Both gender and identity recognition task with hand vein information is solved based on the proposed cross-selected-domain transfer learning model. State-of-the-art recognition results demonstrate the effectiveness of the proposed model for pattern recognition task, and the capability to avoid over-fitting of fine-tuning DCNN with small-scaled database.

We study the problem of determining the minimum number of open-edge guards which guard the interior of a given orthogonal polygon with holes. Here, an open-edge guard is a guard which is allowed to be placed along open edges of a polygon, that is, the endpoints of the edge are not taken into account for visibility purpose. It is shown that finding the minimum number of open-edge guards for a given orthogonal polygon with holes is NP-hard.

This paper explores the potential of orbital angular momentum (OAM) multiplexing as a means to enable high-speed wireless transmission. OAM is a physical property of electro-magnetic waves that are characterized by a helical phase front in the propagation direction. Since the characteristic can be used to create multiple orthogonal channels, wireless transmission using OAM can enhance the wireless transmission rate. Comparisons with other wireless transmission technologies clarify that OAM multiplexing is particularly promising for point-to-point wireless transmission. We also clarify three major issues in OAM multiplexing: beam divergence, mode-dependent performance degradation, and reception (Rx) signal-to-noise-ratio (SNR) reduction. To mitigate mode-dependent performance degradation we first present a simple but practical Rx antenna design method. Exploiting the fact that there are specific location sets with phase differences of 90 or 180 degrees, the method allows each OAM mode to be received at its high SNR region. We also introduce two methods to address the Rx SNR reduction issue by exploiting the property of a Gaussian beam generated by multiple uniform circular arrays and by using a dielectric lens antenna. We confirm the feasibility of OAM multiplexing in a proof of concept experiment at 5.2 GHz. The effectiveness of the proposed Rx antenna design method is validated by computer simulations that use experimentally measured values. The two new Rx SNR enhancement methods are validated by computer simulations using wireless transmission at 60 GHz.

Due to the increase of outsourcing by IC vendors, we face a serious risk that malicious third-party vendors insert hardware Trojans very easily into their IC products. However, detecting hardware Trojans is very difficult because today's ICs are huge and complex. In this paper, we propose a hardware-Trojan classification method for gate-level netlists to identify hardware-Trojan infected nets (or Trojan nets) using a support vector machine (SVM) or a neural network (NN). At first, we extract the five hardware-Trojan features from each net in a netlist. These feature values are complicated so that we cannot give the simple and fixed threshold values to them. Hence we secondly represent them to be a five-dimensional vector and learn them by using SVM or NN. Finally, we can successfully classify all the nets in an unknown netlist into Trojan ones and normal ones based on the learned classifiers. We have applied our machine-learning-based hardware-Trojan classification method to Trust-HUB benchmarks. The results demonstrate that our method increases the true positive rate compared to the existing state-of-the-art results in most of the cases. In some cases, our method can achieve the true positive rate of 100%, which shows that all the Trojan nets in an unknown netlist are completely detected by our method.

In the field of adaptive notch filtering, Monotonically Increasing Gradient (MIG) algorithm has recently been proposed by Sugiura and Shimamura [1], where it is claimed that the MIG algorithm shows monotonically increasing gradient characteristics. However, our analysis has found that the underlying theory in [1] includes crucial errors. This letter shows that the formulation of the gradient characteristics in [1] is incorrect, and reveals that the MIG algorithm fails to realize monotonically increasing gradient characteristics when the input signal includes white noise.

In this letter, we propose two robust and distributed game-based algorithms, which are the modifications of two algorithms proposed in [1], to solve the joint base station selection and resource allocation problem with imperfect information in heterogeneous cellular networks (HCNs). In particular, we repeatedly sample the received payoffs in the exploitation stage of each algorithm to guarantee the convergence when the payoffs of some users (UEs) in [1] cannot accurately be acquired for some reasons. Then, we derive the rational sampling number and prove the convergence of the modified algorithms. Finally, simulation results demonstrate that two modified algorithms achieve good convergence performances and robustness in the incomplete information scheme.

