Data Frame Research Articles

The impact of meteorological changes on the quality of life regarding thermal comfort in the Amazon region

The meteorological imbalances in Brazil have a strong impact on the lives of the population across the country, especially in the Amazon region. These impacts extend to meteorological phenomena, such as extreme rainfall, droughts, and an increase in temperature in several regions, as well as impacts on health, the economy, and, as the object of this study, the quality of life. This study presents the impact of meteorological changes on the quality of life in the Amazon region, based on (i) the thermal discomfort index (TDI), (ii) the temperature and humidity index (THI), and (iii) the effective temperature index as a function of the wind (TEFW). For this, meteorological data from the years 2003–2021 were used, in which the variables include total precipitation, global radiation, air-dry bulb temperature, the maximum temperature in the previous hour, the minimum temperature in the previous hour, relative humidity, and wind speed. This analysis indicates that for this tropical region, the sensation of mild discomfort was predominant in about 70% of the measurements, indicating a certain level of impact on the population's quality of life, in addition to the fluctuation of levels of discomfort during periods of winter in that there are high rates of precipitation. The data control was performed with the removal of null data and calculation of monthly and annual averages, by using the compression (average) empty data frames in order to receive the values without losing the indexing of dates. At first, it was considered to use the kriging process to fill in the missing data; however, due to the existence of microclimates in the regions, the data could characterize remote regions in a generalized way, which would make it difficult to understand these data in comparison with the data from the National Institute of Meteorology (INMET, in Portuguese).

Development of robust communication algorithm between machine vision and boom sprayer for spot application via ISO 11783

A communication algorithm to send data via the standard communication protocol for agricultural implements, ISO 11783, was developed and tested in this research work. The goal was to enable a universal method to integrate machine vision systems to sprayers so that sensor-based real-time spot spraying becomes possible in the future. The algorithm receives pest detection results from a machine vision and constructs messages compatible with ISO 11783 (also known as ISOBUS), which gets sent to the sprayer. This allows for simultaneous opening of different nozzles on the sprayer even when pests are detected by different cameras at different locations across the boom. The results on our spraying apparatus which consisted of 6 nozzles showed that correct spraying commands were sent to the nozzles 100% of the times for all possible 64 simultaneous nozzle opening/closing combinations. The algorithm was designed to construct CAN frames based on arithmetic rather than logic operations. Also, the increase of data load on the ISOBUS of the sprayer was limited to 5.2%. Moreover, no data loss between the machine vision system and the sprayer was observed even when the data frames were sent every 10 milliseconds. The electronic control unit (ECU) on which the algorithm was written has two interfaces, one communicates with the machine vision system using serial communication, and the other communicates with the sprayer using the ISOBUS. The universal serial communication protocol used in this development allows for immediate integration to the ECU which creates the messages according to the specifications of the nozzle control unit of the sprayer.

Modeling desirable futures at local scale by combining the nature futures framework and multi-objective optimization

AbstractEnvisioning positive scenarios that recognize the multiple values of nature is fundamental for designing transformative changes in local socio-ecological systems. This study developed a protocol with three specifications for operationalizing the Nature Futures Framework (NFF) in a landscape scenario analysis using a multi-objective optimization framework composed of: (1) exploring nature-positive futures, (2) seeking alternative pathways for targets satisfying visions of plural values, and (3) screening key direct drivers to achieve the targets. This research conducted a case study of a rural landscape in northeastern Japan. First, 110 strategies of landscape management options were simulated from 2015 to 2100 using a forest landscape model, LANDIS-II. The simulation developed a data frame of four integrated indicators of the NFF values for each year and strategy. Second, nature-positive strategies were screened using the common values. Pareto optimal strategies were then identified to obtain equally good solutions. Finally, the key response options to achieve good nature-positive futures were identified using decision tree analysis. Our protocol identified (1) multiple, but few nature-positive and Pareto optimal strategies that satisfied NFF visions, (2) nature-positive, but not Pareto optimal strategies, and (3) non-nature-positive strategies. In most Pareto optimal strategies, the maximized value perspectives changed over time. Our protocol also identified key response options to achieve three different NFF value perspectives in the case study area: (1) clear or selective cutting in forestry and (2) solar PV installation on abandoned pastureland in agriculture and energy sectors. We discussed the implication for local landscape management, localizing NFF narratives to develop future scenarios and modeling practice of NFF. The protocol does not depend on a specific model and indicator. Thus, our scalable protocol can be applied to scenarios and model practices in any region to support envisioning plausible, feasible, and positive futures, and designing future stakeholder collaboration.

