We consider a classic compressed sensing–based wireless sensor network (WSN) where a large number of sensors observe a common sparse signal. Under the partial-view assumption, each sensor acquires only a portion of the signal, with its support restricted to a unknown subset. To maintain energy efficiency, every sensor employs a sparse sensing vector to measure the (possibly masked) signal and applies the ternary censoring rule proposed in [1] to decide whether its measurement is informative; only qualified measurements are transmitted to the fusion center. The multi-view nature of this setting poses challenges to reconstruction stability as different sensors observe distinct partial views of the signal. To address this, we propose an algorithm, called CSC-modSP, which dynamically refines the qualified measurements to enhance the overall signal reconstruction quality. Simulation results validate the effectiveness of the proposed method.

The generalized Poor-Verdú error lower bound established by Changet al.(2020) for multihypothesis testing is studied in the classical channel coding context. It is proved that for any sequence of block codes sent over the memoryless binary symmetric channel (BSC), the minimum probability of error (under maximum likelihood decoding) has a relative deviation from the generalized bound that grows at most linearly in blocklength. This result directly implies that for arbitrary codes used over the BSC, decoder ties can only affect the subexponential behavior of the minimum probability of error.

The generalized Poor-Verdú error lower bound for multihypothesis testing is revisited. Its asymptotic expression is established in closed-form as its tilting parameter grows to infinity. It is also shown that the asymptotic generalized bound achieves the error exponent (or reliability function) of the memoryless binary symmetric channel at zero coding rates.

Built on the sparse representation framework, sparse subspace clustering (SSC) received considerable attention in the recent years. Conventional SSC employs ℓ1-minimization based sparse regression for neighbor identification on a sample-by-sample basis, and is unaware of the neighbor information revealed by those already computed sparse representation vectors. To rid this drawback, this paper proposes a weighted ℓ1-minimization based sparse regression method, and an associated data ordering rule able to reflect the reliability of neighbor information for further enhancing the clustering accuracy. The selection of weighting coefficients for SSC is also discussed. Computer simulations using both the synthesis and real data are provided to evidence the effectiveness of the proposed method.

We establish a general formula for the maximum size of finite length block codes with minimum pairwise distance no less than d. The achievability argument involves an iterative construction of a set of radius-d balls, each centered at a codeword. We demonstrate that the number of such balls that cover the entire code space cannot exceed this maximum size. Our approach can be applied to codes i) with elements over arbitrary code alphabets, and ii) under a broad class of distance measures. Our formula indicates that the maximum code size can be fully characterized by the cumulative distribution function of the distance measure evaluated at two independent and identically distributed random codewords. When the two random codewords assume a uniform distribution over the entire code alphabet, our formula recovers and thus naturally generalizes the Gilbert-Varshamov (GV) lower bound. Finally, we extend our study to the asymptotic setting.

In this paper, an exact distance spectrum formula for the largest minimum Hamming distance of finite-length binary block codes is presented. The exact formula indicates that the largest minimum distance of finite-length block codes can be fully characterized by the information spectrum of the Hamming distance between two independent and identically distributed (i.i.d.) random codewords. The distance property of finite-length block codes is then connected to the distance spectrum. A side result of this work is a new lower bound to the largest minimum distance of finite-length block codes. Numerical examinations show that the new lower bound improves the finite-length Gilbert-Varshamov lower bound and can reach the minimum distance of existing finite-length block codes.

Our research provides a unified and coherent presentation of the essential concepts and techniques of objectoriented file systems.It consolidates the results of research and development in the semantics and implementation of a full spectrum of information system facilities for object-oriented systems, including data modeling, querying, storage structures, composite objects and integration of a programming language.This approach presents a tool for building an object-oriented file system called object-oriented file system tool (or OOFS for short) for completing the development of a large object-oriented information system, and its associated applications development framework.First we present the technological objectives underlying the project.Then we present the process of developing the information system and detail its architecture and construction, concentrating on the areas in which object-oriented technology has had a significant role.

A sufficient condition reported very recently for perfect recovery of a K-sparse vector via orthogonal matching pursuit (OMP) in K iterations (when there is no noise) is that the restricted isometry constant (RIC) of the sensing matrix satisfies δK+1K+1K+1K+1= (1/(K)). Our approach relies on a newly established near orthogonality condition, characterized via the achievable angles between two orthogonal sparse vectors upon compression, and, thus, better exploits the knowledge about the geometry of the compressed space. The proposed near orthogonality condition can be also exploited to derive less restricted sufficient conditions for signal reconstruction in two other compressive sensing problems, namely, compressive domain interference cancellation and support identification via the subspace pursuit algorithm.

