Background: Rheumatoid arthritis (RA) is a chronic, systematic inflammatory disorder that may affect many tissues and organs, but principally attacks synovial joints and it is a common rheumatic disease with many subtypes. Nuclear Magnetic resonance (1H NMR) spectrometers with high sensitivity, resolution and dynamic range has permitted the rapid, simultaneous investigation of complex mixtures of endogenous or exogenous components present in biological materials. Metabonomics is the systematic study of chemical finger print resulted from cell reactions and could be used as a new biomarker for early disease diagnosis. In the present investigation, we studied serum metabolic profile in rheumatoid arthritis (RA) in order to find out the metabolic finger print pattern of the disease.Materials and methods: In our metabonomics study serum samples were collected from 16 patients with active RA, and from equal number of healthy subjects. They were evaluated during a one-year follow-up with the assessment of disease activity and 1H NMR spectroscopy of sera samples. In all the cases, the presence of active rheumatoid arthritis was shown by an increase in the T1 values of the synovium of the joints. We specified and classified all metabolites using PCA, PLSDA chemometrics methods. Chenomx (Trail Version) and ProMetab codes in Matlab software environments were used for our data analysis. Results were compared with the NMR metabolite data bank (www.metabolomics.ca). Anti-CCP, ANA and urea were also analyzed by ElISA and colorimetric methods respectively.Results: The most changes identified in this study were in the biosynthesis pathways of steroid hormones, biotin, fatty acids, amino acids (Leucine, Valin and isoleucine) and also linoleic acid.Conclusion: In rheumatoid arthritis disease, the activation of the immune system consumes larg amounts of energy. The main donor of free energy in cells is ATP, which is generated by both glycolysis and oxidative phosphorylation. Changes in amino acids and free fatty acids biosynthesis pathways confirm the high energy utilization. In this disease, the increase in free fatty acid metabolism leads to production of Acetyl CoA and ketone bodies. Since there are many diseases subtype in rheumatoid arthritis, more sensitive diagnostic method is required. The result of our investigation suggests that metabolome profiling method could be used as a new biomarker for early diagnosis of rheumatoid arthritis disease.

The Mahalanobis-Taguchi system is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis- Taguchi system and a neural-network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

Communication system recognition can be used in some civilian and military applications. The recognition of the system is done by inspecting the received signal properties like modulation type, carrier frequency, baud rate and so on. Therefore we need Automatic Modulation recognition (AMR) in addition to carrier and baud rate estimation methods. In this paper we introduce a new AMR method based on time and spectral domain features of the received signal. A neural network is used as the classifier. A broad class of analog and digital modulations is considered. Baud rate and carrier frequency estimation is performed by existing methods referred to in this paper. Using this information the protocol used for signal transmission is detected.

An Internet of Things-based security system and OpenCV technology have been developed to improve the efficiency and ease of monitoring video footage from CCTV. The face detection process is carried out using the Haar Cascade method, while facial recognition is carried out using the Local Binary pattern Histogram algorithm. The test results show that light intensity has a significant influence on system accuracy, but this system provides convenience in monitoring CCTV video in real-time through a webserver and improves security, especially in rooms by utilizing Internet of Things technology. The current facial recognition success rate is 72%. Therefore, for the subsequent development of the system, it is recommended to increase the success rate of facial recognition and also implement the File Transfer Protocol to ensure better and better system performance.

In industrial rotatory machines, different forces in rotor bearings are generated due to various impaired mechanical sources, namely bearing misalignment and nonhomogeneous mass distribution (unbalance). By precisely analyzing and diagnosing the produced patterns of bearing forces, one can determine the unbalance parameters such as quantities of masses, their distance from the rotational axis, and characteristics of corresponding parallel planes. Consequently, it will be possible to formulate pragmatic protocols according to which the maintenance engineers of rotatory systems will pinpoint properties of problematic imbalance masses and then straightforwardly balance them. In the procedure of conducting this research, several exemplary imbalance masses are deployed on a rotatory mechanical shaft and the equations of motion and forces in perfectly aligned rigid bearings are extracted. Then, by applying a neural network-oriented system the patterns of bearing forces are recognized and the characteristics of the nominal masses including magnitudes, distances from the rotational axis, angles as well as the unbalance type are determined. The accuracy of predicting 8 variables of balancing masses was 41% and after eliminating the redundant overlaps from principal components, the accuracy of predicted 5 variables of balancing masses significantly increased to 95%. Also, by implementing another comprehensive neural network system, it was shown that by exerting two separate balancing masses, the applicability of this method in balancing any faulty systems with dynamic unbalance is possible.

In this paper, a novel Patch Geodesic Derivative pattern (PGDP) describing the texture map of a face through its shape data is proposed. Geodesic adjusted textures are encoded into derivative patterns for similarity measurement between two 3D images with different pose and expression variations. An extensive experimental investigation is conducted using the publicly available Bosphorus and BU-3DFE databases covering face recognition under pose and expression changes. The performance of the proposed method is compared with the performance of the state-of-the-art benchmark approaches. The encouraging experimental results demonstrate that the proposed method is a new solution for 3D face recognition in single model databases.