Research of In-Vehicle Ethernet Information Fault Detection Based on IEEE 802.1Qci

With features such as the high data transfer rate of 1G/bps and strong compatibility, in-vehicle Ethernet can become the backbone network of next-generation in-vehicle bus network systems. Currently, in-vehicle Ethernet is mainly used in audio and video entertainment systems, to further improve the network security performance of in-vehicle Ethernet, the information security of the data layer and physical layer in the in-vehicle bus network system must be ensured. Based on the characteristics of in-vehicle Ethernet data frame structure, this paper introduces IEEE 802.1 Qci protocol, proposes an information fault detection method through data shifting, detection, and policing, and verifies it by simulation with OMNET++ simulation system. The experimental results show that the accuracy rate of in-vehicle Ethernet data during transmission can reach 99.61%, which further improves the network security performance of in-vehicle Ethernet.

Segmenting Clients using Machine Learning to get the Best Leads in an E-Commerce Business

Customer segmentation is an essential part of modern marketing strategy, as it enables businesses to effectively tailor their marketing efforts and customize their consumer communication. Machine learning algorithms offer a powerful tool for automating the process of customer segmentation by analyzing large amounts of data about customer behavior and identifying patterns that can be used to group customers into segments. We present our experiments and findings for predicting the segment of a customer. We use the Tensorflow library in Python for Machine Learning combined with Pandas for Data frame Manipulation and experiment with various clustering models, including traditional Machine Learning algorithms.

Cancer Tumor Mutation Prediction

Abstract: Every day new technologies are invented to overcome the problems faced by the people. Machine learning can be seen as a boon in the series of technologies inventions within the context of smart digital society. Before 2015, in medical departments, the diagnosis what is serious disease could take a two days or, maybe a week. After the advancements in machine learning the diagnosis time is minimized rapidly. Like the path AI machine learning algorithms helps diagnosis diagnostician in making quick and more correct diagnosis, as well as pinpointing people who may gain from state-of-the-art treatments or therapeutic, our model also could extend to such kind of self learning algorithms. The analysis of our proposed model shows that it is efficient and productive than existing techniques. We have achieved the efficiency of the model by developing certain methods that transforms a data from data frame do a highly correlated data frame. This helps random forest classifier to make a prediction faster while providing a much probable class as an output. Currently this model can assist the diagnosis process and reduces the search space by 90% of the total variance, this on average reduces the time by 1.5 days

Digital adaptive optics with interferometric homodyne encoding for mitigating atmospheric turbulence

Digital adaptive optics (DAO) created using homodyne encoding can mitigate atmospheric turbulence in passive imaging systems. This work demonstrates a self-referencing homodyne interferometry technique that combines the passive imaging utility of multiframe algorithmic procedures with the single-frame correction capability of the Shack–Hartmann adaptive optics technique. This paper presents image reconstruction improvements through the addition of (1) phase diversity modulation techniques within the interferometry reconstruction algorithm and (2) temporal image processing techniques applied after the interferometry reconstruction algorithm. By imaging quick response codes through a turbulent air chamber in the laboratory, it was possible to quantify the machine-readable performance gain provided by DAO when compared with a standard imaging camera. Results from this research verify that DAO from homodyne encoding provides turbulence mitigation for single frames of data, paving the way for environmentally robust, high-speed, self-contained imaging systems.

Explainable human activity recognition based on probabilistic spatial partitions for symbiotic workplaces

ABSTRACT In recent years, smart workplaces that adapt to human activity and motion behavior have been proposed for cognitive production systems. In this respect, methods for identifying the feelings and activities of human workers are being investigated to improve the cognitive capability of smart machines such as robots in shared working spaces. Recognizing human activities and predicting the possible next sequence of operations may simplify robot programming and improve collaboration efficiency. However, human activity recognition still requires explainable models that are versatile, robust, and interpretative. Therefore, recognizing and analyzing human action details using continuous probability density estimates in different workplace layouts is essential. Three scenarios are considered: a standalone, a one-piece flow U-form, and a human-robot hybrid workplace. This work presents a novel approach to human activity recognition based on a probabilistic spatial partition (HAROPP). Its performance is compared to the geometric-bounded activity recognition method. Results show that spatial partitions based on probabilistic density contain 20% fewer data frames and 10% more spatial areas than the geometric bounding box. The approach, on average, detects human activities correctly for 81% of the cases for a pre-known workplace layout. HAROPP has scalability and applicability potential for cognitive workplaces with a digital twin in the loop for pushing the cognitive capabilities of machine systems and realizing human-centered environments.

