Spread Estimation Research Articles

Air quality monitoring and assessment based on AIS data

Abstract Indonesia is an archipelagic country, where sea transportation is highly needed to connect the islands among Indonesian regions. Indonesian waters are also passed by international shipping passing through the Indonesia Archipelagic Sea Lane. Number of ships tends to increase by increasing the economic activities in Indonesia. The increasing number of ships will potentially cause the increase of air pollution in Indonesia waters. AIS consortium has been established since 2020 focusing on conducting join research and development on several issues that can be solved using Automatic Identification System (AIS) data gathered from AIS devices that have been installed in several universities as members of the consortium. One potential use of AIS data is the estimation of capacity and spreading of air pollution caused by shipping activities in Indonesia waters. As AIS can provide ship’s navigation status time-to-time, the estimation of fuel consumption as well as the emission can be estimated in real time. For highly accusation fuel oil consumption prediction is done by using the Random Forest Model and ship emissions including CO2, NO2, SO2, PM2.5, and PM10 are estimated based on emission factors from ships. This analysis is mapping the contribution of air pollution based on ship type in the Tanjung Perak port area. Later, it is expected to become the basis for Indonesia’s maritime operation policy in order to monitor and control the emission contributed by the ships in Indonesian waters.

Evaluating a multi-step collocation approach for an ensemble climatological dataset of actual evapotranspiration over Italy

Accurate estimations of actual evapotranspiration (ET) are key in a variety of water balance applications, but divergent results can be obtained due to the large range of available methodologies. The use of an ensemble approach is a suitable alternative, as it summarizes multiple sources in an optimized strategy. In this study, an expert-based multi-step collocation (MC) approach is tested to merge six ET datasets, with the aim of reconstructing a spatiotemporally-consistent monthly dataset for Italy in the climatological period 1991–2020. The merged products are: three water balance datasets (BIG BANG, LSA SAF, and LISFLOOD), two residual surface energy balance model datasets (SSEBop, and ALEXI), and the MODIS standard ET product. The merged product is analyzed for spatio-temporal consistency and evaluated using flux observations from 11 sites. On average, the merged product has higher accuracy (mean absolute difference = 0.47 ± 0.17 mm/d, relative difference = 27.9 ± 7.5 %) than any single base dataset, and it is characterized by limited bias (mean bias error = -0.17 ± 0.26 mm/d), high correlation (r = 0.83 ± 0.10), and more uniform performance across sites. The merged ET dataset is accompanied by an estimation of the ensemble spread, which highlights large differences in ET estimates in some areas and periods characterized by severe water stress, such as in southern Italy during the summer. This large spread seems to be mostly driven by systematic differences among datasets, which affect the estimation of the reference climatology, suggesting how inter-model spread can have a defining role in further improving the merging strategies.

Investigating the Effect of Blurring and Focusing Current in Cochlear Implant Users with the Panoramic ECAP Method.

For some cochlear implants (CIs), it is possible to focus electrical stimulation by partially returning current from the active electrode to nearby, intra-cochlear electrodes (partial tripolar (pTP) stimulation). Another method achieves the opposite: "blurring" by stimulating multiple electrodes simultaneously. The Panoramic ECAP (PECAP) method provides a platform to investigate their effects in detail by measuring electrically evoked compound action potentials and estimating current spread and neural responsiveness along the length of the CI electrode array. We investigate how sharpening and broadening the electrical current spread are reflected in PECAP estimates. PECAP measurements were recorded at most comfortable level in 12 ears of Advanced Bionics CI users. Focused thresholds were also determined. For the electrodes with the highest and lowest focused thresholds, additional PECAP measurements were recorded while stimulating in pTP mode and in "blurred" mode with 3 or 5 adjacent electrodes simultaneously stimulated. Current spread and neural responsiveness were then estimated along the electrode array using PECAP. PECAP revealed increased current spread estimates across participants for blurred stimulation of the targeted electrodes towards the apex of the cochlea. Variable results for pTP stimulation were found, with two of eight ears appearing to drive a small group-level effect of increased current spread. When stimulating multiple electrodes simultaneously, PECAP detected localized increases in current spread towards the apex (but not the base) of the cochlea. pTP stimulation showed mixed effects on PECAP current spread estimates. These findings are in line with behavioral speech perception studies and have implications for cochlear implant optimization.

