A self-adaptive scaled min-sum algorithm for LDPC decoding based on the difference between the first two minima of the check node messages (Δmin) is proposed. Δmin is utilized for adjusting the scaling factor of the check node messages, and simulation results show that the proposed algorithm improves the error correcting performance compared to existing algorithms.

We reported a secure scan design approach using shift register equivalents (SR-equivalents, for short) that are functionally equivalent but not structurally equivalent to shift registers [10 and also introduced generalized shift registers (GSRs, for short) to apply them to secure scan design [11]-[13]. In this paper, we combine both concepts of SR-equivalents and GSRs and consider the synthesis problem of SR-equivalent GSRs, i.e., how to modify a given GSR to an SR-equivalent GSR. We also consider the enumeration problem of SR-equivalent GFSRs, i.e., the cardinality of the class of SR-equivalent GSRs to clarify the security level of the secure scan architecture.

This paper addresses the issue of implementing a sequence for restoring fiber links and communication paths that have failed due to a catastrophe. We present a mathematical formulation to minimize the total number of steps needed to restore communication paths. We also propose two heuristic algorithms: Minimum spanning tree - based degree order restoration and Congestion link order restoration. Numerical evaluations show that integer linear programming based order restoration yields the fewest number of restoration steps, and that the proposed heuristic algorithms, when used properly with regard to the accommodation rate, are highly effective for real-world networks.

Fine-grained power gating (FGPG) is a power-saving technique by switching off circuit blocks while the blocks are idle. Although FGPG can reduce power consumption without compromising computational performance, switching the power supply on and off causes energy overhead. To prevent power increase caused by the energy overhead, in our prior research we proposed an FGPG control method of the operating system(OS) based on pre-analyzing applications' power usage. However, modern computing systems have a wide variety of use cases and run many types of application; this makes it difficult to analyze the behavior of all these applications in advance. This paper therefore proposes a new FGPG control method without profiling application programs in advance. In the new proposed method, the OS monitors a circuit's idle interval periodically while application programs are running. The OS enables FGPG only if the interval time is long enough to reduce the power consumption. The experimental results in this paper show that the proposed method reduces power consumption by 9.8% on average and up to 17.2% at 25°C. The results also show that the proposed method achieves almost the same power-saving efficiency as the previous profile-based method.

In this paper, the Multi-voltage (multi-Vdd) variable pipeline router is proposed to reduce the power consumption of Network-on-Chips (NoCs) designed for Chip Multi-processors (CMPs). The multi-Vdd variable pipeline router adjusts its pipeline depth (i.e., communication latency) and supply voltage level in response to the applied workload. Unlike Dynamic Voltage and Frequency Scaling (DVFS) routers, the operating frequency remains the same for all routers throughout the CMP; thus, omitting the need to synchronize neighboring routers working at different frequencies. Two types of router architectures are presented: a Coarse-Grained Variable Pipeline (CG-VP) router that changes the voltage supplied to the entire router, and a Fine-Grained Variable Pipeline (FG-VP) router that uses a finer power partition. The evaluation results showed that the CG-VP and FG-VP routers achieve a 22.9% and 35.3% power reduction on average with 14% and 23% area overhead in comparison with a baseline router without variable pipelines, respectively. Thanks to the adopted look-ahead mechanism to switch the supply voltage, the performance overhead is only 4.4%.

This letter presents a simple and explicit formulation of non-unique Wiener filters associated with the linear predictor for processing of sinusoids. It was shown in the literature that, if the input signal consists of only sinusoids and does not include a white noise, the input autocorrelation matrix in the Wiener-Hopf equation becomes rank-deficient and thus the Wiener filter is not uniquely determined. In this letter we deal with this rank-deficient problem and present a mathematical description of non-unique Wiener filters in a simple and explicit form. This description is directly obtained from the tap number, the frequency of sinusoid, and the delay parameter. We derive this result by means of the elementary row operations on the augmented matrix given by the Wiener-Hopf equation. We also show that the conventional Wiener filter for noisy input signal is included as a special case of our description.

Deploying a group of mobile sensor (MS) nodes to monitor a moving phenomenon in an unknown and open area includes a lot of challenges if the phenomenon moves quickly and due to the limited capabilities of MS nodes in terms of mobility, sensing and communication ranges. To address these challenges and achieve a high weighted sensing coverage, in this paper, we propose a new algorithm for moving-phenomenon monitoring, namely VirFID-MP (Virtual Force (VF)-based Interest-Driven phenomenon monitoring with Mobility Prediction). In VirFID-MP, the future movement of the phenomenon is first predicted using the MS nodes' movement history data. Then, the prediction information is used to calculate a virtual force, which is utilized to speed up MS nodes toward the moving phenomenon. In addition, a prediction-based oscillation-avoidance algorithm is incorporated with VirFID-MP movement control to reduce the nodes' energy consumption. Our simulation results show that VirFID-MP outperforms original VirFID schemes in terms of weighted coverage efficiency and energy consumption.

