In this paper, we present a modified genetic algorithm for solving combinatorial optimization problems. The modified genetic algorithm in which crossover and mutation are performed conditionally instead of probabilistically has higher global and local search ability and is more easily applied to a problem than the conventional genetic algorithms. Three optimization problems are used to test the performances of the modified genetic algorithm. Experimental studies show that the modified genetic algorithm produces better results over the conventional one and other methods.

Realization of a planar dual-band fork three-way power divider (PDBF3PD) with Cheng's equivalent structure is discussed. The Cheng's structure consists of two open-circuited stubs and a transmission line, and the characteristic impedances tend to be high. As a result, the realizable range of frequency ratios of upper frequency to lower frequency is limited in a narrow area. In this paper, an impedance scale factor is proposed to transform characteristic impedances into a realizable range and to facilitate the design of PDBF3PDs. Theoretical considerations are verified using a simulator of ADS2008U and by an experiment.

A hybrid Retinex-based image enhancement algorithm is proposed to improve the quality of images captured by unmanned aerial vehicles (UAVs) in this paper. Hyperparameters of the employed multi-scale Retinex with chromaticity preservation (MSRCP) model are automatically tuned via a two-phase evolutionary computing algorithm. In the two-phase optimization algorithm, the Rao-2 algorithm is applied to performing the global search and a solution is obtained by maximizing the objective function. Next, the Nelder-Mead simplex method is used to improve the solution via local search. Real UAV-taken images of bad quality are collected to verify the performance of the proposed algorithm. Meanwhile, four famous image enhancement algorithms, Multi-Scale Retinex, Multi-Scale Retinex with Color Restoration, Automated Multi-Scale Retinex, and MSRCP are utilized as benchmarking methods. Meanwhile, two commonly used evolutionary computing algorithms, particle swarm optimization and flower pollination algorithm, are considered to verify the efficiency of the proposed method in tuning parameters of the MSRCP model. Experimental results demonstrate that the proposed method achieves the best performance compared with benchmarks and thus the proposed method is applicable for real UAV-based applications.

Ant Colony Optimization (ACO) is one of the most recent techniques for solving combinatorial optimization problems, and has been unexpectedly successful. Therefore, many improvements have been proposed to improve the performance of the ACO algorithm. In this paper an ant colony optimization with memory is proposed, which is applied to the classical traveling salesman problem (TSP). In the proposed algorithm, each ant searches the solution not only according to the pheromone and heuristic information but also based on the memory which is from the solution of the last iteration. A large number of simulation runs are performed, and simulation results illustrate that the proposed algorithm performs better than the compared algorithms.

This paper presents a new and robust framework for real-coded genetic algorithm, called real-code conditional genetic algorithm (rc-CGA). The most important characteristic of the proposed rc-CGA is the implicit self-adaptive feature of the crossover and mutation mechanism. Besides, a new crossover operator with laplace distribution following a few promising descent directions (FPDD-LX) is proposed for the rc-CGA. The proposed genetic algorithm (rc-CGA+FPDD-LX) is tested using 31 benchmark functions and compared with four existing algorithms. The simulation results show excellent performance of the proposed rc-CGA+FPDD-LX for continuous function optimization.

A near-optimum parallel algorithm for bipartite subgraph problem using gradient ascent learning algorithm of the Hopfield neural networks is presented. This parallel algorithm, uses the Hopfield neural network updating to get a near-maximum bipartite subgraph and then performs gradient ascent learning on the Hopfield network to help the network escape from the state of the near-maximum bipartite subgraph until the state of the maximum bipartite subgraph or better one is obtained. A large number of instances have been simulated to verify the proposed algorithm, with the simulation result showing that our algorithm finds the solution quality is superior to that of best existing parallel algorithm. We also test the proposed algorithm on maximum cut problem. The simulation results also show the effectiveness of this algorithm.

In this paper, we present a hill-shift learning method of the Hopfield neural network for bipartite subgraph problem. The method uses the Hopfield neural network to get a near-maximum bipartite subgraph, and shifts the local minimum of energy function by adjusts the balance between two terms in the energy function to help the network escape from the state of the near-maximum bipartite subgraph to the state of the maximum bipartite subgraph or better one. A large number of instances are simulated to verify the proposed method with the simulation results showing that the solution quality is superior to that of best existing parallel algorithm.

In a periodic spectrum sensing framework where each frame consists of a sensing block and a data transmitting block, increasing sensing duration decreases the probabilities of both missed opportunity and interference with primary users, but increasing sensing duration also decreases the energy efficiency and the transmitting efficiency of the cognitive network. Therefore, the sensing duration to use is a trade-off between sensing performance and system efficiencies. The relationships between sensing duration and state transition probability are analyzed firstly, when the licensed channel stays in the idle and busy states respectively. Then a state transition probability based sensing duration optimization algorithm is proposed, which can dynamically optimize the sensing duration of each frame in the current idle/busy state by predicting each frame's state transition probability at the beginning of the current state. Analysis and simulation results reveal that the time-varying optimal sensing duration increases as the state transition probability increases and compared to the existing method, the proposed algorithm can use as little sensing duration in each frame as possible to satisfy the sensing performance constraints so as to maximize the energy and transmitting efficiencies of the cognitive networks.

We investigate the problem of joint frequency and power allocation in wireless mesh networks, using a self-pricing game based solution. In traditional pricing game models, the price factor is determined from the global information of the network, which causes heavy communication overhead. To overcome this problem, we propose a self-pricing game model, in which the price factor is determined by the distributed access points processing their individual information; moreover, it is implemented in an autonomous and distributed fashion. The existence and the efficiency of Nash equilibrium (NE) of the proposed game are studied. It is shown that the proposed game based solution achieves near cooperative network throughput while it reduces the communication overhead significantly. Also, a forcing convergence algorithm is proposed to counter the vibration of channel selection. Simulation results verify the effectiveness and robustness of the proposed scheme.

