R Tool Analysis of Gripper Motor Rehabilitation for Post Stroke Therapy

Today, cerebral stroke issues are one of the most terrifying disorders in the clinical era, in which the most common people are highly affected. Worldwide, more than 20,00,000 individuals are exposed to stroke problems like hemiplegia, consistently, where 70% of them pass away at the instance of stroke. Among the survival group after the treatment, more than 85% of them are exposed to long term permanent disability. The affected community practice themselves to survive along with disability due to financial instability and reachability. While comparing with western standards, developing nations like India need keen attention to improve the medical standards. In order to treat the affected at the instance of stroke, home based methodologies shall be introduced for better performance and to improve the standard of daily living. This project involves a gripper motor based regulator for wrist and fingers incorporated with Arduino 328P. This device is a textile fabric tailored with 5 gripper motors to actuate each fingers as a part of post stroke rehabilitation

Indian Sign Language Recognition Using Deep Learning Techniques

By automatically translating Indian sign language into English speech, a portable multimedia Indian sign language translation program can help the deaf and/or speaker connect with hearing people. It could act as a translator for those that do not understand sign language, eliminating the need for a mediator and allowing communication to take place in the speaker's native language. As a result, Deaf-Dumb people are denied regular educational opportunities. Uneducated Deaf-Dumb people have a difficult time communicating with members of their culture. We provide an incorporated Android application to help ignorant Deaf-Dumb people fit into society and connect with others. The newly launched program includes a straight forward keyboard translator that really can convert any term from Indian sign language to English. The proposed system is an interactive application program for mobile phones created with application software. The mobile phone is used to photograph Indian sign language gestures, while the operating system performs vision processing tasks and the constructed audio device output signals speech, limiting the need for extra devices and costs. The perceived latency between both the hand signals as well as the translation is reduced by parallel processing. This allows for a very quick translation of finger and hand motions. This is capable of recognizing one-handed sign representations of the numbers 0 through 9. The findings show that the results are highly reproducible, consistent, and accurate

Analysis of artificial intelligence in industrial drives and development of fault deterrent novel machine learning prediction algorithm

Industrial sectors rely on electrical inverter drives to power their various load segments. Because the majority of their load is nonlinear, their drive system behaviour is unpredictable. Manufacturers continue to invest much in research and development to ensure that the device can resist any disturbances caused by the power system or load-side changes. The literature in this field of study depicts numerous effects caused by harmonics, a sudden inrush of currents, power interruption in all phases, leakage current effects and torque control of the system, among others. These and numerous other effects have been discovered as a result of research, and the inverter drive has been enhanced to a more advanced device than its earlier version. Despite these measures, inverter drives continue to operate poorly and frequently fail throughout the warranty term. This failure analysis is used as the basis for this research work, which presents a method for forecasting faulty sections using power system parameters. The said parameters were obtained by field-test dataset analysis in industrial premises. The prediction parameter is established by the examination of field research test data. The same data are used to train the machine learning system for future pre-emptive action. When exposed to live data feeds, the algorithm may forecast the future and suggest the same. Thus, when comparing the current status of the device to the planned study effort, the latter provides an advantage in terms of safeguarding the device and avoiding a brief period of total shutdown. As a result, the machine learning model was trained using the tested dataset and employed for prediction purposes; as a result, it provides a more accurate prediction, which benefits end consumers rather than improving the power system\u27s grid-side difficulties

An exploration of firm level competitiveness through choices in Manufacturing Strategy: The case of Indian four wheeler passenger vehicle companies

In today’s fast changing uncertain business environment several factors influence competitiveness of a manufacturing firm. There are several competing priorities such as cost, quality, delivery, flexibility and innovation on the basis of which firms compete with each other. Together with these competing priorities, technology plays a significant role in manufacturing competitiveness that ultimately leads to firm competitiveness. This paper explores different perspective of manufacturing strategy, different competing criteria of competitiveness and the role of innovation in manufacturing strategy for achieving competitiveness. Using the cases from Indian four wheeler passenger vehicle companies, the paper presents the use of innovation in gaining competitive positioning within Indian four wheeler passenger vehicle industries. By using case study and systematic review as a methodology this study outlines the role of manufacturing strategy and role of various forms of innovation in achieving competitiveness. The findings have important lessons for firms in their efforts towards innovation. It is seen from the various examples discussed in the study that there is a growing conviction that innovative practices ultimately contribute to the success of firms.     &nbsp