5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models

Channel coding is a fundamental procedure in wireless telecommunication systems and has a strong impact on the data transmission quality. This effect becomes more important when the transmission must be characterised by low latency and low bit error rate, as in the case of vehicle-to-everything (V2X) services. Thus, V2X services must use powerful and efficient coding schemes. In this paper, we thoroughly examine the performance of the most important channel coding schemes in V2X services. More specifically, the impact of use of 4th-Generation Long-Term Evolution (4G-LTE) turbo codes, 5th-Generation New Radio (5G-NR) polar codes and low-density parity-check codes (LDPC) in V2X communication systems is researched. For this purpose, we employ stochastic propagation models that simulate the cases of line of sight (LOS), non-line of sight (NLOS) and line of sight with vehicle blockage (NLOSv) communication. Different communication scenarios are investigated in urban and highway environments using the 3rd-Generation Partnership Project (3GPP) parameters for the stochastic models. Based on these propagation models, we investigate the performance of the communication channels in terms of bit error rate (BER) and frame error rate (FER) performance for different levels of signal to noise ratio (SNR) for all the aforementioned coding schemes and three small V2X-compatible data frames. Our analysis shows that turbo-based coding schemes have superior BER and FER performance than 5G coding schemes for the vast majority of the considered simulation scenarios. This fact, combined with the low-complexity requirements of turbo schemes for small data frames, makes them more suitable for small-frame 5G V2X services.

3D reconstruction of unstained weakly scattering cells from a single defocused hologram.

We investigate the problem of 3D complex field reconstruction corresponding to unstained red blood cells (RBCs) with a single defocused off-axis digital hologram. The main challenge in this problem is the localization of cells to the correct axial range. While investigating the volume recovery problem for a continuous phase object like the RBC, we observe an interesting feature of the backpropagated field that it does not show a clear focusing effect. Therefore, sparsity enforcement within the iterative optimization framework using a single hologram data frame cannot effectively restrict the reconstruction to the true object volume. For phase objects, it is known that the amplitude contrast of the backpropagated object field at the focus plane is minimum. We use this information available in the recovered object field in the hologram plane to device depth-dependent weights that are proportional to the inverse of amplitude contrast. This weight function is employed in the iterative steps of the optimization algorithm to assist the object volume localization. The overall reconstruction process is performed using the mean gradient descent (MGD) framework. Experimental illustrations of 3D volume reconstruction of the healthy as well as malaria-infected RBCs are presented. A test sample of polystyrene microsphere bead is also used to validate the axial localization capability of the proposed iterative technique. The proposed methodology is simple to implement experimentally and provides an approximate tomographic solution, which is axially restricted and consistent with the object field data.

MAC-Based Compression Ratio Improvement for CAN Security

Information security in a controller area network (CAN) is becoming more important as the connections between a vehicle’s internal and external networks increase. Encryption and authentication techniques can be applied to CAN data frames to enhance security. To authenticate a data frame, a message authentication code (MAC) needs to be transmitted with the CAN data frame. Therefore, space for transmitting the MAC is required within the CAN frame. Recently, the Triple ID algorithm has been proposed to create additional space in the data field of the CAN frame. The Triple ID algorithm ensures every CAN frame is authenticated by at least 4 bytes of MAC without changing the original CAN protocol. However, since the Triple ID algorithm uses six header bits, there is a problem associated with low data compression efficiency. In this paper, we propose an algorithm that can remove up to 15 bits from frames compressed with the Triple ID algorithm. Through simulation using CAN signals of a Kia Sorento vehicle and an LS Mtron tractor, we show that the generation of frames containing compressed messages of 4 bytes or more is reduced by up to 99.57% compared to the Triple ID method.

Telemonitoring of Human Biomedical and Biomechanical Signals

Methodological and algorithmic bases of the local-regional and global wireless networks operation for functional states of people long-term monitoring are offered. The construction of efficient wireless networks and portable devices for monitoring biological objects is proposed. High-speed algorithms for processing, encoding, encrypting and transmitting samples of monitoring signals and video data frames using a signaling approach are proposed. The express analysis of monitoring signal samples is offered. The described methods of effective long-term monitoring of functional states of biological objects using wireless networks, algorithms of operational processing, coding, encryption and express analysis of signals are the basis of information technology of wide application. The technology is focused on evidence-based monitoring of the condition of operators of various human-machine complexes, systems, vehicles, aviation and space systems, monitoring of athletes and healthy people in order to prevent various diseases.

