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With the advancement of IoT(Internet of Things), image processing is being studied in various fields. Salt and pepper(S&P) noise in image acquisition devices, such as CT(Computed Tomography), MRI(Magnetic Resonance Imaging), X-ray, and scanning equipment, may develop due to environmental and physical factors, thereby distorting the original image. Numerous studies have been conducted to remove S&P noise, but existing filters have shown significantly insufficient restoration of high-density noise, low-light, and patterned images. Therefore, this paper proposes a filter high-density and low-light noise environments and preserve the patterns properties appearing in the images. In the proposed algorithm, we design local masks transformed for high-density noise and low-light images and defined the patterns represented in the images by determining and grouping similar pixels. Different filters are applied according to the pattern of similar pixel group to preserve the geometry based on its position in the input image. [ABSTRACT FROM AUTHOR]

As the demand for information related to visual data has increased in modern society, so has the technology to process it. These advances have revolutionized many fields and are being applied in our daily lives and various industries. However, in processing visual data, Salt and Pepper noise is generated, degrading the video quality. Various types of filters such as CWMF, EEPA, and IAWMF have been studied to remove this noise, but the existing methods show insufficient performance in extremely high noise-density environments. Therefore, in this paper, a RANSAC switching filter for Salt and Pepper noise removal is proposed, and the noise is removed by switching the filter using the filter with RANSAC algorithm, the average filter, and the weighted Median filter according to the conditions. Simulation results show that the proposed algorithm performs effectively in extremely high noise-density environments and has good visual results. [ABSTRACT FROM AUTHOR]

As a medium for conveying visual information, video is applied in various fields. However, due to an environmental factor or external interference, various noises may appear in images, and salt and pepper noise is typical. In this paper, a homography based salt and pepper denoising method using bilinear interpolation is suggested to effectively remove such noise. Bilinear interpolation is used to estimate the data corrupted by noise and the values between the two axes are interpolated with the selected observing point by dividing a mask into four zones. In addition, a homography technique is used as an auxiliary means to get accurate estimates, and shows excellent visual and numerical performance compared to existing methods, and is expected to contribute to the improvement of image quality as a new noise removal method with strength against changes in the density of noise. [ABSTRACT FROM AUTHOR]

This paper presents a low noise amplifier (LNA) for nonlinear radar applications combined with a composite right-handed and left-handed (CRLH) filter, a type of metamaterial. The proposed LNA consists of an input matching network, output matching network, and a CRLH filter combined. The LNA with a CRLH filter has advantages of broadband, low noise, and high frequency selectivity. It filters the harmonic frequency resulting in broadband operation and has a flat S-parameter value with a gain of more than 10 dBm in a wideband ranging from 4 GHz to 8 GHz. The noise figure value was measured at 1.521 dB and 1.682 dB at 4 GHz and 8 GHz, respectively, and 1.542 dB at 6 GHz, in middle frequency. [ABSTRACT FROM AUTHOR]

As competition between universities intensifies due to the recent decrease in the number of students, it is recognized as an essential task of universities to predict students who are underperforming at an early stage and to make various efforts to prevent dropouts. For this, a high-performance model that accurately predicts student performance is essential. This paper proposes a method to improve prediction performance by removing or amplifying abnormal data in a classification prediction model for identifying underperforming students. Existing anomaly data processing methods have mainly focused on deleting or ignoring data, but this paper presents a criterion to distinguish noise from change indicators, and contributes to improving the performance of predictive models by deleting or amplifying data. In an experiment using open learning performance data for verification of the proposed method, we found a number of cases in which the proposed method can improve classification performance compared to the existing method. [ABSTRACT FROM AUTHOR]

Spectrum sensing is of significance in shared spectrum bands. Traditional energy detectors are limited by the variance in background noise, which can reduce the sensing probability. Order statistics detectors, which are inherently protected against non-uniform background noise, can complement energy detectors. However, to date, the spectrum sensing performance of Nakagami-m fading signals has not been analyzed. In this paper, we fill this gap by analyzing the spectrum sensing performance of Nakagami-m fading signals and proposing a robust sensing method called the GOS-L-m detector for noise-uncertain environments. We derive the false alarm probability formula for the GOS-L-m detector, and calculate a threshold value to maintain a constant false alarm rate. Based on this, we obtain the detection probability in a noise-uncertain environment. Our analysis shows that the GOS-L-m sensing method achieves robust detection performance and maintains the desired constant false alarm rate if the coefficients of the GOS-L-m detector are correctly selected. [ABSTRACT FROM AUTHOR]

