Window Algorithm Research Articles

Instance Segmentation for Governmental Inspection of Small Touristic Infrastructure in Beach Zones Using Multispectral High-Resolution WorldView-3 Imagery

Misappropriation of public lands is an ongoing government concern. In Brazil, the beach zone is public property, but many private establishments use it for economic purposes, requiring constant inspection. Among the undue targets, the individual mapping of straw beach umbrellas (SBUs) attached to the sand is a great challenge due to their small size, high presence, and agglutinated appearance. This study aims to automatically detect and count SBUs on public beaches using high-resolution images and instance segmentation, obtaining pixel-wise semantic information and individual object detection. This study is the first instance segmentation application on coastal areas and the first using WorldView-3 (WV-3) images. We used the Mask-RCNN with some modifications: (a) multispectral input for the WorldView3 imagery (eight channels), (b) improved the sliding window algorithm for large image classification, and (c) comparison of different image resizing ratios to improve small object detection since the SBUs are small objects (<322 pixels) even using high-resolution images (31 cm). The accuracy analysis used standard COCO metrics considering the original image and three scale ratios (2×, 4×, and 8× resolution increase). The average precision (AP) results increased proportionally to the image resolution: 30.49% (original image), 48.24% (2×), 53.45% (4×), and 58.11% (8×). The 8× model presented 94% AP50, classifying nearly all SBUs correctly. Moreover, the improved sliding window approach enables the classification of large areas providing automatic counting and estimating the size of the objects, proving to be effective for inspecting large coastal areas and providing insightful information for public managers. This remote sensing application impacts the inspection cost, tribute, and environmental conditions.

Windowed Decoding for Delayed Bit-Interleaved Coded Modulation

Delayed bit-interleaved coded modulation (DBICM) generalizes bit-interleaved coded modulation (BICM) by modulating differently delayed sub-blocks of codewords onto the same signals. DBICM improves transmission reliability over BICM due to its capability of detecting undelayed sub-blocks with the extrinsic information of the decoded delayed sub-blocks. In this work, we propose a novel windowed decoding algorithm for DBICM, which uses the extrinsic information of both the decoded delayed and undelayed sub-blocks, to improve the detection on all sub-blocks. Numerical results show that the proposed windowed decoding significantly outperforms the original decoding.

보행 역학 데이터를 활용한 대퇴 의지 실시간 제어를 위한 딥러닝 모델 구조 연구

Training data of the deep learning model for the control of the above-knee prosthesis should have a sufficient amount of data to fit with the model. Also, this data can be used for gait classification to control the prosthesis. However, in real-time control, the number of the gait dynamics data counted by the sensor is only measured to the level that the deep learning model is difficult to learn., In this study, the most efficient deep learning model case was developed to resolve this problem in the case of a real-time control situation where data measurement time is insufficient. The data is collected through a hall sensor, load cell and Inertial Measurement Unit (IMU) mounted on the above-knee prosthesis. Subsequently, the collected data is divided into 5 phases (Loading Response (LR), Mid Stance (MS), Push Off (PO), Early Swing (ES), and Late Swing (LS)) of the gait cycle according to the point of inflection of hall sensors and load cells. Afterward, training data of the deep learning were generated by sliding window algorithm and the treated data was exercised on four deep learning models by changing the value of width and depth configurations. The results are assessed on accuracy, loss and F1-Score. In conclusion, the loss function statistically decreased in the case of the values of width and depth of the deep learning model were low. Accuracy and F1-Score were not shown significant difference statistically. Therefore, the results provided an efficient deep learning model for above-knee prosthesis to gait analysis and expected to lead to a more reasonable gait model for the control of the above-knee prosthesis via a more detailed gait classification study in the future.

Relationship between urban heat island and population density of districts in Ho Chi Minh City derived from Sentinel - 3 SLSTR data

The urban heat island occurs due to the causes of the urbanization process, of which the main reason is an increase in population density leading to the changes in artificial objects on the ground surface. Recently, using the Split - Window algorithm for two thermal infrared spectral channels with wavelengths of 11 µm and 12 µm to calculate the daily surface temperature with two times of day and night serves to determine the change of land surface temperature. This method is intended to improve the reliability of the results and high technical efficiency. This study uses Sentinel - 3 SLSTR data to determine urban heat island in the districts of Ho Chi Minh City compared to areas bordering the city on May 15th 2020. In addition, population density is calculated according to the results of the census in 2020. The linear relationship between the urban heat island and population density was built with the coefficient of determination R2=0.64.

