Lower Processing Speed Research Articles

Simulation of High-Speed and Low-Power CMOS Binary Image Sensor Based on Gate/Body-Tied PMOSFET-Type Photodetector Using Double-Tail Comparator

In this paper, we propose a complementary metal-oxide semiconductor (CMOS) binary image sensor with a gate/body-tied (GBT) p-channel metal-oxide-semiconductor field-effect transistor (PMOSFET)-type photodetector using a double-tail comparator for highspeed and low-power operations. The GBT photodetector is based on a PMOSFET tied with a floating gate (n+ polysilicon) and a body that amplifies the photocurrent generated by incident light. A double-tail comparator compares an input signal with a reference voltage and returns the output signal as either 0 or 1. The signal processing speed and power consumption of a double-tail comparator are superior over those of conventional comparator. Further, the use of a double-sampling circuit reduces the standard deviation of the output voltages. Therefore, the proposed CMOS binary image sensor using a double-tail comparator might have advantages, such as low power consumption and high signal processing speed. The proposed CMOS binary image sensor is designed and simulated using the standard 0.18 μm CMOS process.

Moderate-to-vigorous physical activity and processing speed: predicting adaptive change in ADHD levels and related impairments in preschoolers.

Preschool ADHD symptoms have predictive utility for later presence of ADHD diagnoses (Harvey, Youngwirth, Thakar, & Errazuriz, 2009, Journal of Consulting and Clinical Psychology, 77, 349; Lahey et al., 2004, American Journal of Psychiatry, 161(11), 2014), yet some level of inattention, hyperactivity, and impulsivity are present even in typically developing preschoolers. Physical activity (PA) is known to have a broad spectrum of positive effects on the brain in school-age typically developing children (Centers for Disease Control and Prevention, 2010, The association between school based physical activity, including physical education, and academic performance. Atlanta, GA: U.S. Department of Health and Human Services), including functions impaired by ADHD (Halperin, Berwid, & O'Neill, 2014, Child and Adolescent Psychiatric Clinics of North America, 23, 899), yet links between PA and ADHD levels and impairments have rarely been studied in either typically developing or at-risk preschool children. Importantly, impaired processing speed (PS), though not a symptom of ADHD, is a robust neuropsychological correlate (Willcutt & Bidwell, 2011, Treating attention deficit hyperactivity disorder: Assessment and intervention in developmental context. Kingston, NJ: Civic Research Institute) that may indicate additional risk for ADHD. Hence, we examined whether baseline PS moderates the association between preschoolers' PA, specifically moderate-to-vigorous PA (MVPA), and changes in ADHD levels and related behaviors. Eighty-five preschoolers (49.4% female; Mage =4.14, SDage =.64) were drawn from a larger study of the effects of the Kiddie Children and Teachers (CATs) on the Move PA program on school readiness. The sample was largely Head Start eligible (68.2%) and ethnically diverse. Hierarchical regressions were utilized to examine links between MVPA, averaged over a school year, and changes in inattention (IA), hyperactivity/impulsivity (HI), oppositional behaviors, moodiness, and peer functioning, and whether these associations varied based on baseline PS. Results indicated that for IA, HI, and peer functioning, higher amounts of MVPA were associated with greater adaptive change for those with lower (but not higher) levels of PS. Preschool MVPA may be a viable method of reducing ADHD levels and impairments for those with lower PS.

A Review of Data Gathering Algorithms for Real-Time Processing in Internet of Things Environment

Today, Wireless Sensor Network (WSN) has become an enabler technology for the Internet of Things (IoT) applications. The emergence of various applications has then enabled the need for robust and efficient data collection and transfer algorithms. This paper presents a comprehensive review for the existing data gathering algorithms and the technologies adopted for that applications. After reviewing the algorithms and the challenges related to them, which extend the physical reach of the monitoring capability; they possess several constraints such as limited energy availability, low memory size, and low processing speed, which are the principal obstacles to designing efficient management protocols for WSN-IoT integration.

