To optimize the performance of FIR filters that have low computation complexity, this paper proposes a hybrid design consisting of two optimization levels. The first optimization level is based on cyclic-shift synthesis, in which all possible sub filters (or windowed sub filters) with distinct cycle shifts are averaged to generate a synthesized filter. Due to the fact that the ripples of these sub filters' transfer curves can be individually compensated, this synthesized filter attains improved performance (besides two uprushes occur on the edges of a transition band) and thus this synthesis actually plays the role of ‘natural optimization’. Furthermore, this synthesis process can be equivalently summarized into a 3-step closed-form procedure, which converts the multi-variable optimization into a single-variable optimization. Hence, to suppress the uprushes, what the second optimization level (by Differential Evolution (DE) algorithm) needs to do is no more than searching for the optimum transition point which incurs only minimal complexity . Owning to the combination between the cyclic-shift synthesis and DE algorithm, unlike the regular evolutionary computing schemes, our hybrid design is more attractive due to its narrowed search space and higher convergence speed . Numerical results also show that the proposed design is superior to the conventional DE design in both filter performance and design efficiency, and it is comparable to the Remez design.

An automorphism of a graph G=(V, E) is such a one-to-one correspondence from vertex set V to itself that all the adjacencies of the vertices are maintained. Given a subset S of V whose one-to-one correspondence is decided, if the vertices of V-S possess unique correspondence in all the automorphisms that satisfy the decided correspondence for S, S is called determiner set of G. Further, S is called minimal determiner set if no proper subset of S is a determiner set and called kernel set if determiner set S with the smallest number of elements. Moreover, a problem to judge whether or not S is a determiner set is called determiner set decision problem. The purpose of this research is to deal with determiner set decision problem. In this paper, we firstly give the definitions and properties related to determiner sets and then propose an algorithm JDS that judges whether a given S is a determiner set of G in polynomial computation time. Finally, we evaluate the proposed algorithm JDS by applying it to possibly find minimal determiner sets for 100 randomly generated graphs. As the result, all the obtained determiner sets are minimal, which implies JDS is a reasonably effective algorithm for the judgement of determiner sets.

Data generated from the Internet of Things (IoT) devices in smart spaces are utilized in a variety of fields such as context recognition, service recommendation, and anomaly detection. However, the missing values in the data streams of the IoT devices remain a challenging problem owing to various missing patterns and heterogeneous data types from many different data streams. In this regard, while we were analyzing the dataset collected from a smart space with multiple IoT devices, we found a continuous missing pattern that is quite different from the existing missing-value patterns. The pattern has blocks of consecutive missing values over a few seconds and up to a few hours. Therefore, the pattern is a vital factor to the availability and reliability of IoT applications; yet, it cannot be solved by the existing missing-value imputation methods. Therefore, a novel approach for missing-value imputation of the continuous missing pattern is required. We deliberate that even if the missing values of the continuous missing pattern occur in one data stream, missing-values imputation is possible through learning other data streams correlated with this data stream. To solve the missing values of the continuous missing pattern problem, we analyzed multiple IoT data streams in a smart space and figured out the correlations between them that are the interdependencies among the data streams of the IoT devices in a smart space. To impute missing values of the continuous missing pattern, we propose a deep learning-based missing-value imputation model exploiting correlation information, namely, the deep imputation network (DeepIN), in a smart space. The DeepIN uses that multiple long short-term memories are constructed according to the correlation information of each IoT data stream. We evaluated the DeepIN on a real dataset from our campus IoT testbed, and the experimental results show that our proposed approach improves the imputation performance by 57.36% over the state-of-the-art missing-value imputation algorithm. Thus, our approach can be a promising methodology that enables IoT applications and services with a reasonable missing-value imputation accuracy (80∼85%) on average, even if a long-term block of values is missing in IoT environments.

In parallel computing systems, the interconnection network forms the critical infrastructure which enables robust and scalable communication between hundreds of thousands of nodes. The traditional packet-switched network tends to suffer from long communication time when network congestion occurs. In this context, we explore the use of circuit switching (CS) to replace packet switches with custom hardware that supports circuit-based switching efficiently with low latency. In our target CS network, a certain amount of bandwidth is guaranteed for each communication pair so that the network latency can be predictable when a limited number of node pairs exchange messages. The number of allocated time slots in every switch is a direct factor to affect the end-to-end latency, we thereby improve the slot utilization and develop a network topology generator to minimize the number of time slots optimized to target applications whose communication patterns are predictable. By a quantitative discrete-event simulation, we illustrate that the minimum necessary number of slots can be reduced to a small number in a generated topology by our design methodology while maintaining network cost 50% less than that in standard tori topologies.

