Reduce Background Noise Research Articles

PROSPECTS FOR THE USE OF ELECTRIC BUSES IN UKRAINE

The article considers the feasibility of using electric buses in Ukraine. As a result of the literature analysis, it was found that electric transport has a number of advantages over traditional cars. This type of transport in medium-sized cities can be represented by trolleybuses and electric buses. When using trolleybuses, significant capital investments are required for the construction of electrical lines and substations. The main advantages of electric buses are low operating costs, reduction of general background noise in the city, increased comfort for passengers due to low noise and vibrations in the cabin, and the absence of harmful emissions. Performed technical and operational analysis based on planning data for the main types of urban public transport. A method for assessing the economic efficiency of using electric vehicles has been developed. The economic feasibility of using electric buses in medium-sized cities where public transport is represented by buses and trolleybuses is considered. The research methods are theoretical. The savings in operating costs from replacing a bus with an electric bus of similar passenger capacity are UAH 1 million 600 thousand per year, and from replacing a trolleybus – UAH 1 million 250 thousand UAH per year. As of the beginning of 2022, the cost of running 1 km of an electric bus is UAH 7.2, a trolleybus is UAH 18.8, and a bus is UAH 22. The disadvantage of electric buses, which hinders their wider distribution, is the high cost of batteries, which can be up to 50% of the total cost, as well as lower passenger capacity compared to other modes of transport. A way out of the situation can be the installation of cheaper batteries of smaller capacity on electric buses and the use of recharging at the final stops.
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CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism.

Compressed sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically generative adversarial networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning reconstruction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study, we present a novel GAN-based model that delivers superior performance without the model complexity escalating. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby focusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies affirmed the efficacy of the modified modules within the network. Incorporating DR networks and CAM elevated the peak signal-to-noise ratios (PSNR) of the reconstructed images by about 1.2 and 0.8 dB, respectively, on average, even at 10× CS acceleration. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent stability but also outperforming most of the compared networks in terms of PSNR and SSIM. When compared with U-net, DR-CAM-GAN's average gains in SSIM and PSNR were 14% and 15%, respectively. Its MSE was reduced by a factor that ranged from two to seven. The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Enhancements in encoded noisy speech data by background noise reduction

In this paper, we present recent improvements incorporated into an earlier speech encoding system that combined spectral subtraction with voice activity detection (SS-VAD) and linear predictive coding (LPC). The presence of background noise in the speech data reduces the speech audibility, quality, and intelligibility. In the previously proposed system, musical and other types of noises had an adverse effect on encoding performance under low signal-to-noise ratio (SNR) conditions. To address this issue, we propose a technique by amalgamating the minimum mean square error-spectrum power estimator based on the zero-crossing (MMSE-SPZC) and LPC for the development of a noise robust encoding system. The performance evaluation is done for noisy and enhanced encoded speech data using perceptual evaluation of speech quality (PESQ), composite measures (CM), and the normalized covariance metric (NCM). The newly proposed encoding system has provided better PESQ, CM, and NCM values for various types of noises at different SNR levels compared to the earlier reported SS-VAD and LPC-based system. In addition to performance evaluation, we also present the complexity of the computation and memory requirements for both the proposed and existing algorithms. The source code of the algorithms used in this work are made publicly available.1

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter.

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.

A deep unsupervised saliency model for lung segmentation in chest X-ray images

Medical image segmentation is a critical and laborious step in the diagnosis workflow by separating regions of organs or anomalies to be analyzed further by a physician, or classified automatically by an intelligent tool. Deep machine learning techniques have achieved impressive results in image segmentation tasks whenever immense amounts of labeled data are provided. However, having big amounts of manually supervised image data is labor intensive, and costly impractical. Unsupervised learning, contrariwise, can provide results using unlabeled images by reaching decisions, clustering, and deriving insights from the data only. We propose a deep unsupervised learning approach to perform lung segmentation in chest X-ray images, which is guided by heuristic saliency maps as a noisy labeling framework, which through a composite loss of reconstruction and regularization reach saliency estimation as final segmentation. Tests were performed using public chest X-ray images from the Japanese Society of Radiological Technology (JSRT), and from Montgomery County (MC) datasets. The proposed model based on saliency scored a dice coefficient of 0.87, against a state-of-the-art unsupervised method, which scored 0.68, on JSRT, and on the MC dataset 0.88, and 0.64, respectively. Qualitatively comparing, the saliency model worked better in reducing background noise. Considering that unsupervised models can be used as robust preparation steps, and as baselines for further different tasks, either guided by constant feedback from physicians, or as input to semi-supervised learning, the results have shown this to be a promise line of research to medical image segmentation problems.

SpinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content

Droplet microfluidic methods have massively increased the throughput of single-cell sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples and the overall RNA capture efficiency is lower. These drawbacks stem from the lack of strategies to enrich for high-quality material or specific cell types at the moment of cell encapsulation and the absence of implementable multi-step enzymatic processes that increase capture. Here we alleviate both bottlenecks using fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei, fixed cells or target cell types and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half. We harness these properties to deliver a high-quality molecular atlas of mouse brain development, despite starting with highly damaged input material, and provide an atlas of nascent RNA transcription during mouse organogenesis. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.

A turn-on fluorescence strategy for hypochlorous acid detection based on DNAzyme-assisted cyclic signal amplification

Hypochlorous acid (HClO) is a crucial active oxygen component and one of the innate immunity's barrier substances in the body. Abnormal fluctuations in HClO concentration can lead to increased oxidative stress, cellular dysfunction, and the onset of various diseases. Thus, developing convenient, affordable, efficient, and sensitive methods to monitor HClO concentration in healthcare and pathophysiology research is highly significant. In this study, we developed a novel fluorescence strategy for HClO detection based on nucleic acid oxidative cleavage and Pb2+-dependent DNAzyme. By introducing a phosphorothioate site in the hairpin-structured nucleic acid sequence, the nucleic acid probe specifically recognized HClO and underwent oxidative cleavage. Upon cleavage, the enzyme strand is liberated, forming DNAzyme. This DNAzyme then cleaves the substrate strand, liberating the initially quenched fluorescent dyes and generating a turn-on fluorescent signal. The enzyme strand produced by the oxidative cleavage of HClO can be repeatedly utilized, thus realizing the cyclic signal amplification to reduce background noise. We verified the detection mechanism of this strategy through stepwise fluorescence spectroscopy analysis and electrophoresis. Under optimal experimental conditions, the method achieved a detection limit of 5.38 nM and a linear range of 1 nM-800 nM. This method demonstrated exceptional performance in actual biological sample testing and presented an exciting opportunity for expanded utilization in clinical diagnosis and medical research.

A highly sensitive and robust electrochemical biosensor for microRNA detection based on PNA-DNA hetero-three-way junction formation and target-recycling catalytic hairpin assembly amplification

Robust and sensitive methods for the detection of microRNAs (miRNAs) are crucial in the clinical diagnosis of cancers. In this study, a novel electrochemical biosensor with high sensitivity for miRNA-21 detection is developed, which relies on the formation of a peptide nucleic acid (PNA)-DNA hetero-three-way junction (H3WJ) and target-recycling catalytic hairpin assembly (CHA) amplification. The electroneutral PNA probes are initially immobilized onto a gold electrode to construct the sensor. Upon introduction of miRNA-21, target-recycling CHA is initiated, resulting in abundant double-stranded CHA products. Subsequently, association between the PNA probes and these products leads to the formation of PNA-DNA H3WJs. Consequently, the electrode surface is densely populated with numerous electroactive Ferrocene (Fc) groups, resulting in a significantly amplified current response for highly sensitive detection of miRNA-21 at concentrations as low as 0.15 fM. This approach demonstrates remarkable specificity towards target miRNAs and can be utilized for quantitative monitoring of miRNA-21 expression in human cancer cells. More importantly, the sensor exhibits exceptional stability and shows a significant reduction in background noise during miRNA detection, making this method a highly promising sensing platform for monitoring various miRNA biomarkers to facilitate the diagnosis of diverse cancers.

EA-ConvNeXt: An Approach to Script Identification in Natural Scenes Based on Edge Flow and Coordinate Attention

In multilingual scene text understanding, script identification is an important prerequisite step for text image recognition. Due to the complex background of text images in natural scenes, severe noise, and common symbols or similar layouts in different language families, the problem of script identification has not been solved. This paper proposes a new script identification method based on ConvNext improvement, namely EA-ConvNext. Firstly, the method of generating an edge flow map from the original image is proposed, which increases the number of scripts and reduces background noise. Then, based on the feature information extracted by the convolutional neural network ConvNeXt, a coordinate attention module is proposed to enhance the description of spatial position feature information in the vertical direction. The public dataset SIW-13 has been expanded, and the Uyghur script image dataset has been added, named SIW-14. The improved method achieved identification rates of 97.3%, 93.5%, and 92.4% on public script identification datasets CVSI-2015, MLe2e, and SIW-13, respectively, and 92.0% on the expanded dataset SIW-14, verifying the superiority of this method.

