Buffer Size Research Articles

An Energy-Efficient Learning Automata and Cluster-Based Routing Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs), which may be used for a broad variety of applications, have recently emerged as a prominent data collection paradigm. The fundamental concerns in wireless sensor networks are the efficient use of energy and the reliable delivery of data, both of which are largely determined by the rate at which packets are dropped. When developing an energy-efficient routing protocol, one of the most important steps is selecting a node to act as a successor node in a routing path. The application of learning automata theory to guide the routing decisions made by the sensors in a WSN has recently been the subject of research in the field of WSNs, where it has been shown to have several advantages. In this paper, a learning automata-based PSO relay selection scheme for energyefficient relay selection and reliable data delivery is proposed. The network is clustered using the LEACH protocol. The random number in the traditional LEACH protocol will be stabilized with the sensor node energy level for CH stability. Every sensor node in the network estimates the best possible routes to the sink node using the PSO algorithm. Instead of retransmissions, here we introduce learning automata for successor node selection during packet loss. The proposed learning automata calculate the next node’s selection probability in a routing path using multi-objective parameters like communication cost, residual energy, distance from BS, buffer size, and previous selection probability. Performance evaluation clearly showed that the proposed approach decreases energy usage, transmission delays, and data transfers while extending network lifetime. According to the experimental results, the proposed scheme can improve energy efficiency by 21.68%, delay by 31%, PDR by 87%, routing overhead by 0.5%, and throughput by 18.76% as compared to existing techniques like O-LEACH (Optimized Low Energy Adaptive Clustering Hierarchy Protocol) and EEPC (enhanced energy proficient clustering).

Social context‐aware macroscopic routing scheme for opportunistic network

AbstractOpportunistic networks (OppNets) have attracted widespread attention as wireless technologies have advanced. OppNets are widely used in delay‐tolerant applications because they route messages using a store‐carry‐forward mechanism. Recently, socially aware routing has been increasingly modeled for message dissemination in OppNets, where the message is routed selectively through cooperative nodes based on user interests; however, routing becomes extremely difficult as node density and data size increase. However, the current method fails to reduce data redundancy, message overhead, delay, and improve performance efficiency. To address the issues, this article proposes social context‐aware microscopic routing (SCAMR) for OppNets. SCAMR uses cluster‐based communication, novel social‐context association mapping, and an improved lost packet retrieval mechanism with minimal messaging overhead. In this work, the experiment was performed by considering three scenarios: varying node size, varying buffer size, and varying time‐to‐live size. The experimental results show that the SCAMR scheme improves delivery ratio by 71.25%, 67.87%, 69.18%, reduces delay by 33.93%, 26.68%, 35.36%, reduces the number of hop nodes (i.e., messaging overhead) by 77.84%, 71.53%, 76.04% over existing approaches namely, SCARF (SoCial‐Aware Reliable Forwarding Technique for Vehicular Communications), SRS (secure routing strategy), and EDT (effective data transmission) considering different scenarios, respectively.

Qualification of DIRICH readout chain

The DIRICH front-end board (FEB) is a 32+1 channel FPGA based TDC readout module for Multianode Photomultipliers (MAPMT), and also Multianode MCPs. Due to its low cost and excellent timing precision in single photon measurement applications it will be used in the RICH detector of the Compressed Baryonic Matter (CBM) experiment at the future FAIR facility at GSI Darmstadt, Germany.In the regions of highest photon density at the CBM RICH one expects an average hit rate of ≃300 kHz per MAPMT pixel (6 mm × 6 mm) and ∼15% occupancy for minimum bias Au+Au collisions at 35 A GeV at an interaction rate of 10 MHz. In order to validate the high-rate capability of the DIRICH readout, a dedicated lab setup simulating realistic detector signals by employing a pulsed picosecond laser light source in combination with a constant current driven LED was commissioned. In this paper the setup is introduced and first results are discussed. We show that individual readout channels can withstand photon rates up to 2.2 MHz per pixel, limited only by maximum data rate capability and buffer size on the front-end board. Using the same setup, also effects of high photon occupancy on the MAPMTs are investigated, which might cause additional signals due to capacitive cross-talk within the MAPMT or readout chain. Occupancies of up to 55% (simultaneous photon hits on more than half of the MAPMT pixels) are investigated, indicating that in the expected occupancy range of 10%–15% the readout works flawlessly with very low cross-talk.

Online Continual Learning in Acoustic Scene Classification: An Empirical Study.

