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Amputees with lower limb loss need special care during daily life activities to make the movement natural as before amputation. No such work exists covering the main aspects from causes of amputation to the psycho-social impact of the amputees after using the prosthetic device. This review presents for lower limb prosthesis; the study of lower limb amputation, design & development, control strategies & machine learning algorithms, the psycho-social impact of prosthetic users, and design trends in patents. Research articles, review papers, magazines, letters, study reports, surveys, and patents, etc. have been used as sources for this review. Traumatic injuries and different diseases have been found as common causes of amputation. Design & development section illustrates design mechanisms, the categories of passive, active, & semi-active prostheses, an overview of a subset of commercially available prosthetic devices, and 3D printing of the accessories. The control section provides information about control techniques, sensors used, machine learning algorithms, and their key outcomes. Quality of life, phantom limb pain, and psycho-social impact of prosthetic users have been summarized for different countries that are believed to attract the interest of the readers. We have also developed an open-source database “FAKH-50” for patents to emphasize the design trends and advancements in lower limb prostheses from 1970 to 2020. Overall trend analysis determined is in the descending order as the knee (48%) > ankle (28%) > foot (22%) > hip (2%) patents in the current version of our database. The forthcoming section highlights the challenges and prospects of the domain. A mutual observation demands the design of a bio-compatible, lightweight, and economic prosthesis to track the normal human gait by eliminating phantom limb pain. This will empower the amputees to live a quality life in society. This work may be beneficial for researchers, technicians, clinicians, and amputees.

There are several well-known long-term complications following prosthetic valve replacement. Pannus formation is one of the rare complications whose estimated incidence varies between 0.3% and 1.3% per patient-year [1]. The exact etiology of pannus formation is unknown but histopathologically, pannus formation is due to fibroelastic hyperplasia that variably occurs after valve implantation [2]. The rarity and acute coronary syndrome like clinical presentation makes pannus formation a clinically important diagnosis during initial presentation. Workup preferably includes Transthoracic Echocardiography (TEE) with or without CT angiography. Management includes urgent or emergent surgical excision of the pannus with or without re-replacement of the aortic valve [1]. We present a 66-year-old female who presented with typical angina symptoms along with diffuse ischemic EKG changes five years after aortic valve replacement surgery with Trifecta Bioprosthesis due to severe aortic stenosis with Non-ST Elevation Myocardial Infarction (NSTEMI). Coronary angiography done on admission showed left main ostial stenosis of 90-95 % in proximity to the aortic valve prosthesis. A Transthoracic Echocardiogram (TTE) revealed an ejection fraction of 30-35 % with global hypokinesia but did not suggest any valve dysfunction. CT angiogram was done the next day which revealed a noncalcified plaque (pannus) greater than 75% occlusion in the left main in proximity of the aortic valve. Despite expedited care and surgical evaluation, unfortunately the patient decompensated in the surgical operating room and did not survive.

Experimentation and analysis in brain-computer interface (BCI) has increasingly been receiving quite some consideration as a substitute communication possibility for patients who are severely paralyzed in the last few years. To measure brain activities using optical signals a fairly new and non-invasive brain imaging tool can be put to test know as Functional near-infrared spectroscopy (fNIRS). Comparability low cost, safety, portability and wear ability are some of the main advantages of imaging of brain using this non-invasive modality. We propose in this paper to apply this relatively new non-invasive fNIRS technique to make an image of brain activities during four different mental tasks. These tasks include Mental Arithmetic (MA), Motor Imagery (i.e. Left-Hand and Right-Hand Motor Imagery) and Rest. fNIRS data used is an open access dataset which was collected by Continuous-wave imaging system (NIR Scout NIRx GmbH, Berlin, Germany) with the sampling frequency of 10 Hz. The research we have done in this paper Data synchronization is performed before the data is preprocessed. After the preprocessing and signal analysis of data our results shows hemodynamic behavior of multiple patterns during the tasks performed. These unique patterns of hemodynamic behavior can be used to differentiate and distinguish different task. We were able to compare, differentiate and distinguish the brain signal activities captured while performing 4 different tasks using 3 different classifiers i.e. Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The average classification accuracy of above 90% is achieved by using K Nearest Neighbors (KNN).