Local Oscillator Power Adjustment-Based Adaptive Amplification for Coherent TDM-PON With Wide Dynamic Range

Coherent technology is a promising solution for future 100-Gb/s and even 200-Gb/s single-wavelength time-division multiplexing passive optical network (TDM-PON) systems. One of the key issues for coherent TDM-PON is the upstream burst-mode amplification. The limited resolution of the analog-to-digital converter (ADC) makes it challenging to detect burst signals with widely varying power levels. Local oscillator (LO) power adjustment is a promising solution to this problem. In the present study, we verified the feasibility of LO power adjustment for adaptive amplification in a 200-G coherent TDM-PON system. We investigated the overall performance of the system under different implementation conditions, such as ADC sampling scales, TIA MGC mode control voltages, LO optical power levels, and the received optical power (ROP). Simulation and experimental results indicated that LO power adjustment can widen the dynamic range of the coherent PON system. As a proof of concept, we tested the system performance with continuous-mode signals and signals of isolated patterns with a burst-mode data frame. The dynamic range was extended to >30 dB via LO power adjustment, with 38- and 37-dB power budgets for 200-Gb/s/λ coherent TDM-PON continuous signals and signals of isolated patterns, respectively, after a 50-km standard single-mode fiber (SSMF).

Wake-Up Receiver for Underwater Acoustic Communication Using in Shallow Water.

A data frame transmitted over the underwater acoustic channel usually begins with a preamble. Therefore, underwater communication systems have a dedicated receiver that constantly listens to the preamble signals. A receiver that is to work effectively in shallow waters must have solutions that effectively reduce the impact of the permanently occurring multipath propagation. The article presents a solution based on complementary broadband signals. Initial tests were carried out using the Watermark simulator to determine its reliability in such a difficult propagation environment. The results of experimental tests carried out in a model pool are also included. Details of the implementation of the wake-up receiver are presented.

UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework

Abstract. Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. Although the development of remote sensing platforms and deep learning technologies have provided opportunities for UGS mapping from high-resolution images (HRIs), challenges still exist in its large-scale and fine-grained application due to insufficient annotated datasets and specially designed methods for UGS. Moreover, the domain shift between images from different regions is also a problem that must be solved. To address these issues, a general deep learning (DL) framework is proposed for UGS mapping in the large scale, and fine-grained UGS maps of 31 major cities in mainland China are generated (UGS-1m). The DL framework consists of a generator and a discriminator. The generator is a fully convolutional network designed for UGS extraction (UGSNet), which integrates attention mechanisms to improve the discrimination to UGS, and employs a point-rending strategy for edge recovery. The discriminator is a fully connected network aiming to deal with the domain shift between images. To support the model training, an urban green space dataset (UGSet) with a total number of 4544 samples of 512×512 in size is provided. The main steps to obtain UGS-1m can be summarized as follows: (a) first, the UGSNet will be pre-trained on the UGSet in order to obtain a good starting training point for the generator. (b) After pre-training on the UGSet, the discriminator is responsible for adapting the pre-trained UGSNet to different cities through adversarial training. (c) Finally, the UGS results of 31 major cities in China (UGS-1m) are obtained using 2179 Google Earth images with a data frame of 7′30′′ in longitude and 5′00′′ in latitude and a spatial resolution of nearly 1.1 m. An evaluation of the performance of the proposed framework by samples from five different cities shows the validity of the UGS-1m products, with an average overall accuracy (OA) of 87.56 % and an F1 score of 74.86 %. Comparative experiments on UGSet with the existing state-of-the-art (SOTA) DL models proves the effectiveness of UGSNet as the generator, with the highest F1 score of 77.30 %. Furthermore, an ablation study on the discriminator fully reveals the necessity and effectiveness of introducing the discriminator into adversarial learning for domain adaptation. Finally, a comparison with existing products further shows the feasibility of the UGS-1m and the great potential of the proposed DL framework. The UGS-1m can be downloaded from https://doi.org/10.57760/sciencedb.07049 (Shi et al., 2023).

Block-Based Kalman Channel Tracking for LEO Satellite Communication With Massive MIMO

Low Earth orbit (LEO) satellite communication (SATCOM) is one of the most promising systems to be incorporated in the future 5G and beyond wireless networks. The combination of massive multi-input multi-output (mMIMO) with LEO SATCOM can effectively enhance the communication capacity and improve the system throughput. However, the channel acquisition and tracking are challenging due to the severe Doppler shift and long propagation delay under the LEO SATCOM channel’s time-varying propagation environment. In this work, we propose a block-based (BB) Kalman channel tracking scheme for LEO SATCOM with mMIMO. Firstly the mMIMO LEO SATCOM channel model with Doppler shift and propagation delay is proposed. To reduce the tracking times in one data frame, the BB Kalman channel tracking scheme is proposed under LEO SATCOM environment. The optimal block length problem is also studied. Simulation results prove the feasibility of the proposed scheme and the block length selection criteria.