Numerous deep learning methods for acoustic scene classification (ASC) have been proposed to improve the classification accuracy of sound events. However, only a few studies have focused on continual learning (CL) wherein a model continually learns to solve issues with task changes. Therefore, in this study, we systematically analyzed the performance of ten recent CL methods to provide guidelines regarding their performances. The CL methods included two regularization-based methods and eight replay-based methods. First, we defined realistic and difficult scenarios such as online class-incremental (OCI) and online domain-incremental (ODI) cases for three public sound datasets. Then, we systematically analyzed the performance of each CL method in terms of average accuracy, average forgetting, and training time. In OCI scenarios, iCaRL and SCR showed the best performance for small buffer sizes, and GDumb showed the best performance for large buffer sizes. In ODI scenarios, SCR adopting supervised contrastive learning consistently outperformed the other methods, regardless of the memory buffer size. Most replay-based methods have an almost constant training time, regardless of the memory buffer size, and their performance increases with an increase in the memory buffer size. Based on these results, we must first consider GDumb/SCR for the continual learning methods for ASC.

Amazonian pollen assemblages reflect biogeographic gradients and forest cover

AbstractAimPollen assemblages are commonly used to reconstruct past climates yet have not yet been used to reconstruct past human activities, including deforestation. We aim to assess (i) how pollen assemblages vary across biogeographic and environmental gradients, (ii) the source area of pollen assemblages from lake sediment samples and (iii) which pollen taxa can best be used to quantify deforested landscapes.LocationAmazonia.TaxonPlantae.MethodsPollen assemblages (N = 65) from mud‐water interface samples (representing modern conditions) of lake sediment cores were compared with modern gradients of temperature, precipitation and elevation. Pollen assemblages were also compared with local‐scale estimates of forest cover at 1, 2, 5, 10, 20 and 40 km buffers around each lake.ResultsOver 250 pollen types were identified in the samples, and pollen assemblages were able to accurately differentiate biogeographic regions across the basin, corresponding with gradients in temperature and precipitation. Poaceae percentages were the best predictor of deforestation, and had a significant negative relationship with forest cover estimates. These relationships were strongest for the 1 km buffer area, weakening as buffer sizes increased.Main conclusionsThe diverse Amazonian pollen assemblages strongly reflect environmental gradients, and percentages of Poaceae best reflect local‐scale variability in forest cover. Our results of modern pollen‐landscape relationships can be used to provide a foundation for quantitative reconstructions of climate and deforestation in Amazonia.

Gaiwan: A size-polymorphic typesystem for GPU programs

General-purpose computing on graphics processing units (GPGPU) is increasingly used for number crunching tasks such as analyzing time series data. GPUs are a good fit for these tasks as they can execute many computations in parallel. To leverage this parallelism, the programmer is forced to carefully divide their input data into data blocks that are then distributed over the many GPU cores. The optimal block sizes are unrelated to the programmers goals, instead, they are based on characteristics of the used GPU and the input data. GPGPU programmers must additionally be wary of introducing race conditions in their programs. We believe that GPGPU programmers should be able to express GPU transformations without worrying about splitting data or race conditions. For this, we created Gaiwan, a GPGPU programming language with a size-polymorphic type system that only features data race free operations. Programmers can declare the effects of program steps on the sizes of buffers by using affine functions (eg. ). From a step sequence, Gaiwan derives a set of constraints on the size and shape of valid inputs. Gaiwan guarantees that the program will run for any input satisfying these constraints. This means that one program may analyze both a hundred data points and millions of data points, as long as the input satisfies the constraints. We prove that our system is sound and show it works with two usage examples. Our benchmarks show that our initial OpenCL-based implementation of Gaiwan scales to handling large programs.

On the maximum buffer size achieved in a class of constructions of optical priority queues

On the maximum buffer size achieved in a class of constructions of optical priority queues

Математическая модель расчета и анализа характеристик оптического коммутатора в переходном режиме

The article considers a transient behavior of an optical switch described using a single-channel finite buffer queuing system with Poisson input stream and exponential service time distribution. To solve the Kolmogorov equations system and find the probabilities of states in the transition mode, the apparatus of the Laplace transform was used. Analytical expressions are found for determining the probability of losses and the throughput of an optical switch at a given point in time. In a numerical example, the performance metrics of an optical switch with a buffer size of two packets are analyzed for different network loads in transient and stationary modes. The stationary performance metrics obtained using the proposed approach coincided with the known results, which confirms the correctness of the conclusions. The optical switch transient behavior analyses at different network load showed that with a decreasing service rate the transition mode time increases.