This paper presents on-wafer measurement methods for the scattering coefficient, power, and noise figure of a semiconductor circuit in the D band (110∼170 GHz). The calibration algorithms for the S-parameter measurement were compared, and the effect of the total coplanar waveguide (CPW) width of the commercial calibration standards was examined. In the power measurement, the accuracy of the measurement value was improved through precision path loss evaluation. In addition, in the noise figure measurement, the system configuration and calibration method are presented, and the path loss compensation is examined. The detailed configuration and consid- erations for each measurement system are presented, and the measured results of the designed low-noise amplifier (LNA) chip are presented. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep learning-based transmit power control scheme to maximize the sum-rates while satisfying the minimum data-rate in downlink non-orthogonal multiple access (NOMA) systems. In downlink NOMA, we consider the co-channel interference that occurs from a base station other than the cell where the user is located, and the user feeds back the signal-to-interference plus noise power ratio (SINR) information instead of channel state information to reduce system feedback overhead. Therefore, the base station controls transmit power using only SINR information. The use of implicit SINR information has the advantage of decreasing the information dimension, but has disadvantage of reducing the data-rate. In this paper, we resolve this problem with deep learning-based training methods and show that the performance of training can be improved if the dimension of deep learning inputs is effectively reduced. Through simulation, we verify that the proposed deep learning-based power control scheme improves the sum-rate while satisfying the minimum data-rate. [ABSTRACT FROM AUTHOR]

Parameterized multichannel Wiener filter (PMWF) is a linear filter that can control the trade-off between residual noise and signal distortion using the embedded parameter. To apply the PMWF to noisy inputs, accurate noise estimation is important and multichannel minima-controlled recursive averaging (MMCRA) is widely used. However, in the case of the MMCRA, the accuracy of noise estimation decreases when a directional interference is involved into the array inputs. Consequently, the performance of the PMWF is degraded. Therefore, we propose a noise power spectral density (PSD) estimation method for the PMWF in this paper. The proposed method is based on a consecutive process of eigenvalue decomposition on noisy input PSD, estimation of the target component contribution using directional information, and exponential weighting for improved estimation of the target contribution. For evaluation, four objective measures were compared with the MMCRA and we verify that the PMWF with the proposed noise estimation method can improve performance in environments where directional interfereces exist. [ABSTRACT FROM AUTHOR]

As the noise canceler with a small processing delay increases the sampling frequency, a better-quality output can be obtained. For a single buffer, processing delay occurs because it is impossible to write new data while the processor is processing the data. When synthesizing with anti-noise and output signal, this processing delay creates additional buffering overhead to match the phase. In this paper, we propose an accelerator structure that minimizes processing delay and increases processing speed by alternately performing read and write operations using the Symmetric Even-Odd-buffer. In addition, we compare the structural differences between the two methods of noise cancellation (Fast Fourier Transform noise cancellation and adaptive Least Mean Square algorithm). As a result, using an Symmetric Even-Odd-buffer the processing delay was reduced by 29.2% compared to a single buffer. The proposed Symmetric Even-Odd-buffer structure has the advantage that it can be applied to various canceling algorithms. [ABSTRACT FROM AUTHOR]

Recently, many industries are using three dimensional scanning technology. As the performance of the 3D scanner gradually improves, a sampling step to reduce a point data or a remove step to remove a part determined to be noise are generally performed in post processing. However, total point data by long time scanning cannot be processed at once in spite of performing such those additional processes. In general, a method using a multi threaded environment is widely used, but as the scanning process work time increases, the processing performance gradually decreases due to various environmental conditions and accumulated operations. This paper proposes a method to initially remove point data judged to be unnecessary by calculating accumulated fast point feature histogram values from coming point data of the 3D scanner in real time. The entire 3D scanning process can be reduced using this approach. [ABSTRACT FROM AUTHOR]

