In spite of Anderson’s theorem1, disorder is known to affect superconductivity in conventional s-wave superconductors2–11. In most superconductors, the degree of disorder is fixed during sample preparation. Here we ...
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Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients...
Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients. All of this system can be realization by using cryptography. LUC algorithms are introduced by Smith and Lennon in 1993. LUC algorithm is an algorithm that based on the use of Lucas sequence (specific arithmetic operations derived from Lucas row) that rarely used to enhance the security of the messages. In this research, LUC algorithm is combined with visual cryptography to process the encryption and the description of a colored image. Four different images were used in the trial here. The performance of the system is assessed using Structural similarity (SSIM) which has an assessment similar to human eye. If the image quality is the same as the original image, then the SSIM value is one. Whereas if the quality of decryption image is very different from the original image then the value of SSIM is zero.
The design of a tremor estimator is an important part of designing mechanical tremor suppression orthoses. A number of tremor estimators have been developed and applied with the assumption that tremor is a mono-freque...
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The design of a tremor estimator is an important part of designing mechanical tremor suppression orthoses. A number of tremor estimators have been developed and applied with the assumption that tremor is a mono-frequency signal. However, recent experimental studies have shown that Parkinsonian tremor consists of multiple frequencies, and that the second and third harmonics make a large contribution to the tremor. Thus, the current estimators may have limited performance on estimation of the tremor harmonics. In this paper, a high-order tremor estimation algorithm is proposed and compared with its lower-order counterpart and a widely used estimator, the Weighted-frequency Fourier Linear Combiner (WFLC), using 18 Parkinsonian tremor data sets. The results show that the proposed estimator has better performance than its lower-order counterpart and the WFLC. The percentage estimation accuracy of the proposed estimator is 85±2.9%, an average improvement of 13% over the lower-order counterpart. The proposed algorithm holds promise for use in wearable tremor suppression devices.
This work presents an algorithm for automated real-time ramp detection using 3D point cloud data in the context of shared-control powered wheelchairs. Limitations in the interfaces available to those with severe motor...
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ISBN:
(纸本)9781509039302
This work presents an algorithm for automated real-time ramp detection using 3D point cloud data in the context of shared-control powered wheelchairs. Limitations in the interfaces available to those with severe motor impairments can make basic maneuvering tasks with powered wheelchairs difficult. Although a significant amount of work has been done on obstacle detection and avoidance, much less attention has been given to algorithms for the safe and reliable detection of ramps and inclines;even though navigating these structures is an important part of urban life. We provide an algorithmic solution for accurately detecting traversable inclines for applications with powered wheelchairs using the Point Cloud Library (PCL) within the Robotics Operating System (ROS) framework. All algorithms are implemented first in simulation and later evaluated on data obtained from indoor and outdoor urban environments. We measure the performance of our algorithm with systematic testing on several different ramp structures, observed from varied viewpoints. Results show that our algorithm is successful in detecting the orientation, slope, and width of traversable ramps with up to 100% accuracy and an average detection accuracy of 88%.
This paper presents a new and robust algorithm for detection of sleep stages by using the lead I of the Electrocardiography (ECG) and a fingertip Photoplethysmography (PPG) sensor, validated using multiple overnight P...
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This paper presents a new and robust algorithm for detection of sleep stages by using the lead I of the Electrocardiography (ECG) and a fingertip Photoplethysmography (PPG) sensor, validated using multiple overnight PSG recordings consisting of 20 human subjects (9 insomniac and 11 healthy). Heart Rate Variability (HRV) and Pulse Transit Time (PTT) biomarkers which were extracted from ECG and PPG biosignals then employed to extract features. Distance Weighted k-Nearest Neighbours (DWk-NN) was used as classifier to differentiate sleep epochs. The validation of the algorithm was evaluated by Leave-One-Out-Cross-Validation method. The average accuracy of 73.4% with standard deviation of 6.4 was achieved while the algorithm could distinguish stages 2, 3 of non-rapid eye movement sleep by average sensitivity of almost 80%. The lowest mean sensitivity of 53% was for stage 1. These results demonstrate that an algorithm based on PTT and HRV spectral analysis is able to classify and distinguish sleep stages with high accuracy and sensitivity. In addition the proposed algorithm is capable to be improved and implemented as a wearable, comfortable and cheap instrument for sleep screening.
