As research interest in the Internet of Things continue to grow, concerns are raised regarding potential limitations of the current body of knowledge on performance engineering for wireless links. Best practices in this thematic area have insofar largely been focused on human-to-human communications (e.g., wide area cellular wireless networks) and traffic patterns emerging from human actions (e.g., asymmetric bandwidth between the uplink and downlink traffic directions) in urban areas. As a result, studies of the propagation profile that would properly characterize the deployment of wireless technologies (e.g., sensor networks) in rural areas and for specific applications of machine-to-machine (M2M) communications are not sufficiently developed in the literature. To address this shortcoming, we collect and study RSSI measurements as a function of base station antenna height and at a certain distance and transmitting antenna height, under a rural setting. The measurements collected from our testbed instrumentation seem to follow closely the theoretical two-ray model. It is therefore shown here that whenever a second ray has impact on propagation characteristics there is always an optimum and minimum base station antenna height. A method for defining those heights is presented in this paper, which further allows performance optimisation of a wireless sensor network.
A sufficient condition reported very recently for perfect recovery of a K-sparse vector via orthogonal matching pursuit (OMP) in K iterations is that the restricted isometry constant of the sensing matrix satisfies delta_K+1<1/(sqrt(delta_K+1)+1). By exploiting an approximate orthogonality condition characterized via the achievable angles between two orthogonal sparse vectors upon compression, this paper shows that the upper bound on delta can be further relaxed to delta_K+1<(sqrt(1+4*delta_K+1)-1)/(2K).This result thus narrows the gap between the so far best known bound and the ultimate performance guarantee delta_K+1<1/(sqrt(delta_K+1)) that is conjectured by Dai and Milenkovic in 2009. The proposed approximate orthogonality condition is also exploited to derive less restricted sufficient conditions for signal reconstruction in several compressive sensing problems, including signal recovery via OMP in a noisy environment, compressive domain interference cancellation, and support identification via the subspace pursuit algorithm.
Hyperspectral remote sensing often captures imagery where the spectral profiles of the spatial pixels are the result of the reflectance contribution of numerous materials. Spectral unmixing is then used to extract the collection of materials, or endmembers, contained in the measured spectra, and a set of corresponding fractions that indicate the abundance of each material present at each pixel. This work aims at developing a spectral unmixing algorithm directly from compressive measurements acquired using the coded-aperture snapshot spectral imaging (CASSI) system. The proposed method first uses the compressive measurements to find a sparse vector representation of each pixel in a 3-D dictionary formed by a 2-D wavelet basis and a known spectral library of endmembers. The sparse vector representation is estimated by solving a sparsity-constrained optimization problem using an algorithm based on the variable splitting augmented Lagrangian multipliers method. The performance of the proposed spectral unmixing method is improved by taking optimal CASSI compressive measurements obtained when optimal coded apertures are used in the optical system. The optimal coded apertures are designed such that the CASSI sensing matrix satisfies a Restricted Isometry Property (RIP) with high probability. Simulations with synthetic hyperspectral cubes illustrate the accuracy of the proposed unmixing method.
Significant research effort has been drawn over the last few years to reduce the hardware complexity and size of Multiple Input - Multiple Output (MIMO) systems and to push this promising technology even to lightweight and small portable devices. Among other architectures, single-fed MIMO systems with compact parasitic arrays are a possible candidate toward this goal. To facilitate the study and design of parasitic arrays for MIMO applications, this paper presents an alternative signal model considering the currents at the ports of the transmitting array as the input to the system. Based on this model, a novel parasitic array is designed that is able to multiplex 16-QAM signals with the aid of a single radio-frequency (RF) source. The proposed signal model can also be useful for the evaluation of large parasitic arrays in massive MIMO regime, with significantly reduce hardware burden.
In applications of tensor analysis, missing data is an important issue that is usually handled via weighted least-squares fitting, imputation, or iterative expectation-maximization. The resulting algorithms are often cumbersome, and tend to fail when the percentage of missing samples is large. This paper proposes a novel and refreshingly simple approach for handling randomly missing values in big tensor analysis. The stepping stone is random multi-way tensor compression, which enables indirect tensor factorization via analysis of compressed `replicas' of the big tensor. A Bernoulli model for the misses, and two opposite ends of the tensor modeling spectrum are considered: independent and identically distributed (i.i.d.) tensor elements, and low-rank (and in particular rank-one) tensors whose latent factors are i.i.d. In both cases, analytical results are established, showing that the tensor approximation error variance is inversely proportional to the number of available elements. Coupled with recent developments in robust CP decomposition, these results show that it is possible to ignore missing values without losing the ability to identify the underlying model.
