High Frequency Patterns Research Articles

Super-Resolution by Localized Plasmonic Structured Illumination Microscopy Using Self-Assembled Nanoparticle Substrates

Structured illumination microscopy (SIM), an advanced super-resolution methodology, transcends the traditional diffraction limit inherent in optical imaging. This technique utilizes standing-wave illumination generated through the interplay of two obliquely incident light waves. The intrinsic resolution constraint of SIM, traditionally pegged at half the wavelength because of the standing wave’s periodicity, has the potential for enhancement by integrating high spatial frequency illumination patterns, particularly when sourced in the near-field of plasmonic nanostructures. The present study introduces and computationally validates a novel, easily fabricated substrate composed of self-assembled gold nanoparticles designed explicitly for generating these high spatial frequency patterns. Addressing the necessity for diverse patterns in reconstructing super-resolution imagery within plasmonic SIM, this research conducted extensive numerical simulations of nanoparticle arrays under varying illumination scenarios. This undertaking affirmed the feasibility of manipulating high-frequency patterns. Super-resolution reconstruction was actualized by applying Blind-SIM techniques, which verified its effectiveness. This innovative approach notably achieved a resolution threshold of 60 nm, markedly exceeding the conventional 150 nm diffraction barrier and surpassing the 75 nm resolution typically observed in standard SIM applications.

Exogenous intoxication in patients treated at the Toxicological Information and Assistance Center during the COVID-19 pandemic

Poisoning is defined as an adverse event resulting from the action of chemical substances that induces a pathology in an individual. The World Health Organization (WHO) has reported three million hospital admissions occurring annually due to toxic exposure worldwide. The growth of SARS-CoV-2 infection cases in 2020 also significantly increased calls to Poison Centers due to exposure to cleaning products, hand sanitizers, and disinfectants in early March 2020 in the United States. Based on this scenario, it was hypothesized that the pattern of exogenous intoxication has changed. Therefore, the aim of this study was to analyze the epidemiological profile of exogenous intoxications recorded by CIATOX-DF from March 2020 to February 2021, during the COVID-19 pandemic. At that time, there was an increase in accidental, environmental, venomous, and non-venomous animal cases, especially in babies, pre-adolescents, adolescents (11 to 15 years old), and women, due to the greater exposure of this age group and gender who have spent more time in rural environments. Most poisonings were accidental. It was observed that there was a decrease in suicide cases, but within these cases, there was a trend increase in poisoning in pre-adolescents and adolescents (11 to 15 years old) compared to other ages, which can be related to an increase in anxiety within this age group. The main toxic agents were venomous and non-venomous animals. The pattern of high frequency in cases involving medicines, pesticides, and household cleaning products was maintained, with a significant increase in cases involving 70% alcohol. It was found that patients living in towns further away from the Pilot Plan in Brasilia suffer accidents involving venomous animals. Accidental and attempted suicide cases occurred more frequently in cities closer to the Pilot Plan region. We concluded that these changes were explained by the new pattern of social behavior during the pandemic.

IMPLEMENTASI ALGORITMA APRIORI DALAM PEMODELAN POLA PEMBELIAN PRODUK PADA EDUKITS BTC

In competition in the business world, especially the retail industry, requires developers to find an appropriate strategy in order to increase sales of school equipment, one way is to know the sales pattern of school equipment so that we can apply the right steps to provide more facilities, in order to increase selling power. A priori algorithm is a type of association rule in data mining. A priori is a very famous algorithm for finding high frequency patterns. A high frequency pattern is an item pattern in the database that has frequency or support above a certain threshold which is called the minimum support term. Using an a priori algorithm can help develop a marketing strategy. The results showed that with a minimum support of 30%, there were 4 combination patterns of one item set that achieved minimum support, with the highest support for Stationery Office of 72.6%, for a combination of 2 itemsets there were 4 combination patterns with the highest support (School Stationery, Stationery Office) namely 54.8%, for a combination of 3 itemsets there was 1 combination pattern with the highest support (Art & Craft, School Stationery, Office Stationery) namely 32.9%.

Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan

Sales data is generally still rarely used, as well as the Perfume Corner shop just piling up in the database, even though there are problems experienced by the store regarding sales data for the best-selling products and to increase the number of sales of subsequent perfume products, so that the store can survive and develop even better. The algorithm that can be used to manage sales data to overcome this problem is Apriori. The research method used in this research is the KDD (Knowledge Discovery in Database) process. This research produces a high frequency pattern for itemsets with a minimum support value of 20% resulting in products that become The Most Tree Items namely Jo Malone 82.49%, Zarra 28.25%, and Zwitsal 20.34%. While the association rules formed from the value of Min. Supp 20% and Min. Conf 80%, get a combination of 2 itemsets, namely Jo Malone and Zarra. Whereas for the combination of 3 itemsets, namely Jo Malone, Zarra and Baccarte with valid and strong status, it is proven by a lift value greater than 1, therefore the association rules are very appropriate to be used.

Spatiotemporal Variation of Hourly Scale Extreme Rainstorms in the Huang-Huai-Hai Plain and Its Impact on NDVI

This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall at daily and hourly scales in the Huang-Huai-Hai Plain. The paper explores the NDVI’s variability and its relationship with extreme hourly precipitation and analyzes the main factors affecting it. The study made the following observations: (1) The extreme daily precipitation in the Huang-Huai-Hai Plain shows a decreasing trend, with a 13.6 mm/yr reduction rate. In contrast, the proportion of extreme rainfall to total precipitation generally exceeds 20%, and the intensity of extreme rain has gradually increased. The spatial distribution pattern of extreme rainfall follows the distribution pattern of China’s rain belts, with the terrain being an important influencing factor. The high-incidence areas for extreme rainfall are the Huaihe River region and the Shandong Peninsula. (2) The observed significant increase in hourly extreme precipitation events in the Shandong and Henan provinces of the Huang-Huai-Hai Plain has led to an increased risk of flooding, while the corresponding events in the northwest region of the Plain have exhibited a gradual weakening trend over time. (3) The extreme hourly precipitation in the Huang-Huai-Hai plain shows a frequent and scattered pattern, with decreasing intensity over time. Extreme precipitation mainly occurs in the first half of the night, especially between 19:00 and 21:00, with extreme hourly rainfall intensity fluctuating between 0.2 and 0.25 and the proportion of rainfall to total precipitation reaching as high as 10%. The spatial distribution of extreme hourly rainstorms during the peak period (19:00–21:00) exhibits a high rainfall volume, intensity, and frequency pattern in the eastern region, while the western part exhibits low rainfall volume, intensity, and frequency. (4) The incidence of extremely heavy rainfall in an hour has exhibited a more significant increase compared to extreme daily events in the Huang-Huai-Hai Plain, primarily in the form of backward-type precipitation. Hourly extreme precipitation events in the Huang-Huai-Hai Plain are affected by terrain and land use/cover change (LUCC), with the micro-topography of hilly areas leading to a concentrated distribution of precipitation and LUCC suppressing extreme precipitation events in arid climates. (5) At the ten-day scale, the spatial distribution of the NDVI shows a gradually increasing trend from northwest to southeast, with the highest NDVI value reaching up to 0.6 in the southern part of the study area. For extreme hourly precipitation, there is no significant change observed at the multi-year ten-day scale; while the NDVI in the northern and central parts of the Huang-Huai-Hai Plain shows a significant decreasing trend, in contrast, it presents a significant increasing trend in the southern region. (6) Finally, the correlation between NDVI at the ten-day scale and extreme hourly precipitation exhibits a decreasing pattern from north to south, with a correlation coefficient decreasing from 0.48 to 0.08. The lagged correlation analysis of extreme hourly rainfall and NDVI for one, two, and three ten-day periods shows that the lagged effect of extreme hourly precipitation on NDVI is negligible. Analyzing the correlation between extreme hourly rainfall and NDVI for different months, the impact of extreme hourly precipitation on NDVI is predominantly negative, except for June, which shows a positive correlation (0.35), passing the significance test. This study offers a scientific foundation for enhancing disaster warning accuracy and timeliness and strengthening the research on disaster reduction techniques.

