In ultrasound imaging there is compromise between the penetration of signal at certain depths into the object and image resolution as the ultrasound probe only can transmit single frequency signals in one transmission. Using curvilinear ultrasound probe with 2 to 5 MHz frequency bandwidth, this study investigated the use of multi-frequency imaging to enhance the quality of phantom images.
Methods: Siemen Acuson X150 with curvilinear ultrasound transducer was used to scan the organs of interest (kidney, gallbladder and pancreas) of the ultrasound abdominal phantom. Different images at the different selected frequencies (2.5, 3.6 and 5.0 MHz) were created by fixing the position and the orientation of the transducer in each of the scanning process. Different-frequency images were generated and combined to produce composite (multi-frequency) image. Results: In this study, the quality of the composite image was evaluated based on signal-to noise ratio (SNR) and the obtained results were compared with the single frequency images. Besides, the comparison was also made in terms of overall image quality (noise and sharpness of organ outline) through perceived image quality analysis. Based on calculated SNR, the composite image of the kidney, gallbladder and pancreas recorded higher SNR value as compared to the single frequency images. However, through perceived image quality, most of the observers viewed that the quality of the composite image of the kidney, gallbladder and pancreas is poor as compared to the single frequency image. Conclusions: Image quality of ultrasound imaging is improved by combining multiple ultrasound frequency images into a single composite image. This is achieved as high SNR is obtained in the composite image. However, through perceived image quality, the overall image quality of the composite image was poor.
Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality.
We demonstrate a multi-wavelength light source using a semiconductor optical amplifier (SOA) in conjunction with an array waveguide grating (AWG). The experimental results showed more than 20 channels with a wavelength separation of 0.8 nm and an optical signal-to-noise ratio of more than 10 dB under room temperature. The channels operated at the wavelength region from 1530.4 nm to 1548.6 nm, which corresponded to AWG filtering wavelengths with SOA drive current of 350 mA. The proposed light source had the advantages of a simple and compact structure, multi-wavelength operation and the system could be upgraded to generate more wavelengths.
A 2.4 GHz variable-gain low noise amplifier (VGLNA) intended for use in a Wide-band Code Division
Multiple Access receiver was designed in 0.18 um CMOS process for low voltage and low power applications. Rivaling classical designs using voltage mode approach, this design used the current mode approach, utilizing the current mirror principle to obtain a controllable gain range from 8.26 dB to 16.95 dB with good input and output return losses. By varying the current through the widths of transistors and a bias resistor, the VGLNA was capable of exhibiting 8 dB gain tuning range without degrading the noise figure. Therefore, higher gain was possible at lower current and thus at lower power consumption. Total power consumption simulated was 4.63 mW from a 1 V supply and this gave a gain/power quotient of 3.66 dB/mW. Comparing this with available published data, it was observed that this work demonstrated a good gain tuning range and the lowest noise figure with such power consumption.
The paper writes on the possible origin of off-limit cases found in a noise project conducted internally in a factory in Malaysia. Out of 691 sampled workers’ that attended audiometric test results (some repeated), it was found that the mode of hearing ability is between 20 to 30 dB depending on individual worker’s age ranging from 20 to 55 years. Out of the total results, approximately 100 workers are above a limit defined here in this paper as the off-limit condition. The chance of a worker originating from a good condition to an unhealthy condition is about 1 percent. The data are tabulated to show that a sway pattern could be an explanation of workers’ origin. Although the data is profound, there is no evidence of a trace due to a short test period. Possibilities are highlight here to outline the severity of a cross over to the unhealthy condition (here defined as the off-limit condition). Some advises are mentioned here with individual susceptibility on the matter though there is no data to substantiate. Further findings are required to show a trace. In conclusion, the severity is highlight. A chart, developed to know the limits of hearing ability, is illustrated ased the findings.
Environmental factors such as temperature, lighting and noise have very significant impact to workers’ health, safety, comfort, performance and productivity. In an ergonomically design industrial work environment, these factors need to be control at their optimum levels. The main objective of this study is to find the effect of temperature, illuminance and sound pressure level on workers’ productivity in automotive industry. To perform this study a workstation in an automotive component manufacturing was selected as the location of the study. Results of data analysis showed there were relationships between temperature, illuminance and noise on workers’ productivity. Later, the authors’ developed multiple linear equation models to represent the relationships between temperature, illuminance and noise on the workers’ productivity. These multiple linear equation models could be used to predict the production rate for the workstation by referring to the value of temperature, illuminance and noise level.
