Sampling Frequency Research Articles

Quantifying and mitigating recorder-induced variability in ecological acoustic indices

Due to the complexity of soundscapes, Ecological Acoustic Indices (EAI) are frequently used as metrics to summarize ecologically meaningful information from audio recordings. Recent technological advances have allowed the rapid development of many audio recording devices with significant hardware/firmware variations among brands, whose effects in calculating EAI have not yet been determined. In this work, we show how recordings of the same landscape with different devices effectively hinder reproducibility and produce contradictory results. To address these issues, we propose a preprocessing pipeline to reduce EAI variability resulting from different hardware without altering the target information in the audio. To this end, we tested eight EAI commonly used in soundscape analyses. We targeted three common cases of variability caused by recorder characteristics: sampling frequency, microphone gain variation, and frequency response. We quantified the difference in the probability density functions of each index among recorders according to the Kullback-Leibler divergence. As a result, our approach reduced up to 75% variations among recorders from different brands (AudioMoth and SongMeter) and identified the conditions in which these devices are comparable. In conclusion, we demonstrated that different devices effectively affect EAI and show how these variations can be mitigated.

Application of video adaptive sampling based on soft computing in higher education evaluation and E-learning interactive environment

The level of teaching quality largely reflects the overall strength of universities and has a direct impact on the quality of cultivating high-quality talents. In this context, higher education institutions are constantly striving to explore innovative methods, but this diverse educational environment also brings challenges to the evaluation methods of teacher teaching quality. This article explores the application of video adaptive sampling in AI data analysis of university education evaluation based on soft computing technology. In order to solve the data sampling problem in university education evaluation, this paper proposes a video adaptive sampling method based on soft computing. In this paper, an E-learning virtual interactive system is designed, multimedia teaching materials matching the requirements of E-learning environment are created, tools suitable for E-learning virtual interactive environment are selected and integrated, soft computing technology is adopted, intelligent algorithms and data analysis methods are combined. By extracting and analyzing the key information in the video, the video sampling strategy is dynamically adjusted to realize the adaptive video sampling. Modeling the content characteristics of videos through fuzzy logic, and using genetic algorithms to optimize the sampling frequency and accuracy of videos. The experimental results indicate that the video adaptive sampling method based on soft computing can effectively improve the accuracy and efficiency of AI data analysis in university education evaluation. Compared with traditional fixed sampling methods, this method can dynamically adjust according to the characteristics of video content, reduce redundant data sampling, and improve the accuracy of data analysis.

Purported Self-Organized Criticality of the Cardiovascular Function: Methodological Considerations for Zipf's Law Analysis.

Self-organized criticality is a universal theory for dynamical systems that has recently been applied to the cardiovascular system. Precise methodological approaches are essential for understanding the dynamics of cardiovascular self-organized criticality. This study examines how the duration and quality of data recording affect the analysis of cardiovascular self-organized criticality, with a focus on the beat-by-beat heart rate variability time series obtained from seven healthy subjects in a standing position. Drawing a Zipf diagram, we evaluated the distribution of cardiovascular events of bradycardia and tachycardia. We identified tipping points for the distribution of both bradycardia and tachycardia events. By varying the recording durations (1, 2, 5, 10, 20, 30, and 40 min) and sampling frequencies (500, 250, and 100 Hz), we investigated their influence on the observed distributions. While shorter recordings can effectively capture cardiovascular events, they may underestimate the variables describing their distribution. Additionally, the tipping point of the Zipf distribution differs between bradycardia and tachycardia events. Comparisons of the distribution of bradycardia and tachycardia events should be conducted using long data recordings. Utilizing devices with lower sampling frequencies may compromise data fidelity. These insights contribute to refining experimental protocols and advancing our understanding of the complex dynamics underlying cardiovascular regulation.