A PCB-integratable metal cap slot antenna is developed for the 60-GHz band. The antenna is composed of two slots and a T-junction and is fed by a post-wall waveguide on a substrate. The dimensions of the designed antenna are 8.0×4.5×2.5mm3. The designed antenna is insensitive with a metal block behind the antenna. The designed antenna is fabricated by machining a brass block and evaluated by measurement. The measurement shows reflection less than -10.0dB, gain larger than 7.8dBi and beamwidth between 54°-65° over the 60-GHz band with endfire radiation. The antenna showed high gain together with short length of half wavelength in the radiation direction. This antenna also can be integrated with printed circuit board (PCB) and is suitable for mobile terminals such as smart phones and tablets.

We investigate image recognition of multiple food items in a single photo, focusing on a buffet restaurant application, where menu changes at every meal, and only a few images per class are available. After detecting food areas, we perform hierarchical recognition. We evaluate our results, comparing to two baseline methods.

We present an automated lemma generation method for equational, inductive theorem proving based on the term rewriting induction of Reddy and Aoto as well as the divergence critic framework of Walsh. The method effectively works by using the divergence-detection technique to locate differences in diverging sequences, and generates potential lemmas automatically by analyzing these differences. We have incorporated this method in the multi-context inductive theorem prover of Sato and Kurihara to overcome the strategic problems resulting from the unsoundness of the method. The experimental results show that our method is effective especially for some problems diverging with complex differences (i.e., parallel and nested differences).

It is known that correlation-immune (CI) Boolean functions used in the framework of side channel attacks need to have low Hamming weights. In this letter, we determine all unknown values of the minimum Hamming weights of d-CI Boolean functions in n variables, for d ≤ 5 and n ≤ 13.

The fifth generation mobile communications (5G) systems will need to support the ultra-reliable and low-latency communications (URLLC) to enable future mission-critical applications, e.g., self-driving cars and remote control. With the aim of verifying the feasibility of URLLC related 5G requirements in real environments, field trials of URLLC using a new frame structure are conducted in Yokohama, Japan. In this paper, we present the trial results and investigate the impact of the new frame structure and retransmission method on the URLLC performance. To reduce the user-plane latency and improve the packet success probability, a wider subcarrier spacing, self-contained frame structure, and acknowledgement/negative acknowledgement-less (ACK/NACK-less) retransmission are adopted. We verify the feasibility of URLLC in actual field tests using our prototype test-bed while implementing these techniques. The results show that for the packet size of 32 bytes the URLLC related requirements defined by the 3GPP are satisfied even at low signal-to-noise ratios or at non-line-of-sight transmission.

In this study, we propose a statistical reputation approach for constructing a reliable packet route in ad-hoc sensor networks. The proposed method uses reputation as a measurement for router node selection through which a reliable data route is constructed for packet delivery. To refine the reputation, a transaction density is defined here to showcase the influence of node transaction frequency over the reputation. And to balance the energy consumption and avoid choosing repetitively the same node with high reputation, node remaining energy is also considered as a reputation factor in the selection process. Further, a shortest-path-tree routing protocol is designed so that data packets can reach the base station through the minimum intermediate nodes. Simulation tests illustrate the improvements in the packet delivery ratio and the energy utilization.

In this work, we provide the first practical secure email filtering scheme based on homomorphic encryption. Specifically, we construct a secure naïve Bayesian filter (SNBF) using the Paillier scheme, a partially homomorphic encryption (PHE) scheme. We first show that SNBF can be implemented with only the additive homomorphism, thus eliminating the need to employ expensive fully homomorphic schemes. In addition, the design space for specialized hardware architecture realizing SNBF is explored. We utilize a recursive Karatsuba Montgomery structure to accelerate the homomorphic operations, where multiplication of 2048-bit integers are carried out. Through the experiment, both software and hardware versions of the SNBF are implemented. On software, 104-105x runtime and 103x storage reduction are achieved by SNBF, when compared to existing fully homomorphic approaches. By instantiating the designed hardware for SNBF, a further 33x runtime and 1919x power reduction are achieved. The proposed hardware implementation classifies an average-length email in under 0.5s, which is much more practical than existing solutions.

This paper proposes a Petri net based mathematical programming approach to combinatorial optimization, in which we generate integer linear programming problems from Petri net models instead of the direct mathematical formulation. We treat two types of combinatorial optimization problems, ordinary problems and time-dependent problems. Firstly, we present autonomous Petri net modeling for ordinary optimization problems, where we obtain fundamental constraints derived from Petri net properties and additional problem-specific ones. Secondly, we propose a colored timed Petri net modeling approach to time-dependent problems, where we generate variables and constraints for time management and for resolving conflicts. Our Petri net approach can drastically reduce the difficulty of the mathematical formulation in a sense that (1) the Petri net modeling does not require deep knowledge of mathematical programming and technique of integer linear model formulations, (2) our automatic formulation allows us to generate large size of integer linear programming problems, and (3) the Petri net modeling approach is flexible for input parameter changes of the original problem.