Development of Loop-Mediated Isothermal Amplification (LAMP) Assay for In-Field Detection of American Plum Line Pattern Virus.

American plum line pattern virus (APLPV) is the most infrequently reported Ilarvirus infecting stone fruit trees and is of sufficient severity to be classified as an EPPO quarantine A1 pathogen. In late spring, yellow line pattern symptoms were observed on leaves in a few flowering cherries (Prunus serrulata Lindl.) grown in a public garden in Northwest Italy. RNA extracts from twenty flowering cherries were submitted to Ilarvirus multiplex and APLPV-specific RT-PCR assays already reported or developed in this study. One flowering cherry (T22) with mixed prunus necrotic ringspot virus (PNRSV) and prune dwarf virus (PDV) infection also showed infection with APLPV. Blastn analysis of PCR products of the full coat protein (CP) and movement protein (MP) genes obtained from flowering cherry T22 showed 98.23% and 98.34% nucleotide identity with reference APLPV isolate NC_003453.1 from the USA. Then, a LAMP-specific assay was designed to facilitate the fast and low-cost identification of this virus either in the laboratory or directly in the field. The developed assay allowed not only the confirmation of APLPV (PSer22IT isolate) infection in the T22 flowering cherry but also the identification of APLPV in an asymptomatic flowering cherry tree (TL1). The LAMP assay successfully worked with crude flowering cherry extracts, obtained after manually shaking a single plant extract in the ELISA extraction buffer for 3-5 min. The developed rapid, specific and economic LAMP assay was able to detect APLPV using crude plant extracts rather that RNA preparation in less than 20 min, making it suitable for in-field detection. Moreover, the LAMP assay proved to be more sensitive in APLPV detection in flowering cherry compared to the specific one-step RT-PCR assay. The new LAMP assay will permit the estimation of APLPV geographic spread in the territory, paying particular attention to surrounding gardens and propagated flowering cherries in ornamental nurseries.

The Credit Spread: Risk-Free Rate in the Model

This paper proposes a parsimonious credit spread estimation model for valuation of corporate bonds in data-scarce markets. We emphasize the importance of incorporating the risk-free rate directly into credit spread determination. Our model aligns with established literature and demonstrates the ability to capture the observed influence of risk-free rates on credit spreads across economies. We posit that models omitting the risk-free rate component may underestimate credit spreads, particularly impactful in emerging markets with elevated default probabilities and high risk-free rates. Finally, we discuss practical applications of the model, including exchange rate premium calculations, policy analysis, and negative yield spread analysis.

Graph theory based estimation of probable CO2 plume spreading in siliciclastic reservoirs with lithological heterogeneity

Estimating plume spreading in geological CO2 storage reservoirs is critical for several reasons including the assessment of pore space utilization efficiency, preferential CO2 migration pathways and trapping. However, plume spreading critically depends on lithological heterogeneity of the reservoir and CO2 injection rate. It might require numerous high fidelity full physics numerical simulations to constrain the uncertainty in plume spreading for a given reservoir. This might not always be practical due to computational limitations. Hence, reduced physics approaches, such as invasion-percolation method and machine learning, could be useful to answer certain questions on plume spreading in the subsurface. This study presents a new reduced physics approach based on graph theory for estimating probable CO2 plume migration under very low and very high injection rates. The two end-member scenarios are assessed by performing random walk in the 3D reservoir space to constrain 20,000 possible paths of CO2 flow away from the injection well. The resistance to CO2 flow associated with each path is computed for viscous, capillary and gravity forces. The resistances are then transformed into the likelihood of CO2 migration along the path. The algorithm was applied to 45 reservoir models with varying degrees of lithological heterogeneity and the results were compared to those from full physics and invasion percolation simulations. The graph theory results showed a close match with the results from full physics approach for both flow regimes and with results from invasion-percolation approach for capillary-gravity dominated flow regime. The algorithm was further applied to answer key questions on reservoir screening such as pore space utilization potential. The graph theory approach was also integrated with machine learning to predict CO2 saturation. Testing suggested that the graph theory approach can be as much as 50 and 20 times faster than the full physics numerical simulations and invasion-percolation simulations, respectively.

