In this paper, we propose a design of reversible adder/subtractor blocks and arithmetic logic units (ALUs). The main concept of our approach is different from that of the existing related studies; we emphasize the function design. Our approach of investigating the reversible functions includes (a) the embedding of irreversible functions into incompletely-specified reversible functions, (b) the operation assignment, and (c) the permutation of function outputs. We give some extensions of these techniques for further improvements in the design of reversible functions. The resulting reversible circuits are smaller than that of the existing design in terms of the number of multiple-control Toffoli gates. To evaluate the quantum cost of the obtained circuits, we convert the circuits to reduced quantum circuits for experiments. The results also show the superiority of our realization of adder/subtractor blocks and ALUs in quantum cost.

For an odd prime p and a positive integer k ≥ 2, we propose and analyze construction of perfect pk-ary sequences of period pk based on cubic polynomials over the integers modulo pk. The constructed perfect polyphase sequences from cubic polynomials is a subclass of the perfect polyphase sequences from the Mow's unified construction. And then, we give a general approach for constructing optimal families of perfect polyphase sequences with some properties of perfect polyphase sequences and their optimal families. By using this, we construct new optimal families of pk-ary perfect polyphase sequences of period pk. The constructed optimal families of perfect polyphase sequences are of size p-1.

Semi-bent functions have very high nonlinearity and hence they have many applications in symmetric-key cryptography, binary sequence design for communications, and combinatorics. In this paper, we focus on studying the additive autocorrelation of semi-bent functions. We provide a lower bound on the maximum additive autocorrelation absolute value of semi-bent functions with three-level additive autocorrelation. Semi-bent functions with three-level additive autocorrelation achieving this bound with equality are said to have perfect three-level additive autocorrelation. We present two classes of balanced semi-bent functions with optimal algebraic degree and perfect three-level additive autocorrelation.

This paper improves the family size of quadrature amplitude modulation (QAM) complementary sequences with binary inputs. By employing new mathematical description: B-type-2 of 4q-QAM constellation (integer q ≥ 2), a new construction yielding 4q-QAM complementary sequences (CSs) with length 2m (integer m ≥ 2) is developed. The resultant sequences include the known QAM CSs with binary inputs as special cases, and the family sizes of new sequences are approximately 22·2q-4q-1(22·2q-3-1) times as many as the known. Also, both new sequences and the known have the same the peak envelope power (PEP) upper bounds, when they are used in an orthogonal frequency-division multiplexing communication system.

We propose an approach to allocate bandwidth for a satellite communications (SATCOM) system that includes the recent high-throughput satellite (HTS) with frequency flexibility. To efficiently operate the system, we manage the limited bandwidth resources available for SATCOM by employing a control method that allows the allocated bandwidths to exceed the communication demand of user terminals per HTS beam. To this end, we consider bandwidth allocation for SATCOM as an optimal control problem. Then, assuming that the model of communication requests is available, we propose an optimal control method by combining model predictive control and sparse optimization. The resulting control method enables the efficient use of the limited bandwidth and reduces the bandwidth loss and number of control actions for the HTS compared to a setup with conventional frequency allocation and no frequency flexibility. Furthermore, the proposed method allows to allocate bandwidth depending on various control objectives and beam priorities by tuning the corresponding weighting matrices. These findings were verified through numerical simulations by using a simple time variation model of the communication requests and predicted aircraft communication demand obtained from the analysis of actual flight tracking data.

The order/degree problem consists of finding the smallest diameter graph for a given order and degree. Such a graph is beneficial for designing low-latency networks with high performance for massively parallel computers. The average shortest path length (ASPL) of a graph has an influence on latency. In this paper, we propose a novel order adjustment approach. In the proposed approach, we search for Cayley graphs of the given degree that are close to the given order. We then adjust the order of the best Cayley graph to meet the given order. For some order and degree pairs, we explain how to derive the smallest known graphs from the Graph Golf 2016 and 2017 competitions.