Trellis-coded modulation (TCM) is a power and bandwidth efficient digital transmission scheme which offers very low structural delay of the data stream. Classical TCM uses a signal constellation of twice the cardinality compared to an uncoded transmission with one bit of redundancy per PAM symbol, i.e., application of codes with rates n-1/n when 2(n) denotes the cardinality of the signal constellation. Recently published work allows rate adjustment for TCM by means of puncturing the convolutional code (CC) on which a TCM scheme is based on. In this paper it is shown how punctured TCM-signals transmitted over intersymbol interference (ISI) channels can favorably be decoded. Significant complexity reductions at only minor performance loss can be achieved by means of reduced state sequence estimation.
We propose a waveform power allocation technique for a set of clusters of radars in a radar network using a game theoretic method. Each cluster consists of a number of radars forming a MIMO configuration. There is no communication between the clusters in the network, hence the power allocation in the radars within each cluster is performed using a non-cooperative game theoretic technique. The aim of each cluster of radars in the network is to minimise the total power used by the radars in its cluster while achieving a target detection criterion. The convergence of the algorithm to the Nash equilibrium is demonstrated.
This paper presents a mobile product recognition system using bag-of-visual phrase (BoP). It aims to develop a mobile product recognition and recommendation system where a user can recognize a commercial product of interest by taking a picture of it using the mobile phone, and then search for the relevant information (e.g., price, nearby store, consumer recommendation, etc.). In the proposed BoP framework, second-order visual phrases candidates are first obtained from neighborhood visual words. Discriminative visual phrases are then determined, and images are indexed with a two-dimensional inverted index of visual phrases. Geometric verification (GV) is performed to further improve the accuracy of image matching. Experimental results show that the proposed method can achieve 90% recognition rate for a dataset consisting of 3882 reference images and 41 categories.
Although subspace-based direction-finding algorithms have been widely accepted as the most powerful method in estimating the source's directions-of-arrival, its derivation was based on the linear independence of the source's manifold vectors. An undesirable manifoldly ambiguous situation is said to occur when this condition is not satisfied. To minimize the presence of ambiguities, the recent work in [1] proposed a method to resolve ambiguities by placing medium substrates at the front-end of antenna elements, which results in the change of direction and the propagation delay. This effectively breaks down the linear dependence amongst the manifold vectors. In this paper, the use of planar substrates to resolve manifold ambiguities in a diversely-polarized antenna array system is investigated, where the actual causes of ambiguities are assessed from the differential geometry perspective. The criteria is provided to find the allocated placement positions and the total number of required substrates.
An information recovery technique for Orthogonal Frequency Division Multiplexing (OFDM) environments is presented in this paper. It uses fewer OFDM symbols at the side of the receiver as input to the Discrete Fourier Transform (DFT) exploiting the sparse nature of the original data and the properties of the DFT. The Forward Error Correction (FEC) technique employed can further assist the successful information recovery from fewer samples. Up to ¼ of the samples can be omitted and replaced by others at the side of the receiver, reducing the buffer memory size used for sample storage by up to 25% and simplifying the complexity and the power consumption of the DFT and Analog Digital Converter (ADC) implementation.
We study leaderless attitude synchronization problem of multiple underactuated spacecraft in this paper. We adopt the special parameterizations of attitude proposed by Tsiotras et al. (1995) to describe attitude kinematics, which has been shown to be very convenient for control of underactuated axisymmetric spacecraft with two control torques. It is assumed that angular velocity commands are possible and this paper is confined to the kinematic level. We first propose a partial attitude synchronization controller that is based on the exchange of each spacecraft's information with local neighbors. Under a necessary and general connectivity assumption and by use of an appropriate Lyapunov function, we show that the attitudes of spacecraft converge to a fixed or time-varying synchronization trajectory. Then, full attitude synchronization of multiple underactuated spacecraft is considered, where the discontinuous distributed algorithm is proposed. Simulations are given to validate the theoretical results and indicate several interesting observations.