Analysis of visual cues for fruit classification and sorting allows to automate the visual inspection and packaging process in agricultural applications that is performed so far by human workers. Challenges for automated multi-class sorting systems are similarity in color and shape of different fruit varieties and variation among the same category of fruit. A major constraint in using well known deep neural networks for fruit classification arises because deep neural networks require large training datasets for achieving high accuracies which are generally not available in case of agricultural products especially various fruits and vegetable varieties. A thorough analysis is required to find an appropriate combination of various handcrafted features that could give precise and accurate classification results for small datasets. This paper investigates the use of various handcrafted visual features for fruit classification using traditional machine learning techniques. Different color, shape and texture features are analyzed by comparing the results obtained from six supervised machine learning techniques including K nearest neighbors, Support Vector Machines, Naive Bayes, Linear Discriminant Analysis, Decision Trees and Feed forward back propagation neural network. We propose a novel combination of Hue, Color-SIFT, Discrete Wavelet Transform and Haralick features in fruit classification problem that outperforms other handcrafted visual features. This feature combination is found to be invariant to rotation and illumination effects and works well with intra class variations providing good results for identifying subcategories of fruits along with high classification accuracies obtained for difficult fruit categories that are visually similar. It is found that Color-SIFT features alone work very well for fruit classification problem by outperforming other individual handcrafted features. Our approach is trained and tested on publicly available Fruits 360 dataset. Out of different classifiers best results are obtained using Back Propagation Neural Network, SVM and KNN classifier with classification accuracies between 99% and 100%.

The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.

The design objective of the 4G and beyond networks is not only to provide high data rate services but also ensure a good subscriber experience in terms of quality of service. However, the main challenge to this objective is the growing size and heterogeneity of these networks. This paper proposes a genetic-algorithm-based approach for the self-optimization of interference mitigation parameters for downlink inter-cell interference coordination parameter in Long Term Evolution (LTE) networks. The proposed algorithm is generic in nature and operates in an environment with the variations in traffic, user positions and propagation conditions. A comprehensive analysis of the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage in terms of call accept rate as well as capacity in terms of throughput.

This paper presents a human-computer interface system using Electromyography (EMG) signals to design a computer-based video game that enhances the muscle strength and can be used for rehabilitation of fist, wrist or forearm muscles. This game encompasses the rehabilitation of the muscle in itself by asking the user to move the muscle and measure the potential difference generated due to action potential using MyoWare muscle sensors kit. This newly generated raw EMG signal is passed on to Arduino, which is interfaced with a computer through a Graphical User Interface (GUI) based computer game, coded and designed in MATLAB. The gaming console presents an interesting and enjoyable platform as a modern treatment approach for stroke rehabilitation and provides an opportunity to practice activities that are not or cannot be practiced within the clinical environment. It encourages a higher number of repetitions than traditional therapy tasks without the supervision of an expert. However, due to the higher computational delay of 2 to 5 seconds between two consecutive input operations, MATLAB is not suitable for creating a real-time virtual gaming environment. The primary objective of the paper is to design and evaluate the effects of an interactive video game environment for upper limb muscle rehabilitation. Secondary outcomes included process limitations and adverse events based on the current equipment and techniques used.

Weeds are major cause due to which farmers get poor harvest of crops. Many algorithms are developed to classify weeds from crops to autonomously destroy weeds. Color-based, threshold-based and learning-based techniques are deployed in the past. From all techniques, deep-learning-based techniques stand out by showing the best performances. In this paper, deeplearning-based techniques are reviewed in the case where these are applied for weed detection in agricultural crops. Sunflower, carrot, soybean, sugar beet and maize are reviewed with respect to the weeds present in them. Deep learning structures and parameters are presented, and research Gaps are identified for further research.

Diabetic Retinopathy (DR) is an eye disorder that progressively leads to vision loss due to high glucose causing impairment of retinal blood vessels (BVs). ‘Retinal Bright Lesions’ such as ‘Hard Exudates’ (HEs) are plasma leakages from rapture retinal capillaries. HEs appear as hard, waxy, yellowish deposits from tiny spots to fat patches and signify moderate-severe Non-Proliferative Diabetic Retinopathy (NPDR). This paper proposes a simple, compact and computationally inexpensive technique for detection and classification of HEs using Digital Image Processing Techniques on digital fundus images complements and Artificial Neural Networks (ANN). The proposed technique unfolds through five stages i.e. Pre-processing, coarse detection, optimization, features detection & extraction followed by classification. ‘Speed Up Robust Features’ (SURF) algorithm has been used for features detection & extraction while ‘Feed-Forward Back-propagation’ (FFBP) ANN has been used for classification. The proposed technique has yielded 98.7% ‘Sensitivity’ (SE), 97.5% ‘Specificity’ (SP) and 97.7% ‘Accuracy’ (AC) on ‘DIARETDB1’ fundus images.