Alhudaj: CpG islands Detection Tool in Mammalian Genome Using C++

One of the unique combinations in the mammalian genome, that revolutionized concepts in the fields of genetics and molecular pathology is what is termed the CpG islands. However, the accurate and rapid determination of CpG islands for DNA sequences remains experimentally and computationally challenging. The main goal for this project to design an offline, cross-platform CpG islands detection tool. The Algorithm implemented for this study was the traditional sliding window algorithm by using the C++ programming language. Three datasets were used for evaluating the performance of the application. The ANK1 gene, SPTB gene, and RET gene sequence files were obtained from NCBI. In this study, the highest CGIs were reported in ANK1 (ankyrin 1) Gene which scored 13 successive islands whereas the lowest score was reported in RET (ret proto-oncogene) Gene which shows only 6 islands. Generally, the program fulfills the boundaries limits as expected. We strongly recommend for further work, the implementation of other algorithms in addition to the sliding window algorithm such as Hidden Markov Model (HMM).

Spatial Variability and Temporal Heterogeneity of Surface Urban Heat Island Patterns and the Suitability of Local Climate Zones for Land Surface Temperature Characterization

This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool–heat and heat–cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.

THE RUNTIME ANALYSIS OF COMPUTATION OF MODULAR EXPONENTIATION

Context. Providing the problem of fast calculation of the modular exponentiation requires the development of effective algorithmic methods using the latest information technologies. Fast computations of the modular exponentiation are extremely necessary for efficient computations in theoretical-numerical transforms, for provide high crypto capability of information data and in many other applications.
 Objective – the runtime analysis of software functions for computation of modular exponentiation of the developed programs based on parallel organization of computation with using multithreading.
 Method. Modular exponentiation is implemented using a 2k-ary sliding window algorithm, where k is chosen according to the size of the exponent. Parallelization of computation consists in using the calculation of the remainders of numbers raised to the power of 2i modulo, and their further parallel multiplications modulo.
 Results. Comparison of the runtimes of three variants of functions for computing the modular exponentiation is performed. In the algorithm of parallel organization of computation with using multithreading provide faster computation of modular exponentiation for exponent values larger than 1K binary digits compared to the function of modular exponentiation of the MPIR library. The MPIR library with an integer data type with the number of binary digits from 256 to 2048 bits is used to develop an algorithm for computing the modular exponentiation with using multithreading.
 Conclusions. In the work has been considered and analysed the developed software implementation of the computation of modular exponentiation on universal computer systems. One of the ways to implement the speedup of computing modular exponentiation is developing algorithms that can use multithreading technology on multi-cores microprocessors. The multithreading software implementation of modular exponentiation with increasing from 1024 the number of binary digit of exponent shows an improvement of computation time with comparison with the function of modular exponentiation of the MPIR library.

Improvements in space radiation-tolerant FPGA implementation of land surface temperature-split window algorithm

The trend in satellite remote sensing assignments has continuously been concerning using hardware devices with more flexibility, smaller size, and higher computational power. Therefore, field programmable gate arrays (FPGA) technology is often used by the developers of the scientific community and equipment for carrying out different satellite remote sensing algorithms. This article explains hardware implementation of land surface temperature split window (LST-SW) algorithm based on the FPGA. To get a high-speed process and real-time application, VHSIC hardware description language (VHDL) was employed to design the LST-SW algorithm. The paper presents the benefits of the used Virtex-4QV of radiation tolerant series FPGA. The experimental results revealed that the suggested implementation of the algorithm using Virtex4QV achieved higher throughput of 435.392 Mbps, and faster processing time with value of 2.95 ms. Furthermore, a comparison between the proposed implementation and existing work demonstrated that the proposed implementation has better performance in terms of area utilization; 1.17% reduction in number of Slice used and 1.06% reduction in of LUTs. Moreover, the significant advantage of area utilization would be the none use of block RAMs comparing to existing work using three blocks RAMs. Finally, comparison results show improvements using the proposed implementation with rates of 2.28% higher frequency, 3.66 x higher throughput, and 1.19% faster processing time.