When the random variable has a completely monotone density function, we call it bursty (BRST) random variable. At first, we prove that the entropy of inter-arrival time is smaller than or equal to the entropy of inter-departure time in an infinite-server system GI/GI/∞ having general renewal arrivals. On the basis of that result, we prove that a BRST/GI/∞ having bursty arrivals and the associated loss system BRST/GI/c/c have the following paradoxical behavior: In the BRST/GI/∞, the stationary number of customers as well as the inter-departure time become stochastically less variable, as the service time becomes stochastically more variable. Also for the loss system BRST/GI/c/c, the blocking probability decreases and the inter-departure time becomes stochastically less variable, as the service time becomes stochastically more variable.

This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.

Field Programmable Gate Array (FPGA) based robust model fitting enjoys immense popularity in image processing because of its high efficiency. This paper focuses on the tradeoff analysis of real-time FPGA implementation of robust circle and ellipse estimations based on the random sample consensus (RANSAC) algorithm, which estimates parameters of a statistical model from a data set of feature points which contains outliers. In particular, this paper mainly highlights implementation alternatives for solvers of simultaneous equations and compares Gauss-Jordan elimination and Cramer's rule by changing matrix size and arithmetic processes. Experimental evaluation shows a Cramer's rule approach coupled with long integer arithmetic can reduce most hardware resources without unacceptable degradation of estimation accuracy compared to floating point versions.

Based on the reconstruction of the augmented interference-plus-noise (IPN) covariance matrix (CM) and the estimation of the desired signal's extended steering vector (SV), we propose a novel robust widely linear (WL) beamforming algorithm. Firstly, an extension of the iterative adaptive approach (IAA) algorithm is employed to acquire the spatial spectrum. Secondly, the IAA spatial spectrum is adopted to reconstruct the augmented signal-plus-noise (SPN) CM and the augmented IPNCM. Thirdly, the extended SV of the desired signal is estimated by using the iterative robust Capon beamformer with adaptive uncertainty level (AU-IRCB). Compared with several representative robust WL beamforming algorithms, simulation results are provided to confirm that the proposed method can achieve a better performance and has a much lower complexity.

Shrinking feature sizes and higher levels of integration in semiconductor device manufacturing technologies are increasingly causing the gap between defect levels estimated in the design stage and reported ones for fabricated devices. In this paper, we propose a unified weighted fault coverage approach that includes both bridge and open faults, considering the critical area as the incident rate of each fault. We then propose a test pattern reordering scheme that incorporates our weighted fault coverage with an aim to reduce test costs. Here we apply a greedy algorithm to reorder test patterns generated by the bridge and stuck-at automatic test pattern generator (ATPG), evaluating the relationship between the number of patterns and the weighted fault coverage. Experimental results show that by applying this reordering scheme, the number of test patterns was reduced, on average, by approximately 50%. Our results also indicate that relaxing coverage constraints can drastically reduce test pattern set sizes to a level comparable to traditional 100% coverage stuck-at pattern sets, while targeting the majority of bridge faults and keeping the defect level to no more than 10 defective parts per milion (DPPM) with a 99% manufacturing yield.

Approximate computing is considered as a promising approach to design of power- or area-efficient digital circuits. This paper proposes a systematic methodology for design and worst-case accuracy analysis of approximate array multipliers. Our methodology systematically designs a series of approximate array multipliers with different area, delay, power and accuracy characteristics so that an LSI designer can select the one which best fits to the requirements of her/his applications. Our experiments explore the trade-offs among area, delay, power and accuracy of the approximate multipliers.