Prediksi Pola Penjualan Barang pada UMKM XYZ dengan Metode Algoritma Apriori

Through the development of information technology today, the need for clear and accurate information is needed in everyday life, so that information will become an important thing in society. Sometimes high information needs are not accompanied by the presentation of adequate information, often information through the mining process is expected to provide information that was previously hidden in the data warehouse so that it becomes important and valuable information [1]. Utilization of existing data in the information system to support decision-making activities, it is not enough to just rely on operational data, a data analysis is needed to explore the potential of existing information. Decision makers try to take advantage of existing data warehouses to explore useful information to help make decisions, this encourages the emergence of new branches of science to overcome the problem of extracting important or interesting information or patterns from large amounts of data, which is called data mining. 2]. MSME XYZ is one of the leading MSMEs in Lubuklinggau City where this MSME sells various kinds of durian products such as tempoyak, lempok durian, peeled durian, durian pancakes, durian ice cream, durian coffee, durian seed chips etc. Every day MSME XYZ carries out activities such as sales transactions, providing product stock and so on, from the existing sales data so far XYZ has not been able to provide information about the pattern of customer spending habits so that transaction data cannot help leaders in making decisions from data collected. there is. Association analysis or association rule mining is a data mining technique to find the rules of a combination of items. One of the stages of association analysis that has attracted the attention of many researchers to produce efficient algorithms is high frequency pattern analysis (frequent pattern mining). The output of data mining can be used to improve decision making in the future.

Impact of COVID-19 vaccine on epilepsy in adult subjects: an Italian multicentric experience

ObjectivesTo investigate the safety and tolerability of COVID-19 vaccines in people with epilepsy (PwE).MethodsIn this multicentric observational cohort study, we recruited adult patients (age > 18 years old) with epilepsy who attended the Outpatient Epilepsy Clinic from 1st July to 30th October 2021. We administered to the patients a structured questionnaire and interview on demographic and epilepsy characteristics, current treatment, previous SARS-CoV-2 infection, vaccine characteristics, post-vaccine seizure relapse, other side effect, variation of sleep habits, caffeine, or alcohol intake. Seizure frequency worsening was defined as a ratio between mean monthly frequency post-vaccination and mean monthly frequency pre-vaccination superior to 1. Patients were categorized in two groups: patients with seizure frequency worsening (WORSE) and patients with seizure stability (STABLE).ResultsA total of 358 people participated with a mean age of 47.46 ± 19.04. Focal seizure (79.1%), generalized epilepsy (20.4%), and unknown types of epilepsy (0.5%) were detected among participants. In total, 31 (8.7%) people expressed that they were not willing to receive a COVID-19 vaccine; 302 patients (92.35%) did not experience an increase in the seizure frequency (STABLE-group) whereas 25 patients (7.65%) had a seizure worsening (WORSE-group). Post-vaccine seizures occurred mainly in the 7 days following the administration of the vaccine. Patients in the WORSE-group were treated with a mean higher number of anti-seizure medication (ASMs) (p = 0.003) and had a higher pre-vaccine seizure frequency (p = 0.009) compared with patients in the STABLE-group. Drug-resistant epilepsy was also associated with seizure worsening (p = 0.01). One-year pre-vaccination seizure frequency pattern demonstrated that patients in the WORSE-group had a higher frequency pattern (p < 0.001). Multivariate analysis of the vaccinated group showed that only the seizure frequency pattern (confidence interval [CI] = 1.257–2.028; p < 0.001) was significantly associated with seizure worsening.ConclusionIn our cohort of vaccinated PwE, only a little percentage had a transient short-term increase of seizure frequency. The present study demonstrates that COVID-19 vaccines have a good safety and tolerability profile in the short term in PwE.