A cost-effective seed selection model for multi-constraint influence maximization in social networks

The Influence Maximization (IM) problem aims to maximize the diffusion of information or adoption of products among users within a social network by identifying and activating a set of initial users. It is not unrealistic to have a higher activation cost for more influential users in real-life applications. However, existing works on IM focus solely on finding the most influential users as the seed set, without considering either the activation costs of individual nodes and the total budget or the size of the seed set. This oversight may lead to infeasible solutions, particularly from financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation called Multi-Constraint Influence Maximization (MCIM) to achieve a cost-effective solution under both budgetary and cardinality constraints. The MCIM model allows for variable-length solutions, necessitating the exclusion of seed nodes from the influence spread estimation. Consequently, unlike existing IM formulations, the spread function under the MCIM model is no longer monotonic but submodular. As it has also been proven to be an NP-hard problem, we propose a Simple Additive Weighting (SAW)-assisted Differential Evolution (DE) algorithm for solving large-size real-world IM problems. Experimental results on four datasets demonstrate the effectiveness of the proposed formulation and algorithm in finding optimal and cost-effective solutions.

From CountMin to Super kJoin Sketches for Flow Spread Estimation

Real-time data stream processing is key to many Internet applications ranging from e-commerce, social networks, to network monitoring. Sketches (compact data structures) are often used to track large, high-rate streams and estimate their statistics. However, the traditional model for measuring flow size, e.g., number of packets in each flow, is unsuitable for more sophisticated applications that require flow spreads, i.e., number of distinct elements in each flow. When there are numerous flows, most existing work modifies CountMin sketches — which were designed to measure flow size — for measuring flow spread. They inherit a similar design and a commonly-used min operation from the CountMin sketches. This paper casts doubt on such a solution path, arguing that the inherited sketch design and the min operation were both intended for a different purpose and are not the best for flow spread measurement. Replacing the min operation with new position-aware operations, we propose the kJoin sketches that achieve much better average accuracy. We mathematically derive the estimation error bound. We also apply our sketches for network-wide measurement. Trace-driven experiments on software/GPU/FPGA platforms demonstrate our work significantly improves estimation accuracy, streaming rate (recording throughput), and query throughput, compared to the state-of-the-art.

Spatial-temporal risk factors in the occurrence of rabies in Mexico.

Rabies is a zoonotic disease that affects livestock worldwide. The distribution of rabies is highly correlated with the distribution of the vampire bat Desmodus rotundus, the main vector of the disease. In this study, climatic, topographic, livestock population, vampire distribution and urban and rural zones were used to estimate the risk for presentation of cases of rabies in Mexico by co- Kriging interpolation. The highest risk for the presentation of cases is in the endemic areas of the disease, i.e. the States of Yucatán, Chiapas, Campeche, Quintana Roo, Tabasco, Veracruz, San Luis Potosí, Nayarit and Baja California Sur. A transition zone for cases was identified across northern Mexico, involving the States of Sonora, Sinaloa, Chihuahua, and Durango. The variables topography, vampire distribution, bovine population and rural zones are the most important to explain the risk of cases in livestock. This study provides robust estimates of risk and spread of rabies based on geostatistical methods. The information presented should be useful for authorities responsible of public and animal health when they plan and establish strategies preventing the spread of rabies into rabies-free regions of México.

An Architecture for Early Wildfire Detection and Spread Estimation Using Unmanned Aerial Vehicles, Base Stations, and Space Assets

An Architecture for Early Wildfire Detection and Spread Estimation Using Unmanned Aerial Vehicles, Base Stations, and Space Assets

Closed-Form Gaussian Spread Estimation for Small and Large Support Vector Classification

Closed-Form Gaussian Spread Estimation for Small and Large Support Vector Classification

An assessment of the Philippines government’s response to the COVID-19 pandemic based on policy response indices

COVD-19 has resulted in governments worldwide implementing policies for pandemic suppression or containment. The Oxford COVID-19 Government Response Tracker (OxCGRT) global database of pandemic policy responses provide open access data to allow for national assessments of pandemic response policies. Using OxCGRT data for the Philippines, we assessed the temporal trends of pandemic response indices and indicators. Using principal components and principal coordinate analyses, OLS and logistic regression on the ordinal policy response variables, composite policy response indices, and epidemiological estimates of pandemic spread, we assess which policy response is most significant in pandemic management. Stringency and Restrictions to Internal Movement are most significant and this reflects on the primary policing and securitizing strategy used by the Philippines government in the pandemic response. This has limited effective outcomes based on its relationships with health, economic and vaccination policy categories. We contextualize these findings with the outcomes of the government’s vaccination policy.

