Hasballah Zakaria

While public concern regarding smartphone electromagnetic field (EMF) exposure is largely focused on radio frequency (RF) emissions, this study investigates the overlooked extremely low frequency (ELF) magnetic fields originating from internal hardware circuits, such as the Power Management Integrated Circuit (PMIC). This research employs a quantitative experimental methodology to characterize and compare the near-field emissions of two smartphone models with distinct internal architectures: the Xiaomi Redmi Note 8 Pro (12nm mid-range chipset) and the Samsung Galaxy S23 (4nm premium chipset). Magnetic field intensity measurements were conducted using a Hall-effect Gaussmeter, both in free space and with a 3D-printed cubic head phantom fabricated from PETG and filled with a conductive saline-based tissue-simulating liquid (TSL). The primary findings reveal unique ELF emission "fingerprints," where the premium-engineered device exhibits a peak exposure of 0.269 mT—nearly three times lower than its mid-range counterpart at 0.799 mT. Theoretical analysis utilizing the Biot-Savart Law attributes this reduction to the minimized current loop areas inherent in advanced 4nm process nodes compared to older 12nm architectures. Quantitative analysis of mitigation strategies demonstrates that spatial separation (a 15 cm distance) is the most dominant factor, achieving up to 90.7% attenuation, which surpasses the material shielding provided by the phantom (82.0%). Although peak contact exposure can exceed the ICNIRP reference level, the rapid near-field decay ensures compliance at minimal practical distances. This study concludes that ELF exposure is a function of engineering quality rather than network technology, and mitigation is most effectively achieved through physical distance.

Dementia is a term that represents a set of symptoms that affect the ability of the brain’s cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer’s is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient’s quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients.

One common type of vascular dementia (VaD) is poststroke dementia (PSD). Vascular dementia can occur in one-third of stroke patients. The worsening of cognitive function can occur quickly if not detected and treated early. One of the potential medical modalities for observing this disorder by considering costs and safety factors is electroencephalogram (EEG). It is thought that there are differences in the spectral dynamics of the EEG signal between the normal group and stroke patients with cognitive impairment so that it can be used in detection. Therefore, this study proposes an EEG signal characterization method using EEG spectral power complexity measurements to obtain features of poststroke patients with cognitive impairment and normal subjects. Working memory EEGs were collected and analyzed from forty-two participants, consisting of sixteen normal subjects, fifteen poststroke patients with mild cognitive impairment, and eleven poststroke patients with dementia. From the analysis results, it was found that there were differences in the dynamics of the power spectral in each group, where the spectral power of the cognitively impaired group was more regular than the normal group. Notably, (1) significant differences in spectral entropy (SpecEn) with a value Dem < SpecEn_MCI < SpecEn_Normal), and (3) a post hoc multiple comparison test showed significant differences between groups at the F7 electrode. This study shows that spectral complexity analysis can discriminate between normal and poststroke patients with cognitive impairment. For further studies, it is necessary to simulate performance validation so that the proposed approach can be used in the early detection of poststroke dementia and monitoring the development of dementia.

Post-stroke dementia (PSD) is a type of vascular dementia (VaD) that might be occurred in post-stroke patients. Memory, language and behavior tests can be used for the analysis of cognitive impairment caused by PSD. Often a supporting clinical examination such as an electroencephalogram (EEG) is used to support the diagnosis or analyze the characteristic changes that occur in the brain. Conventional analysis or visual inspection of EEG signals can be very difficult, since the nature of the signal tends to be non-stationer. Therefore, this study proposes a quantitative analysis for the characterization of EEG signals in stroke survivors with dementia. It is thought that it has different characteristics with the normal subject so that this study can be used as a reference in supporting dementia detection in post-stroke survivors. The quantitative analysis used in this study is coherence analysis. Coherence analysis was performed on EEG signals recorded from six poststroke patients with dementia and then compared with ten normal healthy subjects. Analysis of coherence between brain areas includes inter and intra-hemispheric coherence. Validation was carried out by using the independent t-test where the confidence level was 95%, indicating that the p-value <0.05 had a significant difference. The test results show that in general the coherence of the electrode pairs in patients with dementia is lower than in the normal healthy group. It is notably, i) In interhemispheric, the C3-C4, T3-T4, and T5-T6 pairs generate significant differences, ii) the highest decrease in intrahemispheric coherence was found in C3-T5 with p = 0.0005. The coherence study presented in this paper is expected to be used for early detection of PSD in the future.

Centella asiatica (CA) has been reported to have a protective effect on cardiovascular disease and cardiovascular disease-related conditions. Frontal QRS-T angle is a non-invasive biomarker that can predict cardiovascular mortality and morbidity in all causes of death. This study aimed to establish the protective effect of CA in animal models of me

Atherosclerosis is a condition indicated by loss of arterial elasticity because of thickening of the arterial wall due to plaques that cause cardiovascular disease. Increased arterial stiffness is one sign of atherosclerosis, so measurement of arterial stiffness can be used as an early detection of cardiovascular disease. One method of measuring arterial stiffness is by analyzing the second derivative of a photoplethysmography (PPG) signal taken from a non-invasive fingertip. PPG signals are obtained from infra red signal transmission to detect changes in blood volume in microvascular vessels. By calculating the delay between direct waves and reflection waves from PPG signals and also from taking the subject's height can be obtained arterial stiffness index. PPG data collection was carried out on 30 subjects, with 10 subjects normal and 10 subjects were cardiac catheterization patients and 10 subjects were athlete. The stiffness index value for cardiac catheterization patients increased compared to athlete and normal subjects. Increased arterial stiffness is one sign of atherosclerosis, so measurement of arterial stiffness can be used as an early detection of cardiovascular disease.

Runway edge light is a runway lighting indicator which provides guidance to pilots on nightfall or bad weather, thereby a regular intensity measurement is required to ensure safety of flights in airport based on aviation regulation. Most instruments that are currently available used ground vehicle to conduct photometry which has a high dependence on airport infrastructure conditions. This paper proposed a new photometric method using an instrument carried by a drone thus lowering its dependency on airport infrastructures conditions. The instrument utilized a photodiode array consisting of an aluminum bar with several photodiode module sensors separated apart with the same distance. To prove the concept the instrument was used to measure a Thorn EL-EAH runway edge light in laboratory setting at Balai Teknik Penerbangan Indonesia (Indonesia Aviation Engineering Center). The vertical scanning method was used to obtain the required measurement points. Computer program was then used to generate the isocandela diagram through the Kriging interpolation. Results showed that the vertical scanning method was successfully measure all required points and the resulted isocandela diagram was adequate to aviation regulation.

Hemoglobin is important part of red blood cell to transport oxygen and carbon dioxide. Hemoglobin concentration in the blood can be used as physical condition parameter. A low hemoglobin level is called anemia and high hemoglobin level is called polycythemia. WHO has determined the anemia cut off level of hemoglobin concentration based on age, sex, and condition (pregnant or not). Currently, accurate and reliable hemoglobin concentration measurement uses invasive methods such as cyanmethemoglobin and automated hematology analyzer. But these methods are expensive, not real time, high infection risk, and need special techniques. Non-invasive methods offer a better alternative because it has low infection risk, instant result, and portable in size. This work developed a non-invasive hemoglobin measurement for anemia diagnosis based on optical spectroscopy. The system utilized LED and photodiode as optical sensor placed on the fingertip. Photodiode just could obtain DC component, so the signal conditioning circuit which consisted of HPF, LPF and amplifier was used to obtain the AC component of the signal. This system used microcontroller to control the operation of the hardware and to calculate the hemoglobin concentration.Hemoglobin is important part of red blood cell to transport oxygen and carbon dioxide. Hemoglobin concentration in the blood can be used as physical condition parameter. A low hemoglobin level is called anemia and high hemoglobin level is called polycythemia. WHO has determined the anemia cut off level of hemoglobin concentration based on age, sex, and condition (pregnant or not). Currently, accurate and reliable hemoglobin concentration measurement uses invasive methods such as cyanmethemoglobin and  automated  hematology analyzer. But these methods are expensive, not real time, high infection risk, and need special techniques. Non-invasive methods offer a better alternative because it has low infection risk, instant result, and portable in size. This work developed a non-invasive hemoglobin measurement for anemia diagnosis based on optical spectroscopy. The system utilized LED and photodiode as optical sensor placed on the fingertip. Photodiode just could obtain DC component, so the signal conditioning circuit which consisted of HPF, LPF and amplifier was used to obtain the AC component of the signal. This system used microcontroller to control the operation of the hardware and to calculate the hemoglobin concentration.

Atherosclerosis, hardening of the arteries, causes heart attacks and strokes that are recorded as the world's number one killer disease. Nowadays, research on atherosclerosis is still limited because the modeling of atherosclerotic animals is difficult to do. Pulse wave velocity (PWV) is used clinically as a measure of arterial stiffness. PWV measurements in animals can be done invasively and non-invasively. PWV values have a strong correlation with cardiovascular conditions. This study aimed to create a PWV measuring instrument in mice to estimate the occurrence of atherosclerosis. The device developed will be highly beneficial in enhancing the research and development of anti-atherosclerotic drugs. PWV measurement was performed non-invasively by using electrocardiogram (ECG) and photoplethysmogram (PPG) sensors. The ECG electrode was placed on the left and right front legs and right rear leg of the test animal. While the PPG sensor placed on the rat tail. PWV is the traveling speed along the arterial segment from the heart position point to the PPG sensor position at the base of the tail. By comparing the EKG and PPG waveforms the amount of travel time (Pulse transit time, PTT) can be determined. The measurement results were obtained in less than a minute. The average PWV measurement results with the proposed method in normal mice aged one to three months was 480 cm/s, within the range of the results obtained by existing standard methods.