In this paper, we introduce a distributed power allocation strategy for random access, that has the capabilities of multipacket reception (MPR) and successive interference cancellation (SIC). The proposed random access scheme is suitable for machine-to-machine (M2M) communication application in fifth-generation (5G) cellular networks. A previous study optimized the probability distribution for discrete transmission power levels, with implicit limitations on the successful decoding of at most two packets from a single collision. We formulate the optimization problem for the general case, where a base station can decode multiple packets from a single collision, and this depends only on the signal-to-interference-plus-noise ratio (SINR). We also propose a feasible suboptimal iterative per-level optimization process; we do this by introducing relationships among the different discrete power levels. Compared with the conventional power allocation scheme with MPR and SIC, our method significantly improves the system throughput; this is confirmed by computer simulations.

Integral image is the sum of input image pixel values. It is mainly used to speed up the process of a box filter operation, such as Haar-like features. However, large memory capacity for integral image data can be an obstacle in an embedded environment with limited hardware. In a previous research, [5] reduced the size of integral image memory using 2×2 block structure with additional calculations. It can be easily extended to n×n block structure for further reduction, but it requires more additional calculations. In this paper, we propose a new block structure for the integral image by modifying the location of the reference pixel in the block. It results in much less additional calculations by reducing the number of memory accesses, while keeping the same amount of memory as the original block structure.

By investigating the properties that the offsets should satisfy, this letter presents a brief proof of general QAM Golay complementary sequences (GCSs) in Cases I-III constructions. Our aim is to provide a brief, clear, and intelligible derivation so that it is easy for the reader to understand the known Cases I-III constructions of general QAM GCSs.

In this paper, we study on an availability analysis for a multibase system with lateral resupply of spare items between bases. We construct a basic model that a spare item of a base is transported for operation to another base without spare upon occurrence of failure, and simultaneously, the base that supplies the spare item receives the failed item of the other base for repair. We propose an approximation method to obtain the availability of the system and show the accuracy of the solution through numerical experiments. Also, two modified models are constructed to show the efficiency of the basic model. The two models modify the assumption on the lateral resupply of spare items between bases in the basic model. We numerically illustrate that the basic model can increase the availability of the system compared with the two modified models through Monte Carlo simulation.

This paper proposes a voice activity detection (VAD) algorithm based on an energy related feature of the frequency modulation of harmonics. A multi-resolution spectro-temporal analysis framework, which was developed to extract texture features of the audio signal from its Fourier spectrogram, is used to extract frequency modulation features of the speech signal. The proposed algorithm labels the voice active segments of the speech signal by comparing the energy related feature of the frequency modulation of harmonics with a threshold. Then, the proposed VAD is implemented on one of Texas Instruments (TI) digital signal processor (DSP) platforms for real-time operation. Simulations conducted on the DSP platform demonstrate the proposed VAD performs significantly better than three standard VADs, ITU-T G.729B, ETSI AMR1 and AMR2, in non-stationary noise in terms of the receiver operating characteristic (ROC) curves and the recognition rates from a practical distributed speech recognition (DSR) system.

This letter introduces an Energy-Aware LED Light System (EA-LLS) that provides adequate illumination to users according to the analysis of the sun's position, the user's movement, and various environmental factors, without sun illumination detection sensors. This letter presents research using algorithms and scenarios. We propose an EA-LLS that offers not only On/Off and dimming control, but dimming control through daylight, space, and user behavior analysis.

Dynamic analysis is frail and insufficient to find hidden paths in environment-intensive program. By analyzing a broad spectrum of different concolic testing systems, we conclude that a number of them cannot handle programs that interact with the environment or require a complete working model. This paper addresses this problem by automatically identifying and modifying outputs of the data input interface function(DIIF). The approach is based on fine-grained taint analysis for detecting and updating the data that interacts with the environment to generate a new set of inputs to execute hidden paths. Moreover, we developed a prototype and conducted extensive experiments using a set of complex and environmentally intensive programs. Finally, the result demonstrates that our approach could identify the DIIF precisely and discover hidden path obviously.

