Increase In Processing Speed Research Articles

The neurocognitive and functional profile of schizophrenia in a genetically homogenous European sample

Schizophrenia is a complex heritable brain disorder that entails significant social, neurocognitive, and functional deficits, and significant psychosocial challenges to affected and unaffected family members. In this cross-sectional study, we explore impairments in specific neurocognitive and social cognition processes in patients affected with schizophrenia, unaffected relatives, and in controls to provide a characterization of a genetically homogenous European sample from an endophenotypic and functional standpoint. A sample of 38 affected patients, 28 first-degree relatives, and 97 controls performed a series of computerized and skills-based assessments. Samples were compared across several neurocognitive, social, and functional domains. Significant impairments in episodic memory, executive function, social cognition, complex cognition, sensorimotor domains were found in patients and first-degree relatives. Findings also showed increased processing speed in memory and other complex cognitive processes relevant to autonomous living. A discriminant function analysis yielded 2 functions allowing 79% of correct group classifications based on social cognition and functional skills, neurocognition, and age. The study highlights the importance of resourcing to wide-ranging assessment methodologies, of developing research efforts to further understand the decline of social and neurocognitive processes, and the need for designing more targeted intervention strategies to be implemented both with affected patients and families.

TLSLeAF: automatic leaf angle estimates from single-scan terrestrial laser scanning.

Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global-scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open-source method of deriving LADs from terrestrial laser scanning. TLSLeAF produces canopy-scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf-wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community. TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer-grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate to c. 25% and c. 11% increases in canopy net photosynthesis (c. 25%) and solar-induced chlorophyll fluorescence (c. 11%). TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote-sensing experts, allowing greater accessibility for addressing ecological hypotheses and large-scale ecosystem modeling efforts.

Embodiment: I sat, I felt, I performed – Posture effects on mood and cognitive performance

Previous embodiment findings indicate a relationship between physical posture and mood states, suggesting upright postures induce positive mood states. Findings also showed a relationship between moods and cognitive performance. While positive mood states were found to be related to increased processing speed, negative mood states were associated with higher processing accuracy in cognitive task performance. This implies that posture may affect the aforementioned sub-aspects of cognitive performance via mood states. Additionally, most studies on posture effects rely on explicit posture manipulation. With that in mind, our research explores the effects of implicitly manipulated postures on processing speed and accuracy and whether these effects are mediated by general mood. The results revealed that subjects in our sample (N = 82, M = 23.09years) who adopted an upright posture showed a more positive general mood (d = 0.50) and higher processing speed (d = 0.42) compared to those in stooped postures. Surprisingly, no differences in processing accuracy were found. There was no evidence of the proposed mediation in our data.

The effect of antipsychotic medications on white matter integrity in first-episode drug-naïve patients with psychosis: A review of DTI studies.

Psychotic episodes have been associated with damage to both grey matter (GM) and white matter (WM). Although a recent meta-analysis suggest that in long term treatment, first generation antipsychotics (FGA) are associated with progressive reduction in GM, second generation antipsychotics (SGA) seem to have benefits to WM microstructure. A search was conducted to identify controlled trials published from January 2000 to January 2021, which assessed WM integrity as measured by DTI in drug-naïve patients with FEP before and after antipsychotic administration. 3 studies met the criteria for inclusion. All studies demonstrated lower FA in psychotic patients vs HC. A 6-week study reported that antipsychotic medication results in a further decrease in FA within the bilateral ACG and right ACR, regions important in emotional processing. An 8-week study found that antipsychotic treatment increase FA in the SLF, resulting in improved symptoms and increased processing speed. A 3rd study found an increase in FA in several regions along with a negative correlation between FA and PANSS at remission. Drug-naïve FEP patients have WM dysfunction at baseline and antipsychotic medications appear to alter or improve WM especially at remission. More controlled trials are warranted to validate these conclusions.