Laser sintering of Cu nanoparticles on PET polymer substrate for printed electronics at different wavelengths and process conditions

This study explores the feasibility of different laser systems to sinter screen-printed lines from nonconductive copper nanoparticles (Cu NPs) on polyethylene terephthalate polymer film. These materials are commonly used in manufacturing functional printed electronics for large-area applications. Here, optical and thermal characterization of the materials is conducted to identify suitable laser sources and process conditions. Direct diode (808 nm), Nd:YAG (1064 nm and second harmonic of 532 nm), and ytterbium fiber (1070 nm) lasers are explored. Optimal parameters for sintering the Cu NPs are identified for each laser system, which targets low resistivity and high processing speed. Finally, the quality of the sintered tracks is quantified, and the laser sintering mechanisms observed under different wavelengths are analyzed. Practical considerations are discussed to improve the laser sintering process of Cu NPs.

Hyperspectral camera application for remote gas analysis

The report reviewed current systems for monitoring gas leaks at industrial facilities. Since in industrial plants the emission of some gases can be technologically justified, and the presence of others indicates a violation of the technological process and is a sign of an emergency situation, a possible option is described not only for detection the fact of leakage, but also for the classification of detected gases. We considered existing hardware-software complexes including acousto-optical, and listed their shortcomings, such as insufficient sensitivity and low data processing speed. In general, we describe the novel technical implementation of imaging spectrometry. For simultaneous detection of spatial and spectral properties of objects, the infrared sensor is divided into a mosaic of the smallest optical filters. Each filter passes individual intervals of the spectrum, blocking the others. The uncooled detector 640×512 can be covered with 16 filters, which spectral characteristics should correlate with the absorption spectra of target gases.

A probabilistic model for assigning queries at the edge

Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results.

Olfactory identification associates with cognitive function and the third ventricle width in patients with relapsing-remitting multiple sclerosis

BackgroundOlfactory dysfunction is a known clinical feature of multiple sclerosis (MS). Some studies have shown that odor identification impairment is an essential feature associated with cognitive function in MS. This study investigates the relationship between olfactory identification and the disease state, including cognitive function and central brain volume, to evaluate the utility of olfactory identification in the clinical assessment of relapsing-remitting (RR) MS. MethodsForty patients with RRMS and 40 healthy controls (HCs) were included. Their olfactory identification was measured using the Odor Stick Identification Test for the Japanese (OSIT-J). Cognitive function was evaluated by the Japanese version of the Wechsler Adult Intelligence Scale, 3rd edition (WAIS-III), and depressive mood was evaluated by the Center for Epidemiologic Studies Depression Scale. Magnetic resonance imaging was used to measure the third ventricle width (3rd VW) as a marker of central brain atrophy. ResultsRRMS patients had a significantly lower OSIT-J score than HCs. The OSIT-J score was significantly lower in RRMS patients with low processing speed (PS) and working memory (WM) scores than RRMS patients with normal PS or WM scores. The OSIT-J score was significantly related to the PS, WM, and the 3rd VW. The OSIT-J score also showed a mild correlation with the expanded disability status scale and disease duration, but not with the number of clinical attacks or patient's age. ConclusionsOur results suggest that olfactory identification impairment occurs in association with cognitive dysfunction and central brain atrophy. Thus, olfactory identification is a possible disease marker of RRMS as with cognitive impairment, especially PS, reflecting the diffuse neurodegeneration in RRMS.