Accuracy Evaluation and Parameter Analysis of Land Surface Temperature Inversion Algorithm for Landsat-8 Data

Many researchers have developed a variety of land surface temperature (LST) inversion algorithms based on satellite data. The main LST inversion algorithms include Radiative Transfer Equation (RTE), Single Channel (SC) algorithm, Mono Window (MW) algorithm, and Split Window (SW) algorithm. In this study, nine LST inversion algorithms were designed using Landsat-8 data and meteorological station data to test the inversion efficiency of different algorithms in different seasons and different locations. The results show that the error of various LST inversion algorithms will increase with the rise of LST. R2 of the inversion results of each LST algorithm and the measured data are all greater than 0.73°C in winter and about 0.5°C in the other seasons. By analyzing the stability of various algorithms inside and outside the city, it is found that the stability of each LST inversion algorithm inside the city is better than that outside the city. For the same surface features, the inversion temperature inside the city is 3–5°C higher than that outside the city. In addition, the sensitivity of various inversion algorithms to parameters was also analyzed. The influence of atmospheric transmittance on RTE, SC, and MW inversion algorithms is in logarithmic form. The effect of emissivity on each algorithm is linear. The influence of NDVI on the algorithms is mainly through the estimation of surface emissivity parameters to affect the inversion results. The effect of ascending radiation on SC (LST4 and LST5) is linear and on RTE (LST1 and LST2) is logarithmic. The effect of downslope radiation on SC and RTE is linear. The influence of atmospheric water vapor content on SW (LST7) is nonlinear.

The gap between automated building management system and office occupants' manual window operations: Towards personalised algorithms

This paper aims to demonstrate how knowledge acquired from occupants' manual window operations can be implemented into BMS automated window operation algorithms. Ten single-occupant offices were selected in a university building in the UK. More than 28,000 hourly data points on indoor and outdoor temperature and open window area (OWA) were analysed from 2015 to 2020. The BMS had adopted nine different automated window operation algorithms during the 5 years. The automated window algorithms could be manually overridden by the office occupants. Automated algorithms were compared against manual window operations. The results showed that the slope and gradient of the regression lines for occupants' manual window operations are smaller than automated operations. OWA of automated window operations increased 20% per 1 °C increase in indoor temperature, however, occupants opened windows 6–8% per 1 °C increase. Occupants react slower to temperature changes than assumed by BMS, which could be considered in BMS automated window operations.

Autonomous Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on an Improved Velocity Obstacle Method

Focusing on the collision avoidance problem for Unmanned Surface Vehicles (USVs) in the scenario of multi-vessel encounters, a USV autonomous obstacle avoidance algorithm based on the improved velocity obstacle method is proposed. The algorithm is composed of two parts: a multi-vessel encounter collision detection model and a path re-planning algorithm. The multi-vessel encounter collision detection model draws on the idea of the velocity obstacle method through the integration of characteristics such as the USV dynamic model in the marine environment, the encountering vessel motion model, and the International Regulations for Preventing Collisions at Sea (COLREGS) to obtain the velocity obstacle region in the scenario of USV and multi-vessel encounters. On this basis, two constraint conditions for the motion state space of USV obstacle avoidance behavior and the velocity obstacle region are added to the dynamic window algorithm to complete a USV collision risk assessment and generate a collision avoidance strategy set. The path re-planning algorithm is based on the premise of the minimum resource cost and uses an improved particle swarm algorithm to obtain the optimal USV control strategy in the collision avoidance strategy set and complete USV path re-planning. Simulation results show that the algorithm can enable USVs to safely evade multiple short-range dynamic targets under COLREGS.

