Sound Monitoring System Research Articles

Real-time bowel sound analysis using newly developed device in patients undergoing gastric surgery for gastric tumor.

Background and AimObjective measurements are not available for determining bowel sounds. The present study sought to evaluate the efficacy of a novel bowel sound monitoring system for perioperative use in patients undergoing gastric surgery.MethodsThe study enrolled 14 patients who underwent surgery for gastric cancer at Kochi Medical School from 2017 to 2018. Preoperative and postoperative bowel sounds were recorded using a newly developed real‐time analysis system in the operating theater and recovery room. Clinical information and bowel sound count data were obtained to compare preoperative and postoperative measures.ResultsThe median preoperative and postoperative bowel sound counts across all patients were 1.4 and 2.5 counts per minute (cpm), respectively. In patients who underwent laparoscopic gastrectomy, the postoperative bowel sound count was significantly higher than that recorded preoperatively (2.3 vs. 1.6 cpm, P = 0.005). The findings also revealed a significant negative correlation between postoperative bowel sound count and operation time (r = −0.714, P = 0.003).ConclusionsThe real‐time bowel sound analysis system tested herein presents a promising diagnostic tool to quantitatively evaluate bowel movements associated with surgery. Our results suggested a need for shorter operation times for gastric procedures with respect to peristalsis recovery and supported the use of minimally invasive surgery.

Application of a continuous respiratory sound monitoring system in thoracic surgery

Application of a continuous respiratory sound monitoring system in thoracic surgery

Evaluation of Perioperative Intestinal Motility Using a Newly Developed Real-Time Monitoring System During Surgery.

This study aimed to investigate perioperative intestinal motility using a novel bowel sound monitoring system in patients undergoing breast and neck surgery. This study enrolled 52 patients who underwent surgery for breast cancer, thyroid tumor, and parathyroid tumor at Kochi Medical School from May 2019 to June 2020. Perioperative bowel sound counts (BSCs) were recorded using a newly developed real-time analysis system in the operating theater. Clinical information and BSC per minute (cpm) data during the preanesthetic, preoperative, operative, postoperative periods, and period in recovery room were obtained to compare between each period. The Mann-Whitney U and Pearson Chi-square tests were used in data analysis. The BSCs during the intraoperative period and postoperative period were significantly decreased compared to those during the preanesthetic period (0.07cpm versus [vs.]. 1.4cpm, P = 0.002 and 0.1cpm vs. 1.4cpm, P = 0.025, respectively). The preoperative BSC with a preanesthetic BSC < 1.4 was significantly lower than that with a preanesthetic BSC ≥ 1.4 (0.40cpm vs. 1.78cpm, P = 0.006). The preanesthetic, preoperative, and postoperative BSCs with an intraoperative BSC < 0.07 were significantly lower than those with an intraoperative BSC ≥ 0.07 (0.48cpm vs. 2.83cpm, P = 0.007; 0.40cpm vs. 1.81cpm, P = 0.008; and 0.07cpm vs. 0.42cpm, P = 0.006, respectively). The real-time bowel sound analysis system demonstrated an inhibitory effect associated with anesthetic and surgical stress on intestinal motility as the BSC sequentially.

Development of a Low-Cost Wireless Bee-Hive Temperature and Sound Monitoring System

Development of a Low-Cost Wireless Bee-Hive Temperature and Sound Monitoring System

Development of a Low-Cost Wireless Bee-Hive Temperature and Sound Monitoring System

Precision beekeeping requires data acquisition, data analysis, and applications where the initial phase data on the beehive plays a fundamental role. This method of apiculture could be used to measure different bee colony parameters in real time, leveraging on wireless sensing technologies, which aid monitoring of a bee colony, and enhances the monitoring of infectious diseases like colony collapse disorder–a major loss in the management of honey bee population. In this paper, a low-cost wireless technology-based system, which measures in real-time, the temperature in and around the beehive, and the sound intensity inside the hive is presented. This monitoring system is developed using an Arduino microprocessor, an ESP8266 communication module, and a web-based server. The proposed system provides valuable information concerning the bee colony behavior in terms of temperature variations and sound characteristics. Based on the measured temperature and sound information, colony beekeepers could easily detect events like increased food usage by the bees, breeding start time, pre-swarming action, actual swarming pattern, and the bee colony's death.

