Due to recent technology progress based on big-data processing, many applications present irregular or unpredictable communication patterns among compute nodes in high-performance computing (HPC) systems. Traditional communication infrastructures, e.g., torus or fat-tree interconnection networks, may not handle well their matchmaking problems with these newly emerging applications. There are already many communication-efficient application mapping algorithms for these typical non-random network topologies, which use nearby compute nodes to reduce the network distances. However, for the above unpredictable communication patterns, it is difficult to efficiently map their applications onto the non-random network topologies. In this context, we recommend using random network topologies as the communication infrastructures, which have drawn increasing attention for the use of HPC interconnects due to their small diameter and average shortest path length (ASPL). We make a comparative study to analyze the impact of application mapping performance on non-random and random network topologies. We propose using topology embedding metrics, i.e., diameter and ASPL, and list several diameter/ASPL-based application mapping algorithms to compare their job scheduling performances, assuming that the communication pattern of each application is unpredictable to the computing system. Evaluation with a large compound application workload shows that, when compared to non-random topologies, random topologies can reduce the average turnaround time up to 39.3% by a random connected mapping method and up to 72.1% by a diameter/ASPL-based mapping algorithm. Moreover, when compared to the baseline topology mapping method, the proposed diameter/ASPL-based topology mapping strategy can reduce up to 48.0% makespan and up to 78.1% average turnaround time, and improve up to 1.9x system utilization over random topologies.

The notion of the signal-to-noise ratio (SNR), proposed by Guilley, et al. in 2004, is a property that attempts to characterize the resilience of (n, m)-functions F=(f1,...,fm) (cryptographic S-boxes) against differential power analysis. But how to study the signal-to-noise ratio for a Boolean function still appears to be an important direction. In this paper, we give a tight upper and tight lower bounds on SNR for any (balanced) Boolean function. We also deduce some tight upper bounds on SNR for balanced Boolean function satisfying propagation criterion. Moreover, we obtain a SNR relationship between an n-variable Boolean function and two (n-1)-variable decomposition functions. Meanwhile, we give SNR(f⊞g) and SNR(f⊡g) for any balanced Boolean functions f, g. Finally, we give a lower bound on SNR(F), which determined by SNR(fi) (1≤i≤m), for (n, m)-function F=(f1,f2,…,fm).

This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.

Path planning and motion control are fundamental components to realize safe and reliable autonomous driving. The discrimination of the role of these two components, however, is somewhat obscure because of strong mathematical interaction between these two components. This often results in a redundant computation in the implementation. One of attracting idea to overcome this redundancy is a simultaneous path planning and motion control (SPPMC) based on a model predictive control framework. SPPMC finds the optimal control input considering not only the vehicle dynamics but also the various constraints which reflect the physical limitations, safety constraints and so on to achieve the goal of a given behavior. In driving in the real traffic environment, decision making has also strong interaction with planning and control. This is much more emphasized in the case that several tasks are switched in some context to realize higher-level tasks. This paper presents a basic idea to integrate decision making, path planning and motion control which is able to be executed in realtime. In particular, lane-changing behavior together with the decision of its initiation is selected as the target task. The proposed idea is based on the nonlinear model predictive control and appropriate switching of the cost function and constraints in it. As the result, the decision of the initiation, planning, and control of the lane-changing behavior are achieved by solving a single optimization problem under several constraints such as safety. The validity of the proposed method is tested by using a vehicle simulator.

Random network topologies have been proposed as a low-latency network for parallel computers. Although multicast is a common collective-communication operation, multicast algorithms each of which consists of a large number of unicasts are not well optimized for random network topologies. In this study, we firstly apply a two-opt algorithm for building efficient multicast on random network topologies. The two-opt algorithm creates a skilled ordered list of visiting nodes to minimize the total path hops or the total possible contention counts of unicasts that form the target multicast. We secondly extend to apply the two-opt algorithm for the other collective-communication operations, e.g., allreduce and allgather. The SimGrid discrete-event simulation results show that the two-opt multicast outperforms that in typical MPI implementation by up to 22% of the execution time of an MPI program that repeats the MPI_Bcast function. The two-opt allreduce and the two-opt allgather operations also improve by up to 15% and 14% the execution time when compared to those used in typical MPI implementations, respectively.