Investigation and improvement of spill quality at XiPAF synchrotron

The Xi’an 200 MeV Proton Application Facility (XiPAF) is a specialized facility designed to simulate space radiation environments. To meet the requirements of space radiation simulation, the proton accelerator must provide a quasi-continuous spill. The facility has achieved this goal by implementing third-order resonance and RF knockout (RF-KO) slow extraction. In this study, the source of the spill ripple at the XiPAF synchrotron was analyzed and a method was developed to calculate the fluctuation ratio of the spill from the spill spectrum. According to the experimental results, the spill quality at XiPAF is mainly affected by the ripple of the magnet power supply. To improve the spill quality, the frequency spectrum of the excitation signal was optimized to suppress the influence of the power supply ripple. We found that the dual-frequency signal, which consists of the two sinusoidal waves corresponding to the extraction and diffusion regions in the third-order resonance, can significantly reduce the influence of the magnet power supply ripple. After adopting the optimized dual-frequency signal, the fluctuation ratio of the spill is reduced from about 0.15 to 0.092 (sample median) at a sampling frequency of 1.24 kHz.

Detail-preserving image warping by enforcing smooth image sampling

Multi-phase dynamic contrast-enhanced magnetic resonance imaging image registration makes a substantial contribution to medical image analysis. However, existing methods (e.g., VoxelMorph, CycleMorph) often encounter the problem of image information misalignment in deformable registration tasks, posing challenges to the practical application. To address this issue, we propose a novel smooth image sampling method to align full organic information to realize detail-preserving image warping. In this paper, we clarify that the phenomenon about image information mismatch is attributed to imbalanced sampling. Then, a sampling frequency map constructed by sampling frequency estimators is utilized to instruct smooth sampling by reducing the spatial gradient and discrepancy between all-ones matrix and sampling frequency map. In addition, our estimator determines the sampling frequency of a grid voxel in the moving image by aggregating the sum of interpolation weights from warped non-grid sampling points in its vicinity and vectorially constructs sampling frequency map through projection and scatteration. We evaluate the effectiveness of our approach through experiments on two in-house datasets. The results showcase that our method preserves nearly complete details with ideal registration accuracy compared with several state-of-the-art registration methods. Additionally, our method exhibits a statistically significant difference in the regularity of the registration field compared to other methods, at a significance level of p < 0.05. Our code will be released at https://github.com/QingRui-Sha/SFM.

Mapping vascular network architecture in primate brain using ferumoxytol-weighted laminar MRI.

Mapping the vascular organization of the brain is of great importance across various domains of basic neuroimaging research, diagnostic radiology, and neurology. However, the intricate task of precisely mapping vasculature across brain regions and cortical layers presents formidable challenges, resulting in a limited understanding of neurometabolic factors influencing the brain's microvasculature. Addressing this gap, our study investigates whole-brain vascular volume using ferumoxytol-weighted laminar-resolution multi-echo gradient-echo imaging in macaque monkeys. We validate the results with published data for vascular densities and compare them with cytoarchitecture, neuron and synaptic densities. The ferumoxytol-induced change in transverse relaxation rate (ΔR2*), an indirect proxy measure of cerebral blood volume (CBV), was mapped onto twelve equivolumetric laminar cortical surfaces. Our findings reveal that CBV varies 3-fold across the brain, with the highest vascular volume observed in the inferior colliculus and lowest in the corpus callosum. In the cerebral cortex, CBV is notably high in early primary sensory areas and low in association areas responsible for higher cognitive functions. Classification of CBV into distinct groups unveils extensive replication of translaminar vascular network motifs, suggesting distinct computational energy supply requirements in areas with varying cytoarchitecture types. Regionally, baseline R2* and CBV exhibit positive correlations with neuron density and negative correlations with receptor densities. Adjusting image resolution based on the critical sampling frequency of penetrating cortical vessels, allows us to delineate approximately 30% of the arterial-venous vessels. Collectively, these results mark significant methodological and conceptual advancements, contributing to the refinement of cerebrovascular MRI. Furthermore, our study establishes a linkage between neurometabolic factors and the vascular network architecture in the primate brain.

