Image Processing Toolbox Research Articles

Development and Evaluation of a Sensor Enabled Smart Herbicide Applicator for Inter Row Crops

A novel self-propelled smart herbicide applicator (1600mm × 500mm × 1100mm) was developed using machine vision to target weeds and apply herbicides variably in wide row-spaced crops. The applicator integrated a weed detection system, smart sprayer, mechanical components, and power transmission system. The weed detection system used a webcam based image sensor, with images processed via MATLAB's image processing toolbox. The system processed images using an RGB-based algorithm (R > G + 5, R > B + 5) to separate weeds from soil. Weed density, calculated as the green area percentage (green pixels to total pixels), was sent to a microcontroller-operated smart sprayer via a serial port. The microcontroller regulates herbicide flow by controlling the valve with a servo motor, following an algorithm based on green area percentage. Field tests showed a 40% reduction in herbicide use, 91.26% weeding efficiency, and 63.4% field efficiency. The smart herbicide applicator's unique features include real-time measurement, portability, and precise herbicide application for wide-row crops, even at night. It also reduced environmental hazards and drudgery among farmers.

89 Streamlining the acquisition and analysis of digital images for the assessment of horn fly abundance

Abstract Horn flies are one of the most prevalent ectoparasites on pasture raised cattle. Studies have shown reasonable evidence of a genetic basis for horn fly attraction in beef cattle. Current methods for assessing fly abundance-related phenotypes (e.g., subjective assessment and image-based counts) suffer from limited accuracy and logistic difficulties. Previous attempts to assess horn fly abundance using image-based methods were hindered by several factors including weather conditions, distance and angle between the image taker and the animal, and lighting during image acquisition. The aim of this study was to address these limitations through the development of practical guidance to optimize image acquisition and to develop an adaptive image analysis algorithm to enhance the accuracy of image-based assessment of fly abundance. Images of beef cattle on pasture over multiple horn fly seasons (late spring to early fall) were collected. For each animal, the best image (where the entire side of the animal was in view) was selected and cropped from the withers to the hooks and from the chest floor to the udder. For every image (n = 314), all the flies were manually counted using the ImageJ software. Several image enhancement and analysis techniques including adaptive thresholding, pixel region connection, and pixel classification were used. The images were then clustered into three quality classes based on quality metrics computed using several algorithms from the image processing toolbox in MATLAB (BRISQUE, NIQE, and PIQE). BRISQUE and NIQE use distortions of natural scene statistics to compute quality scores. PIQE uses image features and models of human perception to compute quality scores. Pearson correlation, mean squared error (MSE), and the coefficient of determination (R2) were used to evaluate the concordance between the manual (ground truth) and the image predicted counts. Across the different scenarios, the Pearson correlation and R2 ranged between 0.21 to 0.67 and 0.04 and 0.45, respectively (Table 1). When the images were first clustered in quality classes, all the assessment metrics showed a substantial increase (Table 2). In fact, the Pearson correlation ranged between 0.47 to 0.80 using the NIQE. When the images were classified as low or medium quality by BRISQUE, the correlation was greater (0.74 to 0.79) compared with NIQE (0.47 to 0.70) and PIQE (0.69 to 0.74). Similar patterns were observed for the other assessment criteria (MSE and R2). These results seem to indicate that NIQE, BRISQUE, or a combination of the two algorithms could be used to assess image quality and determine the appropriate combination of image parameters to maximize the predictive ability. The NIQE method could be used as a filter to detect low quality images that are unsuitable for counting and then BRISQUE could be used on the remaining images.

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer.

