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This paper explores the important role of blind source separation (BSS) techniques in separating M mixtures including N sources using a dual-sensor array, i.e., M = 2 , and proposes an efficient two-stage underdetermined BSS (UBSS) algorithm to estimate the mixing matrix and achieve source recovery by exploiting time–frequency (TF) sparsity. First, we design a mixing matrix estimation method by precisely identifying high clustering property single-source TF points (HCP-SSPs) with a spatial vector dictionary based on the principle of matching pursuit (MP). Second, the problem of source recovery in the TF domain is reformulated as an equivalent sparse recovery model with a relaxed sparse condition, i.e., enabling the number of active sources at each auto-source TF point (ASP) to be larger than M. This sparse recovery model relies on the sparsity of an ASP matrix formed by stacking a set of predefined spatial TF vectors; current sparse recovery tools could be utilized to reconstruct N > 2 sources. Experimental results are provided to demonstrate the effectiveness of the proposed UBSS algorithm with an easily configured two-sensor array. [ABSTRACT FROM AUTHOR]

Thin-film microscale light-emitting diodes (LEDs) are efficient light sources and their integrated applications offer robust capabilities and potential strategies in biomedical science. By leveraging innovations in the design of optoelectronic semiconductor structures, advanced fabrication techniques, biocompatible encapsulation, remote control circuits, wireless power supply strategies, etc., these emerging applications provide implantable probes that differ from conventional tethering techniques such as optical fibers. This review introduces the recent advancements of thin-film microscale LEDs for biomedical applications, covering the device lift-off and transfer printing fabrication processes and the representative biomedical applications for light stimulation, therapy, and photometric biosensing. Wireless power delivery systems have been outlined and discussed to facilitate the operation of implantable probes. With such wireless, battery-free, and minimally invasive implantable light-source probes, these biomedical applications offer excellent opportunities and instruments for both biomedical sciences research and clinical diagnosis and therapy. [ABSTRACT FROM AUTHOR]

Recently, a comment paper on "A New Elliptical Model for Device-Free Localization" (Sensors 2016, 16, 577) has been presented, and the authors have provided a modified model. However, there are still some misunderstandings. In this reply, we further explain the proposed elliptical model in (Sensors 2016, 16, 577) to make it more understandable. [ABSTRACT FROM AUTHOR]

Daily acquisition of large amounts of aerial and satellite images has facilitated subsequent automatic interpretations of these images. One such interpretation is object detection. Despite the great progress made in this domain, the detection of multi-scale objects, especially small objects in high resolution satellite (HRS) images, has not been adequately explored. As a result, the detection performance turns out to be poor. To address this problem, we first propose a unified multi-scale convolutional neural network (CNN) for geospatial object detection in HRS images. It consists of a multi-scale object proposal network and a multi-scale object detection network, both of which share a multi-scale base network. The base network can produce feature maps with different receptive fields to be responsible for objects with different scales. Then, we use the multi-scale object proposal network to generate high quality object proposals from the feature maps. Finally, we use these object proposals with the multi-scale object detection network to train a good object detector. Comprehensive evaluations on a publicly available remote sensing object detection dataset and comparisons with several state-of-the-art approaches demonstrate the effectiveness of the presented method. The proposed method achieves the best mean average precision (mAP) value of 89.6%, runs at 10 frames per second (FPS) on a GTX 1080Ti GPU. [ABSTRACT FROM AUTHOR]

Future wireless communication systems are facing with many challenges due to their complexity and diversification. Orthogonal frequency division multiplexing (OFDM) in 4G cannot meet the requirements in future scenarios, thus alternative multicarrier modulation (MCM) candidates for future physical layer have been extensively studied in the academic field, for example, filter bank multicarrier (FBMC), generalized frequency division multiplexing (GFDM), universal filtered multicarrier (UFMC), filtered OFDM (F-OFDM), and so forth, wherein the prototype filter design is an essential component based on which the synthesis and analysis filters are derived. This paper presents a comprehensive survey on the recent advances of finite impulse response (FIR) filter design methods in MCM based communication systems. Firstly, the fundamental aspects are examined, including the introduction of existing waveform candidates and the principle of FIR filter design. Then the methods of FIR filter design are summarized in details and we focus on the following three categories—frequency sampling methods, windowing based methods and optimization based methods. Finally, the performances of various FIR design methods are evaluated and quantified by power spectral density (PSD) and bit error rate (BER), and different MCM schemes as well as their potential prototype filters are discussed. [ABSTRACT FROM AUTHOR]

Power line detection plays an important role in an automated UAV-based electricity inspection system, which is crucial for real-time motion planning and navigation along power lines. Previous methods which adopt traditional filters and gradients may fail to capture complete power lines due to noisy backgrounds. To overcome this, we develop an accurate power line detection method using convolutional and structured features. Specifically, we first build a convolutional neural network to obtain hierarchical responses from each layer. Simultaneously, the rich feature maps are integrated to produce a fusion output, then we extract the structured information including length, width, orientation and area from the coarsest feature map. Finally, we combine the fusion output with structured information to get a result with clear background. The proposed method fully exploits multiscale and structured prior information to conduct both accurate and efficient detection. In addition, we release two power line datasets due to the scarcity in the public domain. The method is evaluated on the well-annotated power line datasets and achieves competitive performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Based on Time-Frequency (TF) analysis and a-contrario theory, this paper presents a new approach for extraction of linear arranged power transmission tower series in Polarimetric Synthetic Aperture Radar (PolSAR) images. Firstly, the PolSAR multidimensional information is analyzed using a linear TF decomposition approach. The stationarity of each pixel is assessed by testing the maximum likelihood ratio statistics of the coherency matrix. Then, based on the maximum likelihood log-ratio image, a Cell-Averaging Constant False Alarm Rate (CA-CFAR) detector with Weibull clutter background and a post-processing operator is used to detect point-like targets in the image. Finally, a searching approach based on a-contrario theory is applied to extract the linear arranged targets from detected point-like targets. The experimental results on three sets of PolSAR data verify the effectiveness of this approach., Competing Interests: The authors declare no conflict of interest.

Device-free localization (DFL) based on wireless sensor networks (WSNs) is expected to detect and locate a person without the need for any wireless devices. Radio tomographic imaging (RTI) has attracted wide attention from researchers as an emerging important technology in WSNs. However, there is much room for improvement in localization estimation accuracy. In this paper, we propose a geometry-based elliptical model and adopt the orthogonal matching pursuit (OMP) algorithm. The new elliptical model uses not only line-of-sight information, but also non-line-of-sight information, which divides one ellipse into several areas with different weights. Meanwhile the OMP, which can eliminate extra bright spots in image reconstruction, is used to derive an image estimator. The experimental results demonstrate that the proposed algorithm could improve the accuracy of positioning by up to 23.8% for one person and 33.3% for two persons over some state-of-the-art RTI methods.