NeutralNet: Development and testing of a machine learning solution for pulse shape discrimination

Accurate description of radiation fields containing neutrons continues to be a difficult task to complete. This difficulty arises because of the inherent sensitivity of neutron detectors to other types of radiation, and the ability of neutrons to generate secondary particles producing mixed field environments. This research looks at the development and performance of various machine learning architectures when applied to the task of pulse shape discrimination with liquid scintillators. This work was carried out with a neutron sensitive liquid scintillator, EJ-301, with signals digitized at 3.2 GHz with 12 bits of resolution utilizing a CAEN DT-5743 digitizer. Measurements were artificially reduced in sampling depth and frequency to investigate the importance of these parameters for performance of the machine learning algorithms. Two isotopic neutron source, 238Pu9Be and 241Am9Be, and three photon sources: 24Na, 60 Co, and 137Cs were used for generation of the training and validation sets as well as for energy calibration. The greatest performance was achieved with a lightly altered implementation of GoogLeNet and with the full sampling rate and bit depth afforded by the digitizer. A true positive rate of 69.17 % was achieved while correctly rejecting 99.9999 % of photon events. This performance ranges from 1.30 % at events greater than 3 MeVee to 89.96 % between 340–1000 keVee. For neutron events with energy below 200 keVee 21.48 % of the validation neutrons are still identified as neutrons.

Minor Faults Diagnosis for Under-Sampled Lithium-Ion Batteries Based on Static-Dynamic Compensation

With the rapid proliferation of electric vehicles, the safety concerns related to lithium-ion batteries are gaining more and more attention. Fault diagnosis is a key approach to reducing the risk of battery failure. However, existing battery management systems (BMS) apply under-sampled voltage signal acquisition, which leads to misdiagnosis and omission of faults. To address this issue, a minor fault early diagnosis method based on static-dynamic compensation voltage data is proposed. First, the voltages of the series-connected cells are asynchronously collected. Then, the collected voltage sequences from various modules are mapped to the voltage sequence of the target battery using the static-dynamic compensating method, which can obtain a new sequence with a significantly higher equivalent sampling frequency. Finally, the sample entropy method is employed to detect minor faults based on the new sequence after compensation. Experimental results reveal that the presented method can increase the sampling frequency by about 8 times. The proposed method can successfully detect minor short circuits and poor connection faults in the battery under different ambient temperatures.

A cascaded bit-weighted distribution matching for LDPC-coded PS-64QAM DMT in a high-speed Unamplified IM-DD system

We experimentally demonstrate the generation and transmission of probabilistic shaping (PS) 64-ary quadrature amplitude modulation (64-QAM) discrete multi-tone (DMT) signal with low-density parity-check (LDPC)-coded modulation based on two-stage bit-weighted distribution matching (TS-BWDM) in a high-speed unamplified intensity modulation and direct detection system. The TS-BWDM scheme is based on a two-stage bit-weighted inversion algorithm, which has the advantages of no complex multiplication and division operations and low hardware implementation complexity. As the key content of TS-BWDM, before FEC coding, the bit-weighted intervention operation is used to change the bit probability of a specific sequence in the parallel binary sequence. The two-stage bit-weighted inversion operation and one-stage weight-labeling bit insertion is performed for achieving the target symbol probability distribution. As a result, it does not add a significant amount of extra redundant bits. Additionally, the high-accuracy training symbols-based sampling frequency offset (SFO) estimation method and digital interpolation are used to compensate SFO. In our experiments, the TS-BWDM-based PS-64QAM DMT signals with a net rate of 157.3-Gb/s transmission over 10-km non-zero dispersion- shifted fiber can be achieved. The system performance is investigated under two LDPC code rates (3/4 and 9/10) and three PS parameter values (k = 3, 5 and 9). The experimental results show that the receiver power sensitivity and the system fiber nonlinear effect tolerance can be significantly improved compared with uniformly-distributed signals.

