R-CRNN: Region-based Convolutional Recurrent Neural Network for Audio Event Detection

This paper proposes a Region-based Convolutional Recurrent Neural Network (R-CRNN) for audio event detection (AED). The proposed network is inspired by Faster-RCNN, a well known region-based convolutional network framework for visual object detection. Different from the original Faster-RCNN, a recurrent layer is added on top of the convolutional network to capture the long-term temporal context from the extracted high level features. While most of the previous works on AED generate predictions at frame level first, and then use post-processing to predict the onset/offset timestamps of events from a probability sequence; the proposed method generates predictions at event level directly and can be trained end-to-end with a multitask loss, which optimizes the classification and localization of audio events simultaneously. The proposed method is tested on DCASE 2017 Challenge dataset. To the best of our knowledge, R-CRNN is the best performing single-model method among all methods without using ensembles both on development and evaluation sets. Compared to the other region-based network for AED (R-FCN) with an event-based error rate (ER) of 0.18 on the development set, our method reduced the ER to half.Comment: Accepted by Interspeech 201

Concept and Feasibility of One-Embedded System Payload Including Baseband Communication

Traditional approach of payload design develops modules separately such as control, compression and communication. Due to increasing demand of shorter development cycles and lower cost, we shall develop a highly adaptive approach for payload implementation so that we can update it in a short time according to the need of a new mission. Besides, the optimization of payload performance and communication link together becomes possible. Based on these, we propose a “one-embedded system” payload approach. All the control, file management, processing such as compression, and communications are implemented in one built-in embedded system. In other words, after the sensor signal is converted as digital data (after ADC, analog-to-digital-converter), the data gets into the proposed embedded system. And the system “does everything” and then outputs data to DAC (digital-to-analog-converter) and then transmitted it in analog form. The proposed embedded system includes a FPGA implementing a processor IP. Due to the programmable characteristic of FPGA, hardware interfaces can be adjusted quickly according to various mission requirements. Besides, because of the flexibility and adaptability of software, code can be updated to optimize performance according to various tasks during flight. In this work, we provide concept, guideline of optimization, structure, feasibility, benefits and risks of one-embedded system payload approach. An example of implementation for optical remotes sensing payload including interfaces will be investigated

High-Mobility Pentacene-Based Thin-Film Transistors With a Solution-Processed Barium Titanate Insulator

Abstract—Pentacene-based organic thin-film transistors (OTFTs) with solution-processed barium titanate (Ba1.2Ti0.8O3) as a gate insulator are demonstrated. The electrical properties of pentacene-based TFTs show a high field-effect mobility of 8.85 cm2 · V−1 · s−1, a low threshold voltage of −1.89 V, and a low subthreshold slope swing of 310 mV/decade. The chemical composition and binding energy of solution-processed barium titanate thin films are analyzed through X-ray photoelectron spectroscopy. The matching surface energy on the surface of the barium titanate thin film is 43.12 mJ · m−2, which leads to Stranski–Krastanov mode growth, and thus, high mobility is exhibited in pentacene-based TFTs. Index Terms—Barium titanate, high field-effect mobility, high permittivity, organic thin-filmtransistor (OTFT), solution process