This paper introduces a method for localizing reflectors in one dominant direction of a three-dimensional acoustic enclosure. The method is based on the response of the acoustic environment to an acoustic excitation. Only a single sound source and a single microphone are required. A two-step algorithm is proposed. In the first step, the distance between the reflectors is determined from the estimation of resonant frequencies. In the second step, the distance of one of the reflectors from the sound source is determined by numerical optimization. An experiment in a real three-dimensional acoustic environment shows that the position of two reflectors at opposite walls can be localized with high accuracy using a single microphone.
We address the weight-balancing problem for a distributed system whose components (nodes) can exchange information via interconnection links (edges) that form an arbitrary, possibly directed, communication topology (digraph). A weighted digraph is balanced if, for each node, the sum of the weights of the edges outgoing from that node is equal to the sum of the weights of the edges incoming to that node. Weight-balanced digraphs play a key role in a variety of applications, such as coordination of groups of robots, distributed decision making, and distributed averaging which is important for a wide range of applications in signal processing. We propose a distributed algorithm for solving the weight balancing problem in a minimum number of iterations, when the weights are nonnegative real numbers. We also provide examples to corroborate the proposed algorithm.
This paper proposes a new pre-processing approach to a sequence of binarization process, by applying the discrete cosine transform (DCT) to document image prior to binarization. This procedure can be used as a complement or alternative to denoising process used in many binarization methods. Firstly, original document images are transformed by the DCT. Then, the transformed document images are passed to standard image binarization methods, namely Otsu, Niblack, Sauvola and NICK. Secondly, original document images are directly binarized by the same methods. Then performance of the latter are compared with that of the former in terms of recall and precision. Performance evaluation are conducted by using noiseless and noisy ancient Indonesian documents with Arabic characters. It turns out that this pre-processing procedure improves binarization performance, especially for noisy documents.
The IEEE P1619 standard for achieving high degree of security in shared storage media is explored, regarding the Cipher Text Stealing (CTS) feature. Various characteristics that are altered by the inclusion of this feature into an IP core are explored, including area requirements, performance and resource exploitation. Several architectures are considered, either commercially available or proposed by researchers. The results are interesting highlighting a gap in the available IP cores of the market, mainly due to the degradation of performance when CTS is integrated. Furthermore the proposed implementation, presents very good performance characteristics when combined with several available architectures for IEEE P1619 cores.
Two main ingredients that enable wiretap codes to achieve information-theoretic secrecy are their binning structure and the randomization among multiple codewords. The presence of an infinite amount of randomness is an oversimplifying assumption which act as a hurdle that prevents the development of practical codes for wiretap channels. In this paper, we investigate the trade-off between the achievable secrecy rate and the randomness rate for wiretap channels with side information, non-causally known to the transmitter. Using the linear deterministic approach, we present insights into finding near-optimal codes under limited randomness.
In this paper, we consider the impact of traffic burstiness on optimal batching policy for energy-efficient Video-on-Demand (NVoD) services. By introducing batching technology, multiple users can be served by one multicast transmission. The more users in one multicast transmission, the less transmissions are needed, which induces less channel occupancy and energy consumption. However, to expect more users coming leads to longer queueing time. Hence, there is a trade-off between the number of transmissions and the average queueing time of users. We prove that N-Policy is optimal among all feasible policies when the arrivals of users follow a Poisson process. When the inter-arrival time of users follows Gamma distribution, i.e., the arrival process is smooth, it is shown that N-Policy is still optimal. But, when the inter-arrival time of users follows hyper-exponential distribution, i.e., the arrival process is bursty, the N-policy is no more optimal and the energy cost can be further reduced by a Generalized Impatient (GI) policy, especially when the coefficient of variance is large.
The document binarization is a fundamental processing step toward Optical Character Recognition (OCR). It aims to separate the foreground text from the document background. In this article, we propose a novel binarization technique combining local and global approaches using the clustering algorithm Kmeans. The proposed Hybrid Binarization, based on Kmeans (HBK), performs a robust binarization on scanned documents. According to several experiments, we demonstrate that the HBK method improves the binarization quality while minimizing the amount of distortion. Moreover, it outperforms several well-known state of the art methods in the OCR evaluation.
