The Discrete Fourier Transform Test (DFTT) is a randomness test in NIST SP800-22. However, to date, the theoretical reference distribution of the DFTT statistic has not been derived, which is problematic. We propose a new test using power spectrum variance as the test statistic whose reference distribution can be derived theoretically. Note that the purpose of both the DFTT and the proposed test is to detect periodic features. Experimental results demonstrate that the proposed test has stronger detection power than the DFTT and that it test can be used even for short sequences.

To completely utilize the advantages of dynamic voltage and frequency scaling (DVFS) techniques, a quantized decoder (QNT-D) was developed. The QNT-D generates a quantized signal processing quantity (Q) using a predicted signal processing quantity (M). Q is used to produce the optimum frequency (opt.fc) and the optimum supply voltage (opt.VD) that are proportional to Q. To develop a DVFS controlled motion estimation (ME) processor, we used both the QNT-D and a fast ME algorithm called A2BC (Adaptively Assigned Breaking-off Condition) to predict M for each macro-block (MB). A DVFS controlled ME processor was fabricated using 90-nm CMOS technology. The total power dissipation (PT) of the processor was significantly reduced and varied from 38.65 to 99.5 µW, only 3.27 to 8.41 % of PT of a conventional ME processor, depending on the test video picture.

During the execution of software, tremendous amounts of data can be recorded. By exploiting the execution data, one can discover behavioral models to describe the actual software execution. As a well-known open-source process mining toolkit, ProM integrates quantities of process mining techniques and enjoys a variety of applications in a broad range of areas. How to develop a better ProM software, both from user experience and software performance perspective, are of vital importance. To achieve this goal, we need to investigate the real execution behavior of ProM which can provide useful insights on its usage and how it responds to user operations. This paper aims to propose an effective approach to solve this problem. To this end, we first instrument existing ProM framework to capture execution logs without changing its architecture. Then a two-layered framework is introduced to support accurate ProM behavior discovery by characterizing both user interaction behavior and plug-in calling behavior separately. Next, detailed discovery techniques to obtain user interaction behavior model and plug-in calling behavior model are proposed. All proposed approaches have been implemented.

The locally one-dimensional finite-difference time-domain (FDTD) method in cylindrical coordinates is extended to a frequency-dependent version. The fundamental scheme is utilized to perform matrix-operator-free formulations in the right-hand sides. For the analysis of surface plasmon polaritons propagating along a plasmonic grating, the computation time is significantly reduced to less than 10%, compared with the explicit cylindrical FDTD method.

In recent years, with the rapid development of the Internet and cloud computing, an enormous amount of information is exchanged on various social networking services. In order to handle and maintain such a mountain of information properly by limited resources in the network, it is very important to comprehend the dynamics for propagation of information or activity on the social network. One of many indices used by social network analysis which investigates the network structure is “node centrality”. A common characteristic of conventional node centralities is that it depends on the topological structure of network and the value of node centrality does not change unless the topology changes. The network dynamics is generated by interaction between users whose strength is asymmetric in general. Network structure reflecting the asymmetric interaction between users is modeled by a directed graph, and it is described by an asymmetric matrix in matrix-based network model. In this paper, we showed an oscillation model for describing dynamics on networks generated from a certain kind of asymmetric interaction between nodes by using a symmetric matrix. Moreover, we propose a new extended index of well-known two node centralities based on the oscillation model. In addition, we show that the proposed index can describe various aspect of node centrality that considers not only the topological structure of the network, but also asymmetry of links, the distribution of source node of activity, and temporal evolution of activity propagation by properly assigning the weight of each link. The proposed model is regarded as the fundamental framework for different node centralities.

The combination of large-scale antenna arrays and simultaneous wireless information and power transfer (SWIPT), which can provide enormous increase of throughput and energy efficiency is a promising key in next generation wireless system (5G). This paper investigates efficient transceiver design to minimize transmit power, subject to users' required data rates and energy harvesting, in large-scale SWIPT system where the base station utilizes a very large number of antennas for transmitting both data and energy to multiple users equipped with time-switching (TS) or power-splitting (PS) receive structures. We first propose the well-known semidefinite relaxation (SDR) and Gaussian randomization techniques to solve the minimum transmit power problems. However, for these large-scale SWIPT problems, the proposed scheme, which is based on conventional SDR method, is not suitable due to its excessive computation costs, and a consensus alternating direction method of multipliers (ADMM) cannot be directly applied to the case that TS or PS ratios are involved in the optimization problem. Therefore, in the second solution, our first step is to optimize the variables of TS or PS ratios, and to achieve simplified problems. After then, we propose fast algorithms for solving these problems, where the outer loop of sequential parametric convex approximation (SPCA) is combined with the inner loop of ADMM. Numerical simulations show the fast convergence and superiority of the proposed solutions.