The aim of this study was to investigate the potential of multiparametric 18F-FDG PET/MR imaging as a platform for radiomics analysis and machine learning algorithms based on primary cervical cancers to predict N- and M-stage in patients. A total of 30 patients with histopathological confirmation of primary and untreated cervical cancer were prospectively enrolled for a multiparametric 18F-FDG PET/MR examination, comprising a dedicated protocol for imaging of the female pelvis. The primary tumor in the uterine cervix was manually segmented on post-contrast T1-weighted images. Quantitative features were extracted from the segmented tumors using the Radiomic Image Processing Toolbox for the R software environment for statistical computing and graphics. 45 different image features were calculated from non-enhanced as well as post-contrast T1-weighted TSE images, T2-weighted TSE images, the ADC map, the parametric Ktrans, Kep, Ve and iAUC maps and PET images, respectively. Statistical analysis and modeling was performed using Python 3.5 and the scikit-learn software machine learning library for the Python programming language. Prediction of M-stage was superior when compared to N-stage. Prediction of M-stage using SVM with SVM-RFE as feature selection obtained the highest performance providing sensitivity of 91 % and specificity of 92 %. Using receiver operating characteristic (ROC) analysis of the pooled predictions, the area under the curve (AUC) was 0.97. Prediction of N-stage using RBF-SVM with MIFS as feature selection reached sensitivity of 83 %, specificity of 67 % and an AUC of 0.82. M- and N-stage can be predicted based on isolated radiomics analyses of the primary tumor in cervical cancers, thus serving as a template for noninvasive tumor phenotyping and patient stratification using high-dimensional feature vectors extracted from multiparametric PET/MRI data. · Radiomics analysis based on multiparametric PET/MRI enables prediction of the metastatic status of cervical cancers. · Prediction of M-stage is superior to N-stage. · Multiparametric PET/MRI displays a valuable platform forradiomics analyses .

Study of defects in PV generators using image analysis techniques with Matlab

It is evidence that the useful life of the photovoltaic generator, as its correct operation, define the production and the profitability of a photovoltaic installation. There are several aspects that cause descents in the level of production of the generator and therefore losses in the installation. Based on it, it has developed numerous of both corrective and preventive maintenance that avoid these circumstances. Along the document there are revised the possible defects that the photovoltaic generator can have and the techniques of detection of the same ones. Inside the techniques of analysis and examination [1] we will center on the very concrete one: the visual inspection and its development by image analysis tools with Matlab. The techniques used are part of the tool “Image processing toolbox” that provides a finished set of algorithms of standard reference and graphic hardware to the prosecution, the analysis and the visualization of images and the development of complementary algorithms. Across the employment and the adaptation of some of the included algorithms it will be claimed to approach a step more specializing in the conventional visual analysis used in the maintenance of the plants of photovoltaic solar energy.

Dataset of in-situ coaxial monitoring and print’s cross-section images by Direct Energy Deposition fabrication

Coaxial monitoring of the Direct Energy Deposition (DED) machines enables a real-time material deposition study. Coaxial-images contain substantial melt-pool information and incorporate situational information including the sparks’ intensity, numbers, etc. Recent studies have shown that melt-pool observations correlate directly with machine parameters and artifact properties. Therefore, the melt-pool information not only can assist in measuring the machine’s working condition and determining machine operation parameters’ reliability but also facilitates the deposition characteristics studies like print’s regime and dimensions. This information is gathered during the fabrication and can be expanded to perform various process studies and fault registration. This paper utilizes the Optomec DED machine to fabricate single-track prints with multiple process parameters, while a coaxial camera records the deposition. Each deposited track is then cut perpendicular to the print’s direction to facilitate process parameters correlation study with actual geometrical deposition measured using a microscope. The coaxial images taken during fabrication, along with their process parameters, cross-cut measurements, and a developed image-processing toolbox, are presented alongside this paper to empower future research directions.

Comparative Analysis of Colour Perception Models for Realistic Rendering of Synthetic Images

This paper analyses, visualizes, and compares the CIECAM97 and CIECAM02 colour perception models using expert evaluation. Justifications are presented, in which the bases of construction of these models are succinctly described. Also, the features of their implementation are given, which presents some differences in visualization. To implement the models we used MATLAB program, namely, its plugin Image Processing Toolbox, which allowed, by dividing the original image into an array of pixels, to make the necessary operations with them. By defining new observation conditions, visualization was built in such a way that the image obtained in the dark time of day is viewed on a bright monitor display. These implementations show how accurately enough to create an algorithm for colour perception by the human eye. As an initial image to demonstrate the work of the models, the concept of evening illumination of the facade of the building E of NRU “MPEI” is presented. Two presented models were evaluated in a group of 9 fourth year lighting engineering students of NRU “MPEI”. The perception of the images, constructed by using the CIECAM97 and CIECAM02 models of colour perception, was assessed on a 5-point scale. According to the results of the expert evaluation the conclusions were made which of the colour perception models is suitable for integration in virtual reality.

