A new fuzzy level set method (FLSM) based on the global search capability of quantum particle swarm optimization (QPSO) is proposed to improve the stability and precision of image segmentation, and reduce the sensitivity of initialization. The new combination of QPSO-FLSM algorithm iteratively optimizes initial contours using the QPSO method and fuzzy c-means clustering, and then utilizes level set method (LSM) to segment images. The new algorithm exploits the global search capability of QPSO to obtain a stable cluster center and a pre-segmentation contour closer to the region of interest during the iteration. In the implementation of the new method in segmenting liver tumors, brain tissues, and lightning images, the fitness function of the objective function of QPSO-FLSM algorithm is optimized by 10% in comparison to the original FLSM algorithm. The achieved initial contours from the QPSO-FLSM algorithm are also more stable than that from the FLSM. The QPSO-FLSM resulted in improved final image segmentation.

In recent years, algebraic attacks and fast algebraic attacks have received a lot of attention in the cryptographic community. There are three Boolean functions achieving optimal algebraic immunity based on primitive element of F2n. The support of Boolean functions in [1]-[3] have the same parameter s, which makes us have a large number of Boolean functions with good properties. However, we prove that the Boolean functions are affine equivalence when s takes different values.

Very few existing works about inertial sensor based air-writing focused on writing constraints' effects on recognition performance. We proposed a LSTM-based system and made several quantitative analyses under different constraints settings against CHMM, DTW-AP and CNN. The proposed system shows its advantages in accuracy, real-time performance and flexibility.

We examine the feasibility of Deutsch-Jozsa Algorithm, a basic quantum algorithm, on a machine learning-based logistic regression problem. Its major property to distinguish the function type with an exponential speedup can help identify the feature unsuitability much more quickly. Although strict conditions and restrictions to abide exist, we reconfirm the quantum superiority in many aspects of modern computing.

The ability of Software Defined Networking (SDN) to dynamically adjust the network behaviour and to support fine-grained routing policies becomes increasingly attractive beyond the boundaries of Data Centre domains, where SDN has already gained enormous momentum. However, the wider adoption of SDN in ISP (Internet Service Provider) networks is still uncertain due to concerns about the scalability of a centralized traffic management in large-scale environments. This is particularly problematic when ISP offers virtual-link services, which imply a performance guaranteed data transfer between two network points. Our solution is a new approach to virtual-link mapping in SDN-based ISP networks. Within the problem's scope, we address traffic engineering (TE), QoS provisioning and failure recovery issues. In order to decrease the controller load, computational effort, and processing delay, we introduce a function split between online routing and TE. The TE functions are performed periodically, with configurable periodicity. In order to reduce the control overhead, we restrict the traffic optimization problem to load balancing over multiple static tunnels. This allows retention of the traditional MPLS routers in the network core and to achieve fast virtual-link restoration in case of physical-link failures. The online routing and admission control algorithms have been designed with the goal of low complexity, and to minimize Flow-table updates. In our simulation study, we compare the proposed virtual-link mapping solution with the solutions that exploit routing flexibility in fully SDN-enabled networks. We find that the throughput loss due to the use of static traffic tunnels is relatively small, while the control overhead is reduced significantly. A prototype of the proposed SDN control-plane is developed and validated in the Mininet emulator.

In this paper, new classes of binary generalized cyclotomic sequences of period 2pm+1qn+1 are constructed. These sequences are balanced. We calculate the linear complexity of the constructed sequences with a simple method. The results show that the linear complexity of such sequences attains the maximum.

Many autocorrelation-based frequency estimation algorithms have been proposed. However, some of them cannot construct a strict linear prediction (LP) property among the adjacent autocorrelation lags, which affects the estimators' performance. To improve the precision of frequency estimation, two novel autocorrelation based frequency estimation methods of the real sinusoid signal in additive white Gaussian noise (AWGN) are proposed in this paper. Firstly, a simple method is introduced to transform the real sinusoid signal into the noncircular signal. Secondly, the autocorrelation of the noncircular signal is analyzed and a strict LP property is constructed among the adjacent autocorrelation lags of the noncircular signal. Thirdly, the least squares (LS) and reformed Pisarenko harmonic decomposer (RPHD) frameworks are employed to improve estimation accuracy. The simulation results match well with the theoretical values. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.

Malware-infected hosts have typically been detected using network-based Intrusion Detection Systems on the basis of characteristic patterns of HTTP requests collected with dynamic malware analysis. Since attackers continuously modify malicious HTTP requests to evade detection, novel HTTP requests sent from new malware samples need to be exhaustively collected in order to maintain a high detection rate. However, analyzing all new malware samples for a long period is infeasible in a limited amount of time. Therefore, we propose a system for efficiently collecting HTTP requests with dynamic malware analysis. Specifically, our system analyzes a malware sample for a short period and then determines whether the analysis should be continued or suspended. Our system identifies malware samples whose analyses should be continued on the basis of the network behavior in their short-period analyses. To make an accurate determination, we focus on the fact that malware communications resemble natural language from the viewpoint of data structure. We apply the recursive neural network, which has recently exhibited high classification performance in the field of natural language processing, to our proposed system. In the evaluation with 42,856 malware samples, our proposed system collected 94% of novel HTTP requests and reduced analysis time by 82% in comparison with the system that continues all analyses.

