariance Matrix." Defence Science Journal 67, no. 3 (April 25, 2017): 325. http://dx.doi.org/10.14429/dsj.67.10506.

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<p>A novel adaptive threshold spectrum sensing technique based on the covariance matrix of received signal samples is proposed. The adaptive threshold in terms of signal to noise ratio (SNR) and spectrum utilisation ratio of primary user is derived. It considers both the probability of detection and the probability false alarm to minimise the overall decision error probability. The energy- based spectrum sensing scheme shows high vulnerability under noise uncertainty and low SNR. The existing covariance-based spectrum sensing technique overcomes the noise uncertainty problem but its performance deteriorates under low SNR. The proposed covariance-based scheme effectively addresses the low SNR problem. The superior performance of this scheme over the existing covariance-based detection method is confirmed by the simulation results in terms of probability of detection, probability of error, and requirement of samples for reliable detection of spectrum.</p>

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Gupta, Aditi, and Adeiza James Onumanyi. "Performance Analysis of Dynamic Threshold Estimation Techniques Based on the One-Tier Cognitive Radio Network." Journal of Computer and Communications 07, no. 02 (2019): 31–46. http://dx.doi.org/10.4236/jcc.2019.72003.

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Zhang, Wei-liang, Bao-yu Zheng, and Wen-jing Yue. "Joint Optimization of Cooperative Threshold and Power Based on Cooperation Level in Cognitive Radio Networks." Journal of Electronics & Information Technology 33, no. 10 (October 15, 2011): 2322–27. http://dx.doi.org/10.3724/sp.j.1146.2011.00263.

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Bhowmick, Abhijit, Sanjay Dhar Roy, Sumit Kundu, and Aniruddha Chandra. "Double threshold-based cooperative spectrum sensing for a cognitive radio network with improved energy detectors." IET Communications 9, no. 18 (December 17, 2015): 2216–26. http://dx.doi.org/10.1049/iet-com.2014.1098.

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Das, Deepa, and Susmita Das. "A novel approach for energy-efficient resource allocation in double threshold-based cognitive radio network." International Journal of Communication Systems 30, no. 9 (October 27, 2016): e3198. http://dx.doi.org/10.1002/dac.3198.

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