1,721,029 research outputs found
Remote Controlled Operated Prosthetic Arm
No full textM.E. (Electronic Instrumentation and Control Engineering)Prosthesis is an artificial extension that replaces a missing body part. Prostheses are typically used to replace parts lost by injury or missing from birth or to supplement defective body parts. In addition to the standard artificial limb for every-day use, many amputees have special limbs and devices to aid in the participation of sports and recreational activities.
Prosthetic arm is a boon for those persons who have lost their arm (below elbow) due to some mishap. One of the main requirements of artificial arm is that functionally, it should be as near to the natural hand as possible. Various designs of artificial arm are available in the market, categorised as mechanical, electrical and Myo-electric arm. Mechanical devices are functional prostheses that use some motion of the body to provide the force necessary to control the prosthetic component. Electrical arms operate the hand by a motor driven by micro switches and relays. Myo-Electric arm is stimulated by muscle signal available from the stump of amputee.
It has been observed that as the time of amputation increased, the signal strength from amputee stump decreases and muscles lost their activity and acquire a permanent fatigue state. Amputation period of more than about 10 years causes the permanent loss of the muscular action and no EMG output is available from such patients, EMG operated arm is useless for them. So a new prosthetic arm has been proposed, which operates wirelessly by the remote control system consists of four switches for four different operations besides opening & closing of hand with two different levels of grip forces
Enhancing Performance of Classification Techniques for EEG Based Brain–Computer Interface
No full textMental task (MT) Electroencephalography (EEG) is EEG recorded during performance of non-motor general mental tasks. MT based brain-computer interface (BCI) paradigm using non-motor mental tasks can be viewed as generalization of motor imagery (MI) based BCI paradigm. MT based BCI paradigm shows better potential than MI based BCI paradigm in enhancing the quality of life of the physically disabled persons. Consequently, the focus of this current research lies in MT based BCI systems. Classification of such non-motor cognitive Electroencephalography (EEG) signals produced during performance of mental task is the central challenge in developing such type of EEG based non-invasive BCI systems. The human brain shows extremely complex nonlinear and non-stationary spatiotemporal patterns of EEG signals that vary over multiple temporal scales. We believe that accurate representation of such type of complex and subtle patterns contained in the signal dynamics holds the key in enhancing the real time performance of a BCI system. But the neurological control signals driving the MT based BCI paradigm contain a number of different types of sophisticated spatiotemporal patterns which have not been identified yet. Current feature extraction algorithms relying on prior assumptions about the patterns may discard meaningful information contained in the data. Due to this, their ability to accurately identify new type of patterns is limited. The prime motivation of our work stems from this need for discovering new patterns through nonlinear signal processing or from the geometrical and topological properties of the RPS. In this dissertation, we aim at enhancing the classification performance in mental task (MT) based BCI paradigm.
We investigated the following feature extraction approaches:
1. Introducing the largest singular value of the phase space matrix in the multivariate empirical mode decomposition( MEMD) domain as feature for classifying mental task in MT based brain-computer interface.
2. Introducing Eigen values of the covariance matrix of the coefficient matrix of the multivariate autoregressive( MVAR) model in the MEMD domain.
3. Proposing multivariate multi scale entropy values as EEG features for classifying non-motor mental task EEG.
4. Singular values in the phase space of original signal as features.
In the first approach, we employed singular value decomposition (SVD) based phase space analysis of the multivariate intrinsic mode functions (IMFs) and extracted largest singular values from the phase space matrices of the sensitive IMFs for constructing the feature vectors. With these new feature vectors, we achieved highest classification accuracy of 83.33% for binary classification between mental arithmetic and mental letter composing.