Spatio-temporal analysis and simulation of land cover changes and their impacts on land surface temperature in urban agglomeration of Bisha Watershed, Saudi Arabia

The present study investigates the spatiotemporal pattern of Land Use land cover (LULC) and land surface temperature (LST) in Abha for the years 1990, 2000, and 2018. This research also forecasts the future LULC and LST for the year 2028. The support vector machine (SVM) was utilised to classify the LULC for the periods 1990-. The LST for the same period was derived using the mono window algorithm. The artificial neural network-cellular automata model (ANN-CA) was employed to forecast LULC and LST for the year 2028. The results indicated that urban areas rose by 434.6% between 1990 and 2018, while the LST soared to 50 °C in 2018, covering half of the study area. The built-up area, as well as the high LST zone, will be expanded in 2028. As a result, sustainable management strategies should be implemented to limit uncontrolled urban sprawl and LST.

A New Approach for the Development of Grid Models Calculating Tropospheric Key Parameters over China

Pressure, water vapor pressure, temperature, and weighted mean temperature (Tm) are tropospheric parameters that play an important role in high-precision global navigation satellite system navigation (GNSS). As accurate tropospheric parameters are obligatory in GNSS navigation and GNSS water vapor detection, high-precision modeling of tropospheric parameters has gained widespread attention in recent years. A new approach is introduced to develop an empirical tropospheric delay model named the China Tropospheric (CTrop) model, providing meteorological parameters based on the sliding window algorithm. The radiosonde data in 2017 are treated as reference values to validate the performance of the CTrop model, which is compared to the canonical Global Pressure and Temperature 3 (GPT3) model. The accuracy of the CTrop model in regards to pressure, water vapor pressure, temperature, and weighted mean temperature are 5.51 hPa, 2.60 hPa, 3.09 K, and 3.35 K, respectively, achieving an improvement of 6%, 9%, 10%, and 13%, respectively, when compared to the GPT3 model. Moreover, three different resolutions of the CTrop model based on the sliding window algorithm are also developed to reduce the amount of gridded data provided to the users, as well as to speed up the troposphere delay computation process, for which users can access model parameters of different resolutions for their requirements. With better accuracy of estimating the tropospheric parameters than that of the GPT3 model, the CTrop model is recommended to improve the performance of GNSS positioning and navigation.

Applications of neural networks for crack initiation and propagation monitoring in aircraft structures

Timely detection of fatigue cracks on aircraft structural elements is the main task in damage tolerance principle approach. In this regard, much attention in aviation is paid to the methods of non-destructive testing which requires special equipment with the involvement of highly qualified personnel. Nowadays we can see that technologies that can learn to identify defects are preferred to simplify the gap process and minimize human factor errors. A self-learning technology is incorporated in the crack detection program. This makes it possible to increase the sensitivity of defects in the mode of the used technically false equipment. Unlike the detection methods of other machine learning detection systems, the system developed in this paper can also measure the cracks without the use of sophisticated sensors. However, the proposed system requires a photo-capturing device. Compared to similar visual systems, the developed system can work with very noisy images and detect cracks up to 0.3 mm. To do this, the webcam from the mid-range segment with 1920×1080 resolutions is used, that makes such technology easy to access. All modifications in the design of the camera scheme were associated with a change in the focal length, implemented by shifting the lens relative to the matrix. It allows the camera to focus on close distance less than 50 cm. For the fatigue tests compact specimens of duralumin alloy D16T with edge stress concentrator were used. The specimens were cycle tested by cantilever banding with stress ratio R=-1. Loading bogie apply force to specimens in direction normal to specimen surface. A loading value depends on the length of the loading crank and can be adjusted if needed. To measure cracks in the processed images, a visual control program on a convolutional neural network and a sliding window algorithm were used. About 4,000 images were used to train the algorithm. The sliding window algorithm analyzes small images sequentially. One by one, image regions were selected and monitored for cracks using a convolutional neural network. Areas with detected cracks are memorized by the sliding window algorithm.