Design of Digital Image Processing System Based on Machine Vision

Digital image processing technology is a technology that has developed rapidly in recent years[1]. A series of images are processed by computers to improve the visual effect of the image in order to achieve the desired presentation effect. Digital image processing technology has been continuously developed with the innovation of computers, and its application range has expanded from the original image field to traffic command, signal processing, medical treatment and other fields closely related to people's lives. With the support of many algorithms, the speed of digital image processing technology is getting faster and faster, which brings a lot of convenience to people's lives, and which becomes an indispensable technology in life. With the help of MATLAB's GUI user interface, image processing toolbox and MATLAB's comprehensive mathematical function library, this article designs an image processing operating platform that realizes some image processing functions after repeated trials.

Experimental investigation of electrode wear assessment in the EDM process using image processing technique

Abstract One of the widespread non-traditional and non-contact machining processes is the electrical discharge machining (EDM) process, which depends on removing material from the surface by different repeat electrical discharges between a machined workpiece and a cutting tool called the electrode. The electrode wear in the EDM process is one of the parameters of significant technological interest. This article proposed a reliable image-based system that automatically detects and computes electrode wear in EDM of rectangular steel workpiece plate of AISI 314 using a copper electrode with a rectangular shape. MATLAB environment and image processing toolbox are used to identify and manipulate the captured rectangular electrode image for rectangular electrode data extraction needed for the electrode wear identification process. The results show that the proposed image-based approach, using a non-contact measuring system and relatively inexpensive equipment, is suitable with good accuracy and efficiency for measuring and testing electrode wear.

Potential Disease Severity Assessment Approach for Potato Late Blight with Smartphone-Based Image Capturing

Accurate and timely evaluation of the severity of Potato Late Blight (PLB) is vital in disease control and being confident that fungicides are utilized efficiently. PLB is caused by Phytophthora infestans and is one of the most destructive diseases affecting potatoes. Knowledge gap in determining the severity of the PLB using the Late Blight Key (LBK) results in erroneous disease evaluation, unregulated chemical use, and exorbitant management expenses for PLB. The purpose of this research was to build a novel technique based on smartphones to measure the PLB severity in relation to the LBK. Using a smartphone camera, digital images of sick PLB leaves were taken at a consistent height, and simultaneously the related visual disease severity rating score was recorded using the LBK. The MATLAB (R2019b) image processing toolbox was used to process the photos, which included cropping, manually defining the areas of the leaf and lesion, and determining the pixel ratio (the ratio of PLB lesion to leaf pixels). When data from all the images were merged at the conclusion, regression analysis revealed a virtually perfect linear association (R2=86.6%) between the image-derived pixel ratio and the visual ranking scores of the PLB sickness severity. According to these results, image processing analysis that has been built may precisely identify the leaf areas that are infected with PLB and assess the disease's severity level.

Region Growing Segmentation for Brain Tumor Segmentation-Based MRI Images

Abstract: Techniques for brain imaging are crucial in identifying the causes of brain cell damage. Therefore, earlier detection of these disorders can have a significant impact on treatment options and help to prevent any potential problems for the patient. One of the main reasons for fatalities in humans is brain tumours. It is obvious that early detection and proper classification of the tumour can increase the likelihood of survival. Recently, brain tumor segmentation has become a common task in medical image analysis due to its efficacy in diagnosing the type, size, and location of the tumor in automatic methods. Several researchers have developed new methods in order to obtain the best results in brain tumor segmentation, including using deep learning techniques such as the convolutional neural network(CNN).The goal of this paper is to discuss conventional methods of brain tumor segmentation with focus on region-growing segmentation from MRI images. For the implementation of the work brain tumor image from Kaggle and Image Processing Toolbox under Matlab Software have been used.

A Matlab-Based Approach for Estimating the Area Taken Up by Cells Attached to Micropatterned Optically Opaque Surfaces

Abstract Cell attachment is of paramount importance in implant design, bioreactor design, tissue engineering and the design of non-fouling surfaces. Surface roughness is a significant factor that affects cell attachment. To explore the impact of roughness characteristics, micromachining approaches can be used to fabricate surfaces with controlled microscale topography. When optical microscopy is employed to study cell attachment to optically opaque micropatterned surfaces, one needs to separate the area of an image coated with cells from the background. Manual cell counting can be used to assess the amount of attached cells. However, this process is very time consuming, when the studied surface is larger than several square millimeters. This paper describes an approach for the automatic estimation of the area of cells attached to the surfaces of micro-patterned optically opaque platforms. Saccharomyces cerevisiae yeast cells were used to test the developed approach. The approach uses image registration and segmentation tools available in MathWorks MATLAB R2020b Image Processing Toolbox. The factors that affect the accuracy of the developed approach (magnification, contrast and focus) as well as the ways of improving the results are discussed.

Experimental Investigation of Surface Roughness and Material Removal Rate in Wire EDM of Stainless Steel 304.

Its unexcelled mechanical and physical properties, in addition to its biocompatibility, have made stainless steel 304 a prime candidate for a wide range of applications. Among different manufacturing techniques, electrical discharge machining (EDM) has shown high potential in processing stainless steel 304 in a controllable manner. This paper reports the results of an experimental investigation into the effect of the process parameters on the obtainable surface roughness and material removal rate of stainless steel 304, when slotted using wire EDM. A full factorial design of the experiment was followed when conducting experimental trials in which the effects of the different levels of the five process parameters; applied voltage, traverse feed, pulse-on time, pulse-off time, and current intensity were investigated. The geometry of the cut slots was characterized using the MATLAB image processing toolbox to detect the edge and precise width of the cut slot along its entire length to determine the material removal rate. In addition, the surface roughness of the side walls of the slots were characterized, and the roughness average was evaluated for the range of the process parameters being examined. The effect of the five process parameters on both responses were studied, and the results revealed that the material removal rate is significantly influenced by feed (p-value = 9.72 × 10-29), followed by current tension (p-value = 6.02 × 10-7), and voltage (p-value = 3.77 × 10-5), while the most significant parameters affecting the surface roughness are current tension (p-value = 1.89 × 10-7), followed by pulse-on time (1.602 × 10-5), and pulse-off time (0.0204). The developed regression models and associated prediction plots offer a reliable tool to predict the effect of the process parameters, and thus enable the optimizing of their effects on both responses; surface roughness and material removal rate. The results also reveal the trade-off between the effect of significant process parameters on the material removal rate and surface roughness. This points out the need for a robust multi-objective optimization technique to identify the process window for obtaining high quality surfaces while keeping the material removal rate as high as possible.

A Model for Detecting Fungal Diseases in Cotton Cultivation using Segmentation and Machine Learning Approaches

This research detailed a model for detecting fungal diseases via techniques for processing images of cotton leaves. The work allowed to develop a model based on the set of preprocessed data, to formulate the developed model, to simulate and evaluate the model. It is about detecting fungal diseases in cotton cultivation. The image data records were collected in an online data repository consisting of images of cotton leaves infected with fungal diseases and normal leaf images. In addition, other images of infected and uninfected cotton leaves were collected in cotton production fields in the Ségbana region in Benin Republic. The model was formulated based on watershed segmentation technique by applying Edge Detection algorithm and K-Means Clustering; and Support Vector Machine (SVM) for classification. The simulation was done using MATLAB with Image Processing Toolbox 9.4. The results gave an accuracy of 99.05%, specificity 90%, misclassification rate 0.95%, recall rate 99.5% and precision 99.5%. In addition, with less computational effort and in less than a minute, the best results were obtained, showing the efficiency of the image processing technique for the detection and classification of infected and uninfected leaves. It was concluded that this approach was applied to detect fungal diseases on cotton leaves to promote the production and harvest of good quality cotton and valuable cotton products.

Investigating Visual Monitoring of the Scrotum as a Supplementary Tool for Boar Semen Quality Evaluation.