An optimal arrangement problem involves finding a component arrangement to maximize system reliability, namely, the optimal arrangement. It is useful to obtain the optimal arrangement when we design a practical system. An existing study developed an algorithm for finding the optimal arrangement of a connected-(r, s)-out-of-(m, n): F lattice system with r=m-1 and n<2s. However, the algorithm is time-consuming to find the optimal arrangement of a system having many components. In this study, we develop an algorithm for efficiently finding the optimal arrangement of the system with r=m-1 and s=n-1 based on the depth-first branch-and-bound method. In the algorithm, before enumerating arrangements, we assign some components without computing the system reliability. As a result, we can find the optimal arrangement effectively because the number of components which must be assigned decreases. Furthermore, we develop an efficient method for computing the system reliability. The numerical experiment demonstrates the effectiveness of our proposed algorithm.

The enhanced chosen-ciphertext security (ECCA) is motivated by the concept of randomness recovering encryption, which was presented by Dana Dachman-Soled et al. in PKC 2014 [9]. ECCA security is the enhanced version of CCA security. CCA security often appears to be somewhat too strong, so ECCA security is also too strong: there exist encryption schemes that are not ECCA secure but still have some practical application. Canetti et al. proposed a relaxed variant of CCA security called Replayable CCA (RCCA) security in CRYPTO 2003 [3]. In this paper, we propose a relaxed variant of ECCA security called Replayable security (RECCA). RECCA security is the enhanced version of RCCA security. Since RCCA security suffices for the most existing application of CCA security, RECCA security also suffices for them, too. Moreover, RECCA security provides a useful general version of security against active attacks.

Network coding signature (NCS) scheme is a cryptographic tool for network coding against pollution attacks. In [5], Chang et al. first introduced the related-key attack (RKA) to the NCS schemes and tried to give an instantiation of it. However, their instantiation is based on the random oracle (RO) model. In this letter, we present a standard-model instantiation. In particular, we prove that standard-model-based NCS scheme introduced by Boneh et al. in [4] (BFKW scheme, for short) can achieve Φ-RKA security if the underlying signature scheme is also Φ-RKA secure, where Φ is any family of functions defined on signing keys of NCS schemes.

Multiple input multiple output with orthogonal frequency division multiplexing (MIMO-OFDM) is used in various parts of wireless communication systems. Because the MIMO-OFDM system simultaneously transmits parallel data streams and each receive antenna receives all data streams at one time, the detection ability of the receiver is very important. Among the detection schemes suitable for OFDM, maximum likelihood (ML) detection has optimal performance, but its complexity is so high that it is infeasible. Linear detection schemes such as zero-forcing (ZF) and minimum mean square error (MMSE) have low complexity, but also low performance. Among non-linear detection schemes, the near-ML detection which is the sphere detection (SD) or the QR decomposition with M algorithm (QRD-M) also has optimal performance but the complexity of SD and QRD-M detection is also too high. Other non-linear detection schemes like successive interference cancellation (SIC) detection have low complexity. However, the performance of SIC detection is lower than other non-linear detection schemes. In this paper, selectively iterative detection is proposed for MIMO-OFDM system; it offers low complexity and good performance.

This paper proposes a novel dynamic channel assignment scheme named interference-aware dynamic channel assignment (IA-DCA) for the downlink of enterprise small-cell networks (ESNs) that employ orthogonal frequency division multiple access (OFDMA) and frequency division duplexing (FDD). In ESNs, a lot of small-cell access points (SAPs) are densely deployed in a building and thus small-cell user equipments (SUEs) have more serious co-tier interference from neighbor SAPs than the conventional small-cell network. Therefore, in the proposed IA-DCA scheme, a local gateway (LGW) dynamically assigns different numbers of subchannel groups to SUEs through their serving SAPs according to the given traffic load and interference information. Through simulation results, we show that the proposed IA-DCA scheme outperforms other dynamic channel assignment schemes based on graph coloring algorithm in terms of the mean SUE capacity, fairness, and mean SAP channel utilization.