We propose an efficient training method for memristor neural networks. The proposed method is suitable for the mini-batch-based training, which is a common technique for various neural networks. By integrating the two processes of gradient calculation in the backpropagation algorithm and weight update in the write operation to the memristors, the proposed method accelerates the training process and also eliminates the external computing resources required in the existing method, such as multipliers and memories. Through numerical experiments, we demonstrated that the proposed method achieves twice faster convergence of the training process than the existing method, while retaining the same level of the accuracy for the classification results.

This paper discusses the collection of sensor data for power distribution systems. In current power distribution systems, this is usually performed solely by the Remote Terminal Unit (RTU) which is located at the root of a power distribution network. The recent rise of distributed power sources, such as photovoltaic generators, raises the demand to increase the frequency of data collection because the output of these distributed generators varies quickly depending on the weather. Increasing data collection frequency in turn requires shortening the time required for data collection. The paper proposes the use of aggregation points for this purpose. An aggregation point can collect sensor data concurrently with other aggregation points as well as with the RTU. The data collection time can be shortened by having the RTU receive data from aggregation points, instead of from all sensors. This approach then poses the problem of finding the optimal location of aggregation points. To solve this problem, the paper proposes a Mixed Integer Linear Problem (MILP) formulation of the problem. The MILP problem can then be solved with off-the-shelf mathematical optimization software. The results of experiments show that the proposed approach is applicable to rather large scale power distribution systems.

In order to enhance QoE of audio and video IP transmission, this paper proposes a method for mitigating the spatial quality impairment during burst loss periods over the wireless networks in the video output scheme SCS, which is a QoE-based video output scheme. SCS switches between two common video output schemes: frame skipping and error concealment. The proposed method pauses video output with an undamaged frame during the burst loss period in order not to pause video output on a degraded frame. We perform an experiment with constant thresholds, the table-lookup method, and the proposed method under various network conditions. The result shows that the effect of the proposed method on QoE can differ with the contents and GOP structures.

In X-ray computed tomography, scattered X-rays are generally removed by using a post-patient collimator located in front of the detector. In this paper, we show that the scattered X-rays have the potential to improve the estimation accuracy of the attenuation coefficient in computed tomography. In order to clarify the problem, we simplified the geometry of the computed tomography into a thin cylinder composed of a homogeneous material so that only one attenuation coefficient needs to be estimated. We then conducted a Monte Carlo numerical experiment on improving the estimation accuracy of attenuation coefficient by measuring the scattered X-rays with several dedicated toroidal detectors around the cylinder in addition to the primary X-rays. We further present a theoretical analysis to explain the experimental results. We employed a model that uses a T-junction (i.e., T-junction model) to divide the photon transport into primary and scattered components. This division is processed with respect to the attenuation coefficient. Using several T-junction models connected in series, we modeled the case of several scatter detectors. The estimation accuracy was evaluated according to the variance of the efficient estimator, i.e., the Cramer-Rao lower bound. We confirmed that the variance decreases as the number of scatter detectors increases, which implies that using scattered X-rays can reduce the irradiation dose for patients.

We present a modular way of implementing adaptive decisions in performing scientific simulations. The proposed method employs modern software engineering mechanisms to allow for better software management in scientific computing, where software adaptation has often been implemented manually by the programmer or by using in-house tools, which complicates software management over time. By applying the aspect-oriented programming (AOP) paradigm, we consider software adaptation as a separate concern and, using popular AOP constructs, implement adaptive decision separately from the original code base, thereby improving software management. We demonstrate the effectiveness of our approach with applications to stochastic simulation software.

In this letter, we study a wireless powered communication network (WPCN) with non-orthogonal multiple access (NOMA), where the user clustering scheme that groups each two users in a cluster is adopted to guarantee the system performance. The two users in a cluster transmit data simultaneously via NOMA, while time division multiple access (TDMA) is used among clusters. We aim to maximize the system throughput by finding the optimal cluster permutation and the optimal time allocation, which can be obtained by solving the optimization problems corresponding to all cluster permutations. The closed-form solution of each optimization problem is obtained by exploiting its constraint structures. However, the complexity of this exhaustive method is quite high, we further propose a sub-optimal clustering scheme with low complexity. The simulation results demonstrate the superiority of the proposed scheme.

A multi-channel cognitive radio is a powerful solution for recovering the exhaustion of frequency spectrum resources. In a cognitive radio, although master and slave terminals (which construct a communication link) have the freedom to access arbitrary channels, access channel mismatch is caused. A rendezvous scheme based on frequency hopping can compensate for this mismatch by exchanging control signals through a selected channel in accordance with a certain rule. However, conventional frequency hopping schemes do not consider an access protocol of both control signals in the rendezvous scheme and the signal caused by channel access from other systems. Further, they do not consider an information sharing method to reach a consensus between the master and slave terminals. This paper proposes a modified rendezvous scheme based on learning-based channel occupancy rate (COR) estimation and describes a specific channel-access rule in the slave terminal. On the basis of this rule, the master estimates a channel selected by the slave by considering the average COR of the other systems. Since the master can narrow down the number of channels, a fast rendezvous scheme with a few control signals is established.

