Continuous Acquisition Research Articles

A 22.3-Bit Third-Order Delta-Sigma Modulator for EEG Signal Acquisition Systems

This paper presents a high resolution delta-sigma modulator for continuous acquisition of electroencephalography (EEG) signals. The third-order single-loop architecture with a 1-bit quantizer is adopted to achieve 22.3-bit resolution. The effects of thermal noise on the performance of the delta-sigma modulator are analyzed to reasonably allocate the switched-capacitor sizes for optimal signal to noise ratio (SNR) and minimum chip area. The coefficients in feedback path and input path are optimized to avoid the signal distortion under the full-scale input voltage range with almost no increase in total capacitance sizes. Fabricated in 0.5 µm CMOS technology and powered by a 5 V voltage supply, the proposed delta-sigma modulator can achieve 136 dB peak SNR with 16 Hz input and 137 dB dynamic range in 100 Hz signal bandwidth with an oversampling ratio of 512. The modulator dissipates 700 µA. The core chip area is 1.96 mm2. The modulator occupies 1.41 mm2 and the decimator occupies 0.55 mm2.

Analysis of LVMH Group's Business Operation Model and Marketing Strategy - A Case Study Based on Group Mergers and Acquisitions

This article aims to introduce the situation of LVMH Group and its industry-leading position in the global luxury goods market supply chain. Since its establishment, LVMH Group has expanded its business map through continuous acquisitions and expansion, leveraging its strong capabilities and strategic vision. The focus is on analyzing two acquisition cases carried out by LVMH: the Tiffany & Co. acquisition case and the Dior acquisition case. For these two cases, the background, motives, process, and key steps are introduced, and the effects and impacts of the acquisitions are discussed in depth. At the same time, a detailed analysis of LVMH's acquisition strategy is conducted, including acquisition targets, selection criteria, post-acquisition integration and management, etc. Finally, the research findings are summarized, and future development directions are proposed. Since the gradual control of the pandemic, the luxury goods market has witnessed a recovery in consumption and significant growth in sales. This article has certain reference values for understanding trends in the luxury goods industry, corporate strategies, and market competition.

Research on Body Wave Extraction Technology of Mixed Active Source Ambient Noise

In complex surface environments, active source seismic exploration methods are difficult to stimulate and costly, while ambient noise detection technology is fast and convenient, with a little environmental damage and low cost. Therefore, it has aroused extensive research by scholars at home and abroad. At present, the use of passive source surface wave method for subsurface medium imaging has become mature. However, due to the low content and weak energy of the body wave signal in the actual ambient noise, it is particularly difficult to extract body waves using seismic interferometry. At the same time, body wave exploration has larger exploration depth and higher resolution. In this paper, we propose an ambient noise detection technology of mixed active sources, namely, imposing artificial seismic sources in the continuous ambient noise data acquisition process. We refer to the ambient noise of mixed active sources as "mixed ambient noise". It is found that the virtual refraction wave obtained by the ambient noise detection method of mixed active sources has high signal-to-noise ratio, strong anti-interference ability, and can obtain the real velocity information of the deeper subsurface medium, but cannot reflect the accurate time information. Supplementing the active source signal in the far field can obtain virtual refractions with higher signal-to-noise ratio, while effectively reducing the mixing phenomenon of surface wave and surface interference waves in the virtual common-shot record.

Nationwide urban ground deformation in Japan for 15 years detected by ALOS and Sentinel-1

InSAR time series analysis has become a major tool for nationwide land deformation monitoring. Sentinel-1 SAR data have enabled us to measure and monitor ground deformation globally with high accuracy and resolution through InSAR time series analysis, due to its constant and frequent global coverage and open data policy since 2014. Although several datasets from previous SAR satellites were available before Sentinel-1, such comprehensive deformation monitoring was not performed due to several limitations such as data quality, analysis technique, data policy, and processing capacity at that time. However, since a large amount of ALOS InSAR products and an open-source InSAR time series analysis tool LiCSBAS have become openly and freely available, we can easily derive the deformation from 2006 to 2011 by using them. In this study, we detected the deformation time series and velocity in all major urban areas in Japan from 2006 to 2011 and compared the results with the deformation from 2014 to 2020 detected by Sentinel-1 data. The two deformation datasets with different time periods revealed various 15-year deformation histories, such as long-term constant subsidence in Tomakomai and Niigata, changes in deformation areas and/or velocities in Hirosaki, Kujyukuri, Kanazawa, and Matsushiro, and appearance or disappearance of deformation in Joso, Yoyogi, and Kyoto. Future abundant and continuous SAR data acquisitions will reveal more long-term deformation transitions and help to understand the details of the mechanisms.

