Aiming at realizing on-chip real-time human activity recognition based on the miniaturized and integrated radar with limited computational resources, hyperdimensional computing (HDC) is a promising method due to its unique advantages including high energy efficiency, excellent robustness, lightweight model, and superfast learning process. This article reports on the first use of HDC in the radar system to recognize human activities. First, we design an image mask method to minimize the impact of interference in original radar micro-Doppler spectrograms and apply a fast envelope extraction method to fully reflect features of different human activities. Subsequently, we select the most applicable representation methods for HDC to encode and recognize these features. To better accelerate the convergence speed, enable subtle adjustments of the HDC model, and improve the recognition accuracy, we propose a new adaptive retraining method, Mask-Retraining. Finally, the accuracy of recognizing six human activities using HDC with Mask-Retraining reaches 92.93%. Experimental results and comparisons between HDC and ten recognition algorithms demonstrate the validity of our methods. In most cases, HDC outperforms other algorithms being compared both in recognition accuracy and computational cost. Especially, HDC becomes the prime and irreplaceable candidate when only small datasets or finite data points are available.

Permanent magnet (PM) machines inevitably suffer from cogging force, which does not contribute to the average output torque (force) but contributes as a type of torque (force) ripple. This paper provides an overview of the cogging force reduction methods for different types of PM machines. First, a systematic and comprehensive categorization of different kinds of cogging force reduction methods is given according to the reduction principle. Then, the cogging force reduction methods for different types of PM machines are analyzed and discussed based on the categorization. Finally, according to the versatility and feasibility of the cogging force reduction methods, practical methods are recommended for different types of PM machines. The categorization, analyses, and recommendations presented in the paper are useful for the design of different types of PM machines with the requirement of cogging force reduction or output torque (force) ripple suppression.

Background: The incidence of rupture of the quadriceps or patellar tendon s is low, especially that of bilateral quadriceps tendon rupture, and it is generally considered a complication secondary to chronic systemic disorders. We report two rare cases of simultaneous bilateral tendon rupture affecting the extensor function of the knee in patients with chronic kidney disease who have been treated with long-term haemodialysis. Case presentation: Two young males with a history of chronic kidney disease who were being treated with long-term haemodialysis presented to our hospital with clinical signs of disruption of the extensor mechanism of the knee. One patient was diagnosed with bilateral quadriceps tendon rupture, and the other patient had bilateral patellar tendon rupture. They underwent surgical repair of the tendons, and their knees were actively mobilized during physiotherapy. Conclusion: Bilateral quadriceps or patellar tendons rupture is a rare occurrence in patients with chronic kidney disease who are being treated with long-term haemodialysis. Timely surgical treatment and scientific physiotherapy can lead to good recovery of knee joint function.

In this paper, an altered adaptive algorithm on block-compressive sensing (BCS) is developed by using saliency and error analysis. A phenomenon has been observed that the performance of BCS can be improved by means of rational block and uneven sampling ratio as well as adopting error analysis in the process of reconstruction. The weighted mean information entropy is adopted as the basis for partitioning of BCS which results in a flexible block group. Furthermore, the synthetic feature (SF) based on local saliency and variance is introduced to step-less adaptive sampling that works well in distinguishing and sampling between smooth blocks and detail blocks. The error analysis method is used to estimate the optimal number of iterations in sparse reconstruction. Based on the above points, an altered adaptive block-compressive sensing algorithm with flexible partitioning and error analysis is proposed in the article. On the one hand, it provides a feasible solution for the partitioning and sampling of an image, on the other hand, it also changes the iteration stop condition of reconstruction, and then improves the quality of the reconstructed image. The experimental results verify the effectiveness of the proposed algorithm and illustrate a good improvement in the indexes of the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Gradient Magnitude Similarity Deviation (GMSD), and Block Effect Index (BEI).

Outlier detection is an important aspect in the field of data mining. In order to solve the problem of outlier detection in high-dimensional datasets, an outlier detection algorithm based on Gaussian mixture model is proposed. First of all, for the data set to be tested, the global optimization expectation maximization algorithm is used to fit a Gaussian mixture model, and then the three-time standard deviation principle is introduced on each Gaussian component, the outlier is the data point outside the range of the mean deviation of the mean value of three times the standard deviation. Through the experiments on the simulation dataset and the real data set, the effectiveness of the algorithm on the outlier detection of high-dimensional data sets is verified.