In future deep space communication missions, VLBI (Very Long Baseline Interferometry) based on antenna array technology remains a critical detection method, which urgently requires the improvement of synthesis performance for antenna array signals. Considering this, focusing on optimizing the traditional antenna grouping method applied in the phase estimation algorithm, this letter proposes a “L/2 to L/2” antenna grouping method based on the maximum correlation signal-to-noise ratio (SNR). Following this idea, a phase difference estimation algorithm named “Couple” is presented. Theoretical analysis and simulation verification illustrate that: when ρ ＜ -10dB, the proposed “Couple” has the highest performance; increasing the number of antennas can significantly improve its synthetic loss performance and robustness. The research of this letter indicates a promising potential in supporting the rising deep space exploration and communication missions.

Visible-Infrared Person Re-identification (VI-ReID) is a challenging pedestrian retrieval task due to the huge modality discrepancy and appearance discrepancy. To address this tough task, this letter proposes a novel gray augmentation exploration (GAE) method to increase the diversity of training data and seek the best ratio of gray augmentation for learning a more focused model. Additionally, we also propose a strong all-modality center-triplet (AMCT) loss to push the features extracted from the same pedestrian more compact but those from different persons more separate. Experiments conducted on the public dataset SYSU-MM01 demonstrate the superiority of the proposed method in the VI-ReID task.

Network service providers need to appropriately design systems and carefully configuring the settings and parameters to ensure that the systems keep running consistently and deliver the desired services. This can be a heavy and error-prone task. Intent-based system design methods have been developed to help with such tasks. These methods receive service-level requirements and generate service configurations to fulfill the given requirements. One such method is search-based system design, which can flexibly generate systems of various architectures. However, it has difficulty dealing with constraints on the quantitative parameters of systems, e.g., disk volume, RAM size, and QoS. To deal with practical cases, intent-based system design engines need to be able to handle quantitative parameters and constraints. In this work, we propose a new intent-based system design method based on search-based design that augments search states with quantitative constraints. Our method can generate a system that meets both functional and quantitative service requirements by combining a search-based design method with constraint checking. Experimental results show that our method can automatically generate a system that fulfills all given requirements within a reasonable computation time.

We investigate an online problem of a robot exploring the outer boundary of an unknown simple polygon P. The robot starts from a specified vertex s and walks an exploration tour outside P. It has to see all points of the polygon's outer boundary and to return to the start. We provide lower and upper bounds on the ratio of the distance traveled by the robot in comparison to the length of the shortest path. We consider P in two scenarios: convex polygon and concave polygon. For the first scenario, we prove a lower bound of 5 and propose a 23.78-competitive strategy. For the second scenario, we prove a lower bound of 5.03 and propose a 26.5-competitive strategy.

Aiming at the contradiction between exploration and exploitation in deep reinforcement learning, this paper proposes “reward-based exploration strategy combined with Softmax action selection” (RBE-Softmax) as a dynamic exploration strategy to guide the agent to learn. The superiority of the proposed method is that the characteristic of agent's learning process is utilized to adapt exploration parameters online, and the agent is able to select potential optimal action more effectively. The proposed method is evaluated in discrete and continuous control tasks on OpenAI Gym, and the empirical evaluation results show that RBE-Softmax method leads to statistically-significant improvement in the performance of deep reinforcement learning algorithms.

As an important method to solve sequential decision-making problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to large-scale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitative knowledge into reinforcement learning using cloud control systems to represent ‘if-then’ rules. We use it as the heuristics exploration strategy to guide the action selection in deep reinforcement learning. Empirical evaluation results show that our approach can make significant improvement in the learning process.

Color transfer is a simple process to change a color tone in one image (source) to look like another image (target). In transferring colors between images, there are several issues needed to be considered including partial color transfer, trial-and-error, and multiple target color transfer. Our approach enables users to transfer colors partially and locally by letting users select their regions of interest from image segmentation. Since there are many ways that we can transfer colors from a set of target regions to a set of source regions, we introduce the region exploration and navigation approach where users can choose their preferred color tones to transfer one region at a time and gradually customize towards their desired results. The preferred color tones sometimes can come from more than one image; therefore our method is extended to allow users to select their preferred color tones from multiple images. Our experimental results have shown the flexibility of our approach to generate reasonable segmented regions of interest and to enable users to explore the possible results more conveniently.