Theoretical modeling and analysis of Uplink performance for Three-dimension spatial RISs-aided Wireless communication systems

Reconfigurable intelligent surface (RIS) has emerged as a crucial technology capable of improving the performance of future wireless communication (WC) systems. Although a significant body of studies has investigated into the performance analysis of RIS-aided WC systems, most of them fail to consider the impact of multiple RISs on WC systems. Particularly, the influence of RISs randomly distributed in 3-D (three dimension) space is still an open issue. Furthermore, how phase shift error and ARQ (automatic repeat request) scheme affect the transmission behavior of RIS-aided WC systems should also be taken into account. In light of the above limitations, we propose a novel theoretical model to analyze the uplink transmission performance of 3-D spatial RISs-aided WC systems. In the modeling process, we firstly provide an end-to-end (E2E) channel model using a single RIS, where the RIS enables optimal phase shift control. Next we construct a 3-D spatial uplink transmission model, where the multiple RISs are spatially distributed as a homogeneous 3-D PPP (Poisson point process). The impacts of multiple factors including the selection of RISs, buffer size of user, traffic rate and ARQ scheme are comprehensively considered. With this, we derive the closed-form expressions of uplink transmission metrics. Moreover, we further extend the proposed theoretical model under imperfect phase shift control. Finally, we evaluate the uplink transmission performance of 3-D spatial RIS-aided WC systems, and validate the proposed theoretical model.

Small stream predators rely heavily on terrestrial matter energy input in the fall, regardless of riparian buffer size

Stream ecosystems are reliant on the reciprocal exchange of terrestrial and aquatic energy subsides to maintain a productive and stable food web. Land use around streams can have strong effects on the size and availability of resource subsidies for stream and riparian predators such as fish and spiders. A common forestry technique around streams is the establishment of forested buffers to protect aquatic and riparian ecosystems from upland disturbances. Buffer size may determine prey abundance, richness, and spatial extent of prey reach into both the aquatic and terrestrial systems. To test the effects of forested buffers subsidy direction, we explored the carbon and nitrogen stable isotope signatures of brown trout (Salmo trutta), Tetragnathidae and Lycosidae spiders, and their aquatic and terrestrial prey sources around twelve streams in southern Sweden. For both predator groups, buffer presence showed no effect on resource subsidy source. We found that both brown trout and spiders are significantly reliant on terrestrial sources of prey for their diets in the fall. To support the terrestrial subsidy into small streams it is vital to maintain ecologically functional riparian zones by conserving complex surrounding habitats that optimize habitat and both terrestrial and aquatic prey diversity.

Adjacent and downstream effects of forest harvest on the distribution and abundance of larval headwater stream amphibians in the Oregon Coast Range

Forest harvest is a primary landscape-scale management action affecting riparian forests. Although concerns about impacts of forest harvest on stream amphibians is generally limited to areas adjacent to harvest, there is a paucity of information regarding potential downstream effects of forest harvest on these species. We designed a before-after, control-impact (BACI) experiment to quantify potential impacts of clearcut logging that included 12-m buffers or smaller variable-width buffers on the distribution and abundance of headwater stream amphibians in adjacent and downstream areas. We sampled larval coastal tailed frogs (Ascaphus truei), coastal giant salamanders (Dicamptodon tenebrosus), and Columbia torrent salamanders (Rhyacotriton kezeri) across 3,915 sampling occasions that spanned 13 study reaches in 2008–2011 (pre-harvest) and 2013–2016 (post-harvest) as part of the Trask River Watershed Study in the Oregon Coast Range, U.S.A. We analyzed these data using occupancy models to estimate occupancy and (when possible) relative abundance, while accounting for various sources of imperfect detection. All species exhibited reduced occupancy adjacent to clearcuts with variable-width buffers (odds ratios [ORs] ranged = 0.24–0.48), and these negative impacts were not always diminished when increasing the buffer size to 12 m (ORs ranged = 0.20–3.56). Dicamptodon tenebrosus was the only species to have occupancy impacted in downstream areas, and this negative impact was related to clearcut logging with uniform 12-m buffers (OR = 0.60). This species was also the only species to have abundance negatively impacted by forest harvest in downstream areas (OR = 0.41 with uniform 12-m buffers, OR = 0.38 with variable-width buffers), albeit impacts to abundance were not evaluated for R. kezeri. Ascaphus truei abundance increased in areas downstream of clearcut logging with uniform 12-m buffers (OR = 2.92). Although we found the direction and magnitude of responses varied by species, our study confirms that clearcut logging can have negative impacts on amphibians that inhabit the adjacent stream areas. Perhaps more importantly, we also found that forest harvest can have negative effects on stream amphibians downstream of the harvested area and that increasing the buffer size to 12 m did not necessarily diminish these impacts in adjacent and downstream areas. Altogether, our study provides a nuanced picture of adjacent and downstream effects of forest harvest on three endemic headwater stream amphibians, and our findings demonstrate that forest management practices should consider downstream effects on aquatic taxa when assessing the impact of harvesting trees near headwater streams.