Dynamics of the SARS-CoV-2 main protease binding site.

The COVID-19 pandemic has been controlled through quarantines, travel bans, mask mandates, and vaccines. However, due to viral mutations and the unlikelihood of reaching herd immunity, there remains a need for therapeutics. The SARS-CoV-2 main protease (MPro) is necessary for the virus’ replication, thus it is a promising drug target. Additionally, it can be crystallized consistently and its sequence has few changes across virus mutants. MPro crystal structures depict general residue-residue interactions and backbone conformations, but they do not account for the dynamics of flexible regions. Instead, molecular dynamics (MD) simulations are used to identify the flexibility of the binding site, data we hypothesize is essential for successfully designing drugs to bind to MPro using computational drug docking methods. 600 ns of all-atom, explicit solvent MD simulations of the MPro homodimer were conducted, resulting in 1.2 million frames of monomeric data. Principal component analysis (PCA) was conducted on pairwise Cα-Cα distances between selected binding residues within apo and holo PDBs and the MD frames. These distances provide an intuitive set of measurements to characterize the dynamics of the binding site. Contour mapping was used to visualize the presence of clusters in the space defined by a few of the most contributive PC's. Snapshots representative of the most common conformations within the distribution of data were identified through the use of clustering methods and will be used in future drug docking simulations.

Statistical Learning Based Wireless Channel Parameter Estimation for Power Internet of Things

With the rapid increase in the number of sensors in Power Internet of Things (PIOT), the data volume surges and efficient communications are needed. Precise estimation of delay spread is critical to improve the communication performance. In this paper, we propose a statistical learning (SL) based root mean square delay spread (RMSDS) estimation method, which consists of offline training and online estimation. During the offline training process, the instantaneous delay spread (IDS) for each data frame is firstly computed based on the known pilots. The acquired IDS is a random variable and its statistical parameter is related to the actual RMSDS, which is preset and known during offline training. Then, the functional relationship between the statistical parameter and the preset RMSDS is acquired through the SL algorithm. The acquired functional relationship can be used in the online estimation process to accurately estimate the RMSDS. Simulation results indicate that the proposed method can accurately estimate the RMSDS and greatly improve the bit error rate performance.

Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter

As wind energy development increases, accurate wind energy forecasting helps to develop sensible power generation plans and ensure a balance between supply and demand. Machine-learning-based forecasting models possess exceptional predictive capabilities, and data manipulation prior to model training is also a key focus of this research. This study trained a deep Long Short-Term Memory (LSTM) neural network to learn the processing results of the Savitzky-Golay filter, which can avoid overfitting due to fluctuations and noise in measurements, improving the generalization performance. The optimum data frame length to match the second-order filter was determined by comparison. In a single-step prediction, the method reduced the root-mean-square error by 3.8% compared to the model trained directly with the measurements. The method also produced the smallest errors in all steps of the multi-step advance prediction. The proposed method ensures the accuracy of the forecasting and, on that basis, also improves the timeliness of the effective forecasts.

Design and Test of Obstacle Detection and Harvester Pre-Collision System Based on 2D Lidar

Aiming at the need to prevent agricultural machinery from colliding with obstacles in the operation of unmanned agricultural machinery, an obstacle detection algorithm using 2D lidar was proposed, and a pre-collision system was designed using this algorithm, which was tested on a harvester. The method uses the differences between lidar data frames to calculate the collision times between the farm machinery and the obstacles. The algorithm consists of the following steps: pre-processing to determine the region of interest, median filtering, and DBSCAN (density-based spatial clustering of applications with noise) to identify the obstacle and calculate of the collision time according to the 6σ principle. Based on this algorithm, a pre-collision system was developed and integrated with agricultural machinery navigation software. The harvester was refitted electronically, and the system was tested on a harvester. The results showed that the system had an average accuracy rate of 96.67% and an average recall rate of 97.14% for being able to stop safely for obstacles in the area of interest, with a summed average of 97% for both the accuracy and recall rates. The system can be used for an emergency stop when encountering obstacles in the automatic driving of agricultural machinery and provides a basis for the unmanned driving of agricultural machinery in more complex scenarios.