Currently, the demand for image processing is increasing due to the development of IT technology, and active research is being conducted. Since image data generates image noise due to various external causes, and thus degrades the performance of the image, noise removal is essential. Salt and Pepper noise is a representative image noise, and various studies are being conducted to remove it. Existing algorithms include A-TMF, AFMF, LIWF, but these have the disadvantage that their performance is somewhat insufficient. Therefore, in this paper, we propose an algorithm that performs filtering using linear interpolation with effective pixels existing around the central pixel only in case of noise after performing noise judgment in order to efficiently remove salt and pepper noise. In order to judge the performance of the proposed algorithm, it was compared using the processed image of the previously studied algorithm and PSNR. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the dominant noise source of conventional inductively degenerated common-source (CS) cascode low noise amplifier (LNA) when width and gate length of stacked transistors vary. Analytical MOSFET and its noise model are used to estimate the contributions of noise sources. All parameters are based on measured data of 60nm, 90nm and 130nm CMOS devices. Based on the noise analysis for different frequencies and device parameters including process nodes, the dominant noise source can be analyzed to optimize noise figure on the configuration. We verified analytically that the intuctively degenerated CS topology can not sustain its benefits in noise above a certain operation frequency of LNA over different process nodes [ABSTRACT FROM AUTHOR]

In this paper, we analyzed the noise figure of cascode low noise amplifier (LNA) based on the measured data of 65nm CMOS devices. By using the channel thermal noise model of transistors, we expanded noise figure equation and divided the equation into three parts to see its contributions to noise figure. We also varied design parameters such as bias point, transistor gate width, and operating frequency. Our results show that different noise sources dominate at the different operating frequencies. One can easily find the noise transition frequency with device models in ahead of the practical design. Therefore, this research provides a low noise design approach for different operating frequencies [ABSTRACT FROM AUTHOR]

In this paper, a determinant-based two-channel noise reduction method which utilizes speech presence probability (SPP) is proposed. The proposed method improves noise reduction performance from the conventional determinant-based two-channel noise reduction method in [7] by applying SPP to the Wiener filter gain. Consequently, the proposed method adaptively controls the amount of noise reduction depending on the SPP. For performance evaluation, the segmental signal-to-noise ratio (SNR), the perceptual evaluation of speech quality, the short time objective intelligibility, and the log spectral distance were measured in the simulated noisy environments considered various types of noise, reverberation, SNR, and the direction and number of noise sources. The experimental results presented that determinant-based methods outperform phase difference-based methods in most cases. In particular, the proposed method achieved the best noise reduction performance maintaining minimum speech distortion. [ABSTRACT FROM AUTHOR]

The face detection task of detecting a person’s face in an image is used as a preprocess or core process in various image processing-based applications. The neural network models, which have recently been performing well with the development of deep learning, are dependent on 2D images, so if noise occurs in the image, such as poor camera quality or pool focus of the face, the face may not be detected properly. In this paper, we propose a face detection method that uses depth information together to reduce the dependence of 2D images. The proposed model was trained after generating and preprocessing depth information in advance using face detection dataset, and as a result, it was confirmed that the FRN model was 89.16%, which was about 1.2% better than the RetinaNet model, which showed 87.95%. [ABSTRACT FROM AUTHOR]

This paper presents a W-band broadband low noise amplifier (LNA). The proposed LNA was designed and verified using a 100 nm GaAs pHEMT. To achieve broadband low noise and gain characteristics, peak gain distribution was employed. The LNA comprised four gain stages and a shunt inductor at the input stage to prevent the device damage. The LNA achieved a 16.2 dB gain and 5.5 dB noise figure in an area of 1.78×0.743 mm². [ABSTRACT FROM AUTHOR]

Portable systems with built-in active noise control is required low power operation. Excessive anti noise search operation can lead to rapid battery consumption. A method that can adaptively cancel noise according to the operating conditions of the system is required and the methods of reducing power are becoming very important key feature in today's portable systems. In this paper, we propose the method of active noise control(ANC) using binary search algorithm in noisy systems. The implemented architecture detects a frequency component considered as noise from the input signal and by using the binary search algorithm, the system find out an appropriate amplitude value for anti-noise in a much faster time than the general linear search algorithm. Through the experimental results, it was confirmed that the proposed algorithm performs a successful functional operation. [ABSTRACT FROM AUTHOR]

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method. [ABSTRACT FROM AUTHOR]