Based on the study of dormitory sound monitoring system

In order to bring convenience to the management of college dormitory, through the continuous communication and investigation of college students and administrators, and the continuous practice and research on the system functions, a set of student dormitory assistant management system based on sound monitoring is developed to replace the purely artificial way to manage the dormitory of boarding school. This system includes mobile terminal, hardware terminal and server, and the combination of the three, as far as possible to achieve real-time monitoring of dormitory sound, and timely feedback, so that students can abide by the discipline in the dormitory, have a good rest environment purpose. The hardware terminal of the project is able to identify the source of the sound by detecting the decibel value of the sound and artificial intelligence. There is no recording function, so there will be no privacy issues involved.

IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS

Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.

A sound monitoring system for prevention of underground pipeline damage caused by construction

Construction activities have become a common cause of underground pipeline accidents. The detection of construction activities near pipelines based on acoustic signals can effectively reduce pipeline damage. In the framework of “collecting data—feature extraction—classifier training—monitoring scheme”, this paper presents a construction sound monitoring system to prevent the underground pipeline damage caused by construction. The construction sounds of electric hammers, road cutters and excavator breaking hammers are collected as well as environmental noise. A double-layer identification scheme consisting of two random forest-based classifiers makes up the core of the system. The classifier-1 is first established to detect suspicious sounds. Cutting out the suspected segments as inputs, the classifier-2 is then trained to correct the results of the classifier-1 and further identify the types of construction activities. In the test, 95.59% of the construction sounds near pipelines are detected, while only 1.12% of environmental noise causes false positives.

Perancangan Alat Monitoring Pendeteksi Suara di Ruangan Bayi RS Vita Insani Berbasis Arduino Menggunakan Buzzer

Mining age and technology have led to the emergence of an innovation that can help facilitate human activities, especially for parents in controlling and looking after babies. By implementing a sound monitoring system in the baby room based on the Arduino Atmega 328 microcontroller which functions to detect noise. Data from the voice sensor is processed by Arduino via a pin. So Arduino will give instructions to Buzzer to send alarm notifications

Environmental sound monitoring using machine learning on mobile devices

This paper reports on a study to assess the feasibility of creating an intuitive environmental sound monitoring system that can be used on-location and return meaningful measurements beyond the standard LAeq. An iOS app was created using Machine Learning (ML) and Augmented Reality (AR) in conjunction with the Sennheiser AMBEO Smart Headset in order to test this. The app returns readings indicating the human, natural and mechanical sound content of the local acoustic scene, and implements four virtual sound objects which the user can place in the scene to observe their effect on the readings. Testing at various types of urban locations indicates that the app returns meaningful ratings for natural and mechanical sound, though the pattern of variation in the ratings for human sound is less clear. Adding the virtual objects largely has no significant effect aside from the car object, which significantly increases mechanical ratings. Results indicate that using ML to provide meaningful on-location sound monitoring is feasible, though the performance of the app developed could be improved given additional calibration.

Kriging-based spatial interpolation from measurements for sound level mapping in urban areas.

Network-based sound monitoring systems are deployed in various cities over the world and mobile applications allowing participatory sensing are now common. Nevertheless, the sparseness of the collected measurements, either in space or in time, complicates the production of sound maps. This paper describes the results of a measurement campaign that has been conducted in order to test different spatial interpolation strategies for producing sound maps. Mobile measurements have been performed while walking multiple times in every street of the XIIIth district of Paris. By adaptively constructing a noise map on the basis of these measurements, the role of the density of observations and the performance of four different interpolation strategies is investigated. Ordinary and universal Kriging methods are assessed, as well as the effect of using an alternative definition of the distance between observation locations, which takes the topology of the road network into account. The results show that a high density of observation points is necessary to obtain an interpolated sound map close to the reference map.

Financial literacy and SMEs’ potential entrepreneurs: The case of Malaysia

Malaysia, like many other countries, is depending on Small and Medium Enterprises (SMEs) activities in generating national income, creating job opportunities and tapping local and overseas market. As such country like Malaysia is putting a great effort to nurture entrepreneurs to become SME players by providing infrastructure, financial assistance, skills, training and conducive ecosystem for entrepreneurial development. One of the important aspects to address is the ability of the entrepreneurs to manage financial matters effectively. The ability, so called financial literacy, is important for the survival of the enterprise, as acknowledged by many. Practitioners and researchers have proved the significance of financial literacy in determining the success and failure of enterprise management, particularly among young entrepreneurs. As such it is important to understand and investigate the level of financial literacy among entrepreneurs. This paper is presenting a case to investigate the level of financial literacy among the participants of an entrepreneur development program, organised by a State in Malaysia, through a special program entrusted to an implementing agency. The objective of the program is to nurture potential entrepreneurs to develop the development corridor gazetted by the Federal Government in the State. Since the success of this program is critical to the overall objective of the gazetted development corridor this paper intends to investigate the level of financial literacy among the participants, as the main objective of the case. Accordingly, the next objective is to enlighten the importance of enhancing financial literacy so as to provide input to the policy makers and implementing agencies in relevant projects. By employing quantitative approach through questionnaire survey and interview, this paper found that the level of financial literacy among SMEs’ players, particularly those selected to be nurtured as new entrepreneurs is still alarming (Chamwada, 2015). As such this paper advocates the importance of education in financial management among selected participants of entrepreneurial development program as well as formulating a sound monitoring system as part of a good ecosystem in nurturing entrepreneurs to become SME players. Finally the finding of this paper offers an insight evidence to strengthen financial literacy theory, therefore, this paper makes some suggestion regarding the potential coverage of a more comprehensive future study.

Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection.

In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis.

Seasonality constraints to livestock grazing intensity.

Increasing food production is essential to meet the future food demand of a growing world population. In light of pressing sustainability challenges such as climate change and the importance of the global livestock system for food security as well as GHG emissions, finding ways to increasing food production sustainably and without increasing competition for food crops is essential. Yet, many unknowns relate to livestock grazing, in particular grazing intensity, an essential variable to assess the sustainability of livestock systems. Here, we explore ecological limits to grazing intensity (GI; i.e. the fraction of net primary production consumed by grazing animals) by analysing the role of seasonality in natural grasslands. We estimate seasonal limitations to GI by combining monthly net primary production data and a map of global livestock distribution with assumptions on the length of nonfavourable periods that can be bridged by livestock (e.g. by browsing dead standing biomass, storage systems or biomass conservation). This allows us to derive a seasonality-limited potential GI, which we compare with the GI prevailing in 2000. We find that GI in 2000 lies below its potential on 39% of the total global natural grasslands, which has a potential for increasing biomass extraction of up to 181 MtC/yr. In contrast, on 61% of the area GI exceeds the potential, made possible by management. Mobilizing this potential could increase milk production by 5%, meat production by 4% or contribute to free up to 2.8 Mio km² of grassland area at the global scale if the numerous socio-ecological constraints can be overcome. We discuss socio-ecological trade-offs, which may reduce the estimated potential considerably and require the establishment of sound monitoring systems and an improved understanding of livestock system's role in the Earth system.

Development of an underwater sound monitoring system using two hydrophones for a remotely operated underwater vehicle

Recently, a sound monitoring system for hearing fishery sound using three hydrophones installed on an ROV (Rountree and Juanes, 2010) has been developed. However, the operation of the system was not successful because of the high level of the self-noise generated by the ROV. Its thrusters had to be turned off to reduce the self-noise and listen to fishery sound clearly. Currently, Korea Research Institute of Ships and Ocean Engineering (KRISO) is developing a light-work class ROV and a similar sound monitoring system was developed for the ROV. The design of the system was focused on the following two strategies: (1) minimization of self-noise effect; (2) reproducing sound for operators to identify source direction. To minimize the self-noise effect, a digital filter with seven filter banks with octave bandwidth was implemented. A time-domain beamforming technique has been applied to analyze the source location. Reproducing sound signals for the two speakers in the control room were generated according to the estimated source location. The performance of the algorithm was verified by using a receiver array signal simulator. Its detail characteristics will be presented in the talk. [This work was financially supported by the research project # 20130197 funded by KIMST.]

A Wireless Body Sound Sensor with a Dedicated Compact Chipset

A compact wearable smart sensor for the application of wireless body sound monitoring is presented. It supports several kinds of body sound monitoring and has been measured with heart sound as an example. With the sensor, a wireless body sound monitoring system can be realized, which enables us to "build a hospital in our pocket". In order to achieve a small form factor, a dedicated highly integrated chipset has been designed for the wearable sensor. The chipset consists of a sensor readout chip for data acquisition and a 400 MHz band transceiver chip for wireless communication. It has been fabricated in CMOS technologies and can provide all the necessary sensor functions with a commercial EEPROM chip. Excluding the matching network, only eight off-chip components are required to fabricate the hardware system of the sensor, which helps to significantly improve integrated level.

