An overview on the veracity of intraoral digital scanning system and utilization of iTero scanner for analyzing orthodontic study models both In-Vivo and Ex-Vivo.

In the domain of orthodontics, plaster models are contemplated as one of the important tools for diagnosis and treatment planning. In Dentistry, technological advancement has developed in the section of diagnostic devices, for example, the utilization of a 3D intraoral scanner, which can convert plaster models into digital models. With in-office utilization of this system, orthodontists can more meticulously and precisely construct custom braces, clear aligners, and orthodontic appliances. The digital data can be stored as a stereolithography file; it eliminates the disadvantages encountered with the storage of plaster models like breakage, space required, and distortion of the plaster models. ITero®element is the intraoral laser scanner (ILS) which utilizes parallel confocal scanning technology which maximizes the accuracy of the scan. By utilizing the iTero scanner, the dental measurement can be performed in OrthoCADTM software which is highly accurate. The objective of the contemporary study is to review the literature of studies on in-vivo and ex-vivo scanning with the iTero system.

Anthelmintic activity of Tunisian chamomile (Matricaria recutita L.) against Haemonchus contortus.

The chemical treatment of gastrointestinal parasitic diseases has been undermined by increasing resistance and high toxicity. There is an urgent need to search for alternative natural sources for the treatment of such parasites. In this respect, the present study aims to quantify phenolic compounds of chamomile (Matricaria recutita L.) and to study their in vitro anti-oxidant and anthelmintic activities in solvents with increasing polarity. In vitro determination of anti-oxidant capacity was carried out using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2-azino-bis-(3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) radical cation methods. In vitro anthelmintic activity was investigated on egg-hatching inhibition and loss of motility of adult worms of Haemonchus contortus from sheep. The results showed that methanolic and aqueous extracts contain more total polyphenols, total flavonoids and condensed tannins than chloroformic and hexanic extracts. ABTS and DPPH assays showed that methanolic extracts had the highest anti-oxidant potency (IC50 = 1.19 µg/ml and 1.18 µg/ml, respectively). In vitro anthelmintic activity showed that both methanolic (IC50 = 1.559 mg/ml) and aqueous (IC50 = 2.559 mg/ml) extracts had the greatest effect on egg hatching and motility of worms (100% after 8 h post exposure at 8 mg/ml). A significant and positive correlation between DPPH and ABTS tests was observed for all tested extracts. Therefore, total phenolic, total flavonoid and condensed tannin values were correlated with IC50 from both ABTS and DPPH, and with inhibition of egg hatching. To our knowledge, this report is the first of its kind to deal with in vitro anthelmintic activities of chamomile extracts.

Multiresolution forecasting for futures trading using wavelet decompositions.

We investigate the effectiveness of a financial time-series forecasting strategy which exploits the multiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). We apply the Bayesian method of automatic relevance determination to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the individual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representation, or by another perceptron which learns the weight of each scale in the prediction of the original time series. The forecast results are then passed to a money management system to generate trades.

A low-complexity intracardiac electrogram compression algorithm.

Implantable cardioverter defibrillators (ICD's) detect, diagnose and treat the potentially fatal heart arrhythmias known as bradycardia, ventricular tachycardia (VT), and ventricular fibrillation (VF) in cases where these arrhythmias are resistant to surgical and drug-based treatments by direct sensing and electrical stimulation of the heart muscle. Since the ICD is implanted, power consumption, reliability, and size are severe design constraints. This paper targets the problems associated with increasing the signal recording capabilities of an ICD. A data-compression algorithm is described which has been optimized for low power consumption and high reliability implementation. Reliance on a patients morphology or that of a population of patients is avoided by adapting to the intracardiac electrogram (ICEG) amplitude and phase variations and by using adaptive scalar quantization. The algorithm is compared to alternative compression algorithms which are also patient independent using a subset of VT arrhythmias from a data base of 146 patients. At low distortion the algorithm is closest to the Shannon lower bound achieving an average of 3.5 b/sample at 5% root mean square distortion for a 250-Hz sample rate. At higher distortion vector quantization and Karhunen-Loeve Transform approaches are superior but at the cost of considerable additional computational complexity.

Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM).

The adaptive-subspace self-organizing map (ASSOM) proposed by Kohonen is a recent development in self-organizing map (SOM) computation. In this paper, we propose a method to realize ASSOM using a neural learning algorithm in nonlinear autoencoder networks. Our method has the advantage of numerical stability. We have applied our ASSOM model to build a modular classification system for handwritten digit recognition. Ten ASSOM modules are used to capture different features in the ten classes of digits. When a test digit is presented to all the modules, each module provides a reconstructed pattern and the system outputs a class label by comparing the ten reconstruction errors. Our experiments show promising results. For relatively small size modules, the classification accuracy reaches 99.3% on the training set and over 97% on the testing set.

Artificial neural network-based channel selection and loudness mapping.