Experimentation and analysis of Functional near-infrared spectroscopy (fNIRS) in Brain-Computer Interface (BCI) has increasingly been studied as a communication possibility for patients who are severely paralyzed. This study has applied this technique to distinguish brain activities during four different mental tasks. These tasks include Mental Arithmetic (MA), Motor Imagery of Left-Hand (LHMI) and Right-Hand (RHMI) and Rest. fNIRS data used is from an open access dataset of 29 individuals which was collected by Continuous-wave imaging system (NIR Scout). In this research Data integration is performed before the data is preprocessed. Usual preprocessing is done using Butterworth filter to minimize or eliminate any unwanted signal distortion. After that an extensive signal analysis is done in which six different statistical features (Signal Mean (SM), Skewness (SK), Kurtosis (KR), Standard Deviation (SD), Signal Peak (SP), and Signal Variance (SV)) are obtained in the time domain and 13 Mel Frequency Cepstral Coefficients (MFCC) features are obtained from the frequency domain. As per literature review, MFCC has never been used as feature towards classification of fNIRS signal, which is a novel contribution towards this study. Separate Classification analysis is performed on each domain features. We were able to compare, differentiate and distinguish the brain signal activities captured while performing four different tasks using three different classifiers i.e. Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The average classification accuracy of 90.54% is achieved from K Nearest Neighbors (KNN) using the time domain features and accuracy achieved from Support Vector Machine (SVM) using the frequency domain features is 95.7%. Comparison with benchmark study shows the efficiency of MFCC as suitable features for improved classification accuracy.

For many who pertain to particular theological paradigms, their faith cannot be compartmentalised, but is mobilised to inform all aspects of their being, most notably their ethical and moral persuasions. As clinicians, the concept that there are good and bad deaths is already known; understanding the origin and depth of non-physical suffering, and aiming to alleviate it is not possible without learning the individual experiences and beliefs that go with it. Spiritual care forms a fundamental consideration in the endeavor to address the holistic experience of those patients receiving palliative care. Good palliative care seeks to promote the wellbeing and priorities of those with faltering health in a way that continues to support individualised notions of self-determination. The last few decades have resulted in a multicultural and multi-ethnic patient population. Addressing the spiritual and physical needs of patients allows healthcare professionals to deliver truly holistic care. Exploring and understanding the specific nuances of the five major religions of the UK provides healthcare professionals the opportunity to comfort the religiously observant patient at the end of life.

Feature extraction is a pronounced method to infer the information utility which is concealed in electromyography (EMG) signal to study the characteristic properties and behavior of signal. This study gives a comparative analysis of thirteen complete and most up-to-date EMG feature signals in Time-domain and Frequency-domain. Particularly, the EMG signals are obtained from a device MYO gesture control on an embedded system. For this purpose, four healthy male volunteers are considered to perform four different hand movements based on stationary, double tap, single finger movement and finger spread. To be a successful classification of these EMG features in both domains, we prefer attribute selected classifier as it gives the better performance and higher rate of accuracy i.e. 93.8%. The experimental results prove that features in time-domain are superfluity and redundant while features in frequency-domain (measured by statistical parameters of EMG power spectral density) show the ultimate dominance and signal characterization. The findings of this study are highly beneficial for further use in order to predict the behavior of EMG in pattern recognition and in classification of EMG signals for assistive devices or in powered human arm prosthetics.