We developed two tools previously called ISG and DWL and ISG is for generating information systems and DWL is for generating web systems. We have used ISG and DWL to develop a customized web-based system for the 7thUbiquitous-Home Conference UHC2013 and other web-based systems. The advantage of these web-based systems is that it uses object serialization mechanism to fill objects with data which saves CPU time. We used building files of ISG to build the file system of a web-based system and each attribute of an object to be specified for translating these settings to Java. We wrote Input Output programs to read data and write data and these objects with data entry thus created can be stored and retrieved efficiently. Our web-based systems avoid running cooperating processes that share data and resulting in inconsistencies in the shared data. Company produce new products frequently and the web pages of new products need to be updated shortly and using ISG can solve this problem.

The theory of compressive sensing has shown that with a small number of samples from random projections of a sparse signal, one can recover the original signal under certain conditions. In this paper, we use compressive sensing to design a random access protocol for requesting uplink data channels. A wireless node transmits a pseudo-random sequence to an access point (AP) when it requires an uplink channel. The AP receives multiple sequences in a random access shared channel. Due to different propagation delays, the received signals from different wireless nodes are not synchronized at the receiver. Assume that the number of sequence transmissions is substantially less than the number of wireless nodes in the system. Under such circumstances, we design an asynchronous compressive sensing based decoder to recover the original signals in a random access setting. The key difference between our proposed decoder and those presented in the literature is that we do not require any synchronization before sequence transmission which makes our approach practical. Simulation results show the throughput improvement of our proposed scheme compared to two other random access protocols.

The angle between two compressed sparse vectors subject to the norm/distance constraints imposed by the restricted isometry property (RIP) of the sensing matrix plays a crucial role in the studies of many compressive sensing (CS) problems. Assuming that (i) u and v are two sparse vectors separated by an angle thetha, and (ii) the sensing matrix Phi satisfies RIP, this paper is aimed at analytically characterizing the achievable angles between Phi*u and Phi*v. Motivated by geometric interpretations of RIP and with the aid of the well-known law of cosines, we propose a plane geometry based formulation for the study of the considered problem. It is shown that all the RIP-induced norm/distance constraints on Phi*u and Phi*v can be jointly depicted via a simple geometric diagram in the two-dimensional plane. This allows for a joint analysis of all the considered algebraic constraints from a geometric perspective. By conducting plane geometry analyses based on the constructed diagram, closed-form formulae for the maximal and minimal achievable angles are derived. Computer simulations confirm that the proposed solution is tighter than an existing algebraic-based estimate derived using the polarization identity. The obtained results are used to derive a tighter restricted isometry constant of structured sensing matrices of a certain kind, to wit, those in the form of a product of an orthogonal projection matrix and a random sensing matrix. Follow-up applications to three CS problems, namely, compressed-domain interference cancellation, RIP-based analysis of the orthogonal matching pursuit algorithm, and the study of democratic nature of random sensing matrices are investigated.

Energy consumption in wireless sensor networks is dominated by intra-network communication dedicated to coordination and information exchange between sensor nodes and the fusion center. The design of distributed estimation algorithms with reduced communication overheads is thus rather crucial. For amplify-and-forward sensor networks over flat fading channels, this paper proposes a new distributed best-linear-unbiased-estimation (BLUE) scheme by exploiting the statistical characterizations of the sensing noise variance and channel gains. The performance measure is the reciprocal of the mean square error averaged over the considered statistical distributions. We derive a closed-form lower bound for the adopted design metric. By means of this result, we further derive a closed-form universal sensor power amplification factor capable of maintaining a target estimation performance. The proposed scheme has the advantage that repeated power scheduling and message feedback are no longer needed in the parameter estimation phase and, hence, the in-network communication cost is further reduced. Some key features regarding the proposed method are discussed. Computer simulations are conducted to evidence our analytic study.

Knowledge of the achievable restricted isometry constant (RIC) of the sensing matrix is crucial for assessing the signal reconstruction performance of compressive sensing systems. In this paper we consider compressive-domain interference cancellation via orthogonal projection, and study the achievable RIC of the effective sensing matrix, namely, the product of the orthogonal projection matrix and the original sensing matrix. While existing algebraic based methods resorted to the polarization identity to find an upper bound of the considered RIC, motivated by geometric interpretations of the orthogonal projection and the restricted isometry property we derive an improved RIC in a closed form. The proposed solution is shown to be tighter than the existing upper bound. Our analytical results, and the asserted performance advantages, are further evidenced via computer simulations.