An Improved SPICE Model for MEMS Based Capacitive Accelerometers

An improved electrical equivalent circuit for a capacitive MEMS accelerometer, incorporating temperature, pressure and squeezed film effects is reported. The circuit model corresponds to a single degree of freedom (SDOF) vibrating system, including dominant micro physical mechanisms. Static, transient and frequency response analysis are carried out at temperature and pressure ranges of 100 K to 400 K and 30 to 3000 Pa respectively. The effect of these parameters on the resonance frequency, peak displacement and settling time of the accelerometer are determined. Simulations are performed using PSpice® circuit simulator. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3100

NOVA INFORMACIJSKA TEHNOLOGIJA PROCJENE KORISTI IZDVAJANJA CESTA POMOĆU SATELITSKIH SNIMKI VISOKE REZOLUCIJE TEMELJENE NA PCNN I C-V MODELU

Road extraction from high resolution satellite images has been an important research topic for analysis of urban areas. In this paper road extraction based on PCNN and Chan-Vese active contour model are compared. It is difficult and computationally expensive to extract roads from the original image due to presences of other road-like features with straight edges. The image is pre-processed using median filter to reduce the noise. Then road extraction is performed using PCNN and Chan-Vese active contour model. Nonlinear segments are removed using morphological operations. Finally the accuracy for the road extracted images is evaluated based on quality measures.Izdvajanje cesta pomoću satelitskih slika visoke rezolucije je važna istraživačka tema za analizu urbanih područja. U ovom radu ekstrakcije ceste se uspoređuju na PCNN i Chan-Vese aktivnom modelu. Teško je i računalno skupo izdvojiti ceste iz originalne slike zbog prisutnosti drugih elemenata ravnih rubova sličnih cestama. Slika je prethodno obrađena korištenjem filtera za smanjenje smetnji. Zatim se ekstrakcija ceste izvodi pomoću PCNN i Chan-Vese aktivnog modela konture. Nelinearni segmenti su uklonjeni primjenom morfoloških operacija. Konačno, točnost za ceste izdvojene iz slika se ocjenjuje na temelju kvalitativnih mjera

Toxic Comment Classification using Deep Learning

Online Conversation media serves as a means for individuals to engage, cooperate, and exchange ideas; however, it is also considered a platform that facilitates the spread of hateful and offensive comments, which could significantly impact one's emotional and mental health. The rapid growth of online communication makes it impractical to manually identify and filter out hateful tweets. Consequently, there is a pressing need for a method or strategy to eliminate toxic and abusive comments and ensure the safety and cleanliness of social media platforms. Utilizing LSTM, Character-level CNN, Word-level CNN, and Hybrid model (LSTM + CNN) in this toxicity analysis is to classify comments and identify the different types of toxic classes by means of a comparative analysis of various models. The neural network models utilized for this analysis take in comments extracted from online platforms, including both toxic and non-toxic comments. The results of this study can contribute towards the development of a web interface that enables the identification of toxic and hateful comments within a given sentence or phrase, and categorizes them into their respective toxicity classes

A study of antioxidant enzymes on experimental epileptic rat models induced by kainic acid excitotixicity

The word neurodegeneration rightly refers to the degenerative changes in nerve cell structure and so in its functions too. The main characteristics of these neurodegenerative disorders are relentless progression and decline in cognition. Epilepsy is one of the neurodegenerative disorders that affects a major portion of people around the world and the common form of focal epilepsy is Temporal lobe epilepsy (TLE). According to researchers the major cause for nerve cell degeneration is the excess production of free radicals. Preventing their action or quenching them will make a big deal in preventing neurodegeneration. As research work in relation to neurodegeneration is very much limited in India, we aimed one as an initiative. As herbs are always well known for their antioxidant activity with negligible side effects, we planned to apply one of the well-known herb, Acorus calamus and studied the potency of it in maintaining the antioxidant enzyme levels. The aim of this study in particular is to analyze the preventive role of Acorus calamus and its active ingredient Beta asarone in neurodegeneration than treating neurodegeneration in epileptic rat models. For the same, we selected eight groups of animals.&nbsp

Retrieval of Data from Very Large Databases Using Apriori Algorithm

Now-a-days most of the Very Large Databases like Medical Databases, Multimedia Database are consuming Petabytes of memory. For extracting required data from this enormous amount of memory so many retrieval techniques were already developed. But all these techniques are time consuming. We can also use indexing supported by all the commercial databases. When we create an index internally it uses B-Tree data structure which is used for efficient retrieval of data. But, Oracle10g is supporting R-Tree indexes also which are more efficient than B-Tree indexes. On Very Large Databases it is not sufficient to create a single index because these Databases consist of large number of fields. Moreover, it is not necessary to create index on each and every field and it is also not possible because it requires large memory area. In very large databases also most of the times the user concentrates on few fields only. So, in our proposed system, the solution is to create some suggested indexes. And as and when the user’s query pattern changes the suitable index from the suggested index set is dynamically loaded into the main memory from the local storage. Our proposed technique continuously monitors the query patterns and it creates the suggested indexes using Apriori algorithm and this suggested index set is updated periodically based on the query pattern changes

Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data

<div><p>This study describes a novel approach to reducing the challenges of highly nonlinear multiclass gene expression values for cancer diagnosis. To build a fruitful system for cancer diagnosis, in this study, we introduced two levels of gene selection such as filtering and embedding for selection of potential genes and the most relevant genes associated with cancer, respectively. The filter procedure was implemented by developing a fuzzy rough set (FR)-based method for redefining the criterion function of f-information (FI) to identify the potential genes without discretizing the continuous gene expression values. The embedded procedure is implemented by means of a water swirl algorithm (WSA), which attempts to optimize the rule set and membership function required to classify samples using a fuzzy-rule-based multiclassification system (FRBMS). Two novel update equations are proposed in WSA, which have better exploration and exploitation abilities while designing a self-learning FRBMS. The efficiency of our new approach was evaluated on 13 multicategory and 9 binary datasets of cancer gene expression. Additionally, the performance of the proposed FRFI-WSA method in designing an FRBMS was compared with existing methods for gene selection and optimization such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony algorithm (ABC) on all the datasets. In the global cancer map with repeated measurements (GCM_RM) dataset, the FRFI-WSA showed the smallest number of 16 most relevant genes associated with cancer using a minimal number of 26 compact rules with the highest classification accuracy (96.45%). In addition, the statistical validation used in this study revealed that the biological relevance of the most relevant genes associated with cancer and their linguistics detected by the proposed FRFI-WSA approach are better than those in the other methods. The simple interpretable rules with most relevant genes and effectively classified samples suggest that the proposed FRFI-WSA approach is reliable for classification of an individual’s cancer gene expression data with high precision and therefore it could be helpful for clinicians as a clinical decision support system.</p></div