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

Background. Brain computer interface (BCI) is a combination of software and hardware communication protocols that allow brain to control external devices. Main purpose of BCI controlled external devices is to provide communication medium for disabled persons. Now these devices are considered as a new way to rehabilitate patients with impunities. There are certain potentials present in electroencephalogram (EEG) that correspond to specific event. Main issue is to detect such event related potentials online in such a low signal to noise ratio (SNR). In this paper we propose a method that will facilitate the concept of online processing by providing an efficient filtering implementation in a hardware friendly environment by switching to finite impulse response (FIR). Main focus of this research is to minimize latency and computational delay of preprocessing related to any BCI application. Four different finite impulse response (FIR) implementations along with large Laplacian filter are implemented in Xilinx System Generator. Efficiency of 25% is achieved in terms of reduced number of coefficients and multiplications which in turn reduce computational delays accordingly.

This paper presents the design of a stair climbing fixed flipper unmanned ground vehicle (UGV) for urban search and rescue purposes. Mobile flippers are being used in certain UGVs for enhanced mobility in rough terrains, however, the control algorithm of these platforms is complex. To add this enhanced mobility in the UGV and to reduce the intricacy of the control algorithm, anterior end of the tracks are lifted up which enables the UGV to pass over obstacles with relative ease. To prevent the rollover of UGV while moving on an inclined surface, an image processing algorithm was developed which halts the motion of UGV if the calculated slope exceeds the threshold value with a maximum error of about 8%. Furthermore, left and right track velocities along with the turn radius were also calculated.

This paper presents an application of a novel approach for detecting and tracking an object with a 2 DOF robotic manipulator which can be equipped with an array of electrically controlled actuators. The said approach utilizes the Image Based Visual Servoing (IBVS) technique. The developed system is able to determine the object pose in real time from features in the image. Object is detected using shaped based approach algorithms of image processing. The position and orientation of the world coordinates of the object being tracked are calculated from the coordinates of the object in image plane using camera's intrinsic and extrinsic parameters. Experimental results demonstrate the effectiveness of this proposed approach.

Understanding and learning from hospital deaths is an important component of good clinical practice but current approaches and measures are complex, controversial and difficult to understand. Patients who die are not a homogeneous group but fall into three distinct categories; most learning will be achieved by recognising this and investigating categories of deaths in different ways, relying heavily on qualitative approaches. Numerical measures of overall hospital mortality, such as hospital standardised mortality ratio (HSMR) or measures of 'preventable' deaths, are most unlikely to be helpful at a hospital level and may even give false reassurance, as accuracy of measurement is strongly influenced by factors apart from quality of care.

Recent advancements in brain computer-interfacing (BCI) and neuro-robotics have played an indispensable role for people suffering from neural injuries to expect better quality of life by restoring sensory functions and replacement of neuro-muscular pathways as BCI systems work on imagination of movements to control prosthetic limbs. In this research, multiple combinations of features and classifiers have been used to classify electroencephalographic (EEG) signals in order to acquire maximum classification accuracy for a four class EEG based BCI system. Knee and ankle joint movements are executed in a predefined manner during which EEG signals are acquired from sensorimotor cortex of four healthy test subjects, using Emotiv headset. After removing artifacts, five features; average band power, peak, kurtosis, mean and skewness are calculated. Multiple combinations of calculated features are used to classify ankle dorsi-planter flexion and knee extension-flexion movements using five different classifiers; Naive Bayes, A-nearest neighbor (ANN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). It was found that features set comprising; average band power, peak and mean value yielded significantly higher classification accuracy of 81.31% among all other combinations whereas in classifiers, maximum accuracy of 81.48% is obtained through LDA as compared to other classifiers. These novel findings clearly exhibit the feasibility of achieving good classification accuracies using average band power, mean and peak values of EEG signals as feature vectors and LDA as classifier.

In lower limb prosthetics, pistoning and increased volume change of residual limb inside the socket is the main problem. To overcome this problem, a vacuum suspension system has been developed to hold the limb firmly into the socket. This paper aims at the design of the suction geometry made of neoprene rubber to create a vacuum forming system for lower limb prosthetics. The designed geometry was integrated with bionic foot for below knee amputation. The proposed design was modeled using SOLIDWORKS 14 and analyzed by using finite element method (FEM) in ANSYS 14.5. The deformed geometry was further used to calculate the change in internal volume which gives the volume of air evacuated during each step. The value of required pressure to create a vacuum in the socket is −69 KPa (508 mmHg). Further calculations were made to find out the steps to achieve such pressure.