An Enhanced Channel Estimation for IEEE 802.15.4 OFDM Receiver in High-Speed Mobile IoT Communication Systems

Currently, orthogonal frequency division multiplexing (OFDM), standardized in IEEE 802.15.4, has attracted attention as a transmission scheme for higher data rates, long-distance transmissions, and mobile communications for the expansion of IoT applications as well as for legacy IoT applications, such as smart metering systems and home-area networks. In particular, receiving schemes using IEEE 802.15.4-based OFDM have been actively examined to realize mobile communication. Previous studies used the long training field (LTF) and pilot subcarriers for channel estimation of multipath fading, achieving mobile communication with data rates of 100 kbps or above at moving speeds of up to 40 km/h in an urban environment. In a previous study, channel estimates were obtained by repeating channel estimates from the LTF and pilot subcarriers in the time domain and by interpolating the estimates in the frequency domain. This study proposes a receiving scheme for IEEE 802.15.4 OFDM aimed at high-speed mobile IoT communication systems. In the time axis direction, the proposed scheme obtains channel estimates in the same manner as previous schemes. However, in the frequency axis, the proposed scheme obtains the channel estimates by copying estimates of neighboring subcarriers according to the coherence bandwidth of the multipath fading channel to update the channel estimates frequently. Computer simulation results show that the proposed scheme including delay spread estimation can achieve mobile communication with a data rate of 100 kbps at moving speed of 200 km/h in the urban environment.

Real-time Spread Burst Detection in Data Streaming

Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.

Self-Adaptive Sampling Based Per-Flow Traffic Measurement

Per-flow traffic measurement in the high-speed network plays an important role in many practical applications. Due to the limited on-chip memory and the mismatch between off-chip memory speed and line rate, sampling-based methods select and forward a part of flow traffic to off-chip memory, which complements sketch-based solutions in estimation accuracy and online query support. However, most current work uses the same sampling probability for all flows, leading to the waste in storage and communication resources. In practice, different flows often require different sampling rates to meet the same accuracy constraint. This paper presents self-adaptive sampling, a framework to sample each flow with a probability adapted to flow size/spread. Then we propose three algorithms, SAS-LC, SAS-LOG, and SAS-HYB. SAS-LC and SAS-LOG are geared towards per-flow spread estimation and per-flow size estimation by using different compression functions. SAS-HYB combines the advantages of SAS-LC and SAS-LOG, showing higher efficiency when both small flows and large flows are interested. We implement our estimators in hardware using NetFPGA. Experimental results based on real Internet traces show that, compared to the state-of-the-art in per-flow spread estimation, SAS-LC can save around 10% on-chip space and reduce up to 40% communication cost for large flows. In per-flow size estimation, SAS-LOG can save 40% on-chip space and reduce up to 96% communication costs for large flows. Moreover, SAS-HYB’s on-chip memory usage will not be larger than SAS-LC or SAS-LOG and can save up to 19% on-chip space than SAS-LOG when both small flows and large flows are interested.

Real-time Spread Burst Detection in Data Streaming.

Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread, which differs from the traditional problem of burst detection in flow size. It is practically significant with potential applications in cybersecurity, network engineering, and trend identification on the Internet. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

ADAPTIVE CYCLIC PREFIX LENGTH FOR CONVOLUITIONAL CODE OFDM SYSTEM IN FREQUENCY SELECTIVE CHANNEL

Orthogonal Frequency Division Multiplexing (OFDM) is one of recent years multicarrier modulation used in order to combat the Inter Symbol Interference (ISI) introduced by frequency selective mobile radio channel. The circular extension of the data symbol, commonly referred to as cyclic prefix is one of the key elements in an OFDM transmission scheme. This paper study The influence of the cyclic prefix duration on the BER performance of an OFDM-VCPL (Orthogonal frequency division multiplexing - Variable Cyclic Prefix Length) system and the conventional OFDM system with frame 64-QAM modulation is evaluated by means of computer simulation in a multipath fading channel. The adaptation of CP is done with respect to the delay spread estimation of the channel.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.