In this paper, we propose a method for enhancing the visibility of omnidirectional spherical images by enlarging the foreground and compressing the background without provoking a sense of visual incompatibility by using a simplified spring model.

This paper proposes a new design criterion of adaptively scaled belief (ASB) in Gaussian belief propagation (GaBP) for large multi-user multi-input multi-output (MU-MIMO) detection. In practical MU detection (MUD) scenarios, the most vital issue for improving the convergence property of GaBP iterative detection is how to deal with belief outliers in each iteration. Such outliers are caused by modeling errors due to the fact that the law of large number does not work well when it is difficult to satisfy the large system limit. One of the simplest ways to mitigate the harmful impact of outliers is belief scaling. A typical approach for determining the scaling parameter for the belief is to create a look-up table (LUT) based on the received signal-to-noise ratio (SNR) through computer simulations. However, the instantaneous SNR differs among beliefs because the MIMO channels in the MUD problem are random; hence, the creation of LUT is infeasible. To stabilize the dynamics of the random MIMO channels, we propose a new transmission block based criterion that adapts belief scaling to the instantaneous channel state. Finally, we verify the validity of ASB in terms of the suppression of the bit error rate (BER) floor.

Coil-shaped structures are proposed to enhance sensitivity and spatial resolution for EMI near-field probe. This design yields a high sensitivity and a good spatial resolution to find the EMI source in near-field region. Both characteristics are crucial to diagnosis of emissions from electrical and electronic devices. The new design yields a superior sensitivity, which is in general 15 dB greater than conventional probes. This new probe helps practitioners to quickly and correctly locate noise emission source areas on printed circuit boards and devices. Two prototypes of different sizes were fabricated. The larger one provides a high sensitivity while the smaller one can pinpoint emission source locations. The new probe design also has an orientation invariance feature. Its noise response levels are similar for all probe directions. This characteristic can help reduced the probability at miss-detection since sensitivity is largely invariant to its orientation. Extensive measurements were performed to verify the operation mechanism and to assess probe characteristics. It suits well to the electromagnetic interference problem diagnosis.

Workflow nets (WF-nets for short) are a subclass of Petri nets and are used for modeling and analysis of workflows. Soundness is a criterion of logical correctness defined for WF-nets. A WF-net is said to be sound if it satisfies three conditions: (i) option to complete, (ii) proper completion, and (iii) no dead tasks. In this paper, focusing our analysis on acyclic free choice WF-nets, we revealed that (1) Conditions (i) and (ii) of soundness are respectively equivalent to the liveness and the boundedness of its short-circuited net; (2) The decision problem for each condition of soundness is co-NP-complete; and (3) If the short-circuited net has no disjoint paths from a transition to a place (or no disjoint paths from a place to a transition), each condition can be checked in polynomial time.

Electric field induced optical second harmonic generation (EFISHG) measurement at the laser wavelength of 1000nm and 1080nm is conducted to investigate the carrier behaviors in PCPDTBT and PC71BM bulk heterojunction organic solar cells (OSCs). We find that the response time for electrode charging through the external circuit in +1V and -1V are almost identical but the response time for the Maxwell-Wagner-type interfacial charging is different in +1V or -1V. We find that the accumulated charges of PC71BM is more than the accumulated charges of PCPDTBT and consequently affects the efficiency of the bulk heterojunction OSCs.

Two new families of balanced almost binary sequences with a single zero element of period L=2q are presented in this letter, where q=4d+1 is an odd prime number. These sequences have optimal autocorrelation value or optimal autocorrelation magnitude. Our constructions are based on cyclotomy and Chinese Remainder Theorem.

This paper proposes a consensus-based subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and an auxiliary variable as the estimates of an optimal solution and accumulated information of past gradients of neighbor agents. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed consensus-based algorithm with accumulated subgradient information achieves faster convergence than the standard subgradient algorithm.

This paper proposes a novel distributed proximal minimization algorithm for constrained optimization problems over fixed strongly connected networks. At each iteration, each agent updates its own state by evaluating a proximal operator of its objective function under a constraint set and compensating the unbalancing due to unidirectional communications. We show that the states of all agents asymptotically converge to one of the optimal solutions. Numerical results are shown to confirm the validity of the proposed method.