Our second approach is based on deriving multivariate autoregressive (MVAR) models of the set of relevant multivariate intrinsic mode functions (IMFs) generated from the Multivariate empirical mode decomposition (MEMD) of the multi-channel EEG signals. In this approach, the set of statistically significant Eigen values computed from the derived multivariate AR models of the set of relevant IMFs were used for constructing the feature vectors. Finally, we classified the constructed feature vectors by employing LS-SVM classifier with three different kernel functions. We achieved highest average classification accuracy of 94.3% for binary and 77.7% for three class classification.
In the third approach, we proposed multivariate multi scale entropy based complexity measures as EEG features for classifying EEG signals in MT based BCI paradigm. These entropy values computed over selected scales have been employed for constructing the feature vectors. We achieved highest classification accuracy of 100% for binary classification of the two pairs mental tasks.
We tested all our approaches on a bench mark EEG data set and evaluated the results. The accuracy, speed and consistency of the test results show efficacy of the proposed features. In this way, this thesis presents several novel results in the broad area of brain signal classification using EEG recordings which further leads to better understanding of cognitive brain dynamics and improved performance of next generation of noninvasive BCI systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Study and Analysis of SEMG Signal for Enhancement of Above Shoulder Myoelectric Arm Functionality
No full textUpper limb amputation is a traumatic event that can seriously affect the person’s capacity to perform regular tasks and can lead individuals to lose their confidence and autonomy. Prosthetic devices can give relief by acting as substitute the function of missing limb which can help to improve the quality of life of the amputees. In recent years, myoelectric devices have received extensive attraction to provide enhanced degree of freedom over traditional devices. Myoelectric prosthesis is controlled via the acquisition and processing of electromyogram signal produced at the muscles fibre from the surface of body with an array of electrode placed on the residual limb. The acquired signal is a complex one being dependent on the physiological and anatomical property of muscles. The electrodes convert muscles-activity from the torso into information that can be processed by different techniques. The unwanted noise contributes from the electrolyte skin surface, while travelling through the muscles. To make the noisy signal useful, advancement in the detection and processing of the signal becomes a very important requirement in biomedical engineering. The signal has to undergo pre-processing stage consisting of amplification, filtering and adaptive peak detection etc. to reduce the noise level in the raw signal. The different signal processing techniques such as time domain techniques, wavelet coefficients and autoregressive coefficients have been applied to increase the information yield from the EMG signal. Different algorithms to identify the intended movements are available that rely on the feature extraction that provide the user with access to multiple degrees of freedom and have shown great promise in research literature. The identified information of movements is translated into control signal to drive the artificial limb and the force generated by the artificial limb can be varied by the user’s muscles intensity.
The commercially available myoelectric prostheses do not allow to control the transhumeral level and shoulder disarticulation level of amputation. For transhumeral amputee with no muscles-activity or very less muscles-activity in the residual limb there is no intuitive control source for either elbow or hand, therefore controlling the prosthetic device is impossible with existing techniques. As a result, better strategy is required to control a prosthesis for a high-level amputation. Further studies are required to improve the training protocol and analysis of the signal for development of the prosthetic devices for these applications. The main contribution of this thesis to implement the prosthesis based on the EMG signal from the set of shoulder muscles intact in the transhumeral amputee. For this, an overview of the human shoulder muscles anatomy was carried out. The acquired SEMG data with the different shoulder movement are described. Various pre-processing techniques and adaptive peak detection techniques were explored. A new threshold method was developed and applied to filter the unwanted peaks in the pre-processing stage of the SEMG signal. Next step was to investigate the shoulder movements of amputees and non-amputees and compare the EMG activity based on the amplitude level of the signal. Different signal analysis methods such as Fourier transform, short time Fourier transform and wavelet transform were investigated and wavelet transform was applied successfully. The proposed detection scheme focuses on the discrete Daubechies wavelet transform with four decomposition levels. A systematic approach for selecting the optimal wavelet transform method was proposed and demonstrated. A pattern based recognition technique was used with immediate access to four different movements at a time. A set of features was extracted from shoulder muscles of the transhumeral amputee by transforming the wavelet reconstructed coefficients to the new transformed coefficients by using the new proposed transformation method. Subsequently, investigation of classification is presented through various experiments conducted on amputees. The work was carried out with the aim to enhance the robustness in the pattern recognition system to classify different shoulder movements so that it is helpful for making a more reliable and useful device. To classify the different shoulder motions, various machine learning algorithms were compared to select the optimal and efficient algorithm. A data mining Random forest classifier was utilized in this work which was found better than other classifiers. These classification results were evaluated and validated by a prototype using Arduino motor controller for elbow and hand movement.
