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High-precision indoor localization is growing extremely quickly, especially for multi-floor scenarios. The data on existing indoor positioning schemes, mainly, come from wireless, visual, or lidar means, which are limited to a single sensor. With the massive deployment of WiFi access points and low-cost cameras, it is possible to combine the above three methods to achieve more accurate, complete, and reliable location results. However, the existing SLAM rapidly advances, so hybrid visual and wireless approaches take advantage of this, in a straightforward manner, without exploring their interactions. In this paper, a high-precision multi-floor indoor positioning method, based on vision, wireless signal characteristics, and lidar is proposed. In the joint scheme, we, first, use the positioning data output in lidar SLAM as the theoretical reference position for visual images; then, use a WiFi signal to estimate the rough area, with likelihood probability; and, finally, use the visual image to fine-tune the floor-estimation and location results. Based on the numerical results, we show that the proposed joint localization scheme can achieve 0.62 m of 3D localization accuracy, on average, and a 1.24-m MSE for two-dimensional tracking trajectories, with an estimation accuracy for the floor equal to 89.22%. Meanwhile, the localization process takes less than 0.25 s, which is of great importance for practical implementation. [ABSTRACT FROM AUTHOR]

In the important and challenging field of environmental sound classification (ESC), a crucial and even decisive factor is the feature representation ability, which can directly affect the accuracy of classification. Therefore, the classification performance often depends to a large extent on whether the effective representative features can be extracted from the environmental sound. In this paper, we firstly propose a sub-spectrogram segmentation with score level fusion based ESC classification framework, and we adopt the proposed convolutional recurrent neural network (CRNN) for improving the classification accuracy. By evaluating numerous truncation schemes, we numerically figure out the optimal number of sub-spectrograms and the corresponding band ranges, and, on this basis, we propose a joint attention mechanism with temporal and frequency attention mechanisms and use the global attention mechanism when generating the attention map. Finally, the numerical results show that the two frameworks we proposed can achieve 82.1% and 86.4% classification accuracy on the public environmental sound dataset ESC-50, respectively, which is equivalent to more than 13.5% improvement over the traditional baseline scheme. [ABSTRACT FROM AUTHOR]

In this paper, we present a WiFi-based intrusion detection system called Wi-Alarm. Motivated by our observations and analysis that raw channel state information (CSI) of WiFi is sensitive enough to monitor human motion, Wi-Alarm omits data preprocessing. The mean and variance of the amplitudes of raw CSI data are used for feature extraction. Then, a support vector machine (SVM) algorithm is applied to determine detection results. We prototype Wi-Alarm on commercial WiFi devices and evaluate it in a typical indoor scenario. Results show that Wi-Alarm reduces much computational expense without losing accuracy and robustness. Moreover, different influence factors are also discussed in this paper. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets), consisting of macro-cells and overlaying pico-cells, have been recognized as a promising paradigm to support the exponential growth of data traffic demands and high network energy efficiency (EE). However, for two-tier heterogeneous architecture deployment of HetNets, the inter-tier interference will be challenging. Time domain further-enhanced inter-cell interference coordination (FeICIC) proposed in 3GPP Release-11 becomes necessary to mitigate the inter-tier interference by applying low power almost blank subframe (ABS) scheme. Therefore, for HetNets deployment in reality, the pico-cell range expansion (CRE) bias, the power of ABS and the density of pico base stations (PBSs) are three important factors for the network EE improvement. Aiming to improve the network EE, the above three factors are jointly considered in this paper. In particular, we first derive the closed-form expression of the network EE as a function of pico CRE bias, power reduction factor of low power ABS and PBS density based on stochastic geometry model. Then, the approximate relationship between pico CRE bias and power reduction factor is deduced, followed by a linear search algorithm to get the near-optimal pico CRE bias and power reduction factor together at a given PBS density. Next, a linear search algorithm is further proposed to optimize PBS density based on fixed pico CRE bias and power reduction factor. Due to the fact that the above pico CRE bias and power reduction factor optimization and PBS density optimization are optimized separately, a heuristic algorithm is further proposed to optimize pico CRE bias, power reduction factor and PBS density jointly to achieve global network EE maximization. Numerical simulation results show that our proposed heuristic algorithm can significantly enhance the network EE while incurring low computational complexity. [ABSTRACT FROM AUTHOR]