Bistatic multi-input multi-output (MIMO) radar has the capability of measuring the transmit angle from the receiving array, which means the existence of information redundancy and benefits data association. In this paper, a data association decision for bistatic MIMO radar is proposed and the performance advantages of bistatic MIMO radar in data association is analyzed and evaluated. First, the parameters obtained by receiving array are sent to the association center via coordinate conversion. Second, referencing the nearest neighbor association (NN) algorithm, an improved association decision is proposed with the transmit angle and target range as association statistics. This method can evade the adverse effects of the angle system errors to data association. Finally, data association probability in the presence of array directional error is derived and the correctness of derivation result is testified via Monte Carlo simulation experiments. Besides that performance comparison with the conventional phased array radar verifies the excellent performance of bistatic MIMO Radar in data association.

Forming Business Continuity Planning (BCP) is recognized as a significant counter-measure against future large-scale disasters by private enterprises after the 2011 Great East Japan Earthquake more than before. Based on a questionnaire survey, this paper reports business recovery conditions of private enterprises in Miyagi Prefecture affected by the disaster. Analyzing the results of questionnaire, it suggests some important points: (1) estimation of long-term internal/external factors that influence business continuity, (2) development of concrete pre-disaster framework, (3) multi-media-based advertising strategy, and (4) re-allocation of resources.

It is qualitatively considered that emergency information processing by using UTM grids is effective in generating COP (Common Operational Pictures). Here, we conducted a numerical evaluation based on emergency information-processing training to examine the efficiency of the use of UTM grid maps by staff at the Tagajo City Government office. The results of the demonstration experiment were as follows: 1) The time required for information propagation and mapping with UTM coordinates was less than that with address text consisting of area name and block number. 2) There was no measurable difference in subjective estimates of the training performance of participants with or without the use of UTM grids. 3) Fear of real emergency responses decreased among training participants using UTM grids. 4) Many of the negative free answers on a questionnaire evaluation of participants involved requests regarding the reliability and operability of UTM tools.

We present a hard-wired central patter generator (CPG) hardware network that reproduces the periodic oscillations of the typical gaits, namely, walk, trot, and bound. Notably, the three gaits are generated by a single parameter, i.e., the battery voltage EMLR, which acts like a signal from the midbrain's locomotor region. One CPG is composed of two types of hardware neuron models, reproducing neuronal bursting and beating (action potentials), and three types of hardware synapse models: a gap junction, excitatory and inhibitory synapses. When four hardware CPG models were coupled into a Z4 symmetry network in a previous study [22], two neuronal oscillation patterns corresponding to four-legged animal gaits (walk and bound) were generated by manipulating a single control parameter. However, no more than two neuronal oscillation patterns have been stably observed on a hard-wired four-CPG hardware network. In the current study, we indicate that three neuronal oscillation patterns (walk, trot, and bound) can be generated by manipulating a single control parameter on a hard-wired eight-CPG (Z4 × Z2 symmetry) hardware network.

Estimation of the time delay of arrival (TDOA) problem is important to acoustic source localization. The TDOA estimation problem is defined as finding the relative delay between several microphone signals for the direct sound. To estimate TDOA, the generalized cross-correlation (GCC) method is the most frequently used, but it has a disadvantage in terms of reverberant environments. In order to overcome this problem, the adaptive eigenvalue decomposition (AED) method has been developed, which estimates the room transfer function and finds the direct-path delay. However, the algorithm does not take into account the fact that the room transfer function is a sparse channel, and so sometimes the estimated transfer function is too dense, resulting in failure to exact direct-path and delay. In this paper, an enhanced AED algorithm that makes use of a proportionate step-size control and a direct-path constraint is proposed instead of a constant step size and the L2-norm constraint. The simulation results show that the proposed algorithm has enhanced performance as compared to both the conventional AED method and the phase-transform (PHAT) algorithm.

Based on a ternary perfect sequence and a binary orthogonal matrix, the Z-periodic complementary sequence (ZPCS) sets over the 8-QAM+ constellation are constructed. The resultant sequences can be used in multi-carriers code division multiple access (MC-CDMA) systems to remove interference and increase the transmission rate. The proposed construction provides flexible choice for parameters so as to meet different requirements in the application. A construction of shift sequence sets is proposed and the number of 8-QAM ZPCS sets is extended by changing the parameters of shift sequences. As a result, more users can be accommodated in the system.