A frequently occurring subcircuit consists of a loop of a resistor (R), a field-effect transistor (FET), and a capacitor (C). The FET acts as a switch, controlled at its gate terminal by a clock voltage. This subcircuit may be acting as a sample-and-hold (S/H), as a passive mixer (P-M), or as a bandpass filter or bandpass impedance. In this work, we will present a useful analysis that leads to a simple signal flow graph (SFG), which captures the FET-R-C circuit's action completely across a wide range of design parameters. The SFG dissects the circuit into three filtering functions and ideal sampling. This greatly simplifies analysis of frequency response, noise, input impedance, and conversion gain, and leads to guidelines for optimum design. This paper focuses on the analysis of a single-path FET-R-C circuit's signal transfer characteristics including the reconstruction of the complete waveform from the discrete-time sampled voltage.

The accurate assessment of infants' pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants' pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain facial activity features to describe the infants' facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we construct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their effectiveness in infants' pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we find that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%).

This paper proposes MRO-PUF, a new architecture for ring-oscillator-based physically unclonable functions (PUFs) with enhanced resistance against machine learning attacks. In the proposed PUF, an instantaneous output value of a ring oscillator is used as a response, whereas the most existing PUFs directly use propagation delays to determine the response. Since the response of the MRO-PUF is non-linear and discontinuous as the delay of the ring oscillator increases, the prediction of the response by machine learning attacks is difficult. Through the performance evaluation of the MRO-PUF with simulations, it achieves 15 times stronger resistance against machine learning attacks using a support vector machine compared to the existing ones such as an arbiter PUF and a bistable ring PUF. The MRO-PUF also achieves a sufficient level of the basic performance of PUFs in terms of uniqueness and robustness.

For the reconstruction of the feedback polynomial of a synchronous scrambler placed after a convolutional encoder, the existing algorithms require the prior knowledge of a dual word of the convolutional code. To address the case of a dual word being unknown, a new algorithm for the reconstruction of the feedback polynomial based on triple correlation characteristic of an m-sequence is proposed. First, the scrambled convolutional code sequence is divided into bit blocks; the product of the scrambled bit blocks with a dual word is proven to be an m-sequence with the same period as the synchronous scrambler. Second, based on the triple correlation characteristic of the generated m-sequence, a dual word is estimated; the generator polynomial of the generated m-sequence is computed by two locations of the triple correlation peaks. Finally, the feedback polynomial is reconstructed using the generator polynomial of the generated m-sequence. As the received sequence may contain bit errors, a method for detecting triple correlation peaks based on the constant false-alarm criterion is elaborated. Experimental results show that the proposed algorithm is effective. Ulike the existing algorithms available, there is no need to know a dual word a priori and the reconstruction result is more accurate. Moreover, the proposed algorithm is robust to bit errors.

The concept of witness hiding was proposed by Feige and Shamir as a natural relaxation of zero-knowledge. Prior constructions of witness hiding protocol for general hard distribution on NP language consist of at least three rounds. In this paper we construct a two-round witness hiding protocol for all hard distributions on NP language. Our construction is based on two primitives: point obfuscation and adaptive witness encryption scheme.

Heterogeneous Small Cell Network (HSCN) will have wide application given its ability to improve system capacity and hot spot coverage. In order to increase the efficiency of spectrum and energy, a great deal of research has been carried out on radio resource management in HSCN. However, it is a remarkable fact that the user experience in terms of traffic rate demands has been neglected in existing research with excessive concentration on network capacity and energy efficiency. In this paper, we redefined the energy efficiency (EE) and formulate the joint optimization problem of user experience and energy efficiency maximization into a mixed integer non-linear programming (MINLP) problem. After reformulating the optimization problem, the joint subchannel (SC) allocation and power control algorithm is proposed with the help of cluster method and genetic algorithm. Simulation results show that the joint SC allocation and power control algorithm proposed has better performance in terms of user experience and energy consumption than existing algorithms.

An accurate but energy-efficient estimation of a position is important as the number of mobile computing systems grow rapidly. A challenge is to develop a highly accurate but energy efficient estimation method. A particle filter is a key algorithm to estimate and track the position of an object which exhibits non-linear movement behavior. However, it requires high usage of computation resources and energy. In this paper, we propose a scheme which can dynamically adjust the number of particles according to the accuracy of the reference signal for positioning and reduce the energy consumption by 37% on Cortex A7.