HFA-Net: High frequency attention siamese network for building change detection in VHR remote sensing images

Building change detection (BCD) recently can be handled well under the booming of deep-learning based computer vision techniques. However, segmentation and recognition for objects with sharper boundaries still suffer from the poorly acquired high frequency information, which can result in the deteriorated annotation of building boundaries in BCD. To better obtain the high frequency pattern under the deep learning pipeline, we propose a high frequency attention-guided Siamese network (HFA-Net) in which a novel built-in high frequency attention block (HFAB) is applied. HFA-Net is designed to enhance high frequency information of buildings via HFAB which is composed of two main stages, i.e., the spatial-wise attention (SA) and the high frequency enhancement (HF). The SA firstly guides the model to search and focus on buildings, and HF is employed afterwards to highlight the high frequency information of the input feature maps. With high frequency information of buildings enhanced by HFAB, HFA-Net is able to better detect the edges of changed buildings, so as to improve the performance of BCD. Our proposed method is evaluated on three widely-used public datasets, i.e., WHU-CD, LEVIR-CD, and Google dataset. Remarkable experimental results on these datasets indicate that our proposed method can better detect edges of changed buildings and shows a better performance. The source code will be released at: https://github.com/HaiXing-1998/HFANet.

Transforming Discoveries About Cortical Microcircuits and Gamma Oscillations Into New Treatments for Cognitive Deficits in Schizophrenia.

The major cause of disability in schizophrenia is cognitive impairment, which remains largely refractory to existing treatments. This reflects the fact that antipsychotics and other therapies have not been designed to address specific brain abnormalities that cause cognitive impairment. This overview proposes that understanding how specific cellular and synaptic loci within cortical microcircuits contribute to cortical gamma oscillations may reveal treatments for cognitive impairment. Gamma oscillations are rhythmic patterns of high frequency (∼30-100 Hz) neuronal activity that are synchronized within and across brain regions, generated by a class of inhibitory interneurons that express parvalbumin, and recruited during a variety of cognitive tasks. In schizophrenia, both parvalbumin interneuron function and task-evoked gamma oscillations are deficient. While it has long been controversial whether gamma oscillations are merely a biomarker of circuit function or actually contribute to information processing by neuronal networks, recent neurobiological studies in mice have shown that disrupting or enhancing synchronized gamma oscillations can reproduce or ameliorate cognitive deficits resembling those seen in schizophrenia. In fact, transiently enhancing the synchrony of parvalbumin interneuron-generated gamma oscillations can lead to long-lasting improvements in cognition in mice that model aspects of schizophrenia. Gamma oscillations emerge from specific patterns of connections between a variety of cell types within cortical microcircuits. Thus, a critical next step is to understand how specific cell types and synapses generate gamma oscillations, mediate the effects of gamma oscillations on information processing, and/or undergo plasticity following the induction of gamma oscillations. Modulating these circuit loci, potentially in combination with other approaches such as cognitive training and brain stimulation, may yield potent and selective interventions for enhancing cognition in schizophrenia.

The Impact of Using the Association Method in Determining the Pattern of COVID-19 Symptoms in the Sumatra Province

COVID-19 (coronavirus disease 2019) is a new type of disease caused by a coronavirus, namely SARS-CoV-2, also known as the Corona virus. The Corona virus outbreak is causing concern all over the world, including in Indonesia. Many people became paranoid as a result of the virus's widespread spread, which was followed by reports of a number of deaths among victims. The spread of the Corona virus in Indonesia has had a negative impact on all aspects of life. Because of the density of settlements and the vastness of Indonesia's territory, this virus became out of control and spread quickly. The lack of socialization in dealing with this virus has an impact on the community's understanding of the importance of following health protocols. The goal of this research is to use data mining techniques to determine the pattern of symptoms caused by the Corona virus. The Association method is used in data mining. APRIORI is a popular association method that employs a high frequency pattern. This method is expected to be used to determine how likely it is that one case has the first symptom in addition to other symptoms. The parameters Support and Confidence support whether or not the association rules are required. After obtaining the results of the multiplication of Support and Confidence, the rule with the highest multiplication result will be discovered. The rule with the highest result is used as a rule to be applied in the next case, namely "If you have cough symptoms, they will be accompanied by fever symptoms with 90% support and 90% confidence".