With the evolution of broadband mobile networks towards LTE and beyond, the support for the internet and internet based services is growing. However, the size and operational costs of mobile networks are also growing. Self Organizing Networks (SON) are introduced as a part of the specifications of the LTE standard with the purpose of reducing the Operation and Maintenance costs of the mobile networks. This paper introduces a novel framework for automated Radio Resource Management (RRM) in LTE SON. This framework deals with the self-optimization and self-healing features of SON. The data mining technique of linear regression has been used to derive the functional relationship, known as model, between Key Performance Indicators and RRM parameters. The proposed framework uses this model in two ways: first, for network monitoring, which is the first step of the self-healing procedure and secondly, to devise a handover auto-tuning algorithm as part of the self-optimization procedure. The detailed results obtained for the finished case studies, demonstrate the effectiveness and usefulness of this approach.

Transtibial amputation may be caused by a disease or an accident. The rehabilitation for this type of amputation is to replace the amputated part by a prosthetic limb. Most of the commercially available DER (Dynamic Energy return) prosthetic feet contain soft components, that store and release energy. In contrast, the proposed model of prosthetic foot stores and release energy by means of material properties and geometry. Traditionally, the performance analysis of prosthetic feet required the existence of prototype. However, latest numerical techniques allow analysis, during the design stage. ISO-22675 has proposed experimental setup and standard for the analysis of any prosthetic foot. In this work, instead of using experimental setup and physical prototype, the proposed foot has been modeled, analyzed, and optimized using FEM (finite element method) as per the boundary conditions specified by ISO-22675, during the design process. The aim of his paper is to develop a low-cost, fixed prosthetic lower limb, DER foot, that is within the buying power of lower income class and its performance and features are as per ISO standards. The foot has been designed using displacement and stiffness parameters of Niagara FootTM and analyzed as per ISO Protocol 22675 using FEM analysis.

Any device which senses information such as shape, texture, softness, temperature, vibration or shear and normal forces, by physical contact or touch, can be termed a tactile sensor. The importance of tactile sensor technology was recognized in the 1980s, along with a realization of the importance of computers and robotics. Despite this awareness, tactile sensors failed to be strongly adopted in industrial or consumer markets. In this paper, previous expectations of tactile sensors have been reviewed and the reasons for their failure to meet these expectations are discussed. The evolution of different tactile transduction principles, state of art designs and fabrication methods, and their pros and cons, are analyzed. From current development trends, new application areas for tactile sensors have been proposed. Literature from the last few decades has been revisited, and areas which are not appropriate for the use of tactile sensors have been identified. Similarly, the challenges that this technology needs to overcome in order to find its place in the market have been highlighted.

This paper presents design, simulation and fabrication of a surface electromyographic (SEMG) sensor for control of prosthetic devices. EMG activity is mainly the generation of a bio-potential signal (electrical signals) due to muscle action. These signals picked from motor points of muscles are contaminated with various intrinsic/extrinsic noises, which must be removed through different filtering techniques in order to develop a sensor that has a high signal-to-noise ratio. Power spectral density (PSD) of any EMG signal plays a vital role to determine the signal strength. A novel Simulink model has been developed which mimics various elements of an active SEMG sensor. By using this model, low/high/notch filters are designed and optimized. The model is also used to simulate the effects of these filters on power spectral density (PSD) of the EMG signal. Simulation of double rectification and smoothing (envelopment) is also carried out. EMG signals recorded from the Tibialis anterior, monopolar needle, and fine wire isometric contraction were used in this simulation. Finally, on the basis of simulation results, instrumentation of surface EMG sensor is designed and fabricated. Performance/results of developed SEMG sensor are in accordance with the simulation results of the developed Simulink model.

Cure sometimes, treat often and comfort always – Hippocrates As clinicians, we are in a uniquely privileged position; we have passive admittance to the most private aspects of human life and often find ourselves silent observers of death. Sometimes we may be left feeling powerless and that maybe we have failed, given the expectations of modern medicine. In this issue of the Future Healthcare Journal we explore the complexities of creating sustainably resourced end-of-life and palliative care services. These require a foundation of specialist education and skills to empower clinicians, not …

Below-knee amputation is one of the most critical types of amputations related to gait disability. A very effective therapy is to replace the amputated part by a robotic prosthetic lower limb. This limb consists of different design parameters with most important and crucial being its actuator. Since the power transmission to the foot and movement control is directed through the actuator, it is very important to use an actuator which fulfills all the vital parameters required for the prosthesis to work effectively i.e. weight, torque and speed. Conventional actuators available on the market do not control all these parameters. The purpose of this paper is the development of an effective actuator design, for assisting robotic prostheses that can be used by amputated people in the everyday environment to obtain natural gait. The presented design has been validated using finite element analysis (FEA).