In this paper, a lightweight network with fewer parameters compared to the existing object detection method is proposed. In the case of the currently used detection model, the network complexity has been greatly increased to improve accuracy. Therefore, the proposed network uses EfficientNet as a feature extraction network, and the subsequent layers are formed in a pyramid structure to utilize low-level detailed features and high-level semantic features. An attention process was applied between pyramid structures to suppress unnecessary noise for prediction. All computational processes of the network are replaced by depth-wise and point-wise convolutions to minimize the amount of computation. The proposed network was trained and evaluated using the PASCAL VOC dataset. The features fused through the experiment showed robust properties for various objects through a refinement process. Compared with the CNN-based detection model, detection accuracy is improved with a small amount of computation. It is considered necessary to adjust the anchor ratio according to the size of the object as a future study. [ABSTRACT FROM AUTHOR]

One of the most important tasks in image processing is noise filtering. Noise removal in image is a difficult task due to many reasons such as nonstationary sequences and corrupted by various types of noise. Human’s visual perception is heavily based on the edge information. Thus, noise filtering must preserve edges. To remove the noise, we usually use the square-shaped median filter. They possess mathematical simplicity but have the disadvantages that blur the edges. In this paper we consider a new technique for image restoration using a weighted octagonal median filter. The technique consists of simple hypothesis test for edge detection, and we use the weighted octagonal-shaped moving window. The new technique is applied to noise corrupted image and experimental results are compared to the results of the square-shaped median filter and the cross-shaped median filter. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm to remove AWGN by considering the characteristics of noise present in the image. The proposed algorithm uses block matching to calculate the output, and calculates an estimate by determining the similarity between the center mask and the matching mask. The output of the filter is calculated by adding or subtracting the estimated value and the input pixel value, and weighting is given according to the standard deviation of the center mask and the noise constant to obtain the final output. In order to evaluate the proposed algorithm, the simulation was performed in comparison with the existing methods, and analyzed through the enlarged image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves important characteristics of the image, and shows the performance of removing noise efficiently. [ABSTRACT FROM AUTHOR]

Currently, the spread of mobile devices is gradually increasing. Accordingly, various techniques using images or photos are actively being researched. However, image data generates noise for complex reasons, and the accuracy of image processing increases according to the performance of removing noise. Therefore, noise reduction is one of the essential steps. Salt and pepper noise is a typical impulse noise in the image, and various studies are being conducted to remove the noise. However, existing algorithms have poor noise rejection performance in high frequency areas, and average filters have blurring. Therefore, in this paper, we propose an algorithm that effectively removes salt and pepper noise in the high frequency region as well as the low frequency region using entropy. For objective and accurate judgment of proposed algorithms, MSE and PSNR were used to compare and analyze existing algorithms. [ABSTRACT FROM AUTHOR]

Because the sea clutter returns acquired for a long duration cannot guarantee the time-stationarity, the estimation performance for the CCM(clutter covariance matrix) is apt to deteriorate even if a sufficient number of secondary data vectors are provided. This paper presents a detection method with clutter-corrupted target returns for a long duration. This is accomplished by dividing the long acquisition period into a number of shorter periods that maintain the time-stationarity so that the CCM for each short duration can be more accurately estimated. To improve detection performance, we show that maximum SINR(signal to interference plus noise ratio) filters designed with the estimated CCMs are applied to each data for a short period and the filter outputs are coherently combined. The improvement in the SINR at the target Doppler bin is theoretically analyzed and verified by simulation. [ABSTRACT FROM AUTHOR]

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In electronic warfare, to protect allies from enemies, DRFM(digital radio frequency memory) is used to receive threat signals and generate various electronic interference signals to deceive them. The use of noise is an efficient interference method, which is less affected by radar receiver characteristics. It is generated mainly by noise-generating devices. As the signal form of a modern radar changes in real time, the conventional noise generation method has a limitation of interference in response to a change in the threat signal. In this paper, to avoid these limitations, we propose a method to generate DRFM real-time noise. The proposed method generated a noise signal by utilizing phase synthesis with the DRFM self-detection signal without the basic pulse tracking process. The proposed method was verified through a simulation and prototype fabrication and testing, in agreement with the expected results. [ABSTRACT FROM AUTHOR]