A Time Series Analysis of Forest Cover and Land Surface Temperature Change Over Dudpukuria-Dhopachari Wildlife Sanctuary Using Landsat Imagery

Forest cover change is an important criterion as it affects the environmental balance whereas land surface temperature is a significant parameter within the earth climate system. Spatio-temporal change of forest cover can be detected and land surface temperature can be retrieved by applying remote sensing technology. The present study aimed to capture the impact of forest cover change on land surface temperature in Dudpukuria-Dhopachari Wildlife Sanctuary (DDWS), Bangladesh, using multi-spectral and multi-temporal satellite data. To avoid the biasness in the calculation, leaf flash time was targeted for collecting Landsat images from United States Geological Survey (USGS) Earth Explorer and, based on availability, images were collected purposively which ones had closer time period:1990 (March 5, 1990), 2000 (February 5, 2000), 2010 (February 24, 2010) and 2020 (March 23, 2020). Unsupervised classification was applied over the images Landsat 4–5 Thematic Mapper (TM), 7 Enhanced Thematic Mapper Plus (ETM+), and 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data for detecting forest cover change. To retrieve the land surface temperature, Mono Window Algorithm (MWA) method was applied over similar images. Maximum forest degradation was observed in 2010 and the change found was 17% as compared to 1990. After 2010, the forest started to flourish. Land surface temperature dramatically changes over the time period. The highest land surface temperature in the forested area was observed in 2020 (32.2°C) and it was changed 7.7°C from that of the 1990 (24.5°C). In every 10 years, almost 2.3°C–3.0°C temperature change was detected. In the first three decades, a reverse relationship was observed between land surface temperature and forest cover; however, in the last decade, land surface temperature was found to increase with the increase of forest cover. Thus, the results of the study revealed that land surface temperature may not be relevant with the local forest cover change directly. It can be estimated from the results that local forest cover change may have limited impact on local temperature rather than global forest cover change, whereas global warming could play a vital role in changing land surface temperature locally as well as globally.

A CNN model embedded with local feature knowledge and its application to time-varying signal classification

A novel convolutional neural network is proposed for local prior feature embedding and imbalanced dataset modeling for multi-channel time-varying signal classification. This model consists of a single-channel signal feature parallel extraction unit, a multi-channel signal feature integration unit, a local feature embedding and feature similarity measurement unit, a full connection layer, and a Softmax classifier. An algorithm combining dynamic clustering and sliding window was used to select segments signals with typical local features in each pattern class, forming a typical local feature set. The one-dimensional CNNs were used to extract features from the single-channel signal in parallel, a comprehensive feature matrix of the multi-channel signal and the local feature matrix templates were produced. Using the method of external embedding, based on the sliding window and dynamic time warping (DTW) algorithm, the local feature similarities between the local feature template of each pattern class and the comprehensive feature sub-matrix of the input signal were measured, and the maximum values were selected to construct a local feature similarity vector in order. The information fusion was realized through a full connection layer. The proposed methodology can extract and represent both global and local signals features, strengthen the role of prior local feature in classification and improve the modeling properties of imbalanced datasets. A comprehensive learning algorithm is presented in this paper. The classification diagnosis of cardiovascular disease based on 12-lead ECG signals was used as a verification experiment. Results showed that the accuracy and generalization for the proposed technique were significantly improved.

The Sliding Window and SHAP Theory—An Improved System with a Long Short-Term Memory Network Model for State of Charge Prediction in Electric Vehicle Application

The state of charge (SOC) prediction for an electric vehicle battery pack is critical to ensure the reliability, efficiency, and life of the battery pack. Various techniques and statistical systems have been proposed in the past to improve the prediction accuracy, reduce complexity, and increase adaptability. Machine learning techniques have been vigorously introduced in recent years, to be incorporated into the existing prediction algorithms, or as a stand-alone system, with a large amount of recorded past data to interpret the battery characteristics, and further predict for the present and future. This paper presents an overview of the machine learning techniques followed by a proposed pre-processing technique employed as the input to the long short-term memory network (LSTM) algorithm. The proposed pre-processing technique is based on the time-based sliding window algorithm (SW) and the Shapley additive explanation theory (SHAP). The proposed technique showed improvement in accuracy, adaptability, and reliability of SOC prediction when compared to other conventional machine learning models. All the data employed in this investigation were extracted from the actual driving cycle of five different electric vehicles driven by different drivers throughout a year. The computed prediction error, as compared to the original SOC data extracted from the vehicle, was within the range of less than 2%. The proposed enhanced technique also demonstrated the feasibility and robustness of the prediction results through the persistent computed output from a random selection of the data sets, consisting of different driving profiles and ambient conditions.