Fast, computer‐assisted detection of dust and debris impact craters on Stardust interstellar foils

AbstractThe NASA Stardust Interstellar Dust collection provides our current best sample set for direct laboratory analysis of dust grains from the contemporary interstellar dust stream. While a handful of likely interstellar dust grains were identified within the silica aerogel collection media, interstellar dust also impacted Al foils covering the collector frame. Locating these rare impacts requires labor‐intensive collection and examination of tens of thousands of high‐resolution SEM images. Here, we implement a Python‐based algorithm to dramatically reduce the human time investment needed to locate impact craters. The algorithm employs a circular Hough transform to identify circular features in the foil images, followed by several tests to detect characteristic morphological features of impact craters—a dark center and a bright rim, with inclusion of multi‐core processing capabilities to significantly increase processing speed. For most data sets, the code produced a pool of potential crater candidates in 1–5% of the input images, producing a more manageable subset of images for a human expert to review. We used this code to locate 31 impact craters across 12 Stardust interstellar foils, 25 of which were located on three adjacent foils, I1008W,1, I1009N,1, and I1020W,1. Many impacts on these foils formed shallow, oblique craters, with residue compositions consistent with solar cell glass and orientations consistent with debris ejected from the spacecraft solar cells. The code can be integrated into future searches for Stardust interstellar grain impacts and can be implemented as a general utility for dust impact studies on spacecraft materials.

Automatic Identification Fingerprint Based on Machine Learning Method

The fingerprint identification technology has been developed and applied effectively to security systems in financial transactions, personal information security, national security, and other fields. In this paper, we proposed the development of a fingerprint identification system based on image processing methods that clarify fingerprint contours, using machine learning methods to increase processing speed and increase the accuracy of the fingerprint identification process. The identification system consists of the following main steps: improving image quality and image segmentation to identify the fingerprint area, extracting features, and matching the database. The accuracy of the system reached 97.75% on the mixed high- and low-quality fingerprint database.

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.

A network-based cognitive training induces cognitive improvements and neuroplastic changes in patients with relapsing-remitting multiple sclerosis: an exploratory case-control study

Cognitive impairments are commonly observed in patients with multiple sclerosis and are associated with lower levels of quality of life. No consensus has been reached on how to tackle effectively cognitive decline in this clinical population non-pharmacologically. This exploratory case-control study aims to investigate the effectiveness of a hypothesis-based cognitive training designed to target multiple domains by promoting the synchronous co-activation of different brain areas and thereby improve cognition and induce changes in functional connectivity in patients with relapsing-remitting multiple sclerosis. Forty-five patients (36 females and 9 males, mean age 44.62 ± 8.80 years) with clinically stable relapsing-remitting multiple sclerosis were assigned to either a standard cognitive training or to control groups (sham training and non-active control). The standard training included twenty sessions of computerized exercises involving various cognitive functions supported by distinct brain networks. The sham training was a modified version of the standard training that comprised the same exercises and number of sessions but with increased processing speed load. The non-active control group received no cognitive training. All patients underwent comprehensive neuropsychological and magnetic resonance imaging assessments at baseline and after 5 weeks. Cognitive and resting-state magnetic resonance imaging data were analyzed using repeated measures models. At reassessment, the standard training group showed significant cognitive improvements compared to both control groups in memory tasks not specifically targeted by the training: the Buschke Selective Reminding Test and the Semantic Fluency test. The standard training group showed reductions in functional connectivity of the salience network, in the anterior cingulate cortex, associated with improvements on the Buschke Selective Reminding Test. No changes were observed in the sham training group. These findings suggest that multi-domain training that stimulates multiple brain areas synchronously may improve cognition in people with relapsing-remitting multiple sclerosis if sufficient time to process training material is allowed. The associated reduction in functional connectivity of the salience network suggests that training-induced neuroplastic functional reorganization may be the mechanism supporting performance gains. This study was approved by the Regional Ethics Committee of Yorkshire and Humber (approval No. 12/YH/0474) on November 20, 2013.

