Tracking-by-detection is one of the most popular approaches for tracking multiple objects in which the detector plays an important role. Sometimes detector failures caused by occlusions or various poses are unavoidable and lead to tracking failure. To cope with this problem, we construct a heterogeneous association graph that fuses high-level detections and low-level image evidence for target association. Compared with other methods of using low-level information, our proposed Heterogeneous Association Fusion(HAF) tracker is less sensitive to particular parameters and is easier to extend and implement. We use the fused association graph to build track trees for HAF and solve them by the multiple hypotheses tracking (MHT) framework, which has been proven competitive by introducing efficient pruning strategies. In addition, a novel idea of adaptive weights is proposed to analyze the contribution between motion and appearance. We also evaluate our results on the MOT challenge benchmarks and achieve state-of-the-art results on the MOT Challenge 2017.

Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ ₁ minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

This letter considers the design of measurement matrices with low complexity, easy hardware implementation and good sensing performance for practical compressed sensing applications. We construct a class of sparse binary measurement matrices from protograph Low-density parity-check (LDPC) codes, which can satisfy these features simultaneously. The optimal performance of proposed matrices is analyzed from the mutual coherence aspect. Moreover, we obtain a sufficient condition for optimal construction of the matrices using proposed algorithm. Simulation experiments also demonstrate that orthogonal matching pursuit (OMP) algorithm performs better using the constructed matrices as compared with several state-of-the-art measurement matrices, such as random Gaussian matrices.

Parameter convergence is desirable in adaptive control as it brings several attractive features, including accurate online modelling, exponential tracking, and robust adaptation without parameter drift. However, a strong persistent‐excitation (PE) condition must be satisfied to guarantee parameter convergence in the conventional adaptive control. This study proposes a model reference composite learning control strategy to guarantee parameter convergence without the PE condition. In the composite learning, an integral at a moving‐time window is applied to construct a prediction error, an integral transformation is derived for avoiding the time derivation of plant states in the calculation of the prediction error, and both the tracking error and the prediction error are applied to update parametric estimates. Global exponential stability of the closed‐loop system is established under an interval‐excitation condition which is much weaker than the PE condition. Compared with a concurrent learning technique that has the same aim as this study, the proposed composite learning technique avoids the usage of singular value maximisation and fixed‐point smoothing resulting in a considerable reduction of computational cost. Numerical results have verified effectiveness and superiority of the proposed control strategy.

The emerging compressive sensing (CS) paradigm holds considerable promise for improving the energy efficiency of wireless body sensor networks, which enables nodes to employ a sample rate significantly below Nyquist while still able to accurately reconstruct signals. In this paper, we propose a weighted $\ell _{1,2}$ minimization method for multichannel electrocardiogram (ECG) reconstruction by exploiting both the interchannel correlation and multisource prior in wavelet domain. A sufficient and necessary condition for exact recovery via the proposed method is derived. Based upon the condition, the performance gain of the proposed method is analyzed theoretically. Furthermore, a reconstruction error bound of the proposed method is obtained, which indicates that the proposed method is stable and robust in recovering sparse and compressible signals from noisy measurements. Extensive experiments utilizing Physikalisch-Technische Bundesanstalt diagnostic ECG database and open-source electrophysiological toolbox fetal ECG database show that significant performance improvements, in terms of compression rate and reconstruction quality, can be obtained by the proposed method compared with the state-of-the-art CS-based methods.

For semi-closed-loop computer numerical control machine tool, the pitch error of screw caused by thermal expansion can deteriorate the positioning accuracy of ball screw feed drive system. This study presents different prediction models for positioning error of ball screw feed drive system based on the mounting condition, where the total error is separated into geometric error and thermal error. The coefficients in the model are identified using the multiple linear regression method. The prediction model is validated and the error compensation is also done for the X axis of a three-axis computer numerical control milling machine. The test results show that the model developed can well predict the thermal error under any given temperature as well as position during the temperature rising process of ball screw, and it can greatly improve the system positioning accuracy through compensation.