This paper addresses the design of Mismatch Filter (MMF) for Linear Frequency Modulation (LFM) waveform so as to suppress the high range sidelobe. Firstly, the sidelobe level energy of correlation function between LFM and a spectrum weighted MMF is formulated to minimize. To handle this problem, an iterative gradient optimization utilizing Polyak heavy ball method is developed. Finally, numerical simulation are performed to evaluate its effectiveness, revealing that the proposed methodology shares lower Peak Sidelobe Level Ratio (PSLR) and Integral Sidelobe Level Ratio (ISLR), smaller Signal Noise Ratio (SNR) loss, and weaker mainlobe widening in comparison with the traditional windows.

Recently wearable EEG monitoring devices with seizure detection processor using convolutional neural network (CNN) have been proposed to detect the seizure onset of patients in real time for alert or stimulation purpose. High energy efficiency and accuracy are required for the seizure detection processor due to the tight energy constraint of wearable devices. However, the use of CNN and multi-channel processing nature of seizure detection result in significant energy consumption. In this work, an energy-efficient seizure detection processor is proposed, featuring multi-stage CNN classification, segmented data processing and adaptive channel selection to reduce the energy consumption while achieving high accuracy. The design has been fabricated and tested using a 55nm process technology. Compared with several state-of-the-art designs, the proposed design achieves the lowest energy per classification (0.32 μJ) with high sensitivity (97.78%) and low false positive rate per hour (0.5).

Age progression and other sources of variations such as head pose change have adverse effects on the performance of face verification systems. In this paper, we propose to manage the influence of both the age progression and head pose change on the adult face verification system by a general Q-stack age modeling technique, which uses the age and head pose as class-independent quality measures together with the scores from baseline classifiers, in order to obtain better verification performance. This allows for improved long-term class separation by introducing a 2D parameterized decision boundary in the scores-age space using a short-term enrollment model. This new method, based on the concept of classifier stacking with age- and head pose aware decision boundary compares favorably with the conventional face verification approach, which uses age- and head-pose-independent decision threshold calculated only in the score space at the time of enrollment. In this paper, we also show the advantages of user-specific approach for the face verification task over user-independent approach. The proposed approach is evaluated on the MORPH database.