Implementation of a priori algorithm for book lending at state high school library I Silima Pungaga-Punga Parongil

The library plays a role in helping students to enjoy reading books. The availability of books in various fields motivates students to come to visit the library, students / i can read or borrow library books. For this reason, the purpose of this research was carried out including helping library officials apply the rules of how to visit the libraries.&#x0D; In carrying out research methods, including conducting direct observations, conducting interviews to collect the necessary data. The pattern that will be analyzed is the pattern of borrowing what books are often borrowed so that library officials know the information of books that are often borrowed. The result obtained is with the application of a priori algorithms, book data is processed to produce a pattern of borrowing books. After all high frequency patterns are found, then the association rules are sought that meet the minimum requirement for associative confidence A→B minimum confidence = 25%. The final association rules are ordered based on minimum support and minimum confidence, if borrowing IPA, then borrowing MTK Support = 15%, Confidence = 42.8%.

Gabaergic Interneurons in Early Brain Development: Conducting and Orchestrated by Cortical Network Activity.

Throughout early phases of brain development, the two main neural signaling mechanisms—excitation and inhibition—are dynamically sculpted in the neocortex to establish primary functions. Despite its relatively late formation and persistent developmental changes, the GABAergic system promotes the ordered shaping of neuronal circuits at the structural and functional levels. Within this frame, interneurons participate first in spontaneous and later in sensory-evoked activity patterns that precede cortical functions of the mature brain. Upon their subcortical generation, interneurons in the embryonic brain must first orderly migrate to and settle in respective target layers before they can actively engage in cortical network activity. During this process, changes at the molecular and synaptic level of interneurons allow not only their coordinated formation but also the pruning of connections as well as excitatory and inhibitory synapses. At the postsynaptic site, the shift of GABAergic signaling from an excitatory towards an inhibitory response is required to enable synchronization within cortical networks. Concomitantly, the progressive specification of different interneuron subtypes endows the neocortex with distinct local cortical circuits and region-specific modulation of neuronal firing. Finally, the apoptotic process further refines neuronal populations by constantly maintaining a controlled ratio of inhibitory and excitatory neurons. Interestingly, many of these fundamental and complex processes are influenced—if not directly controlled—by electrical activity. Interneurons on the subcellular, cellular, and network level are affected by high frequency patterns, such as spindle burst and gamma oscillations in rodents and delta brushes in humans. Conversely, the maturation of interneuron structure and function on each of these scales feeds back and contributes to the generation of cortical activity patterns that are essential for the proper peri- and postnatal development. Overall, a more precise description of the conducting role of interneurons in terms of how they contribute to specific activity patterns—as well as how specific activity patterns impinge on their maturation as orchestra members—will lead to a better understanding of the physiological and pathophysiological development and function of the nervous system.

High frequency patterns play a key role in the generation of adversarial examples

Deep neural networks (DNNs) have made significant progress in many fields, but the black-box nature hinders their application in critical areas. The emergence of adversarial examples raises serious questions about the safety of neural networks particularly. The interpretability of neural networks has become a research hotspot in the field of deep learning. It is significant to study what features DNNs have learned and how these features evolve within DNNs. Taking adversarial examples as the research object, we find that feature activations of all categories have relatively stable expectations at each layer, and adversarial examples have undergone a feature trajectory from origins to targets, generating the feature activations similar to those of the target category. Using image frequency spectrum, we prove that adversarial examples are mainly caused by the formation of high-frequency patterns widely present in the target category images. The conclusions of this paper contribute to a deeper understanding of feature learning and the generation of adversarial examples.

Decline in independence after three years and its association with dietary patterns and IADL-related factors in community-dwelling older people: an analysis by age stage and sex