Multiscale retinex is one of the most popular image enhancement methods. However, its control parameters, such as Gaussian kernel sizes, gain, and offset, should be tuned carefully according to the image contents. In this letter, we propose a new method that optimizes the parameters using practical swarm optimization and multi-objective function. The method iteratively verifies the visual quality (i.e. brightness, contrast, and colorfulness) of the enhanced image using a multi-objective function while subtly adjusting the parameters. Experimental results shows that the proposed method achieves better image quality qualitatively and quantitatively compared with other image enhancement methods.

We propose a novel hue-correction scheme for multi-exposure image fusion (MEF). Various MEF methods have so far been studied to generate higher-quality images. However, there are few MEF methods considering hue distortion unlike other fields of image processing, due to a lack of a reference image that has correct hue. In the proposed scheme, we generate an HDR image as a reference for hue correction, from input multi-exposure images. After that, hue distortion in images fused by an MEF method is removed by using hue information of the HDR one, on the basis of the constant-hue plane in the RGB color space. In simulations, the proposed scheme is demonstrated to be effective to correct hue-distortion caused by conventional MEF methods. Experimental results also show that the proposed scheme can generate high-quality images, regardless of exposure conditions of input multi-exposure images.

In this paper, we improve upon the automatic building extraction method, which uses a variational inference Gaussian mixture model for performing color clustering, by accelerating its computational speed. The improved method decreases the computational time using an image with reduced resolution upon applying color clustering. According to our experiment, in which we used 106 scenery images, the improved method could extract buildings at a rate 86.54% faster than that of the conventional methods. Furthermore, the improved method significantly increased the extraction accuracy by 1.8% or more by preventing over-clustering using the reduced image, which also had a reduced number of the colors.

Stochastic computing (SC), which is an approximate computation with probabilities, has attracted attention owing to its small area, small power consumption and high fault tolerance. In this paper, we focus on the transient fault tolerance of SC based on linear finite state machines (linear FSMs). We show that state assignment of FSMs considerably affects the fault tolerance of linear FSM-based SC circuits, and present a Markov model for representing the impact of the state assignment on the behavior of faulty FSMs and estimating the expected error significance of the faulty FSM-based SC circuits. Furthermore, we propose a heuristic algorithm for appropriate state assignment that can mitigate the influence of transient faults. Experimental analysis shows that the state assignment has an impact on the transient fault tolerance of linear FSM-based SC circuits and the proposed state assignment algorithm can achieve a quasi-optimal state assignment in terms of high fault tolerance.

The injection locking properties of rotary dissipative solitons developed in a closed traveling-wave field-effect transistor (TWFET) are examined. A TWFET can support the waveform-invariant propagation of solitary pulses called dissipative solitons (DS) by balancing dispersion, nonlinearity, dissipation, and field-effect transistor gain. Applying sinusoidal signals to the closed TWFET assumes the injection-locked behavior of the rotary DS; the solitons' velocity is autonomously tuned to match the rotation and external frequencies. This study clarifies the qualitative properties of injection-locked DS using numerical and experimental approaches.

This paper discusses a static analysis problem, called absolute consistency problem, for relational schema mappings. A given schema mapping is said to be absolutely consistent if every source instance has a corresponding target instance. Absolute consistency is an important property because it guarantees that data exchange never fails for any source instance. Originally, for XML schema mappings, the absolute consistency problem was defined and its complexity was investigated by Amano et al. However, as far as the authors know, there are no known results for relational schema mappings. In this paper, we focus on relational schema mappings such that both the source and the target schemas have functional dependencies, under the assumption that mapping rules are defined by constant-free tuple-generating dependencies. In this setting, we show that the absolute consistency problem is in coNP. We also show that it is solvable in polynomial time if the tuple-generating dependencies are full and the size of the left-hand side of each functional dependency is bounded by some constant. Finally, we show that the absolute consistency problem is coNP-hard even if the source schema has no functional dependency and the target schema has only one; or each of the source and the target schemas has only one functional dependency such that the size of the left-hand side of the functional dependency is at most two.

Multi-cycle Test looks promising a way to reduce the test application time of POST (Power-on Self-Test) for achieving a targeted high fault coverage specified by ISO26262 for testing automotive devices. In this paper, we first analyze the mechanism of Stuck-at Fault Detection Degradation problem in multi-cycle test. Based on the result of our analysis we propose a novel solution named FF-Control Point Insertion technique (FF-CPI) to achieve the reduction of scan-in patterns by multi-cycle test. The FF-CPI technique modifies the captured values of scan Flip-Flops (FFs) during capture operation by directly reversing the value of partial FFs or loading random vectors. The FF-CPI technique enhances the number of detectable stuck-at faults under the capture patterns. The experimental results of ISCAS89 and ITC99 benchmarks validated the effectiveness of FF-CPI technique in scan-in pattern reduction for POST.