Sparsity-based methods have been recently applied to abnormal event detection and have achieved impressive results. However, most such methods suffer from the problem of dimensionality curse; furthermore, they also take no consideration of the relationship among coefficient vectors. In this paper, we propose a novel method called consistent sparse representation (CSR) to overcome the drawbacks. We first reconstruct each feature in the space spanned by the clustering centers of training features so as to reduce the dimensionality of features and preserve the neighboring structure. Then, the consistent regularization is added to the sparse representation model, which explicitly considers the relationship of coefficient vectors. Our method is verified on two challenging databases (UCSD Ped1 database and Subway batabase), and the experimental results demonstrate that our method obtains better results than previous methods in abnormal event detection.

In this paper, we consider a distributed power control scheme that can maximize overall capacity of an interference-limited wireless system in which the same radio resource is spatially reused among different transmitter-receiver pairs. This power control scheme employs a gradient-descent method in each transmitter, which adapts its own transmit power to co-channel interference dynamically to maximize the total weighted sum rate (WSR) of the system over a given interval. The key contribution in this paper is to propose a common feedback channel, over which a backward physical signal is accumulated for computing the gradient of the transmit power in each transmitter, thereby significantly reducing signaling overhead for the distributed power control. We show that the proposed power control scheme can achieve almost 95% of its theoretical upper WSR bound, while outperforming the non-power-controlled system by roughly 63% on average.

Neural networks are widely used in various fields due to their superior learning abilities. This paper proposes a hardware winner-take-all neural network (WTANN) that employs a new winner-take-all (WTA) circuit with phase-modulated pulse signals and digital phase-locked loops (DPLLs). The system uses DPLL as a computing element, so all input values are expressed by phases of rectangular signals. The proposed WTA circuit employs a simple winner search circuit. The proposed WTANN architecture is described by very high speed integrated circuit (VHSIC) hardware description language (VHDL), and its feasibility was tested and verified through simulations and experiments. Conventional WTA takes a global winner search approach, in which vector distances are collected from all neurons and compared. In contrast, the WTA in the proposed system is carried out locally by a distributed winner search circuit among neurons. Therefore, no global communication channels with a wide bandwidth between the winner search module and each neuron are required. Furthermore, the proposed WTANN can easily extend the system scale, merely by increasing the number of neurons. The circuit size and speed were then evaluated by applying the VHDL description to a logic synthesis tool and experiments using a field programmable gate array (FPGA). Vector classifications with WTANN using two kinds of data sets, Iris and Wine, were carried out in VHDL simulations. The results revealed that the proposed WTANN achieved valid learning.

OSEK/VDX, a standard for an automobile OS, has been widely adopted by many manufacturers to design and develop a vehicle-mounted OS. With the increasing functionalities in vehicles, more and more complex applications are be developed based on the OSEK/VDX OS. However, how to ensure the reliability of developed applications is becoming a challenge for developers. To ensure the reliability of developed applications, model checking as an exhaustive technique can be applied to discover subtle errors in the development process. Many model checkers have been successfully applied to verify sequential software and general multi-threaded software. However, it is hard to directly use existing model checkers to precisely verify OSEK/VDX applications, since the execution characteristics of OSEK/VDX applications are different from the sequential software and general multi-threaded software. In this paper, we describe and develop an approach to translate OSEK/VDX applications into sequential programs in order to employ existing model checkers to precisely verify OSEK/VDX applications. The value of our approach is that it can be considered as a front-end translator for enabling existing model checkers to verify OSEK/VDX applications.

Current software behavior models lack the ability to conduct semantic analysis. We propose a new model to detect abnormal behaviors based on a function semantic tree. First, a software behavior model in terms of state graph and software function is developed. Next, anomaly detection based on the model is conducted in two main steps: calculating deviation density of suspicious behaviors by comparison with state graph and detecting function sequence by function semantic rules. Deviation density can well detect control flow attacks by a deviation factor and a period division. In addition, with the help of semantic analysis, function semantic rules can accurately detect application layer attacks that fail in traditional approaches. Finally, a case study of RSS software illustrates how our approach works. Case study and a contrast experiment have shown that our model has strong expressivity and detection ability, which outperforms traditional behavior models.

With aggressive device scaling, timing failures have become more prevalent due to manufacturing defects and process variations. When timing failure occurs, it is important to take corrective actions immediately. Therefore, an efficient and fast diagnosis method is essential. In this paper, we propose a new diagnostic method using timing information. Our method approximately estimates all the segment delays of measured paths in a design, using inequality-constrained least squares methods. Then, the proposed method ranks the possible locations of delay defects based on the difference between estimated segment delays and the expected values of segment delays. The method works well for multiple delay defects as well as single delay defects. Experiment results show that our method yields good diagnostic resolution. With the proposed method, the average first hit rank (FHR), was within 7 for single delay defect and within 8 for multiple delay defects.