Network configuration as a measure of power in global production networks

Power is one of the key components in understanding and analyzing global production and is central to the analytical frameworks of both GVCs and GPNs. By focusing on firms’ power within GPNs, we are able to draw a novel analytical link between the governance structures of GVCs and network configuration presented in recent versions of GPNs. Using global input- output data, we show that the network structure of global production helps determine the distribution of power among firms in different economic sectors and, consequently, it influences the governance structures of supply networks. More specifically, we find a very high correlation between the distribution of profits and a sector’s position in global production, captured by its (total strength) centrality. Based on this, we are able to provide a quantitative measure of power within global production and its governance structures

A new family of donor-acceptor systems comprising tin(IV) porphyrin and anthracene subunits: synthesis, spectroscopy and energy transfer studies

A new family of covalently linked 'Sn(IV) porphyrin-anthracene'diad (1), triad (2) and tetrad (3) donor-acceptor (D-A) systems have been designed and synthesized in good-to-moderate yields. While diad 1 possesses one anthracene subunit at the peripheral (meso) position of the tin(IV) porphyrin scaffold, triad 2 possesses twotrans axial anthracene subunits at the tin(IV) centre. On the other hand, tetrad 3 is endowed with both the peripheral and axial anthracene subunits in its architecture. These D-A systems have been fully characterised by elemental analysis, FAB-MS, UV-Vis, 1H and 13C NMR and electrochemical methods. UV-Vis,NMR and redox data suggest the absence of intramolecular π-π interaction between the porphyrin and the anthracene/s in 1-3. Fluorescence from the anthracene subunit in 1 and 3 is found to be quenched in comparison with the fluorescence of free anthracene in four different solvents. This is not the case with compound 2. Excitation spectral data provides evidence for an intramolecular excitation energy transfer (EET) from the singlet anthracene to the porphyrin in 1 and 3. The energy transfer efficiency is in the order: 2 (almost negligible) < 3 (~30%) < 1 (nearly quantitative), with the peripheral anthracene → porphyrin pathway being largely favoured. This orientation dependence of EET could be analysed using Forster's dipole dipole mechanism

Faceting oscillations in nano-ferroelectrics

We observe periodic faceting of 8-nm diameter ferroelectric disks on a 10s time-scale when thin Pb(Zr0.52Ti0.48)O-3 film is exposed to constant high-resolution transmission electron microscopy beams. The oscillation is between circular disk geometry and sharply faceted hexagons. The behavior is analogous to that of spin structure and magnetic domain wall velocity oscillations in permalloy [Bisig et al., Nat. Commun. 4, 2328 (2013)], involving overshoot and de-pinning from defects [Amann et al., J. Rheol. 57, 149-175 (2013)]

Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image

Nowadays, a massive quantity of remote sensing images is utilized from tremendous earth observation platforms. For processing a wide range of remote sensing data to be transferred based on knowledge and information of them. Therefore, the necessity for providing the automated technologies to deal with multi-spectral image is done in terms of change detection. Multi-spectral images are associated with plenty of corrupted data like noise and illumination. In order to deal with such issues several techniques are utilized but they are not effective for sensitive noise and feature correlation may be missed. Several machine learning-based techniques are introduced to change detection but it is not effective for obtaining the relevant features. In other hand, the only limited datasets are available in open-source platform; therefore, the development of new proposed model is becoming difficult. In this work, an optimized deep belief neural network model is introduced based on semantic modification finding for multi-spectral images. Initially, input images with noise destruction and contrast normalization approaches are applied. Then to notice the semantic changes present in the image, the Semantic Change Detection Deep Belief Neural Network (SCD-DBN) is introduced. This research focusing on providing a change map based on balancing noise suppression and managing the edge of regions in an appropriate way. The new change detection method can automatically create features for different images and improve search results for changed regions. The projected technique shows a lower missed finding rate in the Semantic Change Detection dataset and a more ideal rate than other approaches

Design and development of an instrument for non- destructive fabric weight measurement

In this study, an image processing approach for fabric weight measurement has been proposed and tested. The system involves digital capturing of image using a microscope and then its processing in simple steps using image processing software (MATLAB). The study is conducted using a range of woven fabric samples. The fabrics have been conventionally weighed using an electronic weighing balance, and digital images of the sample fabrics are obtained and processed. The process involves application of suitable filters to obtain weft count, warp count, EPI, PPI and yarn crimp. The values are then substituted in standard formula to obtain the fabric weight. The study shows that the results of the proposed method of image processing, based on fabric weight measurement, are well correlated with the results of conventional method of measurement