The quality of visual comfort and depth perception is a crucial requirement for virtual reality (VR) applications. This paper investigates major causes of visual discomfort and proposes a novel virtual camera controlling method using visual saliency to minimize visual discomfort. We extract the saliency of each scene and properly adjust the convergence plane to preserve realistic 3D effects. We also evaluate the effectiveness of our method on free-form architecture models. The results indicate that the proposed saliency-guided camera control is more comfortable than typical camera control and gives more realistic depth perception.

Due to rapid increases in the number of users and diversity of devices, temporal fluctuation of traffic on information communication network is becoming large and rapid recently. Especially, sudden traffic changes such as flash crowds often cause serious congestion on the network and result in nearly fatal slow down of date-communication speed. In order to keep communication quality high on the network, routing protocols that are scalable and able to quickly respond to rapid, and often unexpected, traffic fluctuation are highly desired. One of the promising approaches is the distributed routing protocol, which works without referring global information of the whole network but requires only limited informatin of it to realize route selection. These approaches include biologically inspired routing protocols based on the Adaptive Response by Attractor Selection model (ARAS), in which routing tables are updated along with only a scalar value reflecting communication quality measured on each router without evaluating communication quality over the whole network. However, the lack of global knowledge of the current status of the network often makes it difficult to respond promptly to traffic changes on the network that occurs at outside of the local scope of the protocol and causes inefficient use of network resources. In order to solve the essential problem of the local scope, we extend ARAS and propose a routing protocol with active and stochastic route exploration. The proposed protocol can obtain current communication quality of the network beyond its local scope and promptly responds to traffic changes occur on the network by utilizing the route exploration. In order to compensate destabilization of routing itself due to the active and stochastic exploration, we also introduce a short-term memory to the dynamics of the proposed attractor selection model. We conform by numerical simulations that the proposed protocol successfully balances rapid exploration with reliable routing owning to the memory term.

The inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.

Routing is a challenging issue in mobile social networks (MSNs) because of time-varying links and intermittent connectivity. In order to enable nodes to make right decisions while forwarding messages, exploiting social relationship has become an important method for designing efficient routing protocols in MSNs. In this paper, we first use the temporal evolution graph model to accurately capture the dynamic topology of the MSN. Based on the model, we introduce the social relationship metric for detecting the quality of human social relationship from contact history records. Utilizing this metric, we propose social relationship based betweenness centrality metric to identify influential nodes to ensure messages forwarded by the nodes with stronger social relationship and higher likelihood of contacting other nodes. Then, we present SRBet, a novel social-based forwarding algorithm, which utilizes the aforementioned metric to enhance routing performance. Simulations have been conducted on two real world data sets and results demonstrate that the proposed forwarding algorithm achieves better performances than the existing algorithms.

We consider the exploration problem with a single agent in an undirected graph. The problem requires the agent starting from an arbitrary node to explore all the nodes and edges in the graph and return to the starting node. Our goal is to minimize both the number of agent moves and the memory size of the agent, which dominate the amount of communication during the exploration. We focus on the local memory called the whiteboard of each node. There are several exploration algorithms which are very fast (i.e. the exploration is completed within a small number of agent moves such as 2m and m+3n) and do not use whiteboards. These algorithms, however, require large agent memory because the agent must keep the entire information in its memory to explore a graph. We achieve the above goal by reducing the agent memory size of such algorithms with using whiteboards. Specifically, we present two algorithms with no agent memory based on the traditional depth-first traversal and two algorithms with O(n) and O(nlog n) space of agent memory respectively based on the fastest algorithms in the literature by Panaite and Pelc [J. Alg., Vol.33 No.2, 1999].