A patient-specific timing protocol for improved CT pulmonary angiography

Rationale and objectivesTo improve the image quality of CT pulmonary angiography (CTPA) using a patient-specific timing protocol. Material and methodsA total of 24 swine (48.5 ± 14.3 kg) underwent continuous contrast-enhanced dynamic CT acquisition over 30 s to capture the pulmonary arterial input function (AIF). Multiple contrast injections were made under different cardiac outputs (1.4–5.1 L/min), resulting in a total of 154 AIF curves. The volume scans with maximal enhancement in these AIF curves were retrospectively selected as the reference standard (group A). Two prospective CTPA protocols with bolus-tracking were then simulated using these AIF curves: one used a fixed delay of 5 s between triggering and CTPA acquisition (group B), while the other used a specific delay based on one-half of the contrast injection duration (group C). The mean attenuation, signal-to-noise (SNR) and contrast-to-noise ratios (CNR) between the three groups were then compared using independent sample t-test. Subjective image quality scores were also compared using Wilcoxon-Mann-Whitney test. ResultsThe mean attenuation of pulmonary arteries for group A, B and C (expressed in [HU]) were 870.1 ± 242.5 HU, 761.1 ± 246.7 HU and 825.2 ± 236.8 HU, respectively. The differences in the mean SNR and CNR between Group A and Group C were not significant (SNR: 65.2 vs. 62.4, CNR: 59.6 vs. 56.4, both p > 0.05), while Group B was significantly lower than Group A (p < 0.05). ConclusionThe image quality of CT pulmonary angiography is significantly improved with a timing protocol determined using contrast injection delivery time, as compared with a standard timing protocol with a fixed delay between bolus triggering and image acquisition.

Daily monitoring of Effective Green Area Index and Vegetation Chlorophyll Content from continuous acquisitions of a multi-band spectrometer over winter wheat

Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R2 of 0.86, LCC (RMSE = 12.06 μg/cm2, RRMSE = 33.34%, R2 = 0.52) and CCC (RMSE = 0.23 g/m2, RRMSE = 24.58%, R2 = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.

Current status and applications of photovoltaic technology in wearable sensors: a review

The rapid development of wearable sensor technology can be attributed to developments in materials, microelectronics, fabrication, communication systems, and Artificial Intelligence (AI). The use of wearable sensors enables continuous acquisition and monitoring of the pathophysiological parameters of a person in real time. The global market for health-related wearables has experienced significant growth, particularly in response to the COVID-19 pandemic. A wearable sensor module is comprised of various components, including a powering unit, sensor(s), acquisition unit, communication unit, and processing unit. The non-fluctuating power source with a long life is of utmost significance to the continuous and real-time operation of a wearable sensor. A wearable device can be powered by a rechargeable battery, such as a lithium-ion battery, which can be charged from a standard power source but requires regular recharging after depletion and has a negative environmental impact. This necessitates using green renewable energy sources like photovoltaic cells, piezoelectric generators, wind energy converters, and thermoelectric generators for powering wearable sensor modules. The photovoltaic cell that converts photonics into electrical energy is deemed a viable green energy source for wearable sensor modules. This article reviews the progress and application of photovoltaic technology in wearable sensor modules.