Myoelectric signal is one of the control signals for controlling the powered prosthesis. A number of commercial products have been developed for these prostheses. But these devices are still insufficient to satisfy the needs of amputee. The precise measurement and analysis of human movements and muscles activity are essential processes in rehabilitation. In India, the maximum amputation rate is for below elbow which is about 52% of the total amputation. Transhumeral amputation is the second largest in upper limb amputation which is about 24% the total. The available literature and systems mostly focus on below elbow amputees and limited work is available on prosthetic design for the shoulder amputees. The researchers have largely ignored the real time SEMG signal for the transhumeral amputees with no activation in the triceps and biceps muscles with the result that a prosthetic device for such amputees is not available off the shelf. The system developed in this work is based on the data from the amputee's muscles activation from different arm movements which allows one to have independent signals required for independent motion of elbow and hand. The system performance has been validated through an actual arm which will be worn by the amputee. The performance of arm has been checked under actual environment. The developed system promises an overall accuracy of more than 90% for correct motion of elbow and hand which shows that its functionality is very close to natural human arm.
This work presents a successful design of an affordable SEMG platform. The effort to achieve this goal will always encourage the researchers into the field of SEMG technologies. Mechanical fabrication using light composite material and electronic assembly using advanced processors can be implemented to make the arm ready for large scale clinical trials. If required, high mechanical functionality in electrode grid to follow the skin surface can be achieved by connecting the wireless sensor to produce better results. High end DSP chip system can be used to handle the large data set and the code that presently exists in MATLAB. The use of energy efficient motors, driving circuits and couplings can further improve the degree of functionalities of the arm.
The main idea of this research is to provide a set of guidelines for researchers and engineers aiming to develop their own low-cost EMG systems applicable in biomechanical, clinical, rehabilitation, sport, and research contexts. EMG signals can be used to generate device control commands for rehabilitation equipment such as robotic prostheses and can be useful in many clinical and industrial applications
Quality assessment of black tea based on physical parameters using machine vision
No full textPHD, EIEDTea is a valuable cash crop throughout the world. It is a major export product of India. As far as social aspect is concerned, about 1.2 million people are directly employed as labour in tea industry. This constitutes a large proportion of human resource of the country. Quality of tea plays a significant role in its marketability as international export price of tea is fixed according to its quality. At present, tea quality is validated by professional ‘Tea Tasters’ who charge exorbitantly for every sip they take. Conventionally, these experts evaluate tea quality by use of organoleptic methods during fermentation and sorting stage. In addition to this, gas chromatography and colorimetery are employed for chemical analysis of tea liquor and for colour analysis, respectively, at various stages of tea processing. These conventional methods have many shortcomings. First of all, being small in number, the Tea Tasters are difficult to hire and there is every possibility of formation of a cartel by them. Their evaluation methods are subjective and suffer from high labour costs, inconsistency and variability. The prominent physical parameters that establish tea quality include colour, texture, grain shape and size. The approach of Tea Tasters does not quantify these parameters and hence, it is difficult to correlate various parameters of tea for assessment of tea quality. Increasing competition and concerns about tea quality leading to rejection of export orders has resulted in substantial fall in tea export from India in the recent past and consequently, tea industry of India is slowly dying. If proper measures are not adopted and lessons not learnt from the past, situation may aggravate in future. There is a dire need to carry out research in this field so as to meet requirements of global standards. There has been lack of research specifically related to grading and quality assessment of tea all over the world.
