Power Quality Signals Classification System Using Time-Frequency Distribution

Difussion-Weighted Imaging (DWI) plays an important role in the diagnosis of brain stroke by providing detailed information regarding the soft tissue contrast in the brain organ. Conventionally, the differential diagnosis of brain stroke lesions is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). The type of stroke lesions consists of acute ischemic, sub-acute ischemic, chronic ischemic and acute hemorrhage. For segmentation, fuzzy c-Means (FCM) and active contour is proposed to segment the lesion’s region. FCM is implemented with active contour to separate the cerebral spinal fluid (CSF) with the hypointense lesion. Pre-processing is applied to the DWI for image normalization, background removal and image enhancement. The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). The average results for the Jaccard index, DC, FPR and FNR are 0.55, 0.68, 0.23 and 0.23, respectively. First statistical order method is applied to the segmentation result to obtain the features for the classifier input. For classification technique, bagged tree classifier is proposed to classify the type of stroke. The accuracy results for the classification is 90.8%. Based on the results, the proposed technique has potential to segment and classify brain stroke lesion from DWI images.

Section: Active Contourmentioning

confidence: 99%

Automated segmentation and classification technique for brain stroke

Noor¹,

Saad²,

Abdullah³

et al. 2019

“…For instance, the proper operation of microcontrollers, depended on the voltage quality supplied to them [14]- [18]. Battery power is used to supply power to the trolley.…”

Section: Battery Powermentioning

confidence: 99%

Automated medical surgical trolley

Saad¹,

Abdullah²,

Noor³

et al. 2019

Operating theatre is a place in a hospital where surgical operations are conducted on patients by surgeons. In the operating theatre, the surgical equipment is placed on stainless steel table or on surgical instrument tray. However, during the operation accidents can occur where the surgical tools placed near to the surgeon could be accidentally be hit by them during the surgical operation. This may cause the surgical tools to fall on the floor which may lead to injuries. Hence, this paper presents an automatic medical surgical trolley for surgeons to grab operating tools easily. The proposed system is implemented for automaticmedical surgical trolley movement using Arduino Uno R3. The invention provides an automatic medical surgical trolley which comprises automatic guidance, a wireless controller, an obstacle avoiding detection device, a touch screen controller via smart phone, an IP camera, a trolley, an integrated power supply and a processor. The trolley with stainless steel shelves is ideal for use in clinical environments and operation theatres. Medical equipment is loaded in the trolley, the wireless remote drives the trolley to move forwards and backwards. Automatic visual guidance is achieved via an IP camera attached to the trolley and a touch screen controller via a smart phone. A large amount of space and a large number of materials are saved, the workload of medical workers will be greatly relieved, and the working efficiency will be improved.

“…Meanwhile, the definition of voltage swell can be described as an increase of 110 to 180 % in rms of the nominal voltage for the duration of 0.5 cycles to a minute [3,4]. Interruption is defined as the complete loss or decreases to less than 0.1 pu of supply voltage for a period not exceeding 1 minute [5][6][7]. Although interruptions are more severe than voltage sag and swell, the financial losses due to voltage sag and swell are comparatively the largest [8,9].…”

Section: Introductionmentioning

confidence: 99%

An improved smooth-windowed Wigner-Ville distribution analysis for voltage variation signal

Manap

Abdullah

Nikolovski

et al. 2020

This paper outlines research conducted using bilinear time-frequency distribution (TFD), a smooth-windowed wigner-ville distribution (SWWVD) used to represent time-varying signals in time-frequency representation (TFR). Good time and frequency resolutions offer superiority in SWWVD to analyze voltage variation signals that consist of variations in magnitude. The separable kernel parameters are estimated from the signal in order to get an accurate TFR. The TFR for various kernel parameters is compared by a set of performance measures. The evaluation shows that different kernel settings are required for different signal parameters. Verification of the TFD that operated at optimal kernel parameters is then conducted. SWWVD exhibits a good performance of TFR which gives high peak-to-side lobe ratio (PSLR) and signal-to-cross-terms ratio (SCR) accompanied by low main-lobe width (MLW) and absolute percentage error (APE). This proved that the technique is appropriate for voltage variation signal analysis and it essential for development in an advanced embedded system.