We introduce the design and deployment of the latest version of the VNode infrastructure, VNode-i. We present new extended VNode-i functions that offer high performance and provide convenient deep programmability to network developers. We extend resource abstraction to the transport network and achieve highly precise slice measurement for resource elasticity. We achieve precise resource isolation for VNode-i. We achieve coexistence of high performance and programmability. We also enhance AGW functions. In addition, we extend network virtualization from the core network to edge networks and terminals. In evaluation experiments, we deploy the enhanced VNode-i on the JGN-X testbed and evaluate its performance. We successfully create international federation slices across VNode-i, GENI, and Fed4FIRE. We also present experimental results on video streaming on a federated slice across VNode-i and GENI. Testbed experiments confirm the practicality of the enhanced VNode-i.

This study investigates a real-time joint channel and hyperparameter estimation method for orthogonal frequency division multiplexing mobile communications. The channel frequency response of the pilot subcarrier and its fixed hyperparameters (such as channel statistics) are estimated using a Liu and West filter (LWF), which is based on the state-space model and sequential Monte Carlo method. For the first time, to our knowledge, we demonstrate that the conventional LWF biases the hyperparameter due to a poor estimate of the likelihood caused by overfitting in noisy environments. Moreover, this problem cannot be solved by conventional smoothing techniques. For this, we modify the conventional LWF and regularize the likelihood using a Kalman smoother. The effectiveness of the proposed method is confirmed via numerical analysis. When both of the Doppler frequency and delay spread hyperparameters are unknown, the conventional LWF significantly degrades the performance, sometimes below that of least squares estimation. By avoiding the hyperparameter estimation failure, our method outperforms the conventional approach and achieves good performance near the lower bound. The coding gain in our proposed method is at most 10 dB higher than that in the conventional LWF. Thus, the proposed method improves the channel and hyperparameter estimation accuracy. Derived from mathematical principles, our proposal is applicable not only to wireless technology but also to a broad range of related areas such as machine learning and econometrics.

This article describes an approximate model of a group of cells in the wireless 4G network with implemented load balancing mechanism. An appropriately modified model of Erlang's Ideal Grading is used to model this group of cells. The model makes it possible to take into account limited availability of resources of individual cells to multi-rate elastic and adaptive traffic streams generated by Erlang and Engset sources. The developed solution allows the basic traffic characteristics in the considered system to be determined, i.e. the occupancy distribution and the blocking probability. Because of the approximate nature of the proposed model, the results obtained based on the model were compared with the results of a digital simulation. The present study validates the adopted assumptions of the proposed model.

With the flourish of applications based on the Internet of Things (IoT), privacy issues have been attracting a lot of attentions. Although the concept of privacy homomorphism was proposed along with the birth of the well-known RSA cryptosystems, cryptographers over the world have spent about three decades for finding the first implementation of the so-called fully homomorphic encryption (FHE). Despite of, currently known FHE schemes, including the original Gentry's scheme and many subsequent improvements as well as the other alternatives, are not appropriate for IoT-oriented applications because most of them suffer from the problems of inefficient key size and noisy restraining. In addition, for providing fully support to IoT-oriented applications, symmetric fully homomorphic encryptions are also highly desirable. This survey presents an analysis on the challenges of designing secure and practical FHE for IoT, from the perspectives of lightweight requirements as well as the security requirements. In particular, some issues about designing noise-free FHE schemes would be addressed.

Location-based services (LBSs) are useful for many applications in internet of things(IoT). However, LBSs has raised serious concerns about users' location privacy. In this paper, we propose a new location privacy attack in LBSs called hidden location inference attack, in which the adversary infers users' hidden locations based on the users' check-in histories. We discover three factors that influence individual check-in behaviors: geographic information, human mobility patterns and user preferences. We first separately evaluate the effects of each of these three factors on users' check-in behaviors. Next, we propose a novel algorithm that integrates the above heterogeneous factors and captures the probability of hidden location privacy leakage. Then, we design a novel privacy alert framework to warn users when their sharing behavior does not match their sharing rules. Finally, we use our experimental results to demonstrate the validity and practicality of the proposed strategy.

In data sharing privacy has become one of the main concerns particularly when sharing datasets involving individuals contain private sensitive information. A model that is widely used to protect the privacy of individuals in publishing micro-data is k-anonymity. It reduces the linking confidence between private sensitive information and specific individual by generalizing the identifier attributes of each individual into at least k-1 others in dataset. K-anonymity can also be defined as clustering with constrain of minimum k tuples in each group. However, the accuracy of the data in k-anonymous dataset decreases due to huge information loss through generalization and suppression. Also most of the current approaches are designed for numerical continuous attributes and for categorical attributes they do not perform efficiently and depend on attributes hierarchical taxonomies, which often do not exist. In this paper we propose a new model for k-anonymization, which is called Similarity-Based Clustering (SBC). It is based on clustering and it measures similarity and calculates distances between tuples containing numerical and categorical attributes without hierarchical taxonomies. Based on this model a bottom up greedy algorithm is proposed. Our extensive study on two real datasets shows that the proposed algorithm in comparison with existing well-known algorithms offers much higher data utility and reduces the information loss significantly. Data utility is maintained above 80% in a wide range of k values.

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.