Farm animals behavior research uses video cameras, mainly for visual observation and recording. The purpose of this feasibility study was to enrich the predictable methods of boar semen production capacity by correlating sperm variables with the scrotal contractions (SC) frequency and intensity. A video camera was used to record the reaction of the scrotum during ejaculation. The respective collected ejaculates were evaluated and semen parameters, such as viability, morphology, membranes functional integrity and kinematics, were determined. The camera recorded the scrotal contractions/relaxations and the video was handled by the Image Processing Toolbox of Matlab (Mathworks Inc., Natick, MA, USA). The SC intensity was verified as a percentage change in the scrotum size among the video frames of maximum contraction and relaxation. The archived data from the frames were analyzed statistically, using a linear mixed effects model that involved sperm assessed parameters. Correlations of the SC intensity with the average path velocity, VAP (R2 = 0.591, p = 0.043) and with the percentage of the cytoplasmic droplets (R2 = 0.509, p = 0.036) were noticed. Previous studies reported the positive correlation of VAP with the number of live-born piglets. In conclusion, video monitoring of the boar scrotal function during ejaculation is useful, but more research is needed to establish its appropriateness as a supplementary method for the prognosis of boar ability to produce high-quality semen.

Sistema de verificación de huellas dactilares basado en DWT, extracción de características de múltiples dominios y clasificador subespacial de conjuntos

This paper describes a fingerprint verification system including preprocessing, Wavelet transform, feature extraction using multiple domains, and ensemble subspace discriminant classifier. The system is implemented in MATLAB using Wavelet Toolbox, Image Processing Toolbox, and Statistics and Machine Learning Toolbox. First, the motivation and novelty, followed by the review of the previous work, are presented. Next, all steps are described in detail. Three fingerprint databases from the literature are used. The proposed method’s performance is compared with state-of-the-art techniques based on different classifiers utilizing the accuracy metric. The proposed algorithm achieves high accuracy at 97.5% for the DB3-FVC2000 subset.

Experimental study on the performance of a mini-scale Y-type mixer with two liquid metal-enabled pumps

The mixing process of two liquids inside an open minichannel was experimentally studied in the presence of liquid metal and an electric field. The Y-type mixers under study were made of Plexiglas, and two liquid metal-enabled pumping systems (based on electrically induced surface tension gradients) were placed at the inlets of the mixer instead of conventional syringe pumps. The effects of the mixing angle, the voltage applied to the liquid metals, and the Reynolds number on the mixing process were investigated. To accurately determine the mixing index, the image processing toolbox of MATLAB software was employed. The results showed that the mixing intensity increased as the applied voltage signal increased, thereby creating a chaotic advection in the minichannel. Furthermore, although the Reynolds number of induced flow and the applied voltages were directly proportional, the input angle plays an important role in the mixing. Among the considered models, in the constant voltage, the 30° and 90° had the best and the worst mixing, respectively. The maximum mixing intensity of 94% was obtained at an input angle of 30° and voltage of 14 V, where, in the absence of an electric field, the maximum mixing intensity was 55%.

Identification of electrode short circuits during the electrolytic refining of copper based on heat radiation

This study is aimed at improving the efficiency of monitoring and automated control over the technological state of electrolytic cells during copper refining using the recognition and identification of short circuits between the electrodes. Experimental works were performed on a laboratory bench consisting of two cells connected in series to the electrical circuit. The method of detecting short circuits is based on measuring infrared radiation using a scanning thermal imager (Оptris PI 400i / PI 450i) for determining areas with elevated temperatures of electrode and electrolyte surfaces. A short-circuit detection algorithm was developed and tested in MATLAB environment using the Image Processing Toolbox MATLAB functions. The proposed method for recognizing and identifying short circuits between the electrodes of an electrolytic cell is based on using a scanning digital thermal imager. This method allows a comprehensive assessment of the contact area, determination of both the start time of a short circuit and the degree of electrode heating in the short-circuit zone, as well as establishment of temperature values in all areas, including the electrolyte temperature in the entire volume of the cell. An algorithm for searching for short-circuit locations in an electrolytic cell is proposed. This algorithm involves the collection of data and its comparison for adjusting the inter-electrode distance, ensuring a stable energy mode for the entire electrolytic line, and determining the exact position of each cathode in the cell using the threshold processing of infrared images obtained by a thermal imager. It was established that timely identification of short circuit locations in the electrolytic bath (since the onset of a contact between the electrodes), as well as recording of the growth rates of a dendritic coalescence area and electrolyte temperature, ensure timely elimination of technological disturbances during the operation of electrolytic baths. Thus, the developed method of short circuit identification for comprehensive determination and recognition of the technological state of an electrolytic cell during the electrolytic refining of copper can be used to reach a stable energy mode with minimum deviations in the process temperature.

Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network

Detecting and counting on road vehicles is a key task in intelligent transport management and surveillance systems. The applicability lies both in urban and highway traffic monitoring and control, particularly in difficult weather and traffic conditions. In the past, the task has been performed through data acquired from sensors and conventional image processing toolbox. However, with the advent of emerging deep learning based smart computer vision systems the task has become computationally efficient and reliable. The data acquired from road mounted surveillance cameras can be used to train models which can detect and track on road vehicles for smart traffic analysis and handling problems such as traffic congestion particularly in harsh weather conditions where there are poor visibility issues because of low illumination and blurring. Different vehicle detection algorithms focusing the same issue deal only with on or two specific conditions. In this research, we address detecting vehicles in a scene in multiple weather scenarios including haze, dust and sandstorms, snowy and rainy weather both in day and nighttime. The proposed architecture uses CSPDarknet53 as baseline architecture modified with spatial pyramid pooling (SPP-NET) layer and reduced Batch Normalization layers. We also augment the DAWN Dataset with different techniques including Hue, Saturation, Exposure, Brightness, Darkness, Blur and Noise. This not only increases the size of the dataset but also make the detection more challenging. The model obtained mean average precision of 81% during training and detected smallest vehicle present in the image

Spatial distribution patterns of trunk internal decay of Euphrates poplar riparian forest along the Tarim River, northwest China

Trunk internal decay is a common phenomenon in forest ecosystems, causing the loss of forest resources. Euphrates poplar (Populus euphratica Oliv.) is the only constructive tree species of the desert riparian forests along the Tarim River in northwestern China. The ecosystem stability of these forests has been damaged due to widespread internal decay or hollow rot in many tree trunks. However, forest resource conservation is difficult due to the unclear spatial patterns of P. euphratica trunk decay under different water conditions and tree morphology in the middle (MTR) and lower reaches (LTR) of the Tarim River. We conducted a field experiment and lab analysis to assess the reliability of the ARBOTOM® 2D stress wave Non-Destructive Testing (NDT) technique on P. euphratica populations. We explored the quantitative characterization method and the spatial distribution patterns of P. euphratica trunk internal decay in the MTR and LTR. Our study found that (1) the accuracy of the stress wave NDT technique in detecting trunk decay of P. euphratica in the arid desert area was more than 80%; The severity of internal trunk decays can be quantitatively characterized by the combination of the Matlab Image Processing Toolbox, (2) the trunk decay proportion of P. euphratica showed an increasing trend with the tree height in the MTR. The LTR first increased, then decreased. The degree of decay was highest when the tree height was 6–7 m. The trunk decay proportion of individual trees positively correlated with the diameter at breast height and crown loss (p < 0.001) and negatively correlated with the crown diameter (p < 0.001). The higher the decay occurrence, the worse the individual size was, and vice versa, and (3) the decay proportion of the P. euphratica showed an overall increasing trend with an increase in groundwater depth. The trunk decay was significantly higher in the LTR compared to the MTR at the same distance of 300–1050 m from the river channel. Our findings suggested that the trunk decay in P. euphratica had a higher difference among different tree morphology, and the occurrence of decay in the LTR was much higher than in the MTR. The water stress at the site is one of the leading factors in internal defects in the riparian forests along the Tarim River. The results provide a theoretical basis and scientific support for desert riparian forest ecosystems’ conservation and sustainable management.

Real-Time Image Processing Toolbox for All-Optical Closed-Loop Control of Neuronal Activities

The development of in vivo imaging and optogenetic tools makes it possible to control neural circuit activities in an all-optical, closed-loop manner, but such applications are limited by the lack of software for online analysis of neuronal imaging data. We developed an analysis software ORCA (Online Real-time activity and offline Cross-session Analysis), which performs image registration, neuron segmentation, and activity extraction at over 100 frames per second, fast enough to support real-time detection and readout of neural activity. Our active neuron detection algorithm is purely statistical, achieving a much higher speed than previous methods. We demonstrated closed-loop control of neurons that were identified on the fly, without prior recording or image processing. ORCA also includes a cross-session alignment module that efficiently tracks neurons across multiple sessions. In summary, ORCA is a powerful toolbox for fast imaging data analysis and provides a solution for all-optical closed-loop control of neuronal activity.