Reduction of cutting tools wear by using new materials in processing

Automation of machine-building equipment, increasing processing speeds and expanding the range of the materials processed necessitated the development of effective abrasive materials that retain the ability to work under extreme conditions. A tool made of diamond-containing composite materials due to the record hardness of diamonds cannot be replaced by any other materials when processing products made of super hard materials, glass, ceramics, natural and synthetic materials. At the present stage of scientific and technical progress strength, viscosity and other characteristics of structural materials are increasing so rapidly that the tool materials available for production in a number of cases do not allow high-performance processing of blanks. Due to these features of modern production, the use of super hard materials is becoming increasingly important in metalworking. Natural (À) and synthetic diamonds (ASO) ASR, ASV, ASK, ASS, A M, ASN are used in industry. A diamond is the hardest material, possessing high red-hardness and wear resistance; there being no adhesion with many materials for diamonds. The disadvantage of diamonds is their fragility. Diamonds are used for the manufacture of diamond tools and fine powders. Other characteristics of construction materials increase so quickly that the tooling materials available for production in some cases do not make it possible to obtain high-performance crystal diamonds used to equip tools. Diamond chisels for thin sharpening and boring of the parts from aluminium alloys, bronzes, composite metals and non-metal materials are most widely used. A diamond instrument is applied for treatment of such hard materials as germanium, silicon, semiconductor materials, ceramics, heat resistant steels and alloys. The quality of the processed surfaces of parts increases with the use of diamond tools. Processing is carried out with the сutting speeds over 1000 m/min. Surface parts processed in these conditions, have low roughness and high accuracy sizes. The article shows that application of superhard materials, acquires still greater value in modern metal-working

Possibility of Wave Prediction by Deep Learning

Numerical wave prediction models require a large amount of computational power to timely complete the required calculations. Artificial Neural Networks (ANN) have been introduced to perform predictions at a lesser computational cost and increased processing speed. Deep learning and specifically Convolutional Neural Networks (CNN) have become accepted for various image recognition applications. Motivation for the examination of wave prediction by deep learning came from the success of CNN in vision applications and the similarity of meteorological weather grid data to visual images. This study investigates a deep learning technique using the Japan Meteorological Agency’s Grid Point Value Mesoscale Model to predict wave height and period. In particular, this study uses the Xception deep learning architecture with depthwise separable convolution to obtain improved wave height and period prediction over artificial neural networks, and gets overall success results.

Motion Detection and Prediction Using Machine Learning Algorithm

Machine learning is a branch of Artificial Intelligence which is gaining importance in the 21st century with increasing processing speeds and miniaturization of sensors, the applications of Artificial Intelligence and cognitive technologies are growing rapidly. An array of ultrasonic sensors i.e., HCSR-04 is placed at different directions, collecting data for a particularinterval of a period during a particular day. The acquired sensor values are subjected to pre-processing, data analytics, and visualization. The prepared data is now split into test and train. A prediction model is designed using logistic regression and linear regression and checked for accuracy, F1 score, and precision compared.

Adaptive block compressed sensing - a technological analysis and survey on challenges, innovation directions and applications

In today’s digital world, data transmission and storage is becoming a massive problem. This is because the data produced by various sensors worldwide is outstripping the ability to store them. Pre-processing the entire data before transmission is the best solution for reducing the storage issue. ‘Compressed sensing’(CS) is a pre-processing technique that exploits the sparsity of the signal for sampling the data. Since most of the natural signals are sparse, CS allows sampling at a rate lesser than that required in Nyquist sampling theorem. However, in conventional CS, sampling is done for the entire image at once which increases processing time and reduces visual quality. In block compressed sensing (BCS), blocks of the images are processed simultaneously which increases processing speed and decreases the processing time. To improve the quality of the reconstructed signal, a variant of BCS, Adaptive block compressed sensing (ABCS) is used. This review paper studies the advantages, challenges and applications of applying ABCS for image compression.