To improve the efficiency of spectrum utilization, cognitive radio systems attempt to use temporarily unoccupied spectrum which is referred to as a spectrum hole. To this end, QoS (Quality of Service) is one of the most important issues in practical cognitive radio systems. In this article, an efficient spectrum management scheme using self-reserving spectrum is proposed to support QoS for cognitive radio users. The self-reservation of a spectrum hole can minimize service dropping probability by using the statistical characteristics of spectrum bands while using optimum amount of resources. In addition, it realizes seamless service for users by eliminating spectrum entry procedure that includes spectrum sensing, spectrum request, and spectrum grant. Performance analysis and intensive system level simulations confirm the efficiency of the proposed algorithms.

To solve the low accuracy problem of the recommender system for long term users, in this paper, we propose a top-N-balanced sequential recommendation based on recurrent neural network. We postulated and verified that the interactions between users and items is time-dependent in the long term, but in the short term, it is time-independent. We balance the top-N recommendation and sequential recommendation to generate a better recommender list by improving the loss function and generation method. The experimental results demonstrate the effectiveness of our method. Compared with a state-of-the-art recommender algorithm, our method clearly improves the performance of the recommendation on hit rate. Besides the improvement of the basic performance, our method can also handle the cold start problem and supply new users with the same quality of service as the old users.

A topological quantum circuit is a representation model for topological quantum computation, which attracts much attention recently as a promising fault-tolerant quantum computation model by using 3D cluster states. A topological quantum circuit can be considered as a set of “loops,” and we can transform the topology of loops without changing the functionality of the circuit if the transformation satisfies certain conditions. Thus, there have been proposed many researches to optimize topological quantum circuits by transforming the topology. There are two directions of research to optimize topological quantum circuits. The first group of research considers so-called a placement and wiring problem where we consider how to place “parts” in a 3D space which corresponds to already optimized sub-circuits. The second group of research focuses on how to optimize the structure and locations of loops in a relatively small circuit which is treated as one part in the above-mentioned first group of research. This paper proposes a new idea for the second group of research; our idea is to consider topological transformations as a placement and wiring problem for modules which we derive from the information how loops are crossed. By using such a formulation, we can use the techniques for placement and wiring problems, and successfully obtain an optimized solution. We confirm by our experiment that our method indeed can reduce the cost much more than the method by Paetznick and Fowler.

This paper concerned the research on a memristive chaotic system and the generated random sequence; by constructing a piecewise-linear memristor model, a kind of chaotic system is constructed, and corresponding numerical simulation and dynamical analysis are carried out to show the dynamics of the new memristive chaotic system. Finally the proposed memristive chaotic system is used to generate random sequence for the possible application in encryption field.

In this letter, a flexible and compatible with fine resolution radar frequency measurement receiver is designed. The receiver is implemented on the platform of Virtex-5 Field Programmable Grid Array (FPGA) from Xilinx. The Digital Down Conversion (DDC) without mixer based on polyphase filter has been successfully introduced in this receiver to obtain lower speed data flow and better resolution. This receiver can adapt to more modulation types and higher density of pulse flow, up to 200000 pulses per second. The measurement results indicate that the receiver is capable of detecting radar pulse signal of 0.2us to 2.5ms width with a major frequency root mean square error (RMSE) within 0.44MHz. Moreover, the wider pulse width and the higher decimation rate of DDC result in better performance. This frequency measurement receiver has been successfully used in a spaceborne radar system.

To address the bandwidth bottleneck that exists between LSI chips, we have proposed a novel, high-sensitivity receiver circuit for differential optical transmission on a silicon optical interposer. Both anodes and cathodes of the differential photodiodes (PDs) were designed to be connected to a transimpedance amplifier (TIA) through coupling capacitors. Reverse bias voltage was applied to each of the differential PDs through load resistance. The proposed receiver circuit achieved double the current signal amplitude of conventional differential receiver circuits. The frequency response of the receiver circuit was analyzed using its equivalent circuit, wherein the temperature dependence of the PD was implemented. The optimal load resistances of the PDs were determined to be 5kΩ by considering the tradeoff between the frequency response and bias voltage drop. A small dark current of the PD was important to reduce the voltage drop, but the bandwidth degradation was negligible if the dark current at room temperature was below 1µA. The proposed circuit achieved 3-dB bandwidths of 18.9 GHz at 25°C and 13.7 GHz at 85°C. Clear eye openings in the TIA output waveforms for 25-Gbps 27-1 pseudorandom binary sequence signals were obtained at both temperatures.