Advanced testbed to assess disturbances in electrical grids with DERs using relays/meters with varying sampling frequencies

Distributed energy resources (DERs) are vital to achieve clean energy goals, but in some cases, their rapid deployment has led to negative effects on their reliability because of possible voltages and/or current disturbances that need to be analyzed in detail. This study created an advanced testbed with a real-time simulator and synchronized time and trigger event systems was created to compare event disturbances from intelligent electronic devices (IEDs) with different sampling frequencies and locations in an electrical grid containing DERs. This testbed is capable of capturing the recorded events of multiple IEDs and the real-time simulator to assess the effect of the disturbances and IEDs’ behaviors, including phase voltages, phase current waveforms, and total harmonic distortion (THD) analysis. In this study, a three-line-to-ground electrical fault was simulated, the disturbance event was detected, and recorded events (from 4 to 133 samples per cycle) were collected from meters and relays located at different sites. A harmonic peak algorithm, which was based on measuring the phase voltage and phase current THD peaks at the first one-cycle of the disturbance events, was created to assess and detect the fault states and locations. Several important findings resulted from performing this study, such as selecting IEDs with high sampling frequencies, installing all IEDs with similar sampling frequencies, assessing the software limitations for computing THD, studying the effect of the IEDs’ locations (downstream or upstream) with respect to the electrical fault site and the distance from the disturbance event point, evaluating the percentage errors of the phase voltage THD peaks at the first one-cycle of disturbance events, and installing synchronized time source and trigger event signal systems for recording the IEDs’ events.

Seven Things to Know About Exercise Classification With Inertial Sensing Wearables.

Exercise monitoring with low-cost wearables could improve the efficacy of remote physical-therapy prescriptions by tracking compliance and informing the delivery of tailored feedback. While a multitude of commercial wearables can detect activities of daily life, such as walking and running, they cannot accurately detect physical-therapy exercises. The goal of this study was to build open-source classifiers for remote physical-therapy monitoring and provide insight on how data collection choices may impact classifier performance. We trained and evaluated multi-class classifiers using data from 19 healthy adults who performed 37 exercises while wearing 10 inertial measurement units (IMUs) on the chest, pelvis, wrists, thighs, shanks, and feet. We investigated the effect of sensor density, location, type, sampling frequency, output granularity, feature engineering, and training-data size on exercise-classification performance. Exercise groups (n = 10) could be classified with 96% accuracy using a set of 10 IMUs and with 89% accuracy using a single pelvis-worn IMU. Multiple sensor modalities (i.e., accelerometers and gyroscopes), high sampling frequencies, and more data from the same population did not improve model performance, but in the future data from diverse populations and better feature engineering could. Given the growing demand for exercise monitoring systems, our sensitivity analyses, along with open-source tools and data, should reduce barriers for product developers, who are balancing accuracy with product formfactor, and increase transparency and trust in clinicians and patients.

Mutation pattern of EGFR gene in Peruvian patients with non-small cell lung cancer.