This paper describes a broadband low phase noise VCO implemented in 0.13 µm CMOS process. A 1-bit switched varactor and a 4-bit capacitor array are adopted in cooperation with the automatic frequency calibration (AFC) circuit to lower the VCO tuning gain (KVCO), with a measured AFC time of 6 µs. Several noise reduction techniques are exploited to minimize the phase noise of the VCO. Measurement results show the VCO generates a high frequency range from 11.37 GHz to 14.8 GHz with a KVCO of less than 270 MHz/V. The prototype exhibits a phase noise of -114.6 dBc/Hz @ 1 MHz at 14.67 GHz carrier frequency and draws 10.5 mA current from a 1.2 V supply. The achieved figure-of-merits (FoM=-186.9dBc/Hz, FoMT=-195.3dBc/Hz) favorably compares with the state-of-the-art.

We propose a parallel precomputation method for real-time model predictive control. The key idea is to use predicted input values produced by model predictive control to solve an optimal control problem in advance. It is well known that control systems are not suitable for multi- or many-core processors because feedback-loop control systems are inherently based on sequential operations. However, since the proposed method does not rely on conventional thread-/data-level parallelism, it can be easily applied to such control systems without changing the algorithm in applications. A practical evaluation using three real-world model predictive control system simulation programs demonstrates drastic performance improvement without degrading control quality offered by the proposed method.

Graphics processing units (GPUs) have become an attractive platform for general-purpose computing (GPGPU) in various domains. Making GPUs a time-multiplexing resource is a key to consolidating GPGPU applications (apps) in multi-tenant cloud platforms. However, advanced GPGPU apps pose a new challenge for consolidation. Such highly functional GPGPU apps, referred to as GPU eaters, can easily monopolize a shared GPU and starve collocated GPGPU apps. This paper presents GLoop, which is a software runtime that enables us to consolidate GPGPU apps including GPU eaters. GLoop offers an event-driven programming model, which allows GLoop-based apps to inherit the GPU eaters' high functionality while proportionally scheduling them on a shared GPU in an isolated manner. We implemented a prototype of GLoop and ported eight GPU eaters on it. The experimental results demonstrate that our prototype successfully schedules the consolidated GPGPU apps on the basis of its scheduling policy and isolates resources among them.

In a multiprocessor system, processors are connected based on various types of network topologies. A network topology is usually represented by a graph. Let G be a graph and u, v be any two distinct vertices of G. We say that G is pancyclic if G has a cycle C of every length l(C) satisfying 3≤l(C)≤|V(G)|, where |V(G)| denotes the total number of vertices in G. Moreover, G is panpositionably pancyclic from r if for any integer m satisfying $r leq m leq rac{|V(G)|}{2}$, G has a cycle C containing u and v such that dC(u,v)=m and 2m≤l(C)≤|V(G)|, where dC(u,v) denotes the distance of u and v in C. In this paper, we investigate the panpositionable pancyclicity problem with respect to the n-dimensional locally twisted cube LTQn, which is a popular topology derived from the hypercube. Let D(LTQn) denote the diameter of LTQn. We show that for n≥4 and for any integer m satisfying $D(LTQ_n) + 2 leq m leq rac{|V(LTQ_n)|}{2}$, there exists a cycle C of LTQn such that dC(u,v)=m, where (i) 2m+1≤l(C)≤|V(LTQn)| if m=D(LTQn)+2 and n is odd, and (ii) 2m≤l(C)≤|V(LTQn)| otherwise. This improves on the recent result that u and v can be positioned with a given distance on C only under the condition that l(C)=|V(LTQn)|. In parallel and distributed computing, if cycles of different lengths can be embedded, we can adjust the number of simulated processors and increase the flexibility of demand. This paper demonstrates that in LTQn, the cycle embedding containing any two distinct vertices with a feasible distance is extremely flexible.

Kernel updates are a part of daily life in contemporary computer systems. They usually require an OS reboot that involves restarting not only the kernel but also all of the running applications, causing downtime that can disrupt software services. This downtime issue has been tackled by numerous approaches. Although dynamic translation of the running kernel image, which is a representative approach, can conduct kernel updates at runtime, its applicability is inherently limited. This paper describes Dwarf, which shortens downtime during kernel updates and covers more types of updates. Dwarf launches the newer kernel in the background on the same physical machine and forces the kernel to inherit the running states of the older kernel. We implemented a prototype of Dwarf on Xen 4.5.2, Linux 2.6.39, Linux 3.18.35, and Linux 4.1.6. Also, we conducted experiments using six applications, such as Apache, MySQL, and memcached, and the results demonstrate that Dwarf's downtime is 1.8 seconds in the shortest case and up to 10× shorter than that of the normal OS reboot.