This paper discusses the development of a customizable FPGA based system for implementing control algorithms on an Unmanned Ground Vehicle (UGV) and its 5 Degree of Freedom (DOF) manipulator. The compact RIO-9012 is used as a controller which is a reconfigurable embedded control and acquisition system using LabVIEW as the programming platform. The developed system enables the control of UGV and its manipulator using a remote joystick controller via Wi-Fi communication. Apart from Joystick, the system can also be controlled optionally using a keyboard. Accuracy of Joystick control has been enhanced by using point to point mapping technique. A user friendly GUI has been developed to view live video feedback obtained from the onboard cameras to control the UGV accordingly. Different features of UGV like path tracker (tracks its path on Google Maps), variable speed modes, battery indicator, camera switch and selector etc. are also managed in the GUI. The system has been developed so that, in future, it can easily be extended to a fully autonomous system.

This paper presents our approach to staircase detection and perception using monoscopic vision. The simulation of the proposed algorithm is conducted on a 4 degree of freedom teleoperated vehicle. The presented solution can detect a staircase and calculate its angle of inclination thus aiding the operator in setting up controls for the vehicle. The approach relies on multiple 5×5 masks to actively predict staircase from the front then by a combination of blob detecting algorithms predict the angle of inclination. One of the challenges was to make the algorithm efficient so that it can be implemented on low power embedded systems. Results of simulations on various acquired images have been discussed within the contents of the paper. The results have been validated by monitoring values over multiple & different staircases. MATLAB's image processing toolbox has been used to conduct simulations of the algorithm.

Electroencephalographic (EEG) signals from test subjects are used for BCI analysis and classification of the four upper limb movements. Brain computer interface (BCI) system provides better neural control to the user, over two antebrachium and two carpus movement. This paper uses Epoc Emotiv head set with fourteen electrodes to acquire signal from the motor area of scalp. A particular protocol for data acquisition is followed. Our method is based on the time domain feature extraction techniques. Activity feature is applied on the signals. In order to discriminate between four upper limb movements within the data set, the paper uses Quadratic support vector machine, RBF SVM and Multilayer perceptron. The best classification accuracy of two antebrachium and two carpus movement is achieved by Quadratic SVM and RBF SVM when Gaussian window is used to calculate the activity feature vector. The number of correctly classified instants in term of percentage is 76.92% and 75.96% for quadratic SVM and RBF SVM. Current classifiers show promising results, where as to make conclusion more generalized and to enhance the classification accuracy, further experiments need to be carried out.

Once a highly competitive specialty, competition rates (CR) for core medical training (CMT) have been falling since 2013. Based on CR (number of applicants received per advertised post), CMT ranked 14th out of 16 specialties in 2016. We sought to further understand and provide evidence for the endemic roots of low morale among all grades of junior doctors.

In this paper a novel shear sensor design is described. The proposed design is able to measure static shear forces. The sensor is fabricated on a printed circuit board and uses a differential capacitor arrangement to detect shear force applied to the sensor surface. Both mathematical and COMSOL models have been developed for the described sensor. The sensor is intended for use in robotic hands for the detection of shear force at the tactile interface. The device can be fabricated at a low cost in both low and high volumes. Prototype sensors with a +/-0.525 mm displacement range were fabricated. Fixed displacements (over the range +/-0.5 mm) are applied to the capacitor common plate, without a silicone covering, and a range of shear forces (over the range +/-4 N) applied to the sensor, once covered with a silicone skin, and the differential capacitance of the transducer is recorded. The maximum standard deviation of the differential capacitance across all force values is 1.35e-15 F. The maximum standard deviation, at each force value, across a range of +/-2 N is 4.28e-16 F.

class="Abstract">This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A non-random and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature -the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10 -1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size. DOI: http://dx.doi.org/10.5755/j01.eee.22.1.14114