In this letter, we propose an earthquake event classification model utilizing a feedback network and curriculum learning (CL). In particular, we propose the CL method with a feature concatenation using gated convolution so that CL can be effectively performed in consideration of the feedback structure. We show that the proposed model is effective through comparison experiments with the existing model using the earthquake dataset for Korean Peninsula and the Stanford earthquake dataset.

This letter proposes an attention-based convolutional neural network architecture for multitasking learning to accurately classify not only the presence of an earthquake but also the event type of the earthquake. In particular, to improve the performance in earthquake-type classification, we develop an attention-based feature aggregation framework embedded in multitask learning architecture. Representative experimental results show that the proposed method provides an effective structure for an earthquake detection and event classification with an earthquake database of the Korean peninsula and the Circum-Pacific belt.

This paper proposes a deep learning method for epicentral distance estimation using a single-channel seismic waveform. The model is based on a convolutional recurrent neural network structure to extract spatial and temporal features. Since the proposed model needs only single-channel data, it can also perform the distance estimation even when some channels of the sensor are adversely disabled. To evaluate our approach, we conduct distance estimation experiments with the Korean peninsula earthquake database from 2016 to 2018, which include microearthquakes and distant earthquakes. The epicentral distance estimation by the proposed method show an absolute mean error of 0.50 km with 9.16km standard deviation of error distribution, which shows the best estimation result among the competing model structures. The promising result indicates that the proposed approach can be deployed for epicentral localization task as part of realizing a robust earthquake monitoring system.

Voice Activity Detection (VAD) systems suffer from unexpected and non-stationary background noises at magnitudes sufficiently high to mask the speech signal.Although several methods of increasing the performance of VAD have been proposed, their approaches have yet to mitigate the influence of the background noise itself. This letter proposes an effective noise-robust VAD system approach. The proposed method uses spectral attention and temporal attention through applying a deep learning-based attention mechanism. The proposed method is demonstrated and compared with several other deep learning-based methods in terms of the area under the curve in experiments with either known or unknown noise-added, and real-world noisy data. The results show that the proposed method outperforms the other methods in all the scenarios considered, but moreover generalizes well in environments of unknown or unexpected noise.

WiFi-based wireless LANs (WLANs) are widely used for Internet access. They were designed such that an Access Points (AP) serves few associated clients with symmetric uplink/downlink traffic patterns. Usage of WiFi hotspots in locations such as airports and large conventions frequently experience poor performance in terms of downlink goodput and responsiveness. We study the various factors responsible for this performance degradation. We analyse and emulate a large conference network environment on our testbed with 45 nodes. We find that presence of asymmetry between the uplink/downlink traffic results in backlogged packets at WiFi Access Point's (AP's) transmission queue and subsequent packet losses. This traffic asymmetry results in maximum performance loss for such an environment along with degradation due to rate diversity, fairness and TCP behaviour. We propose our solution WiFox, which (1) adaptively prioritizes AP's channel access over competing STAs avoiding traffic asymmetry (2) provides a fairness framework alleviating the problem of performance loss due to rate-diversity/fairness and (3) avoids degradation due to TCP behaviour. We demonstrate that WiFox not only improves downlink goodput by 400-700 % but also reduces request's average response time by 30-40 %.

This paper presents a distributed implementation of RAND, a randomized time slot scheduling algorithm, called DRAND. DRAND runs in O(\delta ) time and message complexity where \delta is the maximum size of a two-hop neighborhood in a wireless network while message complexity remains O(\delta ), assuming that message delays can be bounded by an unknown constant. DRAND is the first fully distributed version of RAND. The algorithm is suitable for a wireless network where most nodes do not move, such as wireless mesh networks and wireless sensor networks. We implement the algorithm in TinyOS and demonstrate its performance in a real testbed of Mica2 nodes. The algorithm does not require any time synchronization and is shown to be effective in adapting to local topology changes without incurring global overhead in the scheduling. Because of these features, it can also be used even for other scheduling problems such as frequency or code scheduling (for FDMA or CDMA) or local identifier assignment for wireless networks where time synchronization is not enforced. We further evaluate the effect of the time-varying nature of wireless links on the conflict-free property of DRAND-assigned time slots. This experiment is conducted on a 55-node testbed consisting of the more recent MicaZ sensor nodes.

This paper presents a distributed implementation of RAND, a randomized time slot scheduling algorithm, called DRAND. DRAND runs in O(δ) time and message complexity where δ is the maximum size of a two-hop neighborhood in a wire-less network while message complexity remains O(δ), assuming that message delays can be bounded by an unknown constant.DRAND is the first fully distributed version of RAND. The algorithm is suitable for a wireless network where most nodes do not move,such as wireless mesh networks and wireless sensor networks.We implement the algorithm in TinyOS and demonstrate its performance in a real testbed of Mica2 nodes. The algorithm does not require any time synchronization and is shown to be effective in adapting to local topology changes without incurring global overhead in the scheduling.Because of these features, it can also be used even for other scheduling problems such as frequency or code scheduling (for FDMA or CDMA) or local identifier assignment for wireless networks where time synchronization is not enforced.

This paper presents the design, implementation and performance evaluation of a hybrid MAC protocol, called Z-MAC, for wireless sensor networks that combines the strengths of TDMA and CSMA while offsetting their weaknesses. Like CSMA, Z-MAC achieves high channel utilization and low-latency under low contention and like TDMA, achieves high channel utilization under high contention and reduces collision among two-hop neighbors at a low cost. A distinctive feature of Z-MAC is that its performance is robust to synchronization errors, slot assignment failures and time-varying channel conditions; in the worst case, its performance always falls back to that of CSMA. Z-MAC is implemented in TinyOS.