Edge linking is a fundamental computer vision task, yet presents difficulties arising from the lack of information in the image. Viewed as a constrained optimization problem, it is NP hard-being isomorphic to the classical Traveling Salesman Problem. This paper proposes a gradient ascent learning algorithm of the elastic net approach for edge linking of images. The learning algorithm has two phases: an elastic net phase, and a gradient ascent phase. The elastic net phase minimizes the path through the edge points. The procedure is equivalent to gradient descent of an energy function, and leads to a local minimum of energy that represents a good solution to the problem. Once the elastic net gets stuck in local minima, the gradient ascent phase attempts to fill up the valley by modifying parameters in a gradient ascent direction of the energy function. Thus, these two phases are repeated until the elastic net gets out of local minima and produces the shortest or better contour through edge points. We test the algorithm on a set of artificial images devised with the aim of demonstrating the sort of features that may occur in real images. For all problems, the systems are shown to be capable of escaping from the elastic net local minima and producing more meaningful contours than the original elastic net.

With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on anonymity-based privacy protection, there are still several challenges far from being perfectly solved, such as the negative impact on the security of anonymity by the semantic information, which from anonymous locations and query content. To address these semantic challenges, we propose a dual privacy preservation scheme based on the architecture of multi-anonymizers in this paper. Different from existing approaches, our method enhanced location privacy by integrating location anonymity and the encrypted query. First, the query encryption method that combines improved shamir mechanism and multi-anonymizers is proposed to enhance query safety. Second, we design an anonymity method that enhances semantic location privacy through anonymous locations that satisfy personal semantic diversity and replace sensitive semantic locations. Finally, the experiment on the real dataset shows that our algorithms provide much better privacy and use than previous solutions.

In this paper, based on maximum neural network, we propose a new parallel algorithm that can escape from local minima and has powerful ability of searching the globally optimal or near-optimum solution for the maximum clique problem (MCP). In graph theory a clique is a completely connected subgraph and the MCP is to find a clique of maximum size of a graph. The MCP is a classic optimization problem in computer science and in graph theory with many real-world applications, and is also known to be NP-complete. Lee and Takefuji have presented a very powerful neural approach called maximum neural network for this NP-complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to the local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm.

The endurance time of smartphone still suffer from the limited battery capacity, and smartphone apps will increase the burden of the battery if they download large data over slow network. So how to manage the download tasks is an important work. To this end we propose a smartphone download strategy with low energy consumption which called CLSA (Concentrated Download and Low Power and Stable Link Selection Algorithm). The CLSA is intended to reduce the overhead of large data downloads by appropriate delay for the smartphone, and it based on three major factors: the current network situation, the length of download requests' queue and the local information of smartphone. We evaluate the CLSA using a music player implementation on ZTE V880 smartphone running the Android operation system, and compare it with the other two general download strategies, Minimum Delay and WiFi Only. Experiments show that our download algorithm can achieve a better trade-off between energy and delay than the other two.

Mobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).

In this paper, we propose a new parallel algorithm for cellular radio channel assignment problem that can help the expanded maximum neural network escape from local minima by introducing a transient chaotic neurodynamics. The goal of the channel assignment problem, which is an NP-complete problem, is to minimize the total interference between the assigned channels needed to satisfy all of the communication needs. The expanded maximum neural model always guarantees a valid solution and greatly reduces search space without a burden on the parameter-tuning. However, the model has a tendency to converge to local minima easily because it is based on the steepest descent method. By adding a negative self-feedback to expanded maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm then is fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the expanded maximum neural network and the chaotic neurodynamics. Simulations on benchmark problems demonstrate the superior performance of the proposed algorithm over other heuristics and neural network methods.

This paper proposes a gradient ascent learning algorithm for the elastic net approach to the Traveling Salesman Problem (TSP). The learning model has two phases: an elastic net phase, and a gradient ascent phase. The elastic net phase is equivalent to gradient descent of an energy function, and leads to a local minimum of energy that represents a good solution to the problem. Once the elastic net gets stuck in local minima, the gradient ascent phase attempts to fill up the valley by modifying parameters in a gradient ascent direction of the energy function. Thus, these two phases are iterated until the elastic net gets out of local minima. We test the algorithm on many randomly generated travel salesman problems up to 100 cities. For all problems, the systems are shown to be capable of escaping from the elastic net local minima and generating shorter tour than the original elastic net.

In this paper, introducing a stochastic hill-climbing dynamics into an optimal competitive Hopfield network model (OCHOM), we propose a new algorithm that permits temporary energy increases, which helps the OCHOM escape from local minima. In graph theory, a clique is a completely connected subgraph and the maximum clique problem (MCP) is to find a clique of maximum size of a graph. The MCP is a classic optimization problem in computer science and in graph theory with many real-world applications, and is also known to be NP-complete. Recently, Galan-Marin et al. proposed the OCHOM for the MCP. It can guarantee convergence to a global/local minimum of energy function, and performs better than other competitive neural approaches. However, the OCHOM has no mechanism to escape from local minima. The proposed algorithm introduces stochastic hill-climbing dynamics which helps the OCHOM escape from local minima, and it is applied to the MCP. A number of instances have been simulated to verify the proposed algorithm.

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.

With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

A triangular decomposition-based multipath super-resolution method is proposed to improve the range resolution of small unmanned aerial vehicle (UAV) radar altimeters that use a single channel with continuous direct spread waveform. In the engineering applications of small UAV radar altimeter, multipath scenarios are quite common. When the conventional matched filtering process is used under these environments, it is difficult to identify multiple targets in the same range cell due to the overlap between echoes. To improve the performance, we decompose the overlapped peaks yielded by matched filtering into a series of basic triangular waveforms to identify various targets with different time-shifted correlations of the pseudo-noise (PN) sequence. Shifting the time scale enables targets in the same range resolution unit to be identified. Both theoretical analysis and experiments show that the range resolution can be improved significantly, as it outperforms traditional matched filtering processes.

The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.

Visual object tracking is always a challenging task in computer vision. During the tracking, the shape and appearance of the target may change greatly, and because of the lack of sufficient training samples, most of the online learning tracking algorithms will have performance bottlenecks. In this paper, an improved real-time algorithm based on deep learning features is proposed, which combines multi-feature fusion, multi-scale estimation, adaptive updating of target model and re-detection after target loss. The effectiveness and advantages of the proposed algorithm are proved by a large number of comparative experiments with other excellent algorithms on large benchmark datasets.