Analog Computing and a Hybrid Approach to the Element Base of Artificial Intelligence Applications

Strong demand for artificial intelligence technology is driving the development of complex high-performance applications with less power consumption. Analog computing is of high-performance and has simplified system, which simulates the physical processes occurring in nature. The universality of the digital coding allows getting a fairly accurate calculation result and provides saving without loss and additional restoration. The benefits of digital and analog computing systems can be enhanced by their hybridization. The type and level of hybrid computing depend on the complexity of the task set. Hardware implementation of a neural network offers promising solutions for computing tasks that require compact and low-power computing technologies. Artificial neural networks or ANNs, like biological neurons, are characterized by their capacity of learning and memorizing information, depending on architecture and weight of the latter. The literature review shows that stable weight storage can be achieved using digital weights and analog multipliers to reduce footprint. The proposed methodology for the network architecture provides optimal conditions for maintaining synaptic weights, increasing processing speed by the parallel weight perturbation.

Development of a Portable Electrical Impedance Tomography Device for Online Thrombus Detection in Extracorporeal-Circulation Equipment

A portable Electrical Impedance Tomography (EIT) device has been developed for online thrombus detection in extracorporeal-circulation equipment. For the portable EIT device, circuits of multiplexer module are designed to expend channels, pipeline processing mechanism is introduced to increase processing speed, and data acquisition rate reaches 50 Frames Per Second (FPS) and the online tomography rate reaches 40 FPS with 8-electrode sensor. To test the characteristics of the proposed EIT device, a series of experiments are conducted. Firstly, small sensors with different size and plastic phantoms are used to test the static properties of the device. Testing results show that the quality of the reconstructed images is related to the diameter of sensor, and Image Correlation (IC) reaches 0.75 when diameter of sensor is 30 mm. Secondly, swine blood is used for making thrombus to verify the EIT device's image reconstruction ability of thrombus in stationary conditions instead of human blood. Experiments show that the reconstructed image error is 8.07% when diameter of the sensor is 10 mm, and it reduces to 1.49% when diameter is 30 mm. Finally, experiments are conducted to measure the position of flowing thrombus. Euclidean distance between voltage data is used to judge the occurrence of thrombus. Experimental results indicate that thrombus is clearly distinguished from the background solution in fluids. As a conclusion, the proposed EIT device is of great significance for online thrombus detection in extracorporeal-circulation system.

Smart embedded system based on demosaicking for enhancement of surveillance systems

Demosaicking and denoising are essential elements in digital photography pipelines. The use of convolutional neural networks (CNN)-based image demosaicking and denoising methods has been very successful. However, still there is a room for improvement in the network performance in terms of efficiency and accuracy. The main challenge that remains to be addressed is to guarantee the visual quality of reconstructed images, particularly in the presence of noise. To address these challenges, this paper introduces a novel demosaicking and denoising conjunct strategy using deep adaptive residual learning. The proposed framework has three stages. Initially, zero padding is performed to increase processing speed and preserve the edges of the image. In the second phase, we perform demosaicking using interpolation in order to find missing values using information about neighboring pixels. Finally, the reconstructed image is created using the original image. To evaluate the feasibility of the proposed scheme, we used Pytorch and Google Colab with 400 images for training and 100 images for validation The outcomes show that the proposed scheme beats cutting edge joint demosaicking and denoising schemes regarding both structural similarity index metrics (SSIM) and peak signal-to-noise ratio (PSNR) and basic similitude record measurements (SSIM).

Keyhole mapping to enable closed-loop weld penetration depth control for remote laser welding of aluminum components using optical coherence tomography

Remote laser welding (RLW) combines the positive features of tactile laser welding with additional benefits such as increased processing speed, reduced operational cost and service, and higher process flexibility. A leading challenge preventing the full uptake of RLW technology in industry is the lack of efficient closed loop in-process (CLIP) monitoring and weld quality control solutions. This underpins the need to fuse multiple sensor technologies and data analytics with predictive engineering simulations. Although the development and integration of a variety of sensors covers the radiation spectrum from ultraviolet to far-infrared, the flawless deployment of CLIP solutions is still challenged by the need for the following: signal denoising in the case of process instability; real-time data analytics; and adaptive control engineering architecture to cope with process variations induced by manufacturing tolerances. This paper focuses on the aspect of weld penetration depth control using optical coherence tomography (OCT) as a necessary step to enable adaptive penetration depth control during RLW of aluminum components in the fillet lap joint configuration with consideration to part-to-part gap variation. The approach entails decoupling the welding process parameters in two subsets: (1) in-plane control of the heat input on the upper part to facilitate the droplet formation; and (2) out-of-plane heat management to achieve the desired level of penetration control in the keyhole mode. This paper presents the results of finding the optimal placement of the OCT beam with variable part-to-part gap conditions. Results have shown that statistical signal processing of the raw OCT signal gives insight not only into the depth of the keyhole but can infer the shape of the keyhole itself. Current limitations and next phases of research and development are highlighted based on the experimental study.