Analysis of a tandem queuing system with blocking and group service in the second node

ABSTRACT In this paper, we study a tandem queuing system in which the arrivals occur according to a versatile Markovian point process. The buffer sizes in the first and second nodes are, respectively, infinite and finite. We incorporate the impatience of the customers in both the nodes. The service times in both nodes are assumed to be of phase type. While the services in the first node are offered on a first-come-first-served basis, we assume that in the second node the services are provided in batches of varying sizes based on two pre-determined thresholds. The system is studied as a multi-dimensional continuous time Markov chain and a few illustrative numerical examples are reported.

Colony size buffers interactions between neonicotinoid exposure and cold stress in bumblebees.

Social bees are critical for supporting biodiversity, ecosystem function and crop yields globally. Colony size is a key ecological trait predicted to drive sensitivity to environmental stressors and may be especially important for species with annual cycles of sociality, such as bumblebees. However, there is limited empirical evidence assessing the effect of colony size on sensitivity to environmental stressors or the mechanisms underlying these effects. Here, we examine the relationship between colony size and sensitivity to environmental stressors in bumblebees. We exposed colonies at different developmental stages briefly (2 days) to a common neonicotinoid (imidacloprid) and cold stress, while quantifying behaviour of individuals. Combined imidacloprid and cold exposure had stronger effects on both thermoregulatory behaviour and long-term colony growth in small colonies. We find that imidacloprid's effects on behaviour are mediated by body temperature and spatial location within the nest, suggesting that social thermoregulation provides a buffering effect in large colonies. Finally, we demonstrate qualitatively similar effects in size-manipulated microcolonies, suggesting that group size per se, rather than colony age, drives these patterns. Our results provide evidence that colony size is critical in driving sensitivity to stressors and may help elucidate mechanisms underlying the complex and context-specific impacts of pesticide exposure.

Performance analysis of H2BR: HTTP/2-based segment upgrading to improve the QoE in HAS

HTTP Adaptive Streaming (HAS) plays a key role in over-the-top video streaming with the ability to reduce the video stall duration by adapting the quality of transmitted video segments to the network conditions. However, HAS still suffers from two problems. First, it incurs variations in video quality because of throughput fluctuation. Adaptive bitrate (ABR) algorithms at the HAS client usually select a low-quality segment when the throughput drops to avoid stall events, which impairs the Quality of Experience (QoE) of the end-users. Second, many ABR algorithms choose the lowest-quality segments at the beginning of a video streaming session to ramp up the playout buffer early on. Although this strategy decreases the startup time, clients can be annoyed as they have to watch a low-quality video initially. To address these issues, we introduced the H2BR technique (H TTP/2-B ased R etransmission) (Nguyen et al. 33) that utilizes certain features of HTTP/2 (including server push, multiplexing, stream priority, and stream termination) for late transmissions of higher-quality versions of video segments already in the client buffer, in order to improve video quality. Although H2BR was shown to enhance the QoE, limited streaming scenarios were considered resulting in a lack of general conclusions on H2BR’s performance. Thus, this article provides a profound evaluation to answer three open questions: (i) how H2BR’s performance is impacted by parameters at the server side (i.e., various encoding specifications), at the network side (i.e., packet loss rate), and at the client side (i.e., buffer size) on the performance of H2BR; (ii) how H2BR outperforms other state-of-the-art approaches in different configurations of the parameters above; (iii) how to effectively utilize H2BR on top of ABR algorithms in various streaming scenarios. The experimental results show that H2BR’s performance increases with the buffer size and decreases with increasing packet loss rates and/or video segment duration. The number of quality levels can negatively or positively impact on H2BR’s performance, depending on the ABR algorithm deployed. In general, H2BR is able to enhance the video quality by up to 17% and 14% in scalable video streaming and in non-scalable video streaming, respectively. Compared with an existing retransmission technique (i.e., SQUAD Wang et al., ACM Trans Multimed Comput Commun Applic (TOMM) 13(3s): 45, 49), H2BR shows better results with more than 10% in QoE and 9% in the average video quality.

