We developed a forced non-electric-shock running wheel (FNESRW) system that provides rats with high-intensity exercise training using automatic exercise training patterns that are controlled by a microcontroller. The proposed system successfully makes a breakthrough in the traditional motorized running wheel to allow rats to perform high-intensity training and to enable comparisons with the treadmill at the same exercise intensity without any electric shock. A polyvinyl chloride runway with a rough rubber surface was coated on the periphery of the wheel so as to permit automatic acceleration training, and which allowed the rats to run consistently at high speeds (30 m/min for 1 h). An animal ischemic stroke model was used to validate the proposed system. FNESRW, treadmill, control, and sham groups were studied. The FNESRW and treadmill groups underwent 3 weeks of endurance running training. After 3 weeks, the experiments of middle cerebral artery occlusion, the modified neurological severity score (mNSS), an inclined plane test, and triphenyltetrazolium chloride were performed to evaluate the effectiveness of the proposed platform. The proposed platform showed that enhancement of motor function, mNSS, and infarct volumes was significantly stronger in the FNESRW group than the control group (P<0.05) and similar to the treadmill group. The experimental data demonstrated that the proposed platform can be applied to test the benefit of exercise-preconditioning-induced neuroprotection using the animal stroke model. Additional advantages of the FNESRW system include stand-alone capability, independence of subjective human adjustment, and ease of use.

In this paper, radar target recognition is given by KSDA (kernel scatter-difference discriminant analysis) pattern recognition on RCS (radar cross section). The kernel method converts the traditional FLDA (Fisher linear discriminant analysis) to a nonlinear high-dimensional space and such a kernel technique is called KFDA (kernel Fisher discriminant analysis). The basic concept of KFDA is to map training samples in the original space to a high-dimensional feature space via a nonlinear mapping function. Pattern recognition is then implemented in the feature space through extracted nonlinear discriminant features. However, as the kernel within-class scatter matrix is singular, the optimal discriminant features can not be achieved directly. To improve this drawback of KFDA, this study utilizes the scatter difference as the discriminant function, i.e., KSDA, to implement radar target recognition. The KSDA can modify the Fisher discrimination function and then serves as an efficient tool of radar target recognition. As a result, the computational complexity is reduced and then the computational speed is increased. Of great importance, the proposed target recognition scheme (based on KSDA) can still work well even though the kernel within-class scatter matrix is singular. Our KDSA based target recognition scheme is accurate, efficient and has good ability to tolerate random noises.

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Wearable cuffless photoplethysmographic blood pressure monitors have garnered widespread attention in recent years; however, the long-term performance values of these devices are questionable. Most cuffless blood pressure monitors require initial baseline calibration and regular recalibrations with a cuffed blood pressure monitor to ensure accurate blood pressure estimation, and their estimation accuracy may vary over time if left uncalibrated. Therefore, this study assessed the accuracy and long-term performance of an upper-arm, cuffless photoplethysmographic blood pressure monitor according to the ISO 81060-2 standard. This device was based on a nonlinear machine-learning model architecture with a fine-tuning optimized method. The blood pressure measurement protocol followed a validation procedure according to the standard, with an additional four weekly blood pressure measurements over a 1-month period, to assess the long-term performance values of the upper-arm, cuffless photoplethysmographic blood pressure monitor. The results showed that the photoplethysmographic signals obtained from the upper arm had better qualities when compared with those measured from the wrist. When compared with the cuffed blood pressure monitor, the means ± standard deviations of the difference in BP at week 1 (baseline) were −1.36 ± 7.24 and −2.11 ± 5.71 mmHg for systolic and diastolic blood pressure, respectively, which met the first criterion of ≤5 ± ≤8.0 mmHg and met the second criterion of a systolic blood pressure ≤ 6.89 mmHg and a diastolic blood pressure ≤ 6.84 mmHg. The differences in the uncalibrated blood pressure values between the test and reference blood pressure monitors measured from week 2 to week 5 remained stable and met both criteria 1 and 2 of the ISO 81060-2 standard. The upper-arm, cuffless photoplethysmographic blood pressure monitor in this study generated high-quality photoplethysmographic signals with satisfactory accuracy at both initial calibration and 1-month follow-ups. This device could be a convenient and practical tool to continuously measure blood pressure over long periods of time.

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