Robustness is a key issue in speech recognition. A speech recognition algorithm for Malay digits from zero to nine and an algorithm for noise cancellation by using recursive least squares (RLS) is proposed in this article. This system consisted of speech processing inclusive of digit margin and recognition using zero crossing and energy calculations. Mel-frequency cepstral coefficient vectors were used to provide an estimate of the vocal tract filter. Meanwhile dynamic time warping was used to detect the nearest recorded voice with appropriate global constraint. The global constraint was used to set a valid search region because the variation of the speech rate of the speaker was considered to be limited in a reasonable range which meant that it could prune the unreasonable search space. The algorithm was tested on speech samples that were recorded as part of a Malay corpus. The results showed that the algorithm managed to recognize almost 80.5% of the Malay digits for all recorded words. The addition of a RLS noise canceller in the preprocessing stage increased the accuracy to 94.1%.
This paper demonstrates an erbium/ytterbium co-doped fi bre amplifi er (EYDFA) which used a pumping wavelength of 1058 nm, whereby the amplifi cation was assisted by the energy transfer between Yb and Er ions. The energy transfer increased the erbium doping concentration limit that was imposed by concentration quenching in erbium-doped fi bre. The optimum length was obtained at 4m~6m for erbium/ytterbium co-doped fi bre with Er ion concentration of 1000 p.p.m. This enabled the development of a compact amplifi er with a shorter gain medium compared to erbium-doped fi bre amplifi ers which use a gain medium of up to 15 m. A 1058 nm pumping wave-length was used for the EYDFA, as 1480 nm pumping resulted in severely degraded gain and noise fi gures because the energy transfer could not be achieved. The use of the optical isolator improved the small signal gain and noise fi gure by about 4.8 dB and 1.6 dB, respectively. Without the isolator, gain saturation and a noise fi gure penalty were observed due to the oscillating laser which was created at around 1534 nm by spurious refl ection. This showed that the usage of optical isolators was an important aspect to consider when designing an EYDFA.
Pulse Shaping Amplifier (PSA) is an essential component in nuclear spectroscopy system. This
amplifier has two functions; to shape the output pulse and performs noise filtering. In this paper,
we describe the procedure for the design and development of a pulse shaping amplifier which can
be used in a nuclear spectroscopy system. This prototype was developed using high performance
electronics devices and assembled on a FR4 type printed circuit board. Performance of this
prototype was tested by comparing it with an equivalent commercial spectroscopy amplifier (Model
Silena 7611). The test results showed that the performance of this prototype was comparable
to the commercial spectroscopic amplifier.
This paper analyses electromagnetic signal scattered from the target crossing the Forward Scattering
Radar (FSR) system baseline. The aim of the analysis was to extract the Doppler signal of a target under the influence of high ground clutter and noise interference. The extraction was used for the
automatic target detection (ATD) in the FSR system. Two extraction methods, namely Hilbert Transform and Wavelet Technique, were analyzed. The detection using the Hilbert Transform is only applicable for some conditions; however, the detection using the Wavelet Technique is more robust to any clutter and noise level. From 55 sets of signal, only 4% of false alarm was detected or occurred when the Wavelet Technique was applied as a detection scheme. Two sets of field experimentation were carried out and the target’s signal under the influence of high clutter had successfully been detected using the proposed method.
Accurate inspection of welded materials is important in relation to achieve acceptable standards. Radiography, a non-destructive test method, is commonly used to evaluate the internal condition of a material with respect to defect detection. The presence of noise in low resolution of radiographic images significantly complicates analysis; therefore attaining higher quality radiographic images makes defect detection more readily achievable. This paper presents a study pertaining to the quality enhancement of radiographic images with respect to different types of defects. A series of digital radiographic weld flaw images were smoothed using multiple smoothing techniques to remove inherent noise followed by top and bottom hat morphological transformations. Image quality was evaluated quantitatively with respect to SNR, PSNR and MAE. The results indicate that smoothing enhances the quality of radiographic images, thereby promoting defect detection with the respect to original radiographic images.
Frequency hopping spread spectrum (FHSS) systems with partial band interference require appropriate compounding of spread spectrum modulation, error correcting code, diversity and decoding method to receive improved transmission signal. In this paper, a fast FHSS system with regular low-density parity-check codes was employed to cater some anti-jamming competence by using good waterfall and error floor performance. The performance evalution of the previously mentioned system was conducted in the presence of partial band noise jamming. The best possible design of the system was achieved with the combination of diversity level L=2 with a probability rate of at 0.7 dB which showed the robustness of the system.
This paper deals with the analysis of different Fuzzy membership type performance for Extended Kalman Filter (EKF) based mobile robot navigation. EKF is known to be incompetent in non-Gaussian noise condition and therefore the technique alone is not sufficient to provide solution. Motivated by this shortcoming, a Fuzzy based EKF is proposed in this paper. Three membership types are considered which includes the triangular, trapezoidal and Gaussian membership types to determine the best estimation results for mobile robot and landmarks locations. Minimal rule design and configuration are also other aspects being considered for analysis purposes. The simulation results suggest that the Gaussian memberships surpassed other membership type in providing the best solution in mobile robot navigation.