Predicting and indexing ungulate browse intensity from local to regional scales

The intensity of browsing by white-tailed deer (Odocoileus virginianus; hereafter deer) can be influenced directly by deer densities and food availability, and indirectly by landscape characteristics that influence these factors. However, the variables and spatial scales that influence browsing intensity likely differ in landscapes with varying composition and land use. Furthermore, because high browsing intensity can influence the structure and function of forests, determining the most effective and efficient indices of browsing intensity can aid forest management decisions. To evaluate differences in browsing intensity, we used Bayesian hierarchical models to estimate the probability of browsing for twigs from non-avoided species based upon woodlot and landscape-scale variables, which were calculated within 200, 500, 1000, and 1500 m buffers. We then evaluated the efficacy of the twig age, oak sentinel, and herbaceous indicator methods of indexing browsing intensity by correlating these to browsing intensity and deer density. We also evaluated the expense and required work time associated with each index method. Food availability seemed to drive browsing intensities in our study, however, deer density was also important in the region with the lowest cover of forest. When meaningful, landscape characteristics fit data best at the 500 m buffer size. The twig age method showed the strongest correlations to both browsing intensity and deer density and was among the most efficient methods, suggesting it is a reliable index of browsing intensity within Indiana and similar regions. Together, our results highlight that landscape characteristics can mediate the relationship between deer and forest plant communities, which emphasizes the need to tailor management actions to variable landscapes that may occur within a single state or region. Additionally, our results suggest that the twig age method is the most efficient and effective index to monitor browse intensity in agriculturally dominated landscapes, such as those of the Central Hardwood Forest Region.

Project schedule contingency planning: Building on von Bertalanffy's open systems theory and critical systems practice

AbstractThe research on project schedule contingency suffers from three major shortcomings: (1) it only partly reflects the complexity arising from the high interdependencies among project activities; (2) it is predicated on the idea of closed systems and thus does not address the interaction between a project and its environment; and (3) it does not sufficiently inform industry practices. This paper addresses these gaps by conceptually and analytically linking von Bertalanffy's open systems theory and critical systems practice (CSP) to schedule contingency planning. It focuses on buffer management as a common approach to schedule contingency planning and accordingly develops a method to determine the size of time buffers based on the three foundational dimensions of open systems theory. Further, this research provides guidelines to explain how the simultaneous pursuit of the four stages of CSP can assist project managers and decision‐makers in responding to disruptive events.

Congestion Avoidance Social-Aware Caching Strategy in Cache-Enabled Heterogeneous Networks

In cache-enabled heterogeneous networks (CHetNet), social-aware caching strategies are generally designed under the assumption that the user's sharing willingness (SW) is matched with its sociality, which indicates the probability that users in the real world build social connections with others. In practice, however, social-aware caching will make severe congestion if high-sociality users have weak SW. It is hard to handle the challenge since it is difficult to jointly quantify the impact of sociality, SW, and caching strategy on congestion. In this paper, we study the congestion avoidance caching strategy to maximize throughput capacity, considering user's sociality and SW. Specifically, the congestion mechanism in CHetNet is captured by efficient betweenness (EB), which is defined as the number of content delivery paths through a node. Aided by EB, it is interestingly revealed that popular contents should be preferentially cached in users with high SW and low sociality. Inspired by this, we propose a caching strategy to optimize the offloading ratio between layers and further tune caching probabilities to avoid local congestion. Simulation results show that the proposed strategy outperforms the compared sociality-preference schemes. Moreover, it is shown that the large caching buffer size may conversely decrease throughput capacity due to congestion.

