A Continuous and Static Water Contaminant Detection System Using RF Microwave Principles

A continuous, static, and non-interfering water contaminant detection method is presented to measure specific water contaminants (NaCl, MgCl2, and mixture of NaCl and MgCl2) using RF microwave principles. A coil is mounted on the surface of a glass tube and the liquid sample is placed inside of the tube. An external magnetic field generated by the coil continuously measures changes in radio frequency energy. The non-contact feature of the device allows a long sensor lifetime with high sensitivity for real-time measurements. The measurement parameter is reflection coefficient (S11) and the operating frequency is 10 MHz – 5 GHz. For NaCl and MgCl2, 11 different concentrations (1000 ppm – 400 ppb) liquid solutions are prepared. Amplitude changes and frequency shifts are noticeable among different materials and concentrations. Different test materials have different radio frequencies at which they undergo excitation and the responses are identified in S11 measurement. A machine learning algorithm is introduced to analyze the measured S11 data. A support vector regressor (SVR) model is trained using the measured data of various salt samples. The training data is constructed by concatenating the 20,000 amplitudes and 20,000 phase values from the measured S11 data. The hyperparameters of the SVR are optimized using 10-fold cross-validation method. Based on the trained model, the algorithm predicts the concentrations of the liquid samples. The experimental results indicate that the device can detect concentrations as low as 400 ppb with high accuracy

Convolutional Adaptive Particle Filter with Multiple Models for Visual Tracking

Although particle filters improve the performance of convolutional-correlation trackers, especially in challenging scenarios such as occlusion and deformation, they considerably increase the computational cost. We present an adaptive particle filter to decrease the number of particles in simple frames in which there is no challenging scenario and the target model closely reflects the current appearance of the target. In this method, we consider the estimated position of each particle in the current frame as a particle in the next frame. These refined particles are more reliable than sampling new particles in every frame. In simple frames, target estimation is easier, therefore many particles may converge together. Consequently, the number of particles decreases in these frames. We implement resampling when the number of particles or the weight of the selected particle is too small. We use the weight computed in the first frame as a threshold for resampling because that weight is calculated by the ground truth model. Another contribution of this article is the generation of several target models by applying different adjusting rates to each of the high-likelihood particles. Thus, we create multiple models; some are useful in challenging frames because they are more influenced by the previous model, while other models are suitable for simple frames because they are less affected by the previous model. Experimental results on the Visual Tracker Benchmark v1.1 beta (OTB100) demonstrate that our proposed framework significantly outperforms state-of-the-art methods