Generative adversarial networks are methods of generating images by opposing two neural networks. When generating the image, randomly generated noise is rearranged to generate the image. The image generated by this method is not generated well depending on the noise, and it is difficult to generate a proper image when the number of pixels of the image is small In addition, the speed and size of data accumulation in data classification increases, and there are many difficulties in labeling them. In this paper, to solve this problem, we propose a technique to generate noise based on random noise using real data. Since the proposed system generates an image based on the existing image, it is confirmed that it is possible to generate a more natural image, and if it is used for learning, it shows a higher hit rate than the existing method using the hostile neural network respectively. [ABSTRACT FROM AUTHOR]

In this paper, we present a low noise amplifier with a merged duplexer. The input matching network of the electrical balance duplexer(EBD) and the low-noise amplifier were integrated to minimize input insertion loss in the RX path. The low noise amplifier was designed with a differential structure because its input matching network was integrated with the EBD. The first stage of the low-noise amplifier was designed with a common-source structure while considering noise characteristics. The second stage of the low-noise amplifier was designed with a cascode structure to minimize the effects of impedance mismatch and phase mismatch on the output balun. In addition, the interstage matching network was designed symmetrically using lumped elements while considering the mismatch. The low noise amplifier with a merged duplexer had a gain of over 18.04 dB at the RX path, a return loss of over 8.7 dB, an insertion loss of 4.3 dB at the TX path, a noise figure of 5.21 to 5.23 dB at the RX path in the frequency range of 14.3 to 15.9 GHz, and its chip size was 0.85⨯0.33 mm2. [ABSTRACT FROM AUTHOR]

In a multi-function radar, the adaptive beamforming algorithm is one of the several methods that can be applied to restore the degraded SINR(Signal-to-Interference Noise Ratio) to its original level. This algorithm is employed for the regular operation of radars in jamming situations, when unwanted high-power signals are received. In this paper, we introduce a few adaptive beamforming algorithms that can be applied to a multi-function digital radar in jamming situations. Furthermore, we compare and analyze the adaptive beamforming performance in terms of SINR and sidelobe level characteristics by applying each algorithm to a specific planar array comprising optimized subarray structures. [ABSTRACT FROM AUTHOR]

Fuzzy clustering, represented by FCM(Fuzzy C-Means), is a simple and efficient clustering method. However, the object function in FCM makes clusters affect clustering results proportional to the density of clusters, which can distort clustering results due to density difference between clusters. One method to alleviate this density problem is EDI-FCM(Extended Density-Independent FCM), which adds additional terms to the objective function of FCM to compensate for the density difference. In this paper, proposed is an enhanced EDI-FCM using regularization, Regularized EDI-FCM. Regularization is commonly used to make a solution space smooth and an algorithm noise insensitive. In clustering, regularization can reduce the effect of a high-density cluster on clustering results. The proposed method converges quickly and accurately to real centers when compared with FCM and EDI-FCM, which can be verified with experimental results. [ABSTRACT FROM AUTHOR]

Recently, as the industrial scope of multi-rotor unmanned aerial vehicles(UAV) is greatly expanded, the demands for data collection, processing, and analysis using UAV are also increasing. However, the acoustic data collected by using the UAV is greatly corrupted by the UAV's motor noise and wind noise, which makes it difficult to process and analyze the acoustic data. Therefore, we have studied a method to enhance the target sound from the acoustic signal received through microphones connected to UAV. In this paper, we have extended the densely connected dilated convolutional network, one of the existing single channel acoustic enhancement technique, to consider the inter-channel characteristics of the acoustic signal. As a result, the extended model performed better than the existed model in all evaluation measures such as SDR, PESQ, and STOI. [ABSTRACT FROM AUTHOR]

In this paper, we measure the Electromagnetic Interference(EMI) noise of AC/DC converters in lighting devices to enable us to optimize the design of its filters as compared to the conventional filters. Addition of RC filters to the circuit helps in increasing the noise margin, thereby, reducing the noise generated by high-frequency transformers and switching devices. The characteristics of each converter are compared after addition of RC filter to the circuit. To reduce the noise generated by the high frequency band, ranging from 1 MHz to 30 MHz, high frequency transients and switching devices, with a cut off frequency closer to the band, have been designed. The results are measured using an oscilloscope. A reduction in noise is observed in case of a filter designed using a combination of R and C with a cut-off frequency of 1 MHz. The EMI Receiver shows that the noise is significantly reduced on addition of filters and the noise reduction ranges from 2 dBuV to 11 dBuV, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we present a design technique for the suppression of common-mode(CM) noise in switched-mode power supply boards using the segmentation method. By applying the segmentation method, the example structure is decomposed into two segments with decoupling capacitors and a recombination matrix is extracted for the segments. The effects of the decoupling capacitor on CM noise suppression are examined. The simulation time is significantly reduced on using the segmentation method. [ABSTRACT FROM AUTHOR]