Energy Efficiency of Mobile Devices Using Fuzzy Logic Control by Exponential Weight with Priority-Based Rate Control in Multi-Radio Opportunistic Networks

The rapid development of mobile devices and wireless network technologies have made them indispensable. This has created a demand for faster networks and longer battery life. The assurance of a stable network service and the enhancement of network experiences for mobile devices is equally crucial to meeting these demands. To address these challenges, mobile devices employ various techniques to decrease power consumption when they connect to wireless networks. Moreover, enhancing the endurance of mobile devices to maintain stable network services is critical when using wireless networks. In this paper, we propose a dual-radio opportunistic network for energy efficiency (DRONEE)–exponential weight with priority-based on rate control (WPRC) method which can extend and enhance the DRONEE hybrid network. By leveraging fuzzy logic control (FLC) and quality of service (QoS), our proposed method effectively solves the weighting problem in the DRONEE–weight (DRONEE–W) method. Through efficient allocation of network resources within each cluster, we minimize resource wastage and maximize resource utilization. Simulation results demonstrate the superior performance of our DRONEE–WPRC method in terms of throughput, buffer size, delay time, and power consumption compared to other methods. Therefore, our proposed method achieves optimal network resource allocation and energy efficiency.

Mars: Near-Optimal Throughput with Shallow Buffers in Reconfigurable Datacenter Networks

The performance of large-scale computing systems often critically depends on high-performance communication networks. Dynamically reconfigurable topologies, e.g., based on optical circuit switches, are emerging as an innovative new technology to deal with the explosive growth of datacenter traffic. Specifically, periodic reconfigurable datacenter networks (RDCNs) such as RotorNet (SIGCOMM 2017), Opera (NSDI 2020) and Sirius (SIGCOMM 2020) have been shown to provide high throughput, by emulating a complete graph through fast periodic circuit switch scheduling. However, to achieve such a high throughput, existing reconfigurable network designs pay a high price: in terms of potentially high delays, but also, as we show as a first contribution in this paper, in terms of the high buffer requirements. In particular, we show that under buffer constraints, emulating the high-throughput complete graph is infeasible at scale, and we uncover a spectrum of unvisited and attractive alternative RDCNs, which emulate regular graphs, but with lower node degree than the complete graph. We present Mars, a periodic reconfigurable topology which emulates a d-regular graph with near-optimal throughput. In particular, we systematically analyze how the degree~d can be optimized for throughput given the available buffer and delay tolerance of the datacenter. We further show empirically that Mars achieves higher throughput compared to existing systems when buffer sizes are bounded.

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands.

Impedance cardiography (ICG) is a low-cost, non-invasive technique that enables the clinical assessment of haemodynamic parameters, such as cardiac output and stroke volume (SV). Conventional ICG recordings are taken from the patient's thorax. However, access to ICG vital signs from the upper-arm brachial artery (as an associated surrogate) can enable user-convenient wearable armband sensor devices to provide an attractive option for gathering ICG trend-based indicators of general health, which offers particular advantages in ambulatory long-term monitoring settings. This study considered the upper arm ICG and control Thorax-ICG recordings data from 15 healthy subject cases. A prefiltering stage included a third-order Savitzky-Golay finite impulse response (FIR) filter, which was applied to the raw ICG signals. Then, a multi-stage wavelet-based denoising strategy on a beat-by-beat (BbyB) basis, which was supported by a recursive signal-averaging optimal thresholding adaptation algorithm for Arm-ICG signals, was investigated for robust signal quality enhancement. The performance of the BbyB ICG denoising was evaluated for each case using a 700 ms frame centred on the heartbeat ICG pulse. This frame was extracted from a 600-beat ensemble signal-averaged ICG and was used as the noiseless signal reference vector (gold standard frame). Furthermore, in each subject case, enhanced Arm-ICG and Thorax-ICG above a threshold of correlation of 0.95 with the noiseless vector enabled the analysis of beat inclusion rate (BIR%), yielding an average of 80.9% for Arm-ICG and 100% for Thorax-ICG, and BbyB values of the ICG waveform feature metrics A, B, C and VET accuracy and precision, yielding respective error rates (ER%) of 0.83%, 11.1%, 3.99% and 5.2% for Arm-IG, and 0.41%, 3.82%, 1.66% and 1.25% for Thorax-ICG, respectively. Hence, the functional relationship between ICG metrics within and between the arm and thorax recording modes could be characterised and the linear regression (Arm-ICG vs. Thorax-ICG) trends could be analysed. Overall, it was found in this study that recursive averaging, set with a 36 ICG beats buffer size, was the best Arm-ICG BbyB denoising process, with an average of less than 3.3% in the Arm-ICG time metrics error rate. It was also found that the arm SV versus thorax SV had a linear regression coefficient of determination (R2) of 0.84.