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Ultraviolet-C sourced LED (UVC-LED) has been widely used for disinfection purposes due to its germicidal spectrum. In this study, the efficiencies of UVC-LED for Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) disinfections were investigated at three exposure distances (1, 1.5, and 2 cm) and two exposure times (30 and 60 s). The respective bacterial inhibition zones were measured, followed by a morphological analysis under SEM. The viabilities of human skin fibroblast cells were further evaluated under the treatment of UVC-LED with the adoption of aforesaid exposure parameters. The inhibition zones were increased with the increment of exposure distances and times. The highest records of 5.40 ± 0.10 cm P. aeruginosa inhibition and 5.43 ± 0.11 cm S. aureus inhibition were observed at the UVC-LED distance of 2 cm and 60-s exposure. Bacterial physical damage with debris formation and reduction in size were visualized following the UVC-LED exposures. The cell viability percentages were in a range of 75.20-99.00% and 82-100.00% for the 30- and 60-s exposures, respectively. Thus, UVC-LED with 275-nm wavelength is capable in providing bacterial disinfection while maintaining accountable cell viability which is suitable to be adopted in wound treatment. Bacterial disinfection and human skin fibroblast cell assessment using UVC-LED.

Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a locked-in state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain-Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements.

Lie detection has been studied since a few decades ago, usually for the purpose of producing a scheme to assist in the investigation of identifying the culprit from a list of suspects. Heart Rate Variability (HRV) may be used as a method in lie detection due to its versatility and suitability. However, since its analysis is not instantaneous, a new experiment is described in this paper to overcome the problem. Additionally, a preliminary HRV classification model is designed to further enhance the classification model which is able to distinguish the lie from the truth for up to 80%.

The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automatic diagnosis convolutional neural network (CNN) model which does not require a large training dataset to specifically identify diabetic retinopathy symptoms, which are cotton wool, exudates spots, and red lesion in colour fundus pictures. A novel framework comprised image enhancement method by using upgraded contrast limited adaptive histogram equalization (UCLAHE) filter and transferred pre-trained networks was developed to classify the retinal diseases regarding to the symptoms. The performance of the proposed framework was evaluated based on accuracy, sensitivity, and specificity metrics. The collected results have proven the robustness of the proposed framework in offering good accuracy in retina diseases diagnosis.

The rapid use of ultraviolet light emitting diodes (UV-LEDs) in various disinfection applications is growing tremendously due to their advantages unachievable using UV lamps. In this study, a comparison of standard LED at 460 nm wavelength and UVA LED at 385 nm was conducted to determine their effectiveness in disinfection of frequently isolated pathogens in hospitals (Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). Determination of disinfection efficiency was carried out by measuring inhibition zone. Effects of varied exposure time on the inactivation of pathogenic microorganisms was studied. The results demonstrated that LED does not have germicidal activities. The highest inactivation for UVA LED was achieved for Pseudomonas aeruginosa. Linear relationship was found between exposure time and log reduction. This study showed that UVA LEDs can effectively inactivate significantly higher number of microorganisms hence can be used in disinfection of various applications.

Type 2 diabetes mellitus (T2DM) is a multifactorial disease induced by an intricated interaction of several genetic variants and multiples environmental factors. A total of 281 patients diagnosed with T2DM and 281 non-diabetic control were enrolled in this study. Three SNPs (rs3761547, rs3761549, and rs2232365) in the FOXP3 gene were selected to study their role in the development of T2DM and T2DM-associated hypertension in an Iraqi population. The DNA extraction and genotyping were performed using allele-specific PCR. Our results showed that 212 patients were positive for a family history of T2DM, and 69 patients were negative (P

UV light has become an integral part of human life especially in performing wide range of disinfection. Most of the research on UVLEDs is limited to UVC region because of comparison with mercury based UV lamps which work typically at 254 nm. Limited research is found on the use of UVA-LEDs for inactivation of microorganisms in healthcare. In this study a standard 3 mm LED has been compared with 385 nm UVA-LED for inactivation of Escherichia coli. E. coli strains were swabbed on control, LED and UVA-LED petri dishes using cotton bud. The LED and UVA-LED samples were exposed to standard LED light and UVA light respectively for 1 h. The analysis of bacteria by determining Colony forming units (CFU) and log inactivation were carried out to calculate the number of colonies present in each sample. Result showed negligible to none disinfection properties in standard LED light. LED samples had CFU/ml colonies compared to control which is CFU/ml. UVA-LED samples achieved maximum inactivation and only had CFU/ml. Log inactivation results showed that LED samples observed 0.1-log inactivation whereas the UVA-LED had significant inactivation of 3.8-log inactivation corresponding to approximately 99.99 % E. coli reduction. The results demonstrate that UVA-LED at 385 nm is capable of efficiently providing inactivation of bacteria E. coli.