e20615 Background: The correct characterization of EGFR mutations is crucial to the proper management of NSCLC patients due to the possibility to treat these patients with specific targeted therapies. Herein we report the incidence, mutation pattern and novel mutations of EGFR mutation in Peruvian NSCLC patients. Methods: Between 2016 and 2023 a total of 1489 NSCLC patients from public and private centers were referred to ONCOGENOMICS laboratory for somatic mutation testing. Next Generation Sequencing (NGS) was performed using the Ampliseq for Illumina FOCUS or Custom panel and run on the Illumina MiSeq. Raw data were processed automatically on the BaseSpace Sequence Hub (Illumina) and aligned to the hg19 reference genome The default limit of detection (LOD) was set at 5% allelic frequency (VAF) in tissue samples and 0.5% VAF in cfDNA samples. Results: The study included 1401 NSCLC patients, 61.8% were women. Clinical stages were distributed III (22.5%), IV (77.5%). All tumors were adenocarcinoma. Incidence of EGFR-mutated NSCLC was 33.4% (450), 68.9% were women. We found 519 EGFR mutations in 450 tumors. Classical or common EGFR mutations; L858R or ex19del corresponded to 86.4% with 331 and 383 cases, respectively and L858R or ex19del + T790M corresponded to 13.6% with 2 cases onset and 50 cases in progression. EGFR T790M de novo mutations were found in 4 cases onset. In one case at diagnosis was detected a T790M-like 3R mutant EGFR p.(C797S); c.2390G &gt; C, a drug resistance point mutation in ex20.Non-classical or atypical EGFR mutations corresponded to 14.0% (63/450) of cases. Non-classical EGFR mutations were categorized as follows according to Janning et al.: (i) More frequent uncommon EGFR mutations - 39.7% (25/63) including G719X, S768I, E709X and L861Q (22/25); G719X + S768I (1/25); and combinations with classical EGFR mutation L858R + S768I (2/25). (ii) EGFR exon 20 insertions - 44.5% (28/63), including a novel mutation EGFR p.(769_770VDinsGFV);c.2308_2309ins. All were located in the loop following the C-helix. And (iii) very rare mutations - 15.9% (10/63) including very rare point mutations in ex19 (1/10), ex18del (4/10), ex19ins (1/10) and complex mutations (ex19del + very rare point mutation in ex19 (4/10).Two point mutation no reported previously were found, EGFR, ex19, p.(S752F);c.2255C &gt; T and EGFR, ex19, p.(T751P);c.2251A &gt; C, both as co-mutations with classical EGFR ex19del mutation. Conclusions: The present study underscores the value of NGS to optimally address EGFR mutations, specially the rare and novel ones due to its higher sensitivity and specificity compared to other techniques. This approach is particularly important for the Latin American population which is underrepresented in genomic databases. Clinical correlation of this genomic findings is ongoing.

Microstructural evolution and mechanics behavior of postmortem meat subjected to resonance

The tenderness of meat is determined by muscle components, mainly including myofibrillar and connective tissues. In the present study, the microstructures evolution and mechanics behavior of yak postmortem muscle subjected to resonance vibration treatment was investigated by experimental and numerical investigation, in order to illustrate the resonance tenderization mechanism and the mechanical relationship of muscle components. The results show that there are three orders of natural frequencies of the raw yak meat, and the resonance frequency is determined as 26 Hz. By vibration treatment at this frequency, the shear force and MFI of the vibrated meat is 35.70 N and 62, respectively, exhibiting remarkable tenderizing effect compared with the raw sample and other frequencies treated samples. The microstructures and vibration modal simulation have revealed that the stress concentration happens in the junction of muscle fibers and extracellular matrix (collagenous connective tissues), leading to the detachment of endomysium connective tissues from muscle fibers and the disappearance of M-bands in myofibrils, thus resulting the significant tenderization. The resonance also causes the stress-strain behaviors of meat transiting from plastic deformation to elastic deformation. The experimental and numerical results reveal that the physical destruction of the cross-linkings or interactions between muscle fibers and endomysium connective tissues induced by resonance, plays a crucial role in the detachment of muscle components, transformation of stress-strain behavior and the significant tenderization effect.

The application of a low-cost microcomputer logger with pressure-temperature based sensor for coastal observations: A preliminary study

Abstract Nearshore hydrodynamics, such as water level fluctuations, field observations play a crucial role in understanding and monitoring the dynamics of the coastal region. The observed parameters provide insight into oceanographic processes, climate change impacts, and the behavior of marine ecosystems that could be valuable for coastal management and infrastructure development planning. Most of the available hydrodynamic loggers are provided by companies with relatively high prices. Due to limited budgets, many areas including Indonesia waters, have limited hydrodynamic observation data. In this paper, we presented a prototype of a simple (DIY-Do It Yourself) and low-cost water level logger with a microcomputer that could be applied in coastal regions. The system consists of a pressure-based sensor to detect water level fluctuations, a temperature sensor, a single-board microcomputer and data logger, and a power supply with different sampling frequencies for various coastal applications. The result showed the ability of the microcomputer system to measure high-resolution water levels and temperatures applicable for non-directional waves, tides and non-tides observations, and ecological monitoring. The microcomputer’s low power consumption makes it suitable for long-term coastal observations, even in remote or battery-powered applications. The body of the logger is designed from PVC-nylon with sensors made from waterproof and corrosion-free materials to ensure its applicability for coastal monitoring. Moreover, the flexibility of the microcomputer system allows for customization and adaptation to specific research requirements at relatively affordable prices.