New discrimination algorithm for artificial [formula omitted] radiation sources based on average energy deposition in plastic scintillators

Plastic scintillators are characterized with high detection efficiency, high response speeds, uneven optical efficiency distributions, and poor energy resolution. In this work, we developed a new discrimination algorithm, namely, the average energy (AE) algorithm, for artificial γ radiation sources and implemented it in a hand-held plastic scintillator detector. The algorithm provided excellent discrimination capability of naturally occurring radiation materials (NORM) and artificial radiation Sources. The energy deposited by all radiation sources in small plastic scintillators, was simulated and analyzed using the Monte Carlo method. And all those radiation sources are categorized by the International Atomic Energy Agency. A plastic scintillator detector system was then constructed for proof of concept using 241Am, 137Cs, 60Co, radium-bearing ore, and thorium ore. A large-sized plastic scintillator detector was used for comparison, and the correctness of the AE algorithm was verified. The AEs of the NORM region ranged from 631.5 ± 13.3 keV to 783.6 ± 17.2 keV and 1173.6 keV or more, and the collection time of a single measurement was less than 3 s (5214 photons). Results showed that the AE algorithm could quickly and accurately identify γ artificial radiation sources from the background and NORM. The AE algorithm should always be used in conjunction with the energy window algorithm to avoid misjudgments.

The broken window: An algorithm for quantifying and characterizing misleading trajectories in ecological processes

A core issue in temporal ecology is the concept of trajectory—that is, when can ecologists have reasonable assurance that they know where a system is going? In this paper, we describe a non-random resampling method to directly address the temporal aspects of scaling ecological observations by leveraging existing data. Findings from long-term research sites have been hugely influential in ecology because of their unprecedented longitudinal perspective, yet short-term studies more consistent with typical grant cycles and graduate programs are still the norm. We use long-term insights to create ‘broken windows,’ that is, reanalyze long-term studies from short-term observational perspectives to examine discontinuities in trends at differing temporal scales.The broken window algorithm connects our observations between the short-term and the long-term with an automated, systematic resampling approach: in short, we repeatedly ‘sample’ moving windows of data from existing long-term time series, and analyze these sampled data as if they represented the entire dataset. We then compile typical statistics used to describe the relationship in the sampled data, through repeated samplings, and then use these derived data to gain insights to the questions: 1) how often are the trends observed in short-term data misleading, and 2) can characteristics of these trends be used to predict our likelihood of being misled? We develop a systematic resampling approach, the ‘broken_window algorithm, and illustrate its utility with a case study of firefly observations produced at the Kellogg Biological Station Long-Term Ecological Research Site (KBS LTER). Through a variety of visualizations, summary statistics, and downstream analyses, we provide a standardized approach to evaluating the trajectory of a system, the amount of observation required to find a meaningful trajectory in similar systems, and a means of evaluating our confidence in our conclusions.

A method for compressing AIS trajectory data based on the adaptive-threshold Douglas-Peucker algorithm

The deficiencies of massive data like storage difficulty, computation inefficiency, and information redundancy call for ship trajectory compression. While most studies on ship trajectory compression have one or more of the following drawbacks: the drawback of low compression efficiency; the problem resulted from error ship static information when the distance threshold is set based on ship length or width; poor compression quality for some trajectories, which is caused by experience-based optimal threshold. To solve these problems, we propose the ADP (Adaptive-threshold Douglas-Peucker) algorithm based on DP (Douglas-Peucker) algorithm. By determining the key points of each trajectory through the threshold change rate, ADP no longer relies on ship static information and makes it easier to determine the threshold, which is what traditional algorithms cannot achieve. Additionally, we use the advantage of matrix operation and the method of reducing points to improve the algorithm's computation efficiency. To verify the feasibility and superiority of the proposed algorithm, we compared our algorithm with DP algorithm, Partition-DP algorithm and Sliding Window algorithm from four aspects, namely, compression rate, Synchronous Euclidean distance, Length Loss Rate and running time. The experimental results prove that our algorithm has advantages over the other three algorithms, especially in threshold setting.