Magnetic resonance imaging is an important modality in the diagnosis and pathology detection. Edge detection is used for image segmentation and feature extraction as part of the medical image analysis. There is no ideal and universal algorithm which performs perfectly under all conditions. Conventional Canny edge detector is not suitable to be used in Magnetic resonance images that contaminated by Rician noise. In this paper, we propose the use of customized non-local means into the Canny edge detector instead of Gaussian smoothing in the conventional Canny edge detector to effectively remove Rician noise while preserving edges in Magnetic resonance image of an internal organ. The result shows that our method can yield better edge detection than conventional method, with minimal false edge detection. The proposed method undergoes several attempts of parameter adjustment to detect true edges successfully using optimal parameter setting.

This paper presents to develop a system of wireless power transfer for medical applications.Many surgeons use the bipolar electrosurgery system that runs on a battery for a different kind of surgery. This venture is intended to control a rechargeable cordless remote which is used in operation room/theatre. This venture is framed out from Alternating Current(AC) of 220 Volt(V) 50 Hz to 12V Direct Current(DC) then converted to 12V AC at 20 kHz in the circuit. DC battery is provided in an electrode of the 12 V bipolar electrosurgery system and the arrangement for charging battery is also prudently/properly made. The new design is an improvement based on specialists' complain regarding the visibility of wire/eletrical line of standard electrocautery instrument Theose wire always put them at risk of being tangled on the searing instrument which can burn the hand. Not only that, it also restrict their hands movement from performing surgery. The electrical wire is a weakness/disadvantage as it requires an observation of eletrical wire from the hand position to guarantee that it stays sterile amid surgical operations.

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support vector machine and radial basis function method.

Having a systemic system in recognizing activity in sports is very essential along with enhancing the performance analysis in sport. As the system is required to provide a quality, reliable and unbiased notational data for determining the strength and weakness of field hockey players. Therefore, this study is analysing the accelerometer and gyroscope signal on of the four inertial sensors attached to the upper body chest, waist, right and left wrist and formulate the best model in using the wearable sensor for human activity recognition in the field hockey which are passing, drive, drag flick, dribbling, receiving and tackling. Set of features such as mean, standard deviation, maximum and minimum peak are extracted from each inertial sensor signal as an input vector for classification purpose. Results from the study shows that the recognition using combination of all four sensors achieved the highest performance of 96.7% accuracy; and waist and left wrist is recommended if single sensor based human activity recognition is preferred.

A cardiac circumstance affected through irregular electrical action of the heart is called an arrhythmia. A noninvasive method called Electrocardiogram (ECG) is used to diagnosis arrhythmias or irregularities of the heart. The difficulty encountered by doctors in the analysis of heartbeat irregularities id due to the non-stationary of ECG signal, the existence of noise and the abnormality of the heartbeat. The computer-assisted study of ECG signal supports doctors to diagnoses diseases of cardiovascular. The major limitations of all the ECG signal analysis of arrhythmia detection are because to the non-stationary behavior of the ECG signals and unobserved information existent in the ECG signals. In addition, detection based on Extreme learning machine (ELM) has become a common technique in machine learning. However, it easily suffers from overfitting. This paper proposes a hybrid classification technique using Bayesian and Extreme Learning Machine (B-ELM) technique for heartbeat recognition of arrhythmia detection AD. The proposed technique is capable of detecting arrhythmia classes with a maximum accuracy of (98.09%) and less computational time about 2.5s.

Using found data as training data in statistical parametric speech synthesis can alleviate various problems in tedious database construction. However, the extra silences resided in found data degrades the quality of synthetic speech. Therefore, in this study, silence cutter was created to eliminate the extra silences in the training data. The motivation is the extra silences would be incorrectly assigned to training script and result in unnatural synthetic speech. Therefore, in this study, a Malay speech synthesis system has been constructed using found data from internet. Silence cutter has been utilized to cut out extra silences. The synthetic speech using found data with and without silence cutter was verified and compared to find out the effect of silence cutter. Result showed that silence cutter has help to improve synthetic speech naturalness and reduce the Word Error Rate (WER) in intelligibility test. In short, using found data can alleviate the problem of preparing high quality training data and silence cutter can be used to refine the found data to generate better quality of synthetic speech.