This paper describes the systematic process followed in the development of culturally appropriate equalized speech-in-noise sentences suitable for use in an adaptive Speech-In-Noise training protocol for adults in Malaysia. The process involved three iterative phases of development. They were (1) analysis, (2) design and (3) development phases. In the analysis phase, important variables that needed to be considered for speech-in-noise materials were identified through literature review and discussion with the experts in the field. Next, in the design phase, the compilation and formation of sentences, evaluation of naturalness and recording of the speech materials were done. The last phase was the development phase which involved the evaluation of performance intensity function and equalization of intelligibility. The final outcome of these phases were 171 sentences with equal intelligibility that can be used interchangeably in a speech-in-noise training protocol for adults in Malaysia.
In this study, the asymmetry of the main effects of action, background and tonal frequency during a pitch memory processing
were investigated by means of brain activation. Eighteen participants (mean age 27.6 years) were presented with low and
high frequency tones in quiet and in noise. They listen, discriminate and recognize the target tone against the final tone
in a series of four distracting tones. The main effects were studied using the analysis of variance (ANOVA) with action (to
wring (rubber bulb) vs. not to wring), background (in quiet vs. in noise) and frequency (low vs. high) as the factors (and
levels respectively). The main effect of action is in the right pre-central gyrus (PCG), in conformation with its contralateral
behavior. The main effect of background indicated the bilateral primary auditory cortices (PAC) and is right lateralized,
attributable to white noise. The main effect of frequency is also observed in PAC but bilaterally equal and attributable to
low frequency tones. Despite the argument that the temporo-spectral lateralization dichotomy is not especially rigid as
revealed by the main effect of frequency, right lateralization of PAC for the respective main effect of background clearly
demonstrates its functional asymmetry suggesting different perceptual functionality of the right and left PAC.
In this paper, we propose to use the autoregressive (AR)-based interpolator with Wiener filter and apply the idea to scanning electron microscope (SEM) images. The concept for combining the AR-based interpolator with Wiener filtering comes from the essential requirement of Wiener filtering for accurate and consistent estimation of the power of the noise in images prior to filter implementation. The resultant filter is called AR-Wiener filter. The proposed filter is embedded onto the frame grabber card of the scanning electron microscope (SEM) for real-time image processing. Different images are captured using SEM and used to compare the performances of the conventional Wiener and the proposed AR-Wiener technique.
During the last three decades, several techniques have been proposed for signal-to-noise ratio (SNR) and noise variance estimation in images, with different degrees of success. Recently, a novel technique based on the statistical autoregressive model (AR) was developed and proposed as a solution to SNR estimation in scanning electron microscope (SEM) image. In this paper, the efficiency of the developed technique with different imaging systems is proven and presented as an optimum solution to image noise variance and SNR estimation problems. Simulation results are carried out with images like Lena, remote sensing, and SEM. The two image parameters, SNR and noise variance, are estimated using different techniques and are compared with the AR-based estimator.
In the last two decades, a variety of techniques for signal-to-noise ratio (SNR) estimation in scanning electron microscope (SEM) images have been proposed. However, these techniques can be divided into two groups: first, SNR estimators of good accuracy, but based on impractical assumptions; second, estimators based on realistic assumptions but of poor accuracy. In this paper we propose the implementation of autoregressive (AR)-model interpolation as a solution to the problem. Unlike others, the proposed technique is based on a single SEM image and offers the required accuracy and robustness in estimating SNR values.
Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique's impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.
Low-dose Computed Tomography (LDCT) has gained a great deal of attention in clinical procedures due to its ability to reduce the patient's risk of exposure to the X-ray radiation. However, reducing the X-ray dose increases the quantum noise and artifacts in the acquired LDCT images. As a result, it produces visually low-quality LDCT images that adversely affect the disease diagnosing and treatment planning in clinical procedures. Deep Learning (DL) has recently become the cutting-edge technology of LDCT denoising due to its high performance and data-driven execution compared to conventional denoising approaches. Although the DL-based models perform fairly well in LDCT noise reduction, some noise components are still retained in denoised LDCT images. One reason for this noise retention is the direct transmission of feature maps through the skip connections of contraction and extraction path-based DL modes. Therefore, in this study, we propose a Generative Adversarial Network with Inception network modules (InNetGAN) as a solution for filtering the noise transmission through skip connections and preserving the texture and fine structure of LDCT images. The proposed Generator is modeled based on the U-net architecture. The skip connections in the U-net architecture are modified with three different inception network modules to filter out the noise in the feature maps passing over them. The quantitative and qualitative experimental results have shown the performance of the InNetGAN model in reducing noise and preserving the subtle structures and texture details in LDCT images compared to the other state-of-the-art denoising algorithms.