Investigating three-dimensional vortex evolution in centrifugal pump under rotating stall conditions using tomographic particle image velocimetry

A rotating stall in centrifugal pumps commonly occurs under off-design operations, which is a detrimental phenomenon leading to flow instabilities, pressure fluctuations, and reduced performance. A time-resolved non-intrusive three-dimensional (3D) flow visualization method is developed for investigating complex vortex structures in centrifugal pumps based on Omega vortex identification and tomographic particle image velocimetry (tomo-PIV). A special-made centrifugal pump prototype was developed with acrylic glass allowing for optical access. This method enables both qualitative and quantitative analysis of high spatiotemporal resolution on flow behaviors and dynamics under various stall conditions. The ultra-high sampling frequency realized over 40 time-consecutive observations per revolution under 0.2 Qd, 0.4 Qd, 0.6 Qd, and 0.8 Qd. It captures the instantaneous evolution of vortex structures that undergoes a growth–breakup transition within 7–9 ms. The rotating stall mechanism is revealed experimentally from the evolution of the vortex structure. Our analysis shows the tomo-PIV's additional velocity component aids in understanding the 3D characteristics of the stall. A substantial region of reverse flow in the z-axis direction is observed under 0.2 Qd. Vortex structures are more prone to blockage at the impeller inlet, exacerbating the stall phenomenon. As the flow rate increases, the velocity distributions across different layers exhibit a laminar characteristic with a more uniform profile. The vortex structures extend radially and migrate toward the outlet. The evolutions of the stall vortex, wake vortex, and inlet vortex share the same dominant frequency components (4.75fn and 5.25fn), but the flow rate affects the proportion of different frequency components.

Design and Implementation of Digital Down Converter for WiFi Network

This paper introduces a field-programmable gate array (FPGA)-based digital down converter (DDC) processing a sampling frequency of about 3.64 GHz to a down-converted frequency of 28.4375 MHz to match the IEEE 802.11ah Wi-Fi HaLow standard. The proposed DDC uses a polyphase mixer(PM) and a resampling filter. The PM adopts parallel coordinate rotation digital computer (CORDIC) processors and lowpass filter arrays to reduce high-speed data rates with minimum resource utilization. Again, the resampling filter employs a cascaded integrator comb (CIC) filter associated with a parallel prefixed adder (PPA) and a multi-channel systolic finite impulse response (FIR) filter implemented with canonical expression, attaining optimum hardware cost. Converting floating-point to fixed-point data types provides significant resource savings. Finally, the improved design is coded in the Xilinx Vivado synthesis tool and successfully tested on the FPGA Kintex-7 device. In contrast to other recent architectures, the proposed design substantially reduces area requirements and power utilization. The Matlab tool verifies the design to achieve an acceptable spurious-free dynamic range (SFDR) of 115 dB.

A multistage exergy evaluation-cooperated liquid level optimization approach for multi-equipment evaporation process

The liquid level for the evaporator is a significant parameter related to production safety and evaporation efficiency in the evaporation process. However, the process nonlinearity and time-delay increase the difficulty of operating control. Hence, a multistage exergy evaluation-cooperated liquid level optimization approach for multi-equipment evaporation process is proposed in this paper. An exergy evaluation-paralleled system based on exergy analysis is constructed. In addition, interdependent knowledge between liquid level and key process parameters is mined. Moreover, a multistage liquid level optimization model is established with the goal of minimizing energy consumption and constrained by stable production operation and qualified product quality. Meanwhile, a concentration forecasting strategy with ensemble learning based on gradient boosting decision tree is introduced into the optimization model while considering the asynchronous sampling frequency. Through application verification and analysis, the proposed liquid level optimization model provides feasible operational guidance for actual production, achieving high-quality, efficient, and green production.