BackgroundFew studies have shown age stage and sex differences in the association among dietary patterns and various health factors related to disability in older people. This study aimed to reveal the differences of characteristics, including several dietary patterns, associated with a decline in independence over 3 years in community-dwelling independent older people. Specifically, we examined data by age stage, for people between 65 and 75 years (earlier-stage) and people aged 75 years or above (later-stage), and sex.MethodsWe conducted a nationwide longitudinal study of 25 Japanese prefectures from 2013 to 2016; 2250 participants’ complete data (1294 men and 956 women) were analyzed. Independence was evaluated based on instrumental activities of daily living (IADL) scores (maximum = 12). Dietary patterns were derived from a principal component analysis of the seven food groups. Baseline IADL-related factors linked to independence 3 years later were selected. Multiple logistic regression analysis for having low independence—without a full score of IADL 3 years after baseline—was conducted, adjusted for baseline IADL scores. Finally, to compare differences among age stage and sex groups, we used Multiple-Group Path Analysis.ResultsParticipants with a full IADL score 3 years later were classified as high independence (69.6%), and those without the full score were classified as low independence (30.4%). Only the later-stage older peoples’ proportion of low independence 3 years later was significantly higher than those at baseline. A high meat frequency pattern was associated with a significantly higher risk of decline in independence 3 years later in later-stage older women. The earlier-stage older people showed that 18.5 ≤ BMI < 25 was associated with a lower risk, referring to BMI < 18.5. In the later-stage, exercising three or more times a week with enjoyment and fulfillment was associated with a significantly lower risk.ConclusionsThe IADL disability in older people aged 75 and over showed a rapid change. The different characteristics associated with the risk of decline in independence among age stage and sex were revealed. Targeting age stage and sex separately for community-based comprehensive supportive strategies would be necessary for a long life globally.

Frequency and Antimicrobial Resistance of Shigella Species in Iran During 2000-2020

Context: Numerous studies have shown the high frequency and antibiotic-resistant patterns of Shigella species in different provinces of Iran. In this study, we performed a comprehensive review from 2000 to 2020 in Iran to describe the prevalence rate and antibiotic-resistant patterns of S. sonnei, S. dysenteriae, S. flexneri, and S. boydii. Evidence Acquisition: We systematically searched the biomedical databases including Scopus, Google Scholar, PubMed, SID, and Web of Science for related articles published in English or Persian. Finally, out of 70 articles, 34 studies were included in the study. Results: From 44,292 clinical specimens, 2,742 cases were introduced as positive samples for Shigella species in Iran during 2000-2020. Also, S. sonnei (n = 1484, 54.1%) was the predominant species in Iran, followed by S. flexneri (n = 1100, 40.1%), S. dysenteriae (n = 80,3%), and S. boydii (n = 78, 2.8%). These Shigella species showed maximum resistance to ampicillin (n = 1759, 64%-96%), cotrimoxazole (n = 1220, 87%-100%), nalidixic acid (n = 649, 10%-82%), trimethoprim-sulfamethoxazole (n = 459, 80%-98.5%), cefotaxime (n = 410, 53%-63%), and tetracycline (n = 386, 36%-94%). No resistances were found against imipenem, meropenem, cefoxitin, norfloxacin, levofloxacin, azithromycin, and amoxicillin. Also, 308 and 359 cases were introduced as multidrug resistance (MDR) and extended spectrum beta lactamase (ESBL) producing species, respectively. Conclusions: Evaluation of endemic shigellosis and antibiotic-resistant patterns through epidemiological studies are necessary to promote infection control strategies. These data may be useful to avoid empirical treatment, revise treatment guidelines, and decrease antimicrobial resistance of Shigella spp. in human societies.

Control of Brain State Transitions with a Photoswitchable Muscarinic Agonist.

The ability to control neural activity is essential for research not only in basic neuroscience, as spatiotemporal control of activity is a fundamental experimental tool, but also in clinical neurology for therapeutic brain interventions. Transcranial‐magnetic, ultrasound, and alternating/direct current (AC/DC) stimulation are some available means of spatiotemporal controlled neuromodulation. There is also light‐mediated control, such as optogenetics, which has revolutionized neuroscience research, yet its clinical translation is hampered by the need for gene manipulation. As a drug‐based light‐mediated control, the effect of a photoswitchable muscarinic agonist (Phthalimide‐Azo‐Iper (PAI)) on a brain network is evaluated in this study. First, the conditions to manipulate M2 muscarinic receptors with light in the experimental setup are determined. Next, physiological synchronous emergent cortical activity consisting of slow oscillations—as in slow wave sleep—is transformed into a higher frequency pattern in the cerebral cortex, both in vitro and in vivo, as a consequence of PAI activation with light. These results open the way to study cholinergic neuromodulation and to control spatiotemporal patterns of activity in different brain states, their transitions, and their links to cognition and behavior. The approach can be applied to different organisms and does not require genetic manipulation, which would make it translational to humans.