The above issues are aptly addressed by machine vision based techniques. This work documents the efforts carried out for objective grade assessment of tea quality at the post processing stage with the application of machine vision techniques. In addition to estimation of colour, shape, size and texture by machine vision, direct measurement of two prominent physical parameters namely, moisture and density has been carried out for quality assessment. The present work was taken up to carry out research in this field which has very high socio-economic significance not only for India but for all tea exporting South Asian countries. The main issues required to be addressed by this work include:
• Determination of size, shape, texture, etc. of granules of tea non-destructively by machine vision for assessment of tea grade.
• Determination of colour of brewed tea liquor for assessment of grade.
• Measurement of moisture and density of various grades of tea for grade discrimination.
• Development of a classifier followed by statistical validation of results.
The problems addressed through machine vision technique have certain definite steps to be followed in sequence with image acquisition followed by image pre-processing, feature extraction. Finally, extracted features are classified. Image acquisition is greatly affected by factors such as selection of camera, viewing distance, orientation of illumination source etc. Due care has be taken at this stage to ensure efficient capturing of image data with a high degree of fidelity. Another critical aspect is feature extraction which involves identification and estimation of suitable features that describe the data uniquely. Towards the end, the classification stage deals with selection of appropriate classifier for classification of feature data. In the present work, as a first step, an objective discrimination amongst the various tea grades of tea was carried out on the basis of their morphological features viz. area, perimeter and aspect ratio. The results were compared with the standard samples obtained from tea industry which were duly graded by tea tasters. Finally, with the extracted features when presented to the three inputs MLP, a grading accuracy of 100% was achieved. Statistical analysis by ANOVA (Analysis of Variance) highlighted area and perimeter as key attributes for discrimination between various grades. Further, the possibility of discriminating various grades of tea granules on the basis of their texture was explored. In the present work, four diverse grades of black tea are discriminated using between using textural features on the basis of spatial location of their grey shade intensities. Certain statistical attributes like energy, entropy, contrast, correlation and homogeneity are evaluated for the image database comprising of images of diverse grades. When these features are classified using MLP, an accuracy of 87.5% was achieved. Further, upon decomposition into sub-band images by DWT, the same features were computed and an improved accuracy of 100% was observed. In the next stage, colour estimation was carried out for discriminating the different grades on the basis of colour of tea liquor. Grade assignment was done on the basis of extracted colour features using the RGB colour model and 100% accuracy was observed using MLP classifier.
Another prominent parameter that determines the shelf life and storage quality of black tea i.e. moisture has been investigated for different grades of tea. It has been observed that the moisture retention is more in the grades having larger granules than the grades having smaller granule sizes. Finally, compacted and un-compacted densities were measured for various tea grades and it has been observed that the density enjoys an inverse relation with the granule size.
It is worth mentioning here that the procedures carried out in the present work for quality assessment, except colour analysis, are predominantly non-invasive in nature. If a system is developed using the proposed concept, it is expected that it can successfully assist the traditional methods in the tea industries for quality assessment and monitoring
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Steganography for Invisible Communication: A Review
No full textSteganography is the science and art of embedding secret messages in innocuous looking carriers in such a way that it does not draw the attention of anyone other than the sender and the targeted recipient, thus a method for secret and invisible communication which provides security through obscurity. Its main purpose is to hide the occurrence of communication over a public channel. Steganography has been used since ancient times and has grown exponentially in the recent past because of the improvements in computing power. Earlier, steganography was implemented using some physical medium i.e. some tangible objects but now a days, it is implemented electronically by using several other intangible objects i.e. data can be hidden using any type of media, be it image in bmp, jpeg, gif format or some music file, video clip, text file, SMS etc. In this paper, different types of techniques used to hide data have been discussed with major focus on image based modern steg-anographic techniques
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