The migration of fin whales into the southern Chukchi Sea as monitored with passive acoustics

AbstractFin whales (Balaenoptera physalus) undergo seasonal migration in the Arctic Sea. Because their migration and distribution is likely affected by changes in global climate, we aimed to examine the migration timing of fin whales, and the relationship with prey availability within the oceanographic environment of the Arctic Sea, using passive and active acoustic monitoring methods. Automatic Underwater Sound Monitoring Systems were deployed in the southern Chukchi Sea from July 2012 to 2014 to determine the acoustic presence of fin whales. Furthermore, water temperature and salinity were recorded by a fixed data logger. An Acoustic Zooplankton Fish Profiler was additionally deployed to estimate prey abundance through backscattering strength. Sea ice concentrations were obtained by remote sensing data. Fin whale calls were automatically detected using a custom-made software, and the per cent of half-hours containing calls were counted. Fin whale calls were detected from 4 August to 20 October 2012 (78 d) and 25 July to 1 November 2013 (100 d). The extended period of acoustic presence of fin whales during 2013 when compared with 2012 is likely related to a longer ice-free period during 2013. Furthermore, generalized linear model analyses showed that half-hour periods containing calls increased with a rise in water temperature and zooplankton abundance during the initial call presence period, while they decreased with a decrease in water temperature and salinity during the end of the call presence period. Our results suggest that the rise in water temperature and zooplankton abundance affect the timing of migration of fin whales in a way that is consistent with the expansion of their suitable habitats and the extension of their presence in the Arctic Sea.

Localization of individual call among croakers’ chorus using a stereo recording system

Passive acoustic monitoring (PAM) is widely used in cetacean census in the ocean. In these years, PAM has been applied for various species out of mammals such as fish and crustaceans. For detection, classification and density estimation in PAM, isolation of each biological sound and reliable detection is essentially needed. Choruses of fish sounds were recorded by a stereo sound monitoring system on the seafloor in off Choshi, Chiba prefecture, Japan in May 2013. Numbers of croaker sounds were recorded and the simple separation in time domain was not possible in chorus situation. A blind source separation method based on independent component analysis was applied to separate mixed sounds in order to obtain individual sounds and to localize them. The directions of sound arrivals could be estimated by using the cross correlation function of the separated signals and the estimated directions were plotted. Using proposed methods, individual call sounds could be localized and inter-pulse intervals could be accurately estimated. Using separated sequence of sounds from an individual, the movement of phonating fish could be measured using PAM. [This study was supported by CREST, JST.]

Research and Design of Abnormal Sound Online Monitoring and Positioning System for Substation Equipment

In order to further improve the operation reliability of substation site equipment, abnormal sound online monitoring and positioning system for substation equipment was designed. This system consists of on-site monitoring unit, system server and client three parts. Three key technologies were researched and developed: substation abnormal sound automatic identification technology, substation abnormal sound video positioning technology and substation abnormal sound message warning system technology. Abnormal sound online monitoring and positioning system for substation equipment has reasonable structure and good results.

Mondelēz Hope Kitchen Program, China: a Program Impact Pathways (PIP) analysis.

Mondelēz Hope Kitchen is a community program initiated jointly in 2009 by Mondelēz International and the China Youth Development Foundation (CYDF). In response to the urgent needs of students, parents, and teachers at primary and middle schools in poverty-stricken rural areas of China, the program addresses the complex and intertwined issues of undernutrition and obesity. By funding both kitchen equipment and teacher training in health and nutrition, the Mondelēz Hope Kitchen Program improves the capacity of schools to supply healthy meals, helping students to access safe and nutritious foods and, ultimately, to improve their nutritional status and health. In 2011, the Mondelēz International Foundation awarded CYDF a grant to formally assess the impact of the original program design. The Mondelēz International Foundation encouraged CYDF and six other healthy lifestyles-focused community partners around the world to participate in this program evaluation workshop. The goals of this study were to describe the logic model of the Mondelēz Hope Kitchen Program, summarize a recent evaluation of the Mondelēz Hope Kitchen Program, and conduct a Program Impact Pathways (PIP) analysis to identify Critical Quality Control Points (CCPs) and a suite of impact indicators. The findings were presented at the Healthy Lifestyles Program Evaluation Workshop held in Granada, Spain, 13-14 September 2013, under the auspices of the Mondelēz International Foundation. The authors developed the program's PIP diagram based on deliberations involving the program managers and Director and consulting the "Hope Kitchen Management Rules "and "Hope Kitchen Inspection and Acceptance Report". The PIP analyses identified three CCPs: buy-in from schools, kitchen infrastructure, and changes in teachers' knowledge of nutrition after training. In addition, changes in children's knowledge of nutrition will be added to the core suite of impact evaluation indicators that also includes children's dietary behaviors. Participation in this program evaluation workshop provided the Mondelēz Hope Kitchen team with the tools to improve the program using a sound monitoring system.