Joint angle prediction from EMG signals for lower limb exoskeleton

This paper presents an algorithm for estimation of the intended knee joint angle from sEMG signals acquired from four muscles of upper limb. The algorithm was evaluated with experiments showing the calculated intended motion while performing a simple daily life activity of sitting in a squat position and standing from a squat position. The proposed algorithm uses mean absolute value (MAV) and root mean square (RMS) for feature extraction and a multi-layer back propagation neural network (BPN) for predicting the knee angle. The algorithm and the experimental results are both presented. The predicted knee angle can be used to control a lower limb exoskeleton

Acetone detection using thin tungsten oxide (WO3) film based gas sensor

Acetone being a volatile organic compound is found in human breath. Non-invasive breath analysis for detection of diabetes has gained remarkable attention for the past few years. Human breath has ample of volatile organic compounds pertaining to numerous diseases in the body. Acetone in breath has been proven to be an important biomarker for diagnosis of diabetes through breath. Here, tungsten oxide (WO3) thin films of nanometer thickness have been used for this purpose. The detector films are sputtered over silicon dioxide layer and corresponding connections are made over the film to measure the resistance change. Acetone, being a reducing gas, reduces the resistance of the film as it comes in contact. Different concentrations in parts per million (ppm) have been tested on these nanometer films having thickness of 100 nm over a chip size of 5 mm x 5 mm, as low as 1.2 ppm. Optimum temperature has also been computed to be 300 oC. The topography of film has been characterized by atomic force microscopy (AFM) and a mean grain size of 24.1 nanometers has been observed

Prevalence of vitamin D deficiency and its relationship with thyroid autoimmunity in Asian Indians: a community-based survey

25-Hydroxy vitamin D (25(OH)D) deficiency is linked with predisposition to autoimmune type 1 diabetes and multiple sclerosis. Our objective was to assess the relationship between serum 25(OH)D levels and thyroid autoimmunity. Subjects included students, teachers and staff aged 16-60 years (total 642, 244 males, 398 females). Serum free thyroxine, thyroid-stimulating hormone (TSH), and thyroid peroxidase autoantibodies (TPOAb), intact parathyroid hormone and 25(OH)D were measured by electrochemiluminescence and RIA, respectively. Thyroid dysfunction was defined if (1) serum TSH ≥ 5 μ U/ml and TPOAb>34 IU/ml or (2) TSH ≥ 10 μ U/ml but normal TPOAb. The mean serum 25(OH)D of the study subjects was 17.5 (SD 10.2) nmol/l with 87 % having values ≤ 25 nmol/l. TPOAb positivity was observed in 21 % of subjects. The relationship between 25(OH)D and TPOAb was assessed with and without controlling for age and showed significant inverse correlation (r - 0.08, P = 0.04) when adjusted for age. The prevalence of TPOAb and thyroid dysfunction were comparable between subjects stratified according to serum 25(OH)D into two groups either at cut-off of ≤ 25 or >25 nmol/l or first and second tertiles. Serum 25(OH)D values show only weak inverse correlation with TPOAb titres. The presence of such weak association and narrow range of serum 25(OH)D did not allow us to interpret the present results in terms of quantitative cut-off values of serum 25(OH)D. Further studies in vitamin D-sufficient populations with wider range of serum 25(OH)D levels are required to substantiate the findings of the current study

Dielectric study on human teeth specimen

284-286Electrical and dielectric properties of wet human teeth, in vitro have been studied in detail. The measurements are made in three directions namely longitudinal, lateral-medial and anterior- posterior, at discrete frequencies 10 k Hz to 1.5 MHz using impedance analyzer. The electrical properties of teeth are found frequency dependent. Dielectric loss appears due to heat generation which decreases exponentially from 0.561 at 1 kHz to 0.002 at 13 MHz. It has been observed that the relation frequency is less than 10 kHz

Suppression of Simultaneous Switching Noise Using Embedded Capacitor Method in High-Speed Systems