Use of polarization properties of seismic waves to improve Fresnel Volume Migration of three-component subsurface seismic data

Long-range exploration in mines contributes significantly to work safety, raw material extraction and storage of materials in the subsurface. The distribution and number of source and receiver points for seismic surveys depend on the available gallery passages in the mine or underground buildings. Due to the resulting limited aperture, migration artifacts, spatial ambiguities, and geometrically induced amplitude focusing can occur in migration images. In addition, background noise is often very interfering, and reflectors smear in migration images. It has been shown that along drifts that have directional changes and thus corners or chambers, the signal-to-noise ratio is low due to scattered waves. We have incorporated directional and polarization filters into the workflow for pre-processing subsurface seismic data before starting Fresnel Volume Migration (FVM). This significantly reduces background noise and geometrically induced amplitude focusing as well as migration amplitude smearing effects. By applying directional and polarization filters beforehand, we improve the 3D seismic imaging. This allows us to highlight previously hidden or smeared reflectors when calculating migrations of reflected P and S waves and suppress amplitude focusing effects along drifts. In this paper, we present step-by-step this novel methodological approach that improves 3D seismic imaging for large-scale structure exploration in mining.

Effect of Sub-Surface Damage Layer Removal by Sublimation Etching of 4H-SiC Bulk Wafers on PL Imaging of Crystal Defect Visibility

Improving the visibility of defects in nitrogen-doped 4H-SiC (0001) bare wafers by photoluminescence imaging (PLI) is essential for improving the epitaxial growth process and device yields. This study proposes sub-surface damage (SSD) introduced during the mechanical process of SiC wafers as a new factor in reducing defect visibility in PL images. To verify the effect of SSD, we observed the surface of a SiC wafer, which was thermally etched at about 3 μm. As a result, dramatic defect visibility improvement was observed when the surface roughness was sufficiently flat (Ra < 0.3 nm) after thermal etching. Thus, the results suggest that defect visibility in PL images can be improved by controlling SSD and surface roughness. Using the background noise reduction effect of the SSD removal, not only PLI but also many other wafer surface inspections are expected to be improved.

Sustainable and Rapid Determination of Two Halogenated Pesticides in a Commercial Formulation by Solid Phase Microextraction and Liquid Phase Chemical Ionization Mass Spectrometry

This work presents a sustainable and rapid method for halogenated pesticide analysis without chromatographic separation. The system is composed of a microfluidic open interface (MOI) for solid-phase microextraction (SPME) liquid phase desorption, connected to a liquid electron ionization mass spectrometry interface (LEI-MS). Either a triple quadrupole mass spectrometer (QQQ-MS/MS, (low-resolution) or a quadrupole-time-of-flight tandem MS (QTOF-MS/MS, high-resolution) were employed, each operating in negative chemical ionization (NCI) conditions. The flow rate used (100 µL/min) to rapidly empty the MOI chamber (approximately 2.5 µL) is reduced to the working flow rate of the LEI interface (500 nL/min) by a passive flow splitter (PFS). NCI is an appropriate ionization technique for electrophilic compounds, increasing specificity and reducing background noise. Two halogenated pesticides, dicamba and tefluthrin, were extracted simultaneously from a commercial formulation matrix (CF) using a C18 fiber by direct immersion (3 min under vortex agitation). Analyte desorption occurred in static conditions inside MOI filled with acidified acetonitrile (ACN) (0.2% phosphoric acid, PA). Extraction and desorption steps were optimized to increase efficiency and accelerate the process. No chromatographic separation was involved; therefore, the system fully exploited MS/MS selectivity and HRMS accuracy demonstrating good linearity, repeatability and limits of detection (LODs) and limits of quantification (LOQs) in the pg/mL range (50 and 500 pg/mL, respectively). Low-resolution experiments showed that matrix effects (ME) did not affect the results. The fast workflow (5 min) makes the system suitable for high-throughput analysis observing the principles of green analytical chemistry (GAC).