Association of Cavum Septum Pellucidum and Cavum Vergae With Cognition, Mood, and Brain Volumes in Professional Fighters

Many studies have investigated the imaging findings showing sequelae of repetitive head trauma, with mixed results. To determine whether fighters (boxers and mixed martial arts fighters) with cavum septum pellucidum (CSP) and cavum vergae (CV) have reduced volumes in various brain structures or worse clinical outcomes on cognitive and mood testing. This cohort study assessed participants from the Professional Fighters Brain Health Study. Data were collected from April 14, 2011, to January 17, 2018, and were analyzed from September 1, 2018, to May 23, 2019. This study involved a referred sample of 476 active and retired professional fighters. Eligible participants were at least 18 years of age and had at least a fourth-grade reading level. Healthy age-matched controls with no history of trauma were also enrolled. Presence of CSP, CV, and their total (additive) length (CSPV length). Information regarding depression, impulsivity, and sleepiness among study participants was obtained using the Patient Health Questionnaire depression scale, Barrett Impulsiveness Scale, and the Epworth Sleepiness Scale. Cognition was assessed using raw scores from CNS Vital Signs. Volumes of various brain structures were measured via magnetic resonance imaging. A total of 476 fighters (440 men, 36 women; mean [SD] age, 30.0 [8.2] years [range, 18-72 years]) and 63 control participants (57 men, 6 women; mean [SD] age, 30.8 [9.6] years [range, 18-58 years]) were enrolled in the study. Compared with fighters without CV, fighters with CV had significantly lower mean psychomotor speed (estimated difference, -11.3; 95% CI, -17.4 to -5.2; P = .004) and lower mean volumes in the supratentorium (estimated difference, -31 191 mm3; 95% CI, -61 903 to -479 mm3; P = .05) and other structures. Longer CSPV length was associated with lower processing speed (slope, -0.39; 95% CI, -0.49 to -0.28; P < .001), psychomotor speed (slope, -0.43; 95% CI, -0.53 to -0.32; P < .001), and lower brain volumes in the supratentorium (slope, -1072 mm3 for every 1-mm increase in CSPV length; 95% CI, -1655 to -489 mm3; P < .001) and other structures. This study suggests that the presence of CSP and CV is associated with lower regional brain volumes and cognitive performance in a cohort exposed to repetitive head trauma.

Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search

This paper considers an energy-efficient no-wait permutation flow shop scheduling problem to minimize makespan and total energy consumption, simultaneously. The processing speeds of machines can be dynamically adjusted for different jobs. In general, lower processing speeds require less energy consumption but result in longer processing times, while higher speeds take the opposite effect. To reach the Pareto front of the problem, we propose an adaptive multi-objective variable neighborhood search (AM-VNS) algorithm. Specifically, we first design two basic speed adjusting heuristics which can reduce the energy consumption of a given solution without worsening its makespan. Two widely used neighborhood-generating operations, i.e., insertion and swap, are adapted and integrated into the variable neighborhood descent phase. With respect to their executing order, two variable neighborhood descent structures can be designed. We adopt an adaptive mechanism to dynamically determine which structure will be selected to handle the current solution. To further improve the performance of the algorithm, we develop a novel problem-specific shake procedure. We also introduce accelerating techniques to speed up the algorithm. Computational results show that the AM-VNS algorithm outperforms multi-objective evolutionary algorithms NSGA-II and SPEA-II.

K-Means Cluster Using Rainfall and Storm Prediction in Machine Learning Technique

Data Mining involves extracting meaningful information from the available data in a user understandable manner. Its role is to analyze voluminous data that is being often assembled. Using the approach of Data mining techniques various business related queries can be attended which formerly were extremely time-consuming to answer. There exist uncontrollable natural disasters that critically hampers and costs human life, environment and revenue material. Natural calamities like heavy rainfall and floods cannot be well predicted until it happens, also it’s beyond one’s power to control them. The aftereffect or destruction caused by these calamities prevails for many years. The term disaster is a result of a vulnerable condition caused by heavy rainfall, flood or storm that can have intense effect at a smaller scale such as a village or at a larger scale such as city or state. Clustering model that was developed before confronted the issue of time complexity, low processing speed and were inappropriate for huge datasets. The current research work proposes the approach of K means clustering that is a subset of ML (machine learning) techniques that are capable to process huge datasets and performs quick computation compared to rest of the clustering model. Various stages in this proposed system include Dataset Collection, Pre-processing, Feature selection, K-means clustering. Among these, the K-means clustering tool which is actually a subset of data-mining and ML approach is employed to cluster observations in the form of groups. It’s a form of unsupervised learning that rectifies the clustering problem. The results reveal that the K-means clustering tool performs clustering faster than the other existing technique.