Point cloud generation for critical transportation infrastructure through Bézier curve

While Light Detection and Ranging (LiDAR)-based sensors exhibit considerable potential for transportation infrastructure management, not all infrastructure elements can be comprehensively captured by point clouds, leading to the formation of undesirable “holes” due to both temporary and permanent occlusions. It is imperative to devise mechanisms for identifying and predicting the missing data within these “holes” to ensure the continuous acquisition of critical inventory information. This paper describes a method for generating point clouds based on Bézier curves, which effectively fills the voids within the infrastructure. This method comprises three integral processes, including angle-based boundary detection, identification of principal elevation change, and Bézier curve-based hole filling. The method demonstrates promising results on different roadside surfaces and at different ranges of scales of “holes”. Case studies on the sidewalk, and sound barrier inventories show that the proposed method can significantly improve the quality of the point cloud data for subsequent measurements.

Helical sample-stepping for faster speckle-based multi-modal tomography with the Unified Modulated Pattern Analysis (UMPA) model

Speckle-based imaging (SBI) is a multi-modal X-ray imaging technique that gives access to absorption, phase-contrast, and dark-field signals from a single dataset.However, it is often difficult to disentangle the different signals from a single measurement.Having complementary data obtained by repeating the scan under slightly varied conditions (multiframe approach) can significantly enhance the accuracy of signal extraction and, consequently, improve the overall quality of the final reconstruction.In order to retrieve the different channels, SBI relies on a reference pattern, generated by the addition of a wavefront marker in the beam (i.e., a sandpaper or gratings).Here, we show how a continuous helical acquisition can extend the field of view (FOV) and speed up the acquisition while maintaining a multiframe approach for the signal retrieval of a test object.

GNSS/IMU/LiDAR fusion for vehicle localization in urban driving environments within a consensus framework

The continuous, reliable, and accurate acquisition of position information is a fundamental requirement for autonomous vehicles. It underpins perception, decision-making, and planning modules in automated driving systems. However, the performance of multi-modal sensors, including the Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and LiDAR, typically found in autonomous vehicles, is easily influenced by complex environmental factors encountered in urban driving conditions. This creates challenges for existing localization algorithms. More specifically, while GNSS performs accurately in open-sky areas, it is susceptible to the multi-path effect. Although the IMU-based Inertial Navigation System (INS) provides continuous position data, it suffers from cumulative errors. LiDAR-based map-matching is highly effective when sufficient point cloud features are present but fall short in plain environments. To address these individual sensor deficiencies, we propose the application of a consensus framework for GNSS/IMU/LiDAR fusion, and it is able to be expanded for multi-modal sensor fusion. Our approach seeks to take advantage of the redundancy inherent in these sensor systems. Our method begins with the derivation of the INS mechanization and its error dynamics. Subsequently, based on the Consensus Kalman Filter (CKF), we establish a GNSS measurement node and a Normal Distribution Transformation (NDT) map-matching measurement node. These are designed to estimate position errors in the INS. To counter sensor measurement failure or degradation, we introduce an adaptation mechanism for the CKF. This ensures position accuracy and continuity by normalizing measurement quality across different measurement nodes. Finally, to validate our consensus-based localization framework, we conducted extensive real-world vehicle tests in urban driving conditions. The results have confirmed the superior accuracy and robustness of our approach, even in the face of individual sensor failure. In our experiments, the proposed method reduces the localization mean absolute error by around 17% compared to INS/map-matching fusion, and around 59% compared to INS/GNSS fusion while map-matching fails. While the GNSS and map-matching measurements contain large noises, our framework achieves at least approximately a 30% improvement compared to traditional methods.

The routes of hysterectomy: A survey of current practices amongst members of the International Society for Gynaecologic Endoscopy (ISGE)