Comparison of speech recognition performance with and without a face mask between a basic and a premium hearing aid in hearing-impaired listeners

BackgroundThe mask mandate during the COVID-19 pandemic leads to communication challenges as sound energy gets reduced and the visual cues are lost due to the face mask. This study examines the impact of a face mask on sound energy and compares speech recognition performance between a basic and a premium hearing aid. MethodsParticipants watched four video clips (a female and a male speaker with and without a face mask) and repeated the target sentences in various test conditions. Real-ear measurement was performed to investigate the changes in sound energy in no mask, surgical, and N95 mask conditions. ResultsWith the face mask on, sound energy significantly decreased for all types of masks. For speech recognition, the premium hearing aid showed significant improvement in the mask condition. ConclusionThe findings emphasize and encourage health care professionals to actively use communication strategies, such as speaking slowly and reducing background noise, when interacting with individuals with hearing loss.

Objective Assessment of Binaural Benefit from Acoustical Treatment in Real Primary School Classrooms.

Providing students with an adequate acoustic environment is crucial for ensuring speech intelligibility in primary school classrooms. Two main approaches to control acoustics in educational facilities consist of reducing background noise and late reverberation. Prediction models for speech intelligibility have been developed and implemented to evaluate the effects of these approaches. In this study, two versions of the Binaural Speech Intelligibility Model (BSIM) were used to predict speech intelligibility in realistic spatial configurations of speakers and listeners, considering binaural aspects. Both versions shared the same binaural processing and speech intelligibility backend processes but differed in the pre-processing of the speech signal. An Italian primary school classroom was characterized in terms of acoustics before (reverberation, T20 = 1.6 ± 0.1 s) and after (T20 = 0.6 ± 0.1 s) an acoustical treatment to compare BSIM predictions to well-established room acoustic measures. With shorter reverberation time, speech clarity and definition improved, as well as speech recognition thresholds (SRTs) (by up to ~6 dB), particularly when the noise source was close to the receiver and an energetic masker was present. Conversely, longer reverberation times resulted (i) in poorer SRTs (by ~11 dB on average) and (ii) in an almost non-existent spatial release from masking at an angle (SRM).

Attention-induced semantic and boundary interaction network for camouflaged object detection

The high intrinsic similarity between camouflaged objects and the background makes camouflaged object detection (COD) more challenging than traditional salient object detection (SOD). Existing deep-learning methods often fall into the following shortcomings: (1) When dealing with high-level features, it is difficult to fully and accurately extract semantic information, which is crucial for locating camouflaged targets. (2) The camouflaged targets identified by existing methods in complex scenes have rough boundaries and incomplete spatial information. (3) Existing methods cannot effectively exert the uniqueness and complementarity of multiple features when integrating multiple features. To this end, we propose an attention-induced semantic and boundary interaction network for accurately identifying camouflaged objects. Specifically, we propose a contrastive positioning module (CPM), which adopts a contrastive learning way to separate the camouflaged object from the background, thereby obtaining the exact location of the camouflaged target and retaining more semantic information. Then, we design a boundary exploration module (BEM), which can reduce background noise interference, focus on the structural details of camouflaged targets, and explore rich fine-grained spatial information. Finally, we propose an attention-induced interaction module (AIM) to fuse multivariate information (i.e., semantic location information, boundary information, and the side output information of the backbone), and multivariate information is complementarily fused under attention guidance to generate more powerful camouflaged target features. Extensive experiments show that the proposed method on the different backbones (i.e., ResNet-50, Res2Net-50, PVTv2-B2, EfficientNet-B1) produces state-of-the-art results on several camouflaged benchmarks. The code is available https://github.com/zhangqiao970914/ASBI.

Optimal symbolic entropy: An adaptive feature extraction algorithm for condition monitoring of bearings

Due to their effectiveness in vibration-based fault feature extraction from bearings, entropy-based methods have become a hot research topic. Symbolic dynamic filtering reduces background noise in bearing signals, making it ideal for entropy analysis. However, the partitioning approach selection of symbolic dynamic filtering mainly depends on experience, which may bring over-track and under-track phenomena. The optimal symbolic entropy (OSE) method proposes a solution by using mean spectral kurtosis to evaluate the symbolization performance of partitioning approaches. This method improves the identification of bearing faults through steps such as evaluating frequency and amplitude preservation, selecting the optimal symbolization approach, and using the OSE method with multiscale analysis. Simulative and experimental data analysis demonstrates its superior ability to extract bearing fault characteristics, with better performance and robustness than existing methods.