Vehicle tracking based on shape information and inter-frame motion vector

Vehicle tracking is one of the most fundamental aspects in traffic surveillance systems. However, there is always a trade–off between tracking accuracy and speed. Tracking algorithms with excellent tracking accuracy tend to have low processing speeds, whereas fast tracking algorithms usually have low tracking accuracy. This paper proposes a vehicle tracking algorithm based on the simple motion of vehicles in traffic surveillance videos. In the proposed algorithm, an inter–frame motion vector is defined using vehicle speed and acceleration in the current frame to forecast the vehicle speed in subsequent frames. Further, speed and shape error functions are defined to match the target. The results of experiments conducted comparing the proposed method with kernelized correlation filter, multiple instance learning, and Kalman filter indicate that only the proposed algorithm achieves a balance in terms of both speed and accuracy, resulting in satisfactory tracking accuracy and meeting real–time requirements.

A Survey of Parallel Sequential Pattern Mining

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this article, an in-depth survey of the current status of parallel SPM (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state-of-the art PSPM. We review the related work of PSPM in details including partition-based algorithms for PSPM, apriori-based PSPM, pattern-growth-based PSPM, and hybrid algorithms for PSPM, and provide deep description (i.e., characteristics, advantages, disadvantages, and summarization) of these parallel approaches of PSPM. Some advanced topics for PSPM, including parallel quantitative/weighted/utility SPM, PSPM from uncertain data and stream data, hardware acceleration for PSPM, are further reviewed in details. Besides, we review and provide some well-known open-source software of PSPM. Finally, we summarize some challenges and opportunities of PSPM in the big data era.

Single grain growth of Si thin film on insulating substrate by limited region aluminum induced crystallization

Polycrystalline Si thin films on insulating substrates have been widely used to fabricate thin film transistors. However, since randomly induced grain boundaries in the polycrystalline thin films have been degrading transistor performance, application of thin film transistors has been limited to low speed processing units such as display drivers. Here, in this letter, we have tried to realize single grain growth of Si thin film at controlled position. Aluminum induced layer exchange crystallization of Si thin film was performed at limited region to suppress spontaneous nucleation during the crystal growth, and thus, single grain Si thin films have been successfully realized on quartz glass substrates. This technique will open up a way to realize high speed processing thin film transistors.

Planning of driverless vehicle motion trajectory taking into account its dimensional characteristics and under conditions of dynamically changing environment

This paper presents the solution for the task of the planning of the driverless vehicle (DV) driving trajectory for the modern road conditions. The rapidly-exploring random trees (RRT) algorithm consisting in the generation of the treetops under the equiprobability law and search for an optimum trajectory was used as a base. The minimum length acts as a criterion of optimality. The condition for the trajectory generation using the road map model is added to the new algorithm. Thereby, the problem of low processing (response) speed of the optimum trajectory search algorithm was solved.

Pyrboxes: An efficient multi-scale scene text detector with feature pyramids

Scene text detection has attracted many researches due to its importance to various applications. However, current approaches could not keep a good balance between accuracy and speed, i.e., a high-performance accuracy but with a low processing speed, or vice-versa. In this paper, we propose a novel model, named PyrBoxes, for efficient and effective multi-scale scene text detection. PyrBoxes consists of an SSD-based backbone that utilizes deep layers with strong semantics to detect texts in various sizes, and a proposed grouped pyramid module that leverages basic layers to append detailed locations into detection. Most existing detectors discard features from the basic layers due to the efficiency issue. We argue these layers contain fine-grained information, which is complementary to high-level semantics. Based on this, the grouped pyramid module combines the basic layers recursively into a detection layer via a top-down partition and a bottom-up group. Extensive experiments on both horizontal and oriented benchmarks, including ICDAR2013 Focused Scene Text, ICDAR2015 Incidental Text and COCO-Text, demonstrate that PyrBoxes achieves state-of-the-art or highly competitive performance compared with baselines, while runs significantly faster at inference. Furthermore, by experimenting on another ChiTVText dataset, PyrBoxes shows great generality to Chinese and long text lines. By visualizing some qualitative results, as expected, PyrBoxes provides more accurate locations and reduces the rate of missed detections, especially for small-sized texts.