Generalized recursive circulant graphs (GRCGs for short) are a generalization of recursive circulant graphs and provide a new type of topology for interconnection networks. A graph of n vertices is said to be s-pancyclic for some $3leqslant sleqslant n$ if it contains cycles of every length t for $sleqslant tleqslant n$. The pancyclicity of recursive circulant graphs was investigated by Araki and Shibata (Inf. Process. Lett. vol.81, no.4, pp.187-190, 2002). In this paper, we are concerned with the s-pancyclicity of GRCGs.

The number of IT services that use machine learning (ML) algorithms are continuously and rapidly growing, while many of them are used in practice to make some type of predictions from personal data. Not surprisingly, due to this sudden boom in ML, the way personal data are handled in ML systems are starting to raise serious privacy concerns that were previously unconsidered. Recently, Fredrikson et al. [USENIX 2014] [CCS 2015] proposed a novel attack against ML systems called the model inversion attack that aims to infer sensitive attribute values of a target user. In their work, for the model inversion attack to be successful, the adversary is required to obtain two types of information concerning the target user prior to the attack: the output value (i.e., prediction) of the ML system and all of the non-sensitive values used to learn the output. Therefore, although the attack does raise new privacy concerns, since the adversary is required to know all of the non-sensitive values in advance, it is not completely clear how much risk is incurred by the attack. In particular, even though the users may regard these values as non-sensitive, it may be difficult for the adversary to obtain all of the non-sensitive attribute values prior to the attack, hence making the attack invalid. The goal of this paper is to quantify the risk of model inversion attacks in the case when non-sensitive attributes of a target user are not available to the adversary. To this end, we first propose a general model inversion (GMI) framework, which models the amount of auxiliary information available to the adversary. Our framework captures the model inversion attack of Fredrikson et al. as a special case, while also capturing model inversion attacks that infer sensitive attributes without the knowledge of non-sensitive attributes. For the latter attack, we provide a general methodology on how we can infer sensitive attributes of a target user without knowledge of non-sensitive attributes. At a high level, we use the data poisoning paradigm in a conceptually novel way and inject malicious data into the ML system in order to modify the internal ML model being used into a target ML model; a special type of ML model which allows one to perform model inversion attacks without the knowledge of non-sensitive attributes. Finally, following our general methodology, we cast ML systems that internally use linear regression models into our GMI framework and propose a concrete algorithm for model inversion attacks that does not require knowledge of the non-sensitive attributes. We show the effectiveness of our model inversion attack through experimental evaluation using two real data sets.

Reverberation chambers (RCs) are used widely in the electromagnetic measurement area. An RC is designed to have a long reverberation time, generate numerous modes, and provide good field uniformity within the chamber. The purpose of this paper is to describe the design process and measurement of the KRISS Reverberation Chamber (KRC). KRC models with 4.5m × 3.4m × 2.8m dimensions are simulated by 3D numerical simulation software. The field uniformity and correlation coefficient are then analyzed at 200MHz to obtain the optimized model. The simulation results show good performance in terms of field uniformity and are confirmed by measurement from 200MHz to 1GHz. The lowest usable frequency (LUF) of KRC was confirmed by field uniformity to be 200MHz. However, the stirrer correlation coefficient results show good performance above 300MHz.

By exploiting the inherent sparsity of wireless channels, the channel estimation in an orthogonal frequency division multiplexing (OFDM) system can be cast as a compressed sensing (CS) problem to estimate the channel more accurately. Practically, matching pursuit algorithms such as orthogonal matching pursuit (OMP) are used, where path delays of the channel is guessed based on correlation values for every quantized delay with residual. This full search approach requires a predefined grid of delays with high resolution, which induces the high computational complexity because correlation values with residual at a huge number of grid points should be calculated. Meanwhile, the correlation values with high resolution can be obtained by interpolation between the correlation values at a low resolution grid. Also, the interpolation can be implemented with a low pass filter (LPF). By using this fact, in this paper we substantially reduce the computational complexity to calculate the correlation values in channel estimation using CS.