Fast detection and reduction of local transient artifacts in resting-state fMRI

Image quality control (QC) is a critical and computationally intensive component of functional magnetic resonance imaging (fMRI). Artifacts caused by physiologic signals or hardware malfunctions are usually identified and removed during data processing offline, well after scanning sessions are complete. A system with the computational efficiency to identify and remove artifacts during image acquisition would permit rapid adjustment of protocols as issues arise during experiments. To improve the speed and accuracy of QC and functional image correction, we developed Fast Anatomy-Based Image Correction (Fast ANATICOR) with newly implemented nuisance models and an improved pipeline. We validated its performance on a dataset consisting of normal scans and scans containing known hardware-driven artifacts. Fast ANATICOR's increased processing speed may make real-time QC and image correction feasible as compared with the existing offline method.

Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning.

Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures. Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard. Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text. Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes.

Channel of Measurement and Indication of 220 Voltage in the Device «Quality-E1»

At the V.M. Glushkov Institute of Cybernetics the “Yakist-E1” – electric power quality measuring device, was developed. The device uses new method and structure of a quality determination, and is based on calculating the deviations from the nominal values of voltage and frequency values. The methodical error of level quantization for a 10-bit ADC is ± 0.1%. To achieve an approximation error of ± 0.1%, it is necessary to measure the voltage near the amplitude value after 170 μs. The channel for voltage measuring in the device consists of a voltage transformer, a high-precision resistive divider that scales and biases the input voltage, a reference voltage source (VREF) and an ADC, which built into the microcontroller. The device “Yakist-E1” uses an 8-bit microcontroller type C8051F320, which has a 10-bit ADC with an input signal range from 0 to 2.5 V. The output voltage of the VREF from the chip to the controller chip can vary from 2.38 V to 2.5 V. In addition, different transformer sensors may have different transfer characteristics, which may be non-linear. Therefore, to indicate the values of the measured voltage on the alphanumeric indicator, a piecewise linear approximation of the results of measuring the ADC is used. The transfer and temperature characteristics of the voltage transformer were measured for forward and reverse passages, and, according to the results, was discovered that the temperature error is very small and practically does not affect the accuracy of the sensor and the sensor has hysteresis, the calculated errors do not exceed ± 1%. All this errors were corrected. To determine the deviation of the amplitude value from the nominal value, only 8 low-order bits of the ADC are used. This trick grants that ADC result can be processed by a single command of the microcontroller, which increases processing speed of the channel.

Adaptive Edge Detection Technique Implemented on FPGA

This article proposes an adaptive model that improves the edge detection operation in digital images. One of the disadvantages of traditional edge detection operators such as Prewitt, Sobel and Canny is using fixed-size masks which limit the edge detection operation in images with different degrees of the gray levels. In the proposed model, the image histogram is used as a criterion for selecting a suitable mask according to image specifications. In this model, the mask size of the edge detection operator is determined by measuring the contrast of the image to improve the quality of edge detection. For this purpose, a mask with a small size is used for images that have high contrast to increase processing speed, and for the images that do not have high contrast larger masks are used to increase the accuracy of edge detection. The proposed model is implemented utilizing real-time processing and parallelism capabilities of the Xilinx-Virtex6 FPGA. And, in order to reduce the effect of parallelism on increasing the area size of implemented hardware resources, existing resources are reused appropriately. The results show that with the use of the proposed method the edge detection is done in just 2.2 ms and a trade-off between the quality of detected edge and the hardware resources is established.