Применение голографического кодирования для повышения надежности передачи информации в зашумленных каналах связи

Two options for using previously developed holographic coding to increase the reliability of information transmission in a communication channel with an unmanned aerial vehicle are considered – digital channel coding and spectral coding. It is shown that the holographic code has high noise immunity and provides a probability of correct decoding of more than 0.9999 with a probability of error in the channel of 0.34. Spectral holographic coding ensures reliable communication in the presence of noise and intentional interference, the power of which is 10 dB higher than the signal power, which in turn ensures the secrecy of the communication channel. An additional advantage of spectral coding is increased resistance to intersymbol interference, which makes it possible to eliminate the introduction of guard intervals and increase spectral efficiency, and reduces the requirements for stability of the sampling frequency when performing FFT in the receiver demodulator.

Impact of Variable Sampling on Estimates of HIV-1 Reservoir Formation Dates.

Timing of human immunodeficiency virus-1 (HIV-1) reservoir formation is important for informing HIV cure efforts. It is unclear how much of the variability seen in dating reservoir formation is due to sampling and gene-specific differences. We used a Bayesian extension of root to tip regression (bayroot) to reestimate formation date distributions in participants from Swedish and South African cohorts, and assessed the impact of variable timing, frequency, and depth of sampling on these estimates. Significant shifts in formation date distributions were only observed with use of faster-evolving genes, while timing, frequency, and depth of sampling had minor or no significant effect on estimates.

State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges

Lithium-ion Batteries (LiB) have a wide range of applications in daily life. However, as they get used over time, battery degradation becomes inevitable, which can lead to a drop in performance and a reduction in the battery’s cycle life. The State of Health (SoH) is widely regarded as the health indicator for the battery pack. In Electric Vehicle (EV) applications, the EV user defines the lower limit of SoH when they experience that the battery no longer supports the EV; at that point, the battery is said to be translated from first life to second life. The SoH estimations of Second Life Batteries (SLB) have plenty of uncertainties, such as the availability of battery’s previous history, non-uniform degradation in the EV application, variations in chemistry, and charging protocols defined by vehicle manufacturers, making the SoH estimation of SLB a challenging task. This paper discusses the equipment, timelines, computational complexity, health indicators, and list of parameters that need to be considered for the SoH estimation of SLB. The SoH estimation methods are classified into direct and indirect techniques. Direct assessment techniques involve cyclic ageing experiments followed by dismantling the battery for microscopic studies performed by previous researchers that were explained. Indirect assessment techniques include physical and chemical based approach, electrical, and Artificial Intelligence (AI)-based methods that estimate SoH indirectly through incremental, differential approaches and other parameters such as Integrated Voltage (IV) and Probability Density Function (PDF). Health indicator identifications play a vital role in indirect assessment methods to gain critical insights regarding battery degradation. The challenges involved in SoH estimation are categorized into equipment requirements, parameters, SoH accuracy and efforts required to compute SoH, which are discussed. Of all the SoH estimation methods, comparison of such methods in First Life Batteries (FLB) and SLB perspectives are discussed. To estimate the SoH of SLB, this paper explains all aspects, such as computational methods, filtering data, data sampling frequency, and the need for a specific algorithm to post-process the battery test data. Equipment availability and timelines are interrelated with the cost incurred in the SoH estimation of SLB. The efficacy and practicality of SoH estimation methods that are proposed for SLB is discussed. Overall, this paper provides necessary insights into the parameters required for SoH estimation and the computational and experimental methods that can be considered for estimating the SoH of SLB while some of the methods are applicable to FLB as well.