Cancer is one of the most feared health problems today. Studies on cancer diagnosis and treatment are carried out intensively. In this study, a graphene-based antenna is proposed for cancer diagnosis and treatment with THz radiation therapy, which is a relatively new radiation technique. A graphene-based two-layer monopole antenna is designed for 1.65THz operation frequency. To change the bandwidth and radiation pattern without changing the operating frequency, a graphene ring is placed on the SiO2 substrate (2nd layer).Antenna performance is analyzed for reflection coefficient, realized gain, E-Field. The proposed antenna is obtained approximately %4 bandwidth. A peak gain of 8.52 dB is achieved at 1.65THz within the bandwidth. Antenna design is done in Computer Simulation Technology Studio Suite. It is expected that the results of the THz antenna will make a significant contribution to healthcare applications. The cancer treatment with THz is cheap, easy, and can be used without causing discomfort in patients.
This article presents a new form of exemplar-based learning method, base d on overlapping feature intervals. In this model, a concept is represented by a collection of overlappling intervals for each feature and class. Classificas. tion with Overlapping Feature Intervals COFI is a particular implementation of this technique. In this incremental, inductive, and supervised learning method, the basic unit of the representation is an interval. The COFI algorithm learns the projections of the intervals in each feature dimension for each class. Initially, an interval is a point on a feature-class dimension; then it can be expanded through generalization. No specialization of intervals is done on feature-class dimensions by this algorithm. Classification in the COFI algorithm is base d on a majority voting among the local predictions that are made individually by each feature. An evaluation of COFI and its comparison with similar other classification techniques is give n. Learning refers to a wide spectrum of situations in which a learner increases his knowledge or skill in accomplishing certain tasks. The learner applies inferences to some material in order to construct an appropriate representation of some relevant aspe ct of reality.
Özetçe-Algılama alt sınırı (Limit of Detection-LOD) değeri, mikrosensörün anlamlı olarak algılayabileceği minumum konsantrasyon değerini vermekte olup, beyin hastalıklarında kullanılan mikrosensörün LOD değerinin 1µM dan daha küçük olması istenmektedir. Bu değere yaklaşamayan mikrosensörler deneylerde kullanılamamaktadır. Bu çalışma ile üretim sonucunda elde edilen sensörlerin LOD değerleri azaltılarak, duyarlılığının artırılması hedeflenmiştir. LOD değeri sensörün arkaplan (baseline) gürültüsü ile doğru orantılı olarak artmaktadır. Literatürde yapılan çalışmalarda arka plan gürültüsü filtrelenmeden elde edilen sensör verileri kullanılmıştır. Bu çalışmada ise, ham veriler dalgacık filtreden geçirildikten sonra kalibrasyon yapılarak, LOD değeri 3 kat iyileştirilmiştir.
Anahtar Kelimeler -beyin sensörü; mikroelektrot, kalibrasyon; LOD; dalgacık dönüşümü.Abstract-Limit of detection (LOD) gives the concentration amount that a microsensor can detect. It is desirable to have a LOD value of 1µM for microsensors used in brain diseases. The ones that cannot reach this sensitivity value are disposed and cannot be used in the experiments. The goal of this study is to increase the sensitivity of the produced microsensors by decreasing their LOD values. LOD increases linearly by baseline noise. The sensor data is used generally without any baseline filtering in the literature. In this study, LOD values are enhanced 3 times as much by using wavelet filtering, compared with the ones where no filtering is used.
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