Cortical thinning and flattening in schizophrenia and their unaffected parents.

Background: Schizophrenia is a neurodevelopmental disorder with high heritability. Widespread cortical thinning has been identified in schizophrenia, suggesting that it is a result of cortical development deficit. However, the findings of other cortical morphological indexes of patients are inconsistent, and the research on their relationship with genetic risk factors for schizophrenia is rare.Methods: In order to investigate cortical morphology deficits and their disease-related genetic liability in schizophrenia, we analyzed a sample of 33 patients with schizophrenia, 60 biological parents of the patients, as well as 30 young controls for patients and 28 elderly controls for parents with age, sex and education level being well-matched. We calculated vertex-wise measurements of cortical thickness, surface area, local gyrification index, sulcal depth, and their correlation with the clinical and cognitive characteristics.Results: Widespread cortical thinning of the fronto-temporo-parietal region, sulcal flattening of the insula and gyrification reduction of the frontal cortex were observed in schizophrenia patients. Conjunction analysis revealed that patients with schizophrenia and their parents shared significant cortical thinning of bilateral prefrontal and insula, left lateral occipital and fusiform regions (Monte Carlo correction, P<0.05), as well as a trend-level sulcal depth reduction mainly in bilateral insula and occipital cortex. We observed comprehensive cognitive deficits in patients and similar impairment in the speed of processing of their unaffected parents. Significant associations between lower processing speed and thinning of the frontal cortex and flattening of the parahippocampal gyrus were found in patients and their parents, respectively. However, no significant correlation between abnormal measurements of cortical morphology and clinical characteristics was found.Conclusion: The results suggest that cortical morphology may be susceptible to a genetic risk of schizophrenia and could underlie the cognitive dysfunction in patients and their unaffected relatives. The abnormalities shared with unaffected parents allow us to better understand the disease-specific genetic effect on cortical development.

Questioning Fetal Alcohol Syndrome: a Case Report of Multiple Etiological Factors

A 14-year-old, Caucasian, right-handed male presented for neuropsychological evaluation for diagnostic clarification due to attention-deficit/hyperactivity disorder, bipolar I disorder, and oppositional defiant disorder diagnoses and to rule-out fetal alcohol syndrome (FAS) due to aggression, impulsivity, difficulties with authority, and history of legal issues. Patient was born at 29 weeks gestation without complications. Patient’s adoptive mother reported delays in walking, as well as regression in talking, with suspected prenatal alcohol exposure, neglect, and abuse in first 2 years of life prior to being adopted. Patient is currently in detention setting due to recent homicidal ideation toward family member. Neuropsychological and psychological tests were administered to assess for FAS and clarify previous diagnoses, for which psychostimulant and antipsychotic medications have proven ineffective. Results suggest minimal intellectual impairment beyond low general processing speed abilities, minor academic achievement concerns in only sentence comprehension, executive dysfunction specifically in inhibition, inefficient learning with early plateau, and externalizing behaviors. Adaptive functioning difficulties are limited to self-direction and safety. No evidence of brain atrophy, delayed physical development, or facial dysmorphia in childhood but significant behavioral concerns suggestive of FASDs. A wide range of etiological factors with unconfirmed prenatal alcohol exposure, such as neglect, trauma, and poor school attendance, suggests that FAS may be diagnostically restrictive as it pertains to the broad spectrum of prenatal alcohol effects and common comorbidities. FAS criteria may be revisited as it relates to confirmed alcohol exposure when history does not permit.