For an odd prime p and a positive integer r, new classes of binary sequences with period pr+1 are proposed from Euler quotients in this letter, which include several known classes of binary sequences derived from Fermat quotients and Euler quotients as special cases. The advantage of the new constructions is that they allow one to choose their support sets freely. Furthermore, with some constrains on the support set, the new sequences are proved to possess large linear complexities under the assumption of 2p-1 ≢ 1 mod p2.

We propose efficient algorithms for Sorting k-Sets in Bins. The Sorting k-Sets in Bins problem can be described as follows. We are given numbered n bins with k balls in each bin. Balls in the i-th bin are numbered n-i+1. We can only swap balls between adjacent bins. Our task is to move all of the balls to the same numbered bins. For this problem, we give an efficient greedy algorithm with $rac{k+1}{4}n^2+O(k+n)$ swaps and provide a detailed analysis for k=3. In addition, we give a more efficient recursive algorithm using $rac{15}{16}n^2+O(n)$ swaps for k=3.

Let v=p1m1p2m2…ptmt be the canonical prime factorization of v. In this paper, we give a construction of optimal ((s+1)×v,s+1,1) two-dimensional optical orthogonal codes with both at most one-pulse per wavelength and at most one-pulse per time slot, where s | gcd(p1-1,p2-1,...,pt-1). The method is much simpler than that in [1]. Optimal (m×v,k,1) two-dimensional optical orthogonal codes are also constructed based on the Steiner system S[2,k,m].

In this letter, we present a meet-in-the-middle attack on the 5-round reduced Kuznyechik cipher which has been recently chosen to be standardized by the Russian federation. Our attack is based on the differential enumeration approach. However, the application of the exact approach is not successful on Kuznyechik due to its optimal round diffusion properties. Accordingly, we adopt an equivalent representation for the last round where we can efficiently filter ciphertext pairs and launch the attack in the chosen ciphertext setting. We also utilize partial sequence matching which further reduces the memory and time complexities. For the 5-round reduced cipher, the 256-bit master key is recovered with an online time complexity of 2140.3, a memory complexity of 2153.3, and a data complexity of 2113.

We propose a novel algorithm for the recovery of non-sparse, but compressible signals from linear undersampled measurements. The algorithm proposed in this paper consists of two steps. The first step recovers the signal by the l1-norm minimization. Then, the second step decomposes the l1 reconstruction into major and minor components. By using the major components, measurements for the minor components of the target signal are estimated. The minor components are further estimated using the estimated measurements exploiting a maximum a posterior (MAP) estimation, which leads to a ridge regression with the regularization parameter determined using the error bound for the estimated measurements. After a slight modification to the major components, the final estimate is obtained by combining the two estimates. Computational cost of the proposed algorithm is mostly the same as the l1-nom minimization. Simulation results for one-dimensional computer generated signals show that the proposed algorithm gives 11.8% better results on average than the l1-norm minimization and the lasso estimator. Simulations using standard images also show that the proposed algorithm outperforms those conventional methods.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

We investigate non-orthogonal multiple access (NOMA) with a successive interference canceller (SIC) in the cellular multiple-input multiple-output (MIMO) downlink for systems beyond LTE-Advanced. Taking into account the overhead for the downlink reference signaling for channel estimation at the user terminal in the case of non-orthogonal multiuser multiplexing and the applicability of the SIC receiver in the MIMO downlink, we propose intra-beam superposition coding of a multiuser signal at the transmitter and the spatial filtering of inter-beam interference followed by the intra-beam SIC at the user terminal receiver. The intra-beam SIC cancels out the inter-user interference within a beam. Regarding the transmitter beamforming (precoding), in general, any kind of beamforming matrix determination criteria can be applied to the proposed NOMA method. In the paper, we assume open loop-type random beamforming, which is very efficient in terms of the amount of feedback information from the user terminal. Furthermore, we employ a weighted proportional fair (PF)-based resource (beam of each frequency block and power) allocation for the proposed method. Simulation results show that the proposed NOMA method using the intra-beam superposition coding and SIC simultaneously achieves better sum and cell-edge user throughput compared to orthogonal multiple access (OMA), which is widely used in 3.9 and 4G mobile communication systems.