Gbps signals are commonly used in modern high-speed IO designs. These systems require a high level of signal accuracy. However, these signals can cause current fluctuations at high frequencies due to parasitic inductance on the package and board, leading to significant changes in supply voltage. DDRX interfaces that transmit data up to 4.8 Gbps induce simultaneous switching noise (SSN) in the voltage regulator module (VRM). One of the adverse effects of SSN is timing jitter, also known as power supply-induced jitter. The amount of SSN is directly proportional to the number of signals on a parallel bus. Discrete capacitors may not be effective in reducing SSN impact when data is transmitted at high speeds. The current approach used to predict and evaluate errors in SSNs relies on the designer's expertise, which requires accurate estimates of packaging parasitic. In this paper, a practical approach to lessen SSN is presented through the use of embedded capacitors placed between planes and optimizing discrete capacitors by connecting them in a way that measures the impact of supply noise on the I/O transistor circuit performance at the DDRX interface. Two boards were designed, evaluated, and subsequently compared their results. &nbsp

Package Board Co-Simulation Using Equivalent Circuit Modeling Method for High-Speed System

This research paper focuses on the challenges of system-level simulations for high-speed data transfer and the importance of modeling methodology in achieving accurate results. The simulating of PCB alone without considering the package model is not enough. Instead, to ensure optimal system performance, simulating the packages and PCBs together is essential. When simulating the high-speed parallel bus at the PCB level, it's crucial to consider the package model. If the package model is missing, achieving successful results can be challenging. In this paper, a method is proposed for developing a package model that is specific to the nets required for simulation purposes. The paper explores the challenge of simulating high-speed interfaces with a DDRx example that includes an RLC package model. An interconnect model technique is developed to apply the package RLC model through the equivalent circuit modeling method for the high-speed DDRx test board. This solution aims to simplify the co-design process by using simulation and verifying the model with the assistance of an eye diagram of the data bus. The proposed package model reduces jitter in co-simulation and ensures timing compliance for high-speed interfaces

Shugoshin: From the Perspective of Clinical Disorders

Proper and timely segregation of the cellular genome is a prime requirement of all cell division programs. Mis-segregation of chromosomes and the resulting aneuploidy lead to several clinical consequences. Over the years, shugoshin has emerged as a key protein factor involved in the segregation of genetic material in dividing cells. Deletion or an altered level of shugoshin is reported in several human malignancies; as a result, shugoshin is now emerging as an important tumor-associated gene and a possible target for cancer therapy. Apart from its role in cancer, recent studies have also shown the involvement of shugoshin in several other clinical disorders. In this review, we aim to highlight the clinical relevance of shugoshin

Effect of despeckle filtering on classification of breast tumors using ultrasound images

Ultrasound is the most widely used imaging modality for screening of breast tumors. However, due to the presence of speckle noise in an ultrasound image, the diagnostic information gets masked and the interpretation of the breast abnormalities becomes difficult for the radiologist. The texture of the tumor region and the shape/margin characteristics are considered to be important parameters for the analysis of the breast tumors. In the present work, exhaustive experimentation has been carried out for the design of CAD systems for classification of breast tumors by considering (a) original images only, (b) despeckled images only and (c) both original and despeckled images together (hybrid approach). Total 100 breast ultrasound images (40 benign and 60 malignant) have been used for the analysis. Initially, these images have been despeckled using six filters namely Lee sigma, BayesShrink, DPAD, FI, FB and HFB filters. Total 162 features (149 texture and 13 morphological features) have been computed from both original and despeckled breast ultrasound images and SVM classifier has been used extensively for the classification. The results of the study indicate that the hybrid approach of CAD system design using texture features computed from original images combined with morphological features computed from images despeckled by DPAD filter yield optimal performance for classification of benign and malignant breast tumors with a classification accuracy of 96.0%. From the promising results of the study it can be concluded that the proposed hybrid CAD system design could be used as a second opinion tool in clinical setting

Diagnosis of Diabetes Using Computer Methods: Soft Computing Methods for Diabetes Detection Using Iris

Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier