Development of Embedded System for Centralized Insomnia System

Insomnia is a common health problem in medical field as well as in psychiatry. The measurement of those factors could be collected by using polysomnography as one of the current standards. However, due to the routine of clinical assessment, the polysomnography is impractical and limited to be used in certain place. The rapid progress of electronic sensors to support IoT in health telemonitoring should provide the real time diagnosis of patient at home too. In this research, the development of centralized insomnia system for recording and analysis of patient with chronic-insomnia data is proposed. The system is composed from multi body sensors that connected to main IOT server. The test has been done for 5 patients and the result has been successfully retrieved in real time

Spesh Enterprise / Zainol Abidin Salleh... [et al.]

Spesh Enterpise is a company that provide and selling sport equipments. It was started on 1 January 2005.This company was located at Star Parade, Alor Setar. The business was selling sport equipment like shoes, T-shirt, Track bottom and others. It was a partnership business that include four members

Insomnia analysis based on internet of things using electrocardiography and electromyography

Insomnia is a disorder to start, maintain, and wake up from sleep, has many sufferers in the world. For patients in remote locations who suffer from insomnia, which requires testing, the gold standard performed requires patients to take the time and travel to the health care center. By making alternatives to remote sleep insomnia testing using electrocardiography and electromyography connected to the internet of things can solve the problem of patients' access to treatment. Delivery of patient data to the server is done to make observations from the visualization of patient data in real-time. Furthermore, using artificial neural networks was used to classify EMG, ECG, and combine patient data to determine patients who have Insomnia get resulted in patient classification errors around 0.2% to 2.7%

Digital Medical Images Segmentation by Active Contour Model based on the Signed Pressure Force Function

The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM demonstrates effectiveness in image segmentation. However, it may fail if the targeted object is close to a neighbouring object. Additionally, the presence of intensity inhomogeneity and noise in medical images degrades segmentation accuracy and local target areas. Thus, we proposed a new SPF-based ACM, namely the Selective Segmentation with Signed Pressure Force 1 (SSPF1) model, by incorporating the ideas of the SPF function and the distance fitting term based on geometrical constraints. Then, the new SSPF1 model was extended by incorporating an image enhancement technique to develop our second new model, termed the Selective Segmentation with Signed Pressure Force 2 (SSPF2). Numerical results indicated that the SSPF2 model was more recommended than SSPF1 as the SSPF2 model was approximately 4.7% more accurate, as indicated by the Jaccard value and was about 112 times faster in segmenting noisy images compared to the existing selective segmentation mode

Alteration in the functional organization of the default mode network following closed non-severe traumatic brain injury

The debilitating effect of traumatic brain injury (TBI) extends years after the initial injury and hampers the recovery process and quality of life. In this study, we explore the functional reorganization of the default mode network (DMN) of those affected with non-severe TBI. Traumatic brain injury (TBI) is a wide-spectrum disease that has heterogeneous effects on its victims and impacts everyday functioning. The functional disruption of the default mode network (DMN) after TBI has been established, but its link to causal effective connectivity remains to be explored. This study investigated the differences in the DMN between healthy participants and mild and moderate TBI, in terms of functional and effective connectivity using resting-state functional magnetic resonance imaging (fMRI). Nineteen non-severe TBI (mean age 30.84 ± 14.56) and twenty-two healthy (HC; mean age 27.23 ± 6.32) participants were recruited for this study. Resting-state fMRI data were obtained at the subacute phase (mean days 40.63 ± 10.14) and analyzed for functional activation and connectivity, independent component analysis, and effective connectivity within and between the DMN. Neuropsychological tests were also performed to assess the cognitive and memory domains. Compared to the HC, the TBI group exhibited lower activation in the thalamus, as well as significant functional hypoconnectivity between DMN and LN. Within the DMN nodes, decreased activations were detected in the left inferior parietal lobule, precuneus, and right superior frontal gyrus. Altered effective connectivities were also observed in the TBI group and were linked to the diminished activation in the left parietal region and precuneus. With regard to intra-DMN connectivity within the TBI group, positive correlations were found in verbal and visual memory with the language network, while a negative correlation was found in the cognitive domain with the visual network. Our results suggested that aberrant activities and functional connectivities within the DMN and with other RSNs were accompanied by the altered effective connectivities in the TBI group. These alterations were associated with impaired cognitive and memory domains in the TBI group, in particular within the language domain. These findings may provide insight for future TBI observational and interventional research

Determining Potassium Nitrate performance as solid rocket propellant for rocket engine

Research and development on solid rocket propellant were carried out extensively on the whole process of production and performance testing. Solid rocket propellant was produced by using potassium nitrate as oxidiser and sucrose as organic fuel. Press- moulding method was selected to produce the propellant. Based on experiments, it was found that the compressed moulding method gives more uniform and stable propellant compared to the other method of preparation. Propellant burning rate test and static thrust test were carried out in order to determine the characteristic of solid propellant. Burning rate test was conducted on the standard strand burner test rig under atmospheric pressure. The effect of oxidizer and fuel composition on burning rate was studied. It was found that the burning rate was influenced by oxidizer-fuel (O/F) ratio. There are several characteristic parameters of solid propellant that could be found from the static thrust test. The thrust produced by the rocket motor was measured directly from static thrust test. Based on the propellant with O/F ratio of 65/35, the thrust produced was more than 300 N with total impulse of 150.04 Ns and specific impulse of 154.8 s. From experiments, it was found that the potassium nitrate based solid propellant has good characteristics and has the potential to be develop further as propulsion system for a launch vehicle

Geochemical contrast between Beroga (Kuala Kelawang pluton) and Semenyih (Kuala Lumpur pluton) granites in Kuala Kelawang area, Negeri Sembilan

Geochemical data of both Semenyih and Beroga granites, in the central part of Main Range Granite, show that they are madeup of different magma pulses. The Beroga granite is more basic, containing 67.22 - 68.02% SiO2, whereas the Semenyih granite consists of more evolved rock (SiO2 : 70.36 to 73.48%). Both are S-type granite which suggests that the source rocks were metasediments. Geochemically, they are part of the Main Range Granite. The Semenyih granite is charcterised by high Rb/Sr ratio, which suggests that the magma is highly evolved and contains less enclaves compared to the Beroga granite