Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

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Handbook Of Image And Video Processing

This paper introduces a review article on indoor localization techniques and technologies. The paper starts with current localization systems and summarizes comparisons between these systems in terms of accuracy, cost, advantages, and disadvantages. Also, the paper presents different detection techniques and compare them in terms of accuracy and cost. Finally, localization methods and algorithms, including angle of arrival (AOA), time of arrival (TOA), and recived signal strength (RSS) are introduced. The study contains concepts, requirements, and specifications for each category of methods presents pros and cons for investigated methods, and conducts comparisons between them.

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A Review of Indoor Localization Techniques and Wireless Technologies

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Proteins interact with each other to play critical roles in many biological processes in cells. Although promising, laboratory experiments usually suffer from the disadvantages of being time-consuming and labor-intensive. The results obtained are often not robust and considerably uncertain. Due recently to advances in high-throughput technologies, a large amount of proteomics data has been collected and this presents a significant opportunity and also a challenge to develop computational models to predict protein-protein interactions (PPIs) based on these data. In this paper, we present a comprehensive survey of the recent efforts that have been made towards the development of effective computational models for PPI prediction. The survey introduces the algorithms that can be used to learn computational models for predicting PPIs, and it classifies these models into different categories. To understand their relative merits, the paper discusses different validation schemes and metrics to evaluate the prediction performance. Biological databases that are commonly used in different experiments for performance comparison are also described and their use in a series of extensive experiments to compare different prediction models are discussed. Finally, we present some open issues in PPI prediction for future work. We explain how the performance of PPI prediction can be improved if these issues are effectively tackled.

A survey on computational models for predicting protein-protein interactions.

Link prediction aims to predict the existence of unknown links via the network information. However, most similarity-based algorithms only utilize the current common neighbor information and cannot get high enough prediction accuracy in evolving networks. So this paper firstly defines the future common neighbors that can turn into the common neighbors in the future. To analyse whether the future common neighbors contribute to the current link prediction, we propose the similarity-based future common neighbors (SFCN) model for link prediction, which accurately locate all the future common neighbors besides the current common neighbors in networks and effectively measure their contributions. We also design and observe three MATLAB simulation experiments. The first experiment, which adjusts two parameter weights in the SFCN model, reveals that the future common neighbors make more contributions than the current common neighbors in complex networks. And two more experiments, which compares the SFCN model with eight algorithms in five networks, demonstrate that the SFCN model has higher accuracy and better performance robustness.

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Similarity-based future common neighbors model for link prediction in complex networks

Complex networks have found many applications in various fields. An important problem in theories of complex networks is to find factors that aid link prediction, which is needed for network reconstruction and to study network evolution mechanisms. Though current similarity-based algorithms study factors of common neighbors and local paths connecting a target node pair, they ignore factor information on paths between a node and its neighbors. Therefore, this paper first supposes that paths between nodes and neighbors provide basic similarity features. Accordingly, we propose a so-called relative-path-based method. This method utilizes factor information on paths between nodes and neighbors, besides paths between node pairs, in similarity calculation for link prediction. Furthermore, we solve the problem of determining the parameters in our algorithm as well as in other algorithms after a series of discoveries and validations. Experimental results on six disparate real networks demonstrate that the relative-path-based method can obtain greater prediction accuracy than other methods, as well as performance robustness.

Relative-path-based algorithm for link prediction on complex networks using a basic similarity factor

Unmanned Aerial Vehicles (UAVs) have wide applications in many fields, e.g. multiple Unmanned Aerial Vehicles (UAVs) cooperatively tracking multiple targets. This paper studies the multiple UAVs cooperatively tracking multiple targets by vision surveillance system, where the images/videos of targets have direction requirements. One target is covered by a UAV if and only if its position is within the Field Of View (UAV) as well as the UAV is within a requested angle of the target's face direction. The objective is to maximize the total covered targets number by the UAVs, which can not be solved by existing models. A simple effective distributed, online cooperation algorithm for this problem is designed in this paper. The theoretical analysis shows our algorithm achieves constant factor to the optimal.

Directional Monitoring of Multiple Moving Targets by Multiple Unmanned Aerial Vehicles

In order to reduce the latency of polar codes, several decoding methods have been proposed. However, most of them need a particular structure of nodes in the decoding tree. Thus, they ignore some unstructured nodes. To this end, we propose a modified version of the simplified successive cancellation decoder based on node error probability to complement the structured scheme. By a proper design, simulation results in additive white Gaussian noise channel show that the proposed method can efficiently reduce the decoding latency of the structured scheme while keeping the error performance almost unchanged.

A Low-Latency Simplified Successive Cancellation Decoder for Polar Codes Based on Node Error Probability

In wireless sensor networks, ranging or positioning via ultra-wideband (UWB) has caused widespread research interests where the non-coherent energy detection (ED) method with low sampling rate and low complexity is widely studied. However, the traditional energy detection methods only analyze the signal energy in the time domain, so their error is relatively large. In this paper, the simulation results show that most of the signal energy concentrates in the low-frequency band, so a novel threshold selection method for time of arrival (TOA) estimation is proposed that analyzes the signals in both time domain and frequency domain. In this method, the received signal is decomposed by “db6” wavelet and the kurtosis of energy blocks of the low-frequency wavelet coefficients (K
 c) is analyzed. At last, the mapping relationship between K
 c and the normalized threshold for TOA estimation is created using polynomial fitting with degree 3. The simulation results show that the TOA estimation error of the proposed method is significantly less than the method without wavelet decomposition.

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Jianhang Liu

Papers

Similarity-based future common neighbors model for link prediction in complex networks

Relative-path-based algorithm for link prediction on complex networks using a basic similarity factor

Directional Monitoring of Multiple Moving Targets by Multiple Unmanned Aerial Vehicles

A Low-Latency Simplified Successive Cancellation Decoder for Polar Codes Based on Node Error Probability

Threshold selection method for UWB TOA estimation based on wavelet decomposition and kurtosis analysis