ObjectiveThis study aimed to explore the current opinion on the routes of hysterectomy amongst the members of the International Society for Gynaecologic Endoscopy (ISGE), as well as the perceptions of potential barriers that inhibit gynaecologists from offering a minimally invasive hysterectomies (MIHs) to their patients. Study designAn anonymous, electronic survey was designed, including the questions about the surgeon location and length of gynaecological practice, preferred approach to hysterectomy, approximate number of surgical cases performed during the year preceding the survey (2021), and potential barriers and contraindications to performing MIHs. It was validated by 12 practicing gynaecologists. Subsequently, the survey was sent to all practicing gynaecologists who are the members of the ISGE. ResultsWe received a response from 159 members of ISGE (29 % response rate), of which 92 % with ≥ 5 years in practice since the completion of their residency training in Gynaecology and Obstetrics. When asked about the preferred route of hysterectomy for themselves or their relatives, 59 % chose total laparoscopic hysterectomy (TLH), 19 % vaginal hysterectomy (VH), 8 % chose laparoscopically-assisted vaginal hysterectomy (LAVH) and 5 % chose total abdominal hysterectomy (TAH). However, TAH was the most performed hysterectomy procedure undertaken by the respondents in the year preceding the survey. When asked about the main obstacles to performing MIHs, more than half of the respondents highlighted insufficient training during residency and insufficient surgical experience. Only 25 % of the responders acknowledged reading the ISGE guidelines on performing vaginal hysterectomy. ConclusionsContrary to a full understanding of the benefits of MIHs, lack of training, as well as insufficient surgical experience and unawareness of existing evidence-based guidelines were the main reasons for the high rate of TAH among the ISGE members who participated in the study. All efforts should be directed at teaching VH and TLH techniques during residency, continuous acquisition of practical experience, and use of validated patient selection guidelines for MIH in daily clinical practice.

Seismic background noise levels in the Italian strong-motion network

Abstract. The Italian strong-motion network monitors the seismic activity in the region, with more than 585 stations with continuous data acquisition. In this study, we determine the background seismic noise characteristics of the network by using the data collected in 2022. We analyse the spatial and temporal characteristics of the background noise. It is found that most of the stations suffer from anthropogenic noises, since the strong-motion network is designed to capture the peak ground motions in populated areas. Hence, human activities enrich the low periods of noise. Therefore, land usage of the area where the stations are located affects the background noise levels. Stations can be noisier during the day, up to 12 dB, and during the weekday, up to 5 dB, in short periods. In long periods (≥ 5 s), accelerometric stations converge to similar noise levels and there are no significant daily or weekly changes. It is found that more than half of the stations exceed the background noise model designed for strong-motion stations in Switzerland by Cauzzi and Clinton (2013) in at least one of the calculated periods. We also develop an accelerometric seismic background noise model for periods between 0.0124 and 100 s for Italy by using the power spectral densities of the network. The model is in agreement with the background noise model developed by D’Alessandro et al. (2021) using broadband data for Italy in short periods, but in long periods there is no correlation among studies.

Advanced Phase-Retrieval for Stepping-Free X-Ray Dark-Field Computed Tomography.

Grating-based phase- and dark-field-contrast X-ray imaging is a novel technology that aims to extend conventional attenuation-based X-ray imaging by unlocking two additional contrast modalities. The so called phase-contrast and dark-field channels provide enhanced soft tissue contrast and additional microstructural information. Accessing this additional information comes at the expense of a more intricate measurement setup and necessitates sophisticated data processing. A big challenge for translating grating-based dark-field computed tomography to medical applications lies in minimizing the data acquisition time. While a continuously moving detector is ideal, it prohibits conventional phase stepping techniques that require multiple projections under the same angle with different grating positions. One solution to this problem is the so-called sliding window processing approach that is compatible with continuous data acquisition. However, conventional sliding window techniques lead to crosstalk-artifacts between the three image channels, if the projection of the sample moves too fast on the detector within a processing window. In this work we introduce a new interpretation of the phase retrieval problem for continuous acquisitions as a demodulation problem. In this interpretation, we identify the origin of the crosstalk-artifacts as partially overlapping modulation side bands. Furthermore, we present three algorithmic extensions that improve the conventional sliding-window-based phase retrieval and mitigate crosstalk-artifacts. The presented algorithms are tested in a simulation study and on experimental data from a human-scale dark-field CT prototype. In both cases they achieve a substantial reduction of the occurring crosstalk-artifacts.