Recently six conjectures on linear complexities (LC) of some Kronecker sequences of two or three component sequences are proposed by Karkkainen. In, the LC of Kronecker sequences of two component sequences were studied by Uehara and Imamura, their results are true except in the case when eb 2 or when ea = eb = 1. In this paper the LC for Kronecker sequences of two component sequences are determined completely, and it is shown that all the six conjectures are true except in some special cases, which are listed and corrected.

This letter proposes a novel connected dominanting set based decentralized cooperative spectrum sensing algorithm for cognitive radio networks. It is analytically shown that the proposed algorithm distributively converges to the average consensus as that of traditional distributed consensus algorithm, while reducing both the convergence time and message complexity significantly.

A real-time road-direction point detection model is developed based on convolutional neural network architecture which can adapt to complex environment. Firstly, the concept of road-direction point is defined for either single road or crossroad. For single road, the predicted road-direction point can serve as a guiding point for a self-driving vehicle to go ahead. In the situation of crossroad, multiple road-direction points can also be detected which will help this vehicle to make a choice from possible directions. Meanwhile, different types of road surface can be classified by this model for both paved roads and unpaved roads. This information will be beneficial for a self-driving vehicle to speed up or slow down according to various road conditions. Finally, the performance of this model is evaluated on different platforms including Jetson TX1. The processing speed can reach 12 FPS on this portable embedded system so that it provides an effective and economic solution of road-direction estimation in the applications of autonomous navigation.

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

Soil moisture sensor calibration based on the Multivariate Adaptive Regression Splines (MARSplines) model is studied in this paper. Different from the generic polynomial fitting methods, the MARSplines model is a non-parametric model, and it is able to model the complex relationship between the actual and measured soil moisture. Rao-1 algorithm is employed to tune the hyper-parameters of the calibration model and thus the performance of the proposed method is further improved. Data collected from four commercial soil moisture sensors is utilized to verify the effectiveness of the proposed method. To assess the calibration performance, the proposed model is compared with the model without using the temperature information. The numeric studies prove that it is promising to apply the proposed model for real applications.

In CRYPTO 2019, Gohr first introduced the deep learning method to cryptanalysis for SPECK32/64. A differential-neural distinguisher was obtained using ResNet neural network. Zhang et al. used multiple parallel convolutional layers with different kernel sizes to capture information from multiple dimensions, thus improving the accuracy or obtaining a more round of distinguisher for SPECK32/64 and SIMON32/64. Inspired by Zhang's work, we apply the network structure to other ciphers. We not only improve the accuracy of the distinguisher, but also increase the number of rounds of the distinguisher, that is, distinguish more rounds of ciphertext and random number for DES, Chaskey and PRESENT.

This paper describes a new learning method for Multiple-Value Logic (MVL) networks using the local search method. It is a "non-back-propagation" learning method which constructs a layered MVL network based on canonical realization of MVL functions, defines an error measure between the actual output value and teacher's value and updates a randomly selected parameter of the MVL network if and only if the updating results in a decrease of the error measure. The learning capability of the MVL network is confirmed by simulations on a large number of 2-variable 4-valued problems and 2-variable 16-valued problems. The simulation results show that the method performs satisfactorily and exhibits good properties for those relatively small problems.

Single-packet attack can be tracked with logging-based IP traceback approaches, whereas DDoS attack can be tracked with marking-based approaches. However, both approaches have their limits. Logging-based approaches incur heavy overhead for packet-digest storage as well as time overhead for both path recording and recovery. Marking-based approaches incur little traceback overhead but are unable to track single packets. Simply deploying both approaches in the same network to deal with single-packet and DDoS attacks is not an efficient solution due to the heavy traceback overhead. Recent studies suggest that hybrid approaches are more efficient as they consume less router memory to store packet digests and require fewer attack packets to recover attack paths. Thus, the hybrid single packet traceback approach is more promising in efficiently tracking both single-packet and DDoS attacks. The major challenge lies in reducing storage and time overhead while maintaining single-packet traceback capability. We present in this paper a new hybrid approach to efficiently track single-packet attacks by designing a novel path fragment encoding scheme using the orthogonality of Walsh matrix and the degree distribution characteristic of router-level topologies. Compared to HIT (Hybrid IP Traceback), which, to the best of our knowledge, is the most efficient hybrid approach for single-packet traceback, our approach has three advantages. First, it reduces the overhead by 2/3 in both storage and time for recording packet paths. Second, the time overhead for recovering packet paths is also reduced by a calculatable amount. Finally, our approach generates no more than 2/3 of the false-positive paths generated by HIT.

Multicarrier communications including orthogonal frequency-division multiplexing (OFDM) is a technique which has been adopted for various wireless applications. However, a major drawback to the widespread acceptance of OFDM is the high peak-to-mean envelope power ratio (PMEPR) of uncoded OFDM signals. Finding methods for construction of sequences with low PMEPR is an active research area. In this paper, by employing some new shortened and extended Golay complementary pairs as the seeds, we enlarge the family size of near-complementary sequences given by Yu and Gong. We also show that the new set of sequences we obtained is just a reversal of the original set. Numerical results show that the enlarged family size is almost twice of the original one. Besides, the Hamming distances of the binary near-complementary sequences are also analyzed.

In this letter, we study cooperative transmission in wireless multicast networks. An opportunistic cooperative multicast scheme based on coded cooperation (OCM-CC) is proposed and its closed-form expression of outage performance is obtained. Through numeric evaluation, we analyze its outage probability with different numbers of relays and different cooperative ratios.

This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.