Forty years after the first application of Superconducting Quantum Interference Devices (SQUIDs) [1], [2] for geophysical purposes, they have recently become a valued tool for mineral exploration. One of the most common applications is time domain (or transient) electromagnetics (TEM), an active method, where the inductive response from the ground to a changing current (mostly rectangular) in a loop on the surface is measured. After the current in the transmitter coil is switched, eddy currents are excited in the ground, which decay in a manner dependent on the conductivity of the underlying geologic structure. The resulting secondary magnetic field at the surface is measured during the off-time by a receiver coil (induced voltage) or by a magnetometer (e.g. SQUID or fluxgate). The recorded transient signal quality is improved by stacking positive and negative decays. Alternatively, the TEM results can be inverted and give the electric conductivity of the ground over depth. Since SQUIDs measure the magnetic field with high sensitivity and a constant frequency transfer function, they show a superior performance compared to conventional induction coils, especially in the presence of strong conductors. As the primary field, and especially its slew rate, are quite large, SQUID systems need to have a large slew rate and dynamic range. Any flux jump would make the use of standard stacking algorithms impossible. IPHT and Supracon are developing and producing SQUID systems based on low temperature superconductors (LTS, in our case niobium), which are now state-of-the-art. Due to the large demand, we are additionally supplying systems with high temperature superconductors (HTS, in our case YBCO). While the low temperature SQUID systems have a better performance (noise and slew rate), the high temperature SQUID systems are easier to handle in the field. The superior performance of SQUIDs compared to induction coils is the most important factor for the detection of good conductors at large depth or ore bodies underneath conductive overburden.

While Triple modular Redundancy (TMR) is effective in eliminating soft errors in LSIs, the overhead of the triplicated area as well as the triplicated energy consumption is the problem. In addition to the spatial TMR mode where executions are simply tripricated and the majority is taken, the temporal TMR mode is available where only two copies of an operation are executed and the results are compared, then if the results differ, the third copy is executed to get the correct result. Appropriately selecting the power supply voltage is also an effective technique to reduce the energy consumption. In this paper, a method to derive a TMR design is proposed which selects the TMR mode and supply voltage for each operation to minimize the energy consumption within the time and area constraints.

A fast and accurate architecture exploration for high performance and low energy VLIW data-path is proposed. The main contribution is a method to find Pareto optimal FU structures, i.e., the optimal number of FUs and the best instruction assignment for each FU. The proposed architecture exploration method is based on GA and enables the effective exploration of vast solution space. Experimental results showed that proposed method was able to achieve fast and accurate architecture exploration. For most cases, the estimation error was less than 1%.

Robot covering problem has gained attention as having the most promising applications in our real life. Previous spanning tree coverage algorithm addressed this problem well in a static environment, but not in a dynamic one. In this paper, we present and analyze our algorithm workable in a dynamic environment with less shadow areas.

We outline a number of high temperature superconducting Josephson junction-based devices including superconducting quantum interference devices (SQUIDs) developed for a wide range of applications including geophysical exploration, magnetic anomaly detection, terahertz (THz) imaging and microwave communications. All these devices are based on our patented technology for fabricating YBCO step-edge junction on MgO substrates. A key feature to the successful application of devices based on this technology is good stability, long term reliability, low noise and inherent flexibility of locating junctions anywhere on a substrate.

This paper considers online vertex exploration problems in a simple polygon where starting from a point in the inside of a simple polygon, a searcher is required to explore a simple polygon to visit all its vertices and finally return to the initial position as quickly as possible. The information of the polygon is given online. As the exploration proceeds, the searcher gains more information of the polygon. We give a 1.219-competitive algorithm for this problem. We also study the case of a rectilinear simple polygon, and give a 1.167-competitive algorithm.

Kennedy has proposed the bare bones particle swarm (BBPS) by the elimination of the velocity formula and its replacement by the Gaussian sampling strategy without parameter tuning. However, a delicate balance between exploitation and exploration is the key to the success of an optimizer. This paper firstly analyzes the sampling distribution in BBPS, based on which we propose an adaptive BBPS inspired by the cloud model (ACM-BBPS). The cloud model adaptively produces a different standard deviation of the Gaussian sampling for each particle according to the evolutionary state in the swarm, which provides an adaptive balance between exploitation and exploration on different objective functions. Meanwhile, the diversity of the swarms is further enhanced by the randomness of the cloud model itself. Experimental results show that the proposed ACM-BBPS achieves faster convergence speed and more accurate solutions than five other contenders on twenty-five unimodal, basic multimodal, extended multimodal and hybrid composition benchmark functions. The diversity enhancement by the randomness in the cloud model itself is also illustrated.

To reduce the huge search space when customizing accelerators for the application specific instruction-set processor (ASIP), this paper proposes an automated customization method based on the data flow graph exploration. This method integrates the instruction identification and selection using an iterative improvement strategy, which uses a seed-growth algorithm to select the valid patterns that can bring higher performance enhancement. The search space is reduced by considering the performance factors during the identification stage. The experimental results indicate that the proposed method is feasible enough compared to the previous exhaustive algorithms.