Search

Many driver monitoring systems (DMSs) have been proposed to reduce the risk of human-caused accidents. Traditional DMSs focus on detecting specific predefined abnormal driving behaviors, such as drowsiness or distracted driving, using generic models trained with the data collected during abnormal driving. However, it is difficult to collect sufficient representative training data to construct generic detection models, which are applicable to all drivers. Consequently, this paper proposes a new personal-based hierarchical DMS (HDMS). During driving, the first layer of the proposed HDMS detects normal and abnormal driving behavior based on normal personal driving models represented by sparse representations. When abnormal driving behavior is detected, the second layer of the HDMS further determines whether the behavior is drowsy driving behavior or distracted driving behavior. The experimental results obtained for three datasets show that the proposed HDMS outperforms existing state-of-the-art DMS methods in detecting normal driving behavior, drowsy driving behavior, and distracted driving behavior. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel annotation placement approach for revealing information about foreground objects in surveillance videos. To arrange positions of annotations, spatiotemporal coherence between annotations and foreground objects is applied. The annotation placement problem is formulated as an optimization problem with respect to spatiotemporal coherence of annotations and foreground objects. The optimization problem is effectively solved using Markov random fields. To the best of our knowledge, this paper is the first work that discusses and solves the annotation placement problem for surveillance videos by considering the relationships between annotations and foreground objects with trajectories. As shown in the experiments, the proposed approach can arrange annotations based on the moving trajectories of foreground objects and prevent the occlusions between different annotations and foreground objects. It also achieves better quantitative and qualitative results compared with state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

This paper aims to develop a mathematical model for predicting the temperature response of tissues in electromagnetic heating therapy (EHT), when using a magnetic flux concentrator to improve heating efficiency. EHT has two critical challenges when applied to deep-seated tissue heating, i.e., the temperature might not be accurately measured and the magnetic field intensity decreases with increasing depth. The finite-element method (FEM) is suitable for coupled analysis with electromagnetic fields and heat transfer, which can be used to predict temperature profiles in deep tissue implanted with magnetic materials. To improve the accuracy of the FEM model, an adaptive network fuzzy inference system (ANFIS) model is implemented on the basis of measured data and simulated data, which were generated by the FEM model. The ANFIS model can provide a large number of testing data to optimize the parameters in the FEM model; moreover, it can be used to expedite the optimization process. [ABSTRACT FROM AUTHOR]

This paper presents a deep-magnetic-field generator for thermotherapy, which has several advantages over traditional hard-copper coils. First, the new device is flexible and can therefore be used to treat different zones of the body. The device enables electromagnetic cauterization in thermotherapy of tumors deep within the body. Another advantage is that the flexible coil produces a magnetic field that penetrates deeper into the body compared with traditional helical hard-copper coils of the same diameter. For a treatment depth of 10 cm, we compare the results for in vitro electromagnetic-induction ablation experiments on porcine liver tissue done: 1) with a flexible laminated copper coil and 2) with a hard-copper coil. The results of the experiments show that the temperature increases 3.9 times better for the flexible coil than the traditional hard-copper coil. [ABSTRACT FROM AUTHOR]