At present the general trend of modern electronics is toward smaller packages and high performance. As an antenna requires high powers, the EMC(Electromagnetic Compatibility) problems of the transmission line stage is becoming crucial day by day. In this paper, a transmission line excited by the electromagnetic fields from an infinitesimal electric dipole antenna is analyzed using the modified telegrapher’s equations. The analytical equations are derived for arbitrarily positioned electric dipole with reference to a transmission line. To verify our approach, the induced voltage and current at the terminal were computed by the proposed approach and compared with those obtained by the electromagnetic simulation solver. Furthermore, the induced currents at the terminal of a transmission line excited by the electric dipoles at various positions were investigated using our approach. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the signal characteristic changes in a single-ended dual-stripline configuration, which is often used in high-speed memory system chip packages and module circuit boards, and developed a methodology to mitigate crosstalk. Although using a dual-stripline configuration can result in a reduced number of layers and increased cost of PCB, it inevitably deteriorates the crosstalk and reflection characteristics formed by capacitive coupling. This is because there is no reference layer between the two signal layers. We analyzed the characteristic change according to the self and mutual capacitance values and proposed an airgap structure located between adjacent signal layers to mitigate capacitance and crosstalk by using inhomogeneity. The proposed method was demonstrated to mitigate the signal noise caused by near-end and far-end crosstalk not only in a single coupled line but also in a multi-coupled line and actual structure with micro-stripline and vias. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to analyze noise experience and response to noise by patients and nursing staff in intensive care units (ICU). Methods: This study was a cross-sectional survey based on questionnaires administered to patients and staff in ICU to analyze the perceived noise level in the ICU environment, frequency of noise experience and noise level emitted by different sources, noise induced discomfort, response to noise, and the quality of sleep. The data were analyzed using descriptive statistics, independent t-test, ANOVA, and Pearson's correlation coefficient. Results: Regarding the frequency of experience and noise levels emitted by various sources, patients carried the maximum number of human factors, and staff carried the highest number of medical devices or medical supplies. The quality of sleep caused by noise in patients was lower. Based on the correlation between the various variables, the higher the perceived noise level in the ICU environment, the higher the response to the noise (positive correlation) and the lower the quality of sleep (negative correlation). Conclusion: Based on the results of this study, a follow-up study is suggested to compare perceived noise levels of patients and staff. Practical strategies are needed to reduce noise based on noise levels and sources of noise experienced by ICU patients and staff. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the actual noise conditions in adult Intensive Care Units (ICUs) according to type, time, day of the week, and area. Methods: This study was conducted from February to March 2018. ICU noise levels were examined using a noise meter with a microphone an amplifier, auditory correction circuit, and indicator meter capable of directly reading A-weighted decibels (dBA). Noise was measured for 24 hours for seven days and the average dBA, maximum dBA, and minimum dBA were recorded. Results: The highest mean noise level was 58.48 dBA (range of 57.62-65.27), while lowest was 51.65 dBA (range of 51.36-52.86). Average noise levels on weekdays and weekends were over 50 dBA(the open zone was measured at 56.61 dBA, while the isolation zone was measured at 52.45 dBA. Further, daytime, evening work-times and weekdays were measured above 60 dBA, while nighttime noise levels were below 60 dBA. Finally, average noise levels during turnarounds, shift changes, and rounding times were above 60 dBA; open zone had higher average noise levels than the isolation zone. Conclusion: This study showed that ICU noise levels exceeded those recommended by the World Health Organization regardless of type of noise, day of the week, or time. Therefore, studies are proposed to identify the need or importance of noise management by ICU personnel to reduce noise in ICUs. It also proposes studies to develop and apply noise reduction strategies that can be easily used in practice, reflecting the various characteristics of noise in ICUs. [ABSTRACT FROM AUTHOR]