The discrepancy between verbal and visuoperceptual IQ in children with a specific learning disorder: An analysis of 1624 cases

Children with a specific learning disorder (SLD) are often characterized by marked intellectual strengths and weaknesses. In the last few years, research has focused on a common discrepancy between low working memory and processing speed on the one hand, and high verbal and visuoperceptual intelligence on the other. SLD profiles featuring a specific discrepancy between verbal and visuoperceptual abilities have been only marginally considered, however, and their systematic comparison vis-à-vis typically-developing (TD) populations has yet to be conducted. The present study examined a dataset of 1624 WISC-IV profiles of children with a diagnosis of SLD. It emerged that the proportion of children with a Verbal Comprehension Index (VCI) at least 1.5 SD (22 standardized points) lower than their scores on the Perceptual Reasoning Index (PRI) was larger than the proportion of SLD children with the opposite discrepant profile; it was also larger than the same proportion found among TD children. Comparing the two discrepant profiles revealed that the children also differed by type of learning difficulty, gender, and performance in the WISC-IV Symbol search task. Further examination suggested that children who were discrepant and also distinctly poor in visuoperceptual intelligence were particularly slow in general processing.

Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching

To meet the requirement of vehicle real-time and precise ego-localization on the flat road of city, a vehicle ego-localization method based on the fusion of optical flow and feature points matching is proposed. A novel FAST algorithm with self-adaptive threshold is applied to detect feature points. Based on the assumption of flat plane, the improved Lucas-Kanade algorithm is carried out to track feature points, and then the custom LARSAE is used to amend vehicle offsets. Meanwhile, Hu moments are used as the feature descriptor to complete image matching, realizing vehicle motion estimation. These two methods are fused by the discrete kalman filter to update and optimize vehicle position. Experimental results show that the fusion algorithm overcomes the shortcomings of poor positioning accuracy of optical flow and the low processing speed of feature matching, and is able to provide more accurate real-time positioning output, having a certain robustness for circumstances such as illumination change and low pavement texture.

Универсальные и специфические индивидуально-психологические факторы риска попадания в дорожно-транспортное происшествие у психически здоровых и лиц с психическими расстройствами

The article presents the results of the research aimed to highlight the individual, psychological and cognitive risk factors of getting into a traffic accident depending on the presence of a mental disorder. 139 people aged from 18 to 59 years old (average age 30.44 years ± 9.56) were examined using a set of methods aimed at assessing the individual and typological features of self-control, individual, psychological and cognitive features of the driver's personality. Analysis of the results allowed identifying the universal individual and psychological risk factors for getting into a traffic accident. Specifically: systemic violations of self-regulation in the form of a violation of the connectedness of its stages, impulsiveness, risk-taking, physical aggression. When assessing the contribution of the parameter “guilty / not guilty in accident”, statistically significant differences in the indicators of cognitive methods were revealed. The convicted drivers were significantly distinguished by a high degree of workability, selectivity and accuracy of attention, providing a reactive response, with low processing speed of visual information and fatigue. Characteristics that significantly distinguish the convicted drivers, depending on the presence or absence of the factor “mental disorder”, were also highlighted. Mentally healthy drivers can be characterized by violations of the activation system, due to the orientation of the reward; low level of selectivity of attention, as well as high rates of dissociative driving style, which is characterized by disregard for the rules on the road; low level of planning and evaluation of the results of their own activities. Drivers with mental disorders were statistically significantly distinguished by high rates of risk-taking in the structure of self-control, low level of conscious planning, situational response conditions combined with suspiciousness, irritability, impulsiveness, and a decrease in criticality of mental activity. Characteristic features of their behavior on the road are high rates of anxious driving style, characterized by a heightened sense of alertness and tension while driving. Fatigue and difficulties in workability can be defined among the cognitive variables.