LSTM network have shown to outperform in facial expression recognition of video sequence. In view of limited representation ability of single-layer LSTM, a hierarchical attention model with enhanced feature branch is proposed. This new network architecture consists of traditional VGG-16-FACE with enhanced feature branch followed by a cross-layer LSTM. The VGG-16-FACE with enhanced branch extracts the spatial features as well as the cross-layer LSTM extracts the temporal relations between different frames in the video. The proposed method is evaluated on the public emotion databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.

In this paper, we reformulate a sensitivity analysis method for function-expansion-based topology optimization method without using gray area. In the conventional approach based on function expansion method, permittivity distribution contains gray materials, which are intermediate materials between core and cladding ones, so as to let the permittivity differentiable with respect to design variables. Since this approach using gray area dose not express material boundary exactly, it is not desirable to apply this approach to design problems of strongly guiding waveguide devices, especially for plasmonic waveguides. In this study, we present function-expansion-method-based topology optimization without gray area. In this approach, use of gray area can be avoided by replacing the area integral of the derivative of the matrix with the line integral taking into acount the rate of boundary deviation with respect to design variables. We verify the validity of our approach through applying it to design problems of a T-branching power splitter and a mode order converter.

Two-ray (TR) fading model is one of the fading models to represent a worst-case fading scenario. We derive the exact closed-form expressions of the generalized moment generating function (G-MGF) for the TR fading model, which enables us to analyze the numerous types of wireless communication applications. Among them, we carry out several analytical results for the TR fading model, including the exact ergodic capacity along with asymptotic expressions and energy detection performance. Finally, we provide numerical results to validate our evaluations.

High dynamic range (HDR) imaging refers to digital image processing that modifies the range of color and contrast to enhance image visibility. To create an HDR image, two or more images that include various information are needed. In order to convert low dynamic range (LDR) images to HDR images, we consider the possibility of using a generative adversarial network (GAN) as an appropriate deep neural network. Deep learning requires a great deal of data in order to build a module, but once the module is created, it is convenient to use. In this paper, we propose a weight map for local luminance based on learning to reconstruct locally tone-mapped images.

The communications quality of content delivery networks (CDNs), which are geographically distributed networks that have been optimized for content delivery, deteriorates when interflow congestion conditions are severe. Herein, we propose an adaptive server and path switching scheme that is based on the estimated acquisition throughput of each path. We also provide simulation results that show our proposed method can provide higher throughput performance levels than existing methods.

Troika is a recently proposed sponge-based hash function for IOTA's ternary architecture and platform, which is developed by CYBERCRYPT and is now used in IOTA's blockchain. In this paper, we introduce the preimage attack on 2/3 rounds of Troika with a divide-and-conquer approach. Firstly, we propose the equivalent conditions to determine whether a message is the preimage with an algebraic method. As a result, for the preimage attack on two-round Troika, we can search the preimage only in a valid smaller space and efficiently enumerate the messages which can satisfy most of the equivalent conditions with a guess-and-determine technique. Our experiments show that the time complexity of the preimage attack on 2-round Troika can be improved to 379 from 3243. For the preimage attack on 3-round Troika, the MILP-based method is applied to achieve the optimal time complexity, which is 327 times faster than brute force.

For low-density parity-check (LDPC) codes, the penalized decoding method based on the alternating direction method of multipliers (ADMM) can improve the decoding performance at low signal-to-noise ratios and also has low decoding complexity. There are three effective methods that could increase the ADMM penalized decoding speed, which are reducing the number of Euclidean projections in ADMM penalized decoding, designing an effective penalty function and selecting an appropriate layered scheduling strategy for message transmission. In order to further increase the ADMM penalized decoding speed, through reducing the number of Euclidean projections and using the vertical layered scheduling strategy, this paper designs a fast converging ADMM penalized decoding method based on the improved penalty function. Simulation results show that the proposed method not only improves the decoding performance but also reduces the average number of iterations and the average decoding time.

We present a card-based protocol for computing a three-input majority using six cards. The protocol essentially consists of performing a simple XOR protocol two times. Compared to the existing protocols, our protocol does not require private operations other than choosing cards.