This letter proposes a heuristic algorithm to select check variables, which are points of comparison for error detection, for soft-error tolerant datapaths. Our soft-error tolerance scheme is based on check-and-retry computation and an efficient resource management named speculative resource sharing (SRS). Starting with the smallest set of check variables, the proposed algorithm repeats to add new check variable one by one incrementally and find the minimum latency solution among the series of generated solutions. During the process, each new check variable is selected so that the opportunity of SRS is enlarged. Experimental results show that improvements in latency are achieved compared with the choice of the smallest set of check variables.

For a service-oriented architecture based system, the problem of synthesizing a concrete model, i.e., behavioral model, for each service configuring the system from an abstract specification, which is referred to as choreography, is known as the choreography realization problem. In this paper, we assume that choreography is given by an acyclic relation. We have already shown that the condition for the behavioral model is given by lower and upper bounds of acyclic relations. Thus, the degree of freedom for behavioral models increases; developing algorithms of synthesizing an intelligible model for users becomes possible. In this paper, we introduce several metrics for intelligibility of state machines, and study the algorithm of synthesizing Pareto efficient state machines.

In this paper, we consider a networked control system where bounded network delays and packet dropouts exist in the network. The physical plant is abstracted by a transition system whose states are quantized states of the plant measured by a sensor, and a control specification for the abstracted plant is given by a transition system when no network disturbance occurs. Then, we design a prediction-based controller that determines a control input by predicting a set of all feasible abstracted states at time when the actuator receives the delayed input. It is proved that the prediction-based controller suppresses effects of network delays and packet dropouts and that the controlled plant still achieves the specification in spite of the existence of network delays and packet dropouts.

This paper discusses the use of a common computer mouse as a pointing interface for tabletop displays. In the use of a common computer mouse for tabletop displays, there might be an angular distance between the screen coordinates and the mouse control coordinates. To align those coordinates, this paper introduces a screen coordinates calibration technique using a shadow cursor. A shadow cursor is the basic idea of manipulating a mouse cursor without any visual feedbacks. The shadow cursor plays an important role in obtaining the angular distance between the two coordinates. It enables the user to perform a simple mouse manipulation so that screen coordinates calibration will be completed in less than a second.

Spritz is a stream cipher proposed by Rivest and Schuldt at the rump session of CRYPTO 2014. It is intended to be a replacement of the popular RC4 stream cipher. In this paper we propose distinguishing attacks on the full Spritz, based on a short-term bias in the first two bytes of a keystream and a long-term bias in the first two bytes of every cycle of N keystream bytes, where N is the size of the internal permutation. Our attacks are able to distinguish a keystream of the full Spritz from a random sequence with samples of first two bytes produced by 244.8 multiple key-IV pairs or 260.8 keystream bytes produced by a single key-IV pair. These biases are also useful in the event of plaintext recovery in a broadcast attack. In the second part of the paper, we look at a state recovery attack on Spritz, in a special situation when the cipher enters a class of weak states. We determine the probability of encountering such a state, and demonstrate a state recovery algorithm that betters the 21400 step algorithm of Ankele et al. at Latincrypt 2015. Finally we propose a simple fix that removes the bias in the first two keystream bytes. The countermeasure requires only one additional memory access and hence does not diminish software performance substantially, and in fact the loss in software speed is only around 1.5%.

For a multiuser multi-input multi-output (MU-MIMO) multicell network, the Han-Kobayashi strategy aims to improve the achievable rate region by splitting the data information intended to a serviced user (UE) into a common message and a private message. The common message is decodable by this UE and another UE from an adjacent cell so that the corresponding intercell interference is cancelled off. This work aims to design optimal precoders for both common and private messages to maximize the network sum-rate, which is a highly nonlinear and nonsmooth function in the precoder matrix variables. Existing approaches are unable to address this difficult problem. In this paper, we develop a successive convex quadratic programming algorithm that generates a sequence of improved points. We prove that the proposed algorithm converges to at least a local optimum of the considered problem. Numerical results confirm the advantages of our proposed algorithm over conventional coordinated precoding approaches where the intercell interference is treated as noise.