To minimize the damage caused by landslides resulting from torrential rain, residents must quickly evacuate to a place of refuge. To make the decision to evacuate, residents must be able to collect and share disaster information. Firstly, this paper introduces the Grass-roots Information Distribution System and a fixed type monitoring system which our research group has been developing. The fixed type monitoring system is deployed at the location of apparent danger, whereas the Grass-roots Information Distribution System distributes disaster information acquired from the fixed type monitoring system through a mobile ad hoc network (MANET) to residents. The MANET is configured using mobile terminals of residents. Next, in this paper, an information dissemination scheme utilizing a MANET and cellular networks to communicate among mobile terminals is proposed and simulated in the area where our research group has been deploying the distribution system. The MANET topology and information distribution obtained from the simulation results for further field experiments are then discussed.

To recognize transportation modes without any additional sensor devices, we demonstrate that the transportation modes can be recognized from communication quality factors. In the demonstration, instead of using global positioning system (GPS) and accelerometer sensors, we collect mobile TCP throughputs, received-signal strength indicators (RSSIs), and cellular base-station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming. In accuracy evaluations, we conduct two different field experiments to collect the data in six typical transportation modes (static, walking, riding a bicycle, riding a bus, riding a train and riding a subway), and then construct the classifiers by applying a support-vector machine (SVM), k-nearest neighbor (k-NN), random forest (RF), and convolutional neural network (CNN). Our results show that these transportation modes can be recognized with high accuracy by using communication quality factors as well as the use of accelerometer sensors.

This study proposes a design and calibration method for a small-footprint, low-frequency, and low-power gate leakage timer using a differential leakage technique for IoT applications. The proposed gate leakage timer is different from conventional ones because it is composed of two leakage sources and exploits differential leakage current for the charging capacitor. This solution alleviates the inherent trade-off between small-footprint and low-frequency in the conventional gate leakage timer. Furthermore, a calibration method to suppress variations of the output frequency is proposed in this paper. To verify the effectiveness of the proposed gate leakage timer, a test chip was fabricated using 55-nm-DDC-CMOS technology. The test chip successfully demonstrates the highest figure of merit (FoM) of the product of the capacitor area (0.072µm2) and output frequency (0.11Hz), which corresponds to an improvement by a factor of 2,121 compared to the conventional one. It also demonstrates the operation with 4.5pW power consumption. The total footprint can be reduced to be 28µm2, which enables low-cost and low-power IoT edges. The scaling scenario shows that the proposed technique is conducive to technology scaling.

Wireless communication security has become a hot topic in recent years. The directional modulation (DM) is a promising secure communication technique that has attracted attentions of many researchers. Several different frequency diverse arrays (FDAs) are used to obtain the direction-range-dependent DM signals in previous literatures. However, most of them are not ideal enough to obtain a nonperiodic dot-shaped secure area. In this paper, the symmetrical multi-carrier frequency diverse array with logarithmical frequency increment, named the symmetrical-multilog-FDA, is used to obtain the direction-range-dependent DM signals that are normal at the desired locations while disordered at other locations. Based on the symmetrical-multilog-FDA, we derive the closed-form expression of baseband-weighted vector using the artificial-noise-aided zero-forcing approach. Compared with previous schemes, the proposed scheme can obtain a more fine-focusing nonperiodic dot-shaped secure area at the desired location. In addition, it can achieve a point-to-multipoint secure communication for multiple cooperative receivers at different locations.

Notes on two constructions of zero-difference balanced (ZDB) functions are made in this letter. Then ZDB functions over Ze×∏ki=0 Fqi are obtained. And it shows that all the known ZDB functions using cyclotomic cosets over Zn are special cases of a generic construction. Moreover, applications of these ZDB functions are presented.

In recent years we have witnessed an increasing demand to process queries on large datasets in Non-ordered Discrete Data Spaces (NDDS). In particular, one type of query in an NDDS, called box queries, is used in many emerging applications including error corrections in bioinformatics and network intrusion detection in cybersecurity. Effective indexing methods are necessary for efficiently processing queries on large datasets in disk. However, most existing NDDS indexing methods were not designed for box queries. Several recent indexing methods developed for box queries on a large NDDS dataset in disk are based on the popular data-partitioning approach. Unfortunately, a space-partitioning based indexing scheme, which is more effective for box queries in an NDDS, has not been studied before. In this paper, we propose a novel indexing method based on space-partitioning, called the BINDS-tree, for supporting efficient box queries on a large NDDS dataset in disk. A number of effective strategies such as node split based on minimum span and cross optimal balance, redundancy reduction utilizing a singleton dimension inheritance property, and a space-efficient structure for the split history are incorporated in the constructing algorithm for the BINDS-tree. Experimental results demonstrate that the proposed BINDS-tree significantly improves the box query I/O performance, comparing to that of the state-of-the-artdata-partitioning based NDDS indexing method.