A Basic Study on the Diagnosis Method of Internal Short Circuit based on Feature Extraction and Separation Technique

Lithium-ion batteries are widely used in energy storage systems (ESS) and electric vehicles due to superior performance. However, most safety problems in the use process of the lithium-ion battery are attributed to internal short circuits, there are a lot of factors leading to the internal short circuit. Short circuit in the lithium-ion battery affects the electrochemical reaction and then generating a larger short circuit current. Besides, the thermal runaway is the result of a failure of the separator inside Li-ion batteries, small discharge pulses of a few mV were detected at the time of separator damages. Therefore, the fault diagnosis by internal short circuit of lithium batteries has attracted more attention to prevent the fire accidents. This paper aimed to suggest a diagnostic model for separator of LFP (Lithium iron phosphate) pouch cell as a basic research work using continuous pulse analysis method such as feature extraction and separation technique of pulse source for timely diagnose the short circuit fault. In other words, this paper provided a method that can identify the separator failure before thermal runaway in the aspect of a potential explosion and fire prevention. The internal short circuit signal for diagnosis of LFP pouch cell was investigated by forming artificial defects at the separator part compared with normal battery. To investigate the short circuit signal, artificial defect specimens are prepared as a pouch type, pulse signal was analyzed in terms of pulse waveshape (Skewness) and frequency (Fast Fourier Transform, FFT) compared with normal battery and artificial defect pouch cell. To calculate the continuous acquisition pulse signal, python code was developed. As a result, it is found that the pulse signal of artificial defect specimen shown high frequency and low skewness characteristics. Whereas, in the case of a normal battery, it showed characteristics of relatively low frequency and high or middle skewness. From our research work, the results revealed that the pulse shape and feature contain useful information which can help to understand degradation behavior of the LFP pouch cells. This result is expected to be applicable various batteries with presence a defect at the separator of battery

Hardware and Software Implementation of the Embedded Controlling System for the TuMag Camera

The main objective of this paper is to present the design and implementation of an embedded controlling system for the Tunable Magnetograph (TuMag) instrument camera sensor and test-bench software application for camera laboratory characterization. The TuMag camera is based on a new scientific sensor GPIXEL SENSE400 device family equipped with an FPGA. The camera sensor has an active area of 2048 × 2048 pixels and reaches a frame rate of up to 48 frames per second. The embedded controlling system was implemented on an Artix 7 FPGA, which oversees controlling the image sensor through a configuration interface. It controls the read-out of the sensor data and the communication of commands with the host device. The camera has a standard CoaXPress communication interface of up to 3125 Gbps. The FPGA embedded control system allows bit, word, and serial channel calibration and the modification of several parameters, such as the region of interest (RoI) size, exposition time, hardware or software trigger, single or continuous acquisition modes, sensor gain, and adjustment of the black level offset and exposition time. In addition, the firmware has a temperature controller, voltage level monitoring, and an enable–disable power sensor. In addition, the test-bench software application is a library developed in Python 3.7. It is a wrapper for the standard GeniCan commercial frame grabber interpreter. This wrapper permits the improvement of the interaction and interface between the user and the hardware in the camera calibration. The test-bench software application permits the reduction of costs and the transport risk of the TuMag camera between different laboratories. All TuMag embedded control systems and test-bench software application functionalities were successfully tested according to scientific requirements.

Research on Chain Store Operations and Financing Problems of the Self-service KTV in China

At present, the development of various industries is characterized by branding. Through standardized management, they can develop rapidly in cities, regions, and even countries, occupying a huge share of the local market. For example, Chayanyuese, a tea brand in Changsha, China, has won the favor of consumers with its fresh and classic brand image, Chinese-styled concept of brand culture, and high-quality service. Examples of success due to standardized management also include Luckin Coffee, Haidilao Hot pot, and other brands in China. Therefore, in this context, the future development and business model of KTV should also be changed accordingly to cope with the KTV market in the post-epidemic era. Based on the literature review, this paper studies the chain store operations and financing problems of the self-service KTV in China from the perspective of supply chain finance. The results show that the operation of the self-service KTV in China faces many problems, among which the lack of a large amount of funds is the main problem. To solve this problem, supply chain financing, which makes a continuous acquisition of high-quality assets and has a low financing risk, can be an appropriate financing method.

Train Trajectory Generation Method to Mitigate Delay Propagation based on Continuous Train Position Acquisition

Train Trajectory Generation Method to Mitigate Delay Propagation based on Continuous Train Position Acquisition

Sensor-Location-Specific Joint Acquisition of Peripheral Artery Bioimpedance and Photoplethysmogram for Wearable Applications.