We present in this paper artificial neural network techniques for implementing loudness mapping and "smart" channel selection for cochlear implant systems. For loudness mapping, a multilayer perceptron (MLP) is trained to perform the mapping for each channel according to threshold and comfort levels. It is shown that good accuracy mapping can be performed by a very simple MLP architecture. For channel selection, we propose a neural network-based method that can make "smart" selection. We describe and report results for the case in which 6 channels are to be selected from 18. The neural network-based selection system is trained on a multispeaker labeled speech database and tested on a database of different speakers and spoken sentences. Compared with methods used by leading cochlear implant systems, our approach produces significantly better results, and it is easy to implement in the speech processor of the cochlear implant system.

A low-power VLSI arrhythmia classifier.

The design, implementation, and operation of a low-power multilayer perceptron chip (Kakadu) in the framework of a cardiac arrhythmia classification system is presented in this paper. This classifier, called MATIC, makes timing decisions using a decision tree, and a neural network is used to identify heartbeats with abnormal morphologies. This classifier was designed to be suitable for use in implantable devices and a VLSI (very large scale integration) neural-network chip (Kakadu) was designed so that the computationally expensive neural-network algorithm can be implemented with low power consumption. Kakadu implements a (10,6,4) perceptron and has a typical power consumption of tens of microwatts. When used with the arrhythmia classification system, the chip can operate with an average power consumption of less than 25 nW.

Kakadu--a low power analogue neural network classifier.

An analogue neural network VLSI chip designed for low power operation is presented. This chip consists of 84 synapse elements arranged as arrays of size 10 x 6 and 6 x 4 and was fabricated using a standard 1.2 micron double metal single poly CMOS process. The synapses are digitally programmable and static weight storage is provided. The chip has a typical power consumption of tens of microwatts. It has been successfully trained and tested on a range of classification problems including 4-bit parity, character recognition and morphological-based classification of intracardiac electrogram signals.

MATIC--an intracardiac tachycardia classification system.

The use of an additional atrial sensing electrode together with a morphology recognition algorithm provides a significant improvement in classification performance over the current rate based algorithms used in implantable cardioverter defibrillator (ICD) devices. The classification system, called morphology and timing intracardiac classifier (MATIC), follows a classification process similar to that used by cardiologists. Timing between the atrial and ventricular channels is examined using a decision tree and forms the primary criterion for arrhythmia classification. A neural network based morphology classifier is used for cases such as ventricular tachycardia with 1:1 retrograde conduction where timing alone cannot make a reliable decision. MATIC achieves 99.6% correct classification on a database of intracardiac electrogram (ICEG) signals containing 12,483 QRS complexes recorded from 67 patients during electrophysiological studies. Arrhythmias in this database include sinus tachycardia, normal sinus rhythm, normal sinus rhythm with bundle branch block, sinus tachycardia with bundle branch block, atrial fibrillation (AF), various supraventricular tachycardias, ventricular tachycardia, ventricular tachycardia with 1:1 retrograde conduction, and ventricular fibrillation. Within these arrhythmias, there were numerous ventricular ectopic beats, fusion beats, noise, and other artifacts. MATIC addresses the classification problem from start to finish, inputs being raw intracardiac electrogram signals and the outputs being the recommended ICD therapy. Results achieved with MATIC were compared with a classifier used in the Telectronics Guardian ATP 4210, which achieved 75.9% correct classification on the same database. MATIC is simple and efficient, making it suitable for use in a low power implantable device.

Predicting the number of contacts and dimensions of full-custom integrated circuit blocks using neural network techniques.

Block layout dimension prediction is an important activity in many very large scale integration computer-aided design tasks, among them structural synthesis, floor planning and physical synthesis. Block layout dimension prediction is harder than block area prediction and has been previously considered to be intractable. The authors present a solution to this problem using a neural network machine learning approach. The method uses a neural network to predict first the number of contacts; then another neural network uses this prediction and other circuit features to predict the width and the height of its layout. The approach has produced much better results than those published-a dimension (aspect ratio) prediction average error of less than 18% with a corresponding area prediction average error of less than 15%. Furthermore, the technique predicts the number of contacts in a circuit with less than 4% error on average.

Analysis of the effects of quantization in multilayer neural networks using a statistical model.

A statistical quantization model is used to analyze the effects of quantization when digital techniques are used to implement a real-valued feedforward multilayer neural network. In this process, a parameter called the effective nonlinearity coefficient, which is important in the studying of quantization effects, is introduced. General statistical formulations of the performance degradation of the neural network caused by quantization are developed as functions of the quantization parameters. The formulations predict that the network's performance degradation gets worse when the number of bits is decreased; that a change of the number of hidden units in a layer has no effect on the degradation; that for a constant effective nonlinearity coefficient and number of bits, an increase in the number of layers leads to worse performance degradation; and the number of bits in successive layers can be reduced if the neurons of the lower layer are nonlinear.