Causal speech enhancement using dynamical-weighted loss and attention encoder-decoder recurrent neural network.

Speech enhancement (SE) reduces background noise signals in target speech and is applied at the front end in various real-world applications, including robust ASRs and real-time processing in mobile phone communications. SE systems are commonly integrated into mobile phones to increase quality and intelligibility. As a result, a low-latency system is required to operate in real-world applications. On the other hand, these systems need efficient optimization. This research focuses on the single-microphone SE operating in real-time systems with better optimization. We propose a causal data-driven model that uses attention encoder-decoder long short-term memory (LSTM) to estimate the time-frequency mask from a noisy speech in order to make a clean speech for real-time applications that need low-latency causal processing. The encoder-decoder LSTM and a causal attention mechanism are used in the proposed model. Furthermore, a dynamical-weighted (DW) loss function is proposed to improve model learning by varying the weight loss values. Experiments demonstrated that the proposed model consistently improves voice quality, intelligibility, and noise suppression. In the causal processing mode, the LSTM-based estimated suppression time-frequency mask outperforms the baseline model for unseen noise types. The proposed SE improved the STOI by 2.64% (baseline LSTM-IRM), 6.6% (LSTM-KF), 4.18% (DeepXi-KF), and 3.58% (DeepResGRU-KF). In addition, we examine word error rates (WERs) using Google's Automatic Speech Recognition (ASR). The ASR results show that error rates decreased from 46.33% (noisy signals) to 13.11% (proposed) 15.73% (LSTM), and 14.97% (LSTM-KF).

Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring

The popularity of dogs has been increasing owing to factors such as the physical and mental health benefits associated with raising them. While owners care about their dogs' health and welfare, it is difficult for them to assess these, and frequent veterinary checkups represent a growing financial burden. In this study, we propose a behavior-based video summarization and visualization system for monitoring a dog's behavioral patterns to help assess its health and welfare. The system proceeds in four modules: (1) a video data collection and preprocessing module; (2) an object detection-based module for retrieving image sequences where the dog is alone and cropping them to reduce background noise; (3) a dog behavior recognition module using two-stream EfficientNetV2 to extract appearance and motion features from the cropped images and their respective optical flow, followed by a long short-term memory (LSTM) model to recognize the dog's behaviors; and (4) a summarization and visualization module to provide effective visual summaries of the dog's location and behavior information to help assess and understand its health and welfare. The experimental results show that the system achieved an average F1 score of 0.955 for behavior recognition, with an execution time allowing real-time processing, while the summarization and visualization results demonstrate how the system can help owners assess and understand their dog's health and welfare.

A sub-region Unet for weak defects segmentation with global information and mask-aware loss

In recent years, diverse detection methods have been proposed to achieve highly accurate image segmentation. However, low contrast and background noise interference still pose challenges to defect detection. Existing deep learning-based methods for defect detection mostly learn rich features on the whole image. This can lead to overfitting as the parameters get bigger, and it also loses the spatial connection. In order to solve this problem, we suggest a network based on spatial sub-region feature extraction and mask-aware loss that can detect metal surface defects. In this network framework, we obtain the contextual information of each region through two streams: one branch encodes the spatial sub-region information and reduces the effect of geometric and illumination deformation under u-shape architecture. A skip-connected structure in another stream is designed to learn global information and compensate for boundary features. Next, we propose a mask-aware loss to reduce background noise interference further. Finally, we validate the effectiveness of our method on two challenging datasets, reaching 88.45% and 79.16% on the NEU-Seg and USB-Seg datasets, respectively. Also, our model is only 5.48 MB, and can achieve 172 fps for processing images with 200 × 200 size defects.

Using crowd-sourced data to optimize conversational sound levels in hospitality venues

One of the top complaints about a restaurant is usually related to noise. The sound level in a venue can significantly affect whether or not a customer will return. Statistical analysis of the crowdsourced data collected with the SoundPrint smartphone app at more than 10,000 U.S.-based hospitality venues (restaurants, coffee shops, and bars) provides a comparison of the objective sound level data versus the patrons’ subjective ratings for conversational ability. These insights show a customer's tolerance for noisy environments can be highly dependent on the venue type, specifically when communication is important. The findings can also help venue managers know what interior sound levels to target when optimizing the acoustics for their venue, whether by reducing background noise levels or improving acoustical treatments, in order to attract and keep their patrons happy.