Cardiovascular diseases (CVDs), being the culprit for one-third of deaths globally, constitute a challenge for biomedical instrumentation development, especially for early disease detection. Pulsating arterial blood flow, providing access to cardiac-related parameters, involves the whole body. Unobtrusive and continuous acquisition of electrical bioimpedance (EBI) and photoplethysmography (PPG) constitute important techniques for monitoring the peripheral arteries, requiring novel approaches and clever means. In this work, five peripheral arteries were selected for EBI and PPG signal acquisition. The acquisition sites were evaluated based on the signal morphological parameters. A small-data-based deep learning model, which increases the data by dividing them into cardiac periods, was proposed to evaluate the continuity of the signals. The highest sensitivity of EBI was gained for the carotid artery (0.86%), three times higher than that for the next best, the posterior tibial artery (0.27%). The excitation signal parameters affect the measured EBI, confirming the suitability of classical 100 kHz frequency (average probability of 52.35%). The continuity evaluation of the EBI signals confirmed the advantage of the carotid artery (59.4%), while the posterior tibial artery (49.26%) surpasses the radial artery (48.17%). The PPG signal, conversely, commends the location of the posterior tibial artery (97.87%). The peripheral arteries are highly suitable for non-invasive EBI and PPG signal acquisition. The posterior tibial artery constitutes a candidate for the joint acquisition of EBI and PPG signals in sensor-fusion-based wearable devices-an important finding of this research.

Impact of Shelf-Life Simulation on a Self-Adhesive Composite: Polymerization Kinetics, Chemical and Color Stability.

To determine the polymerization kinetics and color stability of a self-adhesive and conventional resin composite after accelerated shelf-life simulation. Two composites were tested - universal Filtek Z250 (3M Oral Care) and self-adhesive Constic (DMG). They were stored for 2 months in an incubator to simulate an Arrhenius aging model (60°C) and tested at 5 different time points. Polymerization kinetics (n = 3) were studied using an attenuated total reflectance technique (ATR), through continuous FTIR spectral acquisition (20 min). Spectra were obtained before, during and after 20 s of light curing. With the spectral data, qualitative analysis was performed yielding chemical stability, and quantitative data including extrapolated degree of conversion (DCmax) and polymerization rate (Rpmax) were assessed. To evaluate color stability (n = 3), a spectrophotometer was used to record CIELAB color parameters. Inferential statistics, including repeated measures two-way ANOVA were carried out at a significance level of 5%. The composites did not appear to undergo significant chemical changes after 2 months of accelerated aging. There was a significant impact of aging on the mean DCmax (p < 0.001). Similarly, a reduction in Rpmax, measured for both composites, was also noted (ANOVA; Z = 203.7; p < 0.001). The two-way ANOVA confirmed that the composite had no influence on the color stability (F = 0.94; p = 0.34), while aging did (p = 0.013). Minimal changes in absorbance levels were noted for both composites, without overly affecting their chemical composition. The presence of an acidic monomer did not seem to potentiate the degradation of the self-adhesive composite. This composite even showed greater color stability after aging.

In this work, we have used several configurations of single-component seismic sensors array for the detection and analysis of the signals produced by lahars at the Cotopaxi Volcano, Ecuador. We have developed a compact, flexible, and reliable prototype enough to be used in volcanic monitoring that includes a new generation Field Programmable Gate Arrays (FPGA) as the basis of the

In this work, we have used several configurations of single-component seismic sensors array for the detection and analysis of the signals produced by lahars at the Cotopaxi Volcano, Ecuador. We have developed a compact, flexible, and reliable prototype enough to be used in volcanic monitoring that includes a new generation Field Programmable Gate Arrays (FPGA) as the basis of the embedded system for continuous signal acquisition, digitalization and storage. We have used an array of twelve single-component seismic sensors in linear geometric configuration. The information is also sent to the central base (Instituto Geofísico) located in the city of Quito through radiofrequency communication. Subsequently, the information is analyzed by means of a mathematical model developed during the time of execution of this research as a result, we have obtained a quick lahar detection including calculation of the speed with which the lahar descends, providing reliable information from the moment the event arises, generating an early warning to the affected population.