A feature invariant generative adversarial network for head and neck MRI/CT image synthesis

With the emergence of online MRI radiotherapy treatments, MR-based workflows have increased in importance in the clinical workflow. However proper dose planning still requires CT images to calculate dose attenuation due to bony structures. In this paper, we present a novel deep image synthesis model that generates in an unsupervised manner CT images from diagnostic MRI for radiotherapy planning. The proposed model based on a generative adversarial network (GAN) consists of learning a new invariant representation to generate synthetic CT (sCT) images based on high frequency and appearance patterns. This new representation encodes each convolutional feature map of the convolutional GAN discriminator, leading the training of the proposed model to be particularly robust in terms of image synthesis quality. Our model includes an analysis of common histogram features in the training process, thus reinforcing the generator such that the output sCT image exhibits a histogram matching that of the ground-truth CT. This CT-matched histogram is embedded then in a multi-resolution framework by assessing the evaluation over all layers of the discriminator network, which then allows the model to robustly classify the output synthetic image. Experiments were conducted on head and neck images of 56 cancer patients with a wide range of shape sizes and spatial image resolutions. The obtained results confirm the efficiency of the proposed model compared to other generative models, where the mean absolute error yielded by our model was 26.44(0.62), with a Hounsfield unit error of 45.3(1.87), and an overall Dice coefficient of 0.74(0.05), demonstrating the potential of the synthesis model for radiotherapy planning applications.

Two-stage adaptive random Fourier sampling method for image reconstruction

We propose a random Fourier sampling scheme to enhance the accuracy of the high frequency pattern estimation for image reconstruction. This method is designed to work in a constrained ℓ1 minimization based on the Fourier-Haar interplay revealing a column-wise maximum coherent structure that we provide. Essential in the scheme is to generate a data-driven density function by a small percentage of Fourier samples. The density function governs a random sampling procedure to acquire high frequency information, resulting in better reconstruction of the Haar wavelet coefficients. We also discuss a few examples of exact recovery of the Haar wavelet coefficients from which the proposed sampling scheme has emerged. Numerical experiments confirm superiority of the proposed sampling scheme to other conventional sampling schemes in the ℓ1 framework.

A new PDAF correction method of CMOS image sensor with Nonacell and Super PD to improve image quality in binning mode

This paper presents a new PDAF correction method to improve the binning mode image quality in the world’s first 0.8um 108 mega pixel CMOS Image Sensor with Samsung Nonacell and Super PD technology. PDAF pixels had been fixed by bad-pixel-correction (BPC), referring to the adjacent non-PDAF pixels in the conventional correction method. We demonstrated a new method, named Dilution mode which output their own seed value within the 3x3 same color-channel pixels to video images and deliver AF information through the separate embedded data. As a result, the PDAF artifact, such as a false color, broken line and dot artifact in a high frequency pattern and overall image detail have dramatically improved in dilution mode.

IMPLEMENTASI ASSOCIATION RULE MINING UNTUK MENENTUKAN POLA KOMBINASI MAKANAN DENGAN ALGORITMA APRIORI

OH5 Hash Cafe is a business that is engaged in the food sector and there is a lot of competition in doing business that is increasingly difficult to do so it is necessary to develop a strategy, this study aims to determine the pattern of food combinations, the method used in this research is the Apriori Algorithm to be able to find out and processed using the Rapid Miner 9.7 software in determining food combination patterns, the Apriori Algorithm is an interesting type of association rule in data mining and an interesting association analysis to produce an efficient algorithm that is high frequency pattern analysis, an association can be identified with two benchmarks, namely: Support and Cofindence. Support is the percentage of item combinations in the database, while Confidence is the strong relationship between items in the association rule.&#x0D; &#x0D;