Overnight continuous blood pressure measurement provides simultaneous monitoring of blood pressure and sleep architecture. By this means, we are able to investigate whether different sleep events are associated to blo...
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Overnight continuous blood pressure measurement provides simultaneous monitoring of blood pressure and sleep architecture. By this means, we are able to investigate whether different sleep events are associated to blood pressure fluctuations. In this paper, we used the Pulse Transit Time (PTT) to develop and evaluate functions for measurement of blood pressure. We focused on the first and second derivatives of fingertip Photoplethysmography (PPG) recordings to detect PPG critical points. By applying R wave of ECG and PPG critical points, we created two PTT-based models for estimation of systolic and diastolic blood pressure (SBP and DBP). Seven subjects polysomnography datasets that contained PPG, ECG and blood pressure recordings were utilised to validate and compare developed PTT-BP functions. Results found that if the peak of the first derivative of PPG (VPG) was considered as the pulse pressure arrival point, the resulted PTT (PTT V ) would more accurately predict both SBP and DBP. The average R-squared coefficient for SBP and DBP were correspondingly 0.593 and 0.416. The obtained mean error for PTT V based functions in SBP was ±3.96 mmHg with standard deviation of 1.41 mmHg and in DBP was ±6.88 mmHg with standard deviation of 3.03 mmHg. We concluded PTT detected from VPG is a reliable and suitable maker for overnight continuous blood pressure monitoring.
The human metabolome has remained largely unknown, with most studies annotating ~10% of features. In nucleic acid sequencing, annotating transcripts by source has proven essential for understanding gene function. Here...
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Objective 1. To evaluate and optimize commonly used nonlinear deformation algorithms when applied to human brain MRI. 2. To evaluate and optimize accuracy of automated atlas-based segmentation of the two most commonly...
Objective 1. To evaluate and optimize commonly used nonlinear deformation algorithms when applied to human brain MRI. 2. To evaluate and optimize accuracy of automated atlas-based segmentation of the two most commonly used target regions for deep brain stimulation (DBS), the nucleus subthalamicus (STN) and the internal part of the globus pallidus (GPi). 3. To guide clinicians as to which preoperative MRI sequences to acquire in patients undergoing DBS. Background Translating single-subject imaging into a common reference frame such as the MNI space is a ‘core concept within the field of brain mapping’ (Evans et al., 2012) making an accurate transformation into this space necessary. Here, we evaluated and optimized 6 commonly used deformation algorithms and compared each outcome to manually labeled brain regions of 103 brains. Methods Algorithms evaluated were: New Segment, DARTEL and SHOOT (SPM); FNIRT (FSL); SyN and BSpline (ANTs). Target space was the MNI 2009b NLIN space. An atlas of the STN and GPi in MNI space (Ewert et al., 2017) was transformed to native space by inverting each deformation matrix. The resulting atlas-based segmentations were then compared to expert manual segmentations of the native brains. Overlap between the two was quantified using the Dice coefficient (Dice et al., 1945), mean surface distance and correlation of volumes. Two open source datasets were used: The IXI-dataset ( http://***/ixi-dataset/ ) and the HCP-dataset ( http://***/ ). Results The best performing algorithms were New Segment with a custom tissue probability map (TPM) (1) and ANTs SyN (2) with an additional step to refine subcortical coregistration. Performance is compared to inter-rater results (3) of manual segmentations. Fig. 1 shows results for the HCP data for STN (L, in red) and GPi (R, in blue) separately: median Dice for STN (1): 0.70 (std: 0.1); (2): 0.72 (0.06); (3): 0.76 (0.09). Median of mean surface distance in m
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