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Design for test (DFT) is a technique for testing hardware and software functional designs. Through DFT, the chip testing process is achieved in a very efficient and cost-effective manner. In this paper, five different tests, namely the Checker Board Test, March Algorithm, Pattern Generation, Zero-One Test, and Pseudo Random Test, are used. These four tests are applied to the memory, and its timing is checked. The minimum time interval taken for the memory to complete these tasks is noted. AND-self gating is a helpful clock gating approach for eliminating pointless clock toggling and resulting in significant power savings. The minimum time taken for completing all four tests will be displayed, and it makes the memory more effective. The Model sim is used for simulation purposes. [ABSTRACT FROM AUTHOR]

Detecting brain cancers in their earliest stages is the most important difficulty for a radiologist. The tumor doubles in size in approximately twenty-five days as it develops fast. Survival rates for those who are not adequately cared for are typically in the half-year range. This may quickly lead to death. A technology that can detect brain cancers at an early stage is preferable because of this. Magnetic resonance imaging (MRI) scans are often preferred over computed tomography (CT) scans for the detection of malignant and noncancerous lesions. Thus, a novel noise-removal approach, the modified iterative grouping median filter, is developed. Also offered for feature extraction is a kernel principal component analysis based on a maximum likelihood estimate (MLE). Segmentation was accomplished with the help of VGG16 architecture trained using deep reinforcement learning. Both qualitative and quantitative evaluations have revealed that the suggested method outperforms the well-known strategies. [ABSTRACT FROM AUTHOR]

In order to analyze and improve in physical therapy, professional sports, and the performing arts, it is crucial to monitor human joint angle movement. For both acute and chronic injuries, as well as to assess how well a treatment is functioning, range of motion assessment is essential. Athletes are more susceptible to fractures from acute elbow injuries (e.g. in gymnasts falling on an extended elbow). The industry standard for assessing joint ROM is now manual ROM measurements using a hand-held goniometer because of their accessibility and ease of use. We now present the fibre optic approach to assess elbow range of motion because the precision and repeatability of manual goniometer measurement have been questioned. In terms of size and robustness, optical fibre technology provides an exceptional solution for motion capture sensing. The method under study is based on an approach that has been used in the past to quantify spine movement and respiration rate: angular displacement between two separated fibres using intensity modulated optical fibre. By adding IoT, all data are uploaded to the cloud, where they are processed and conclusions are drawn for use in Rehabilation therapy using Functional Electrical Stimulation Therapy. [ABSTRACT FROM AUTHOR]

The Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, which has been around for many years, is still gaining the expertise of research networks engaged in the Wireless Sensor Network (WSN) industry. The effort of researchers has resulted in many extensive modifications to the LEACH method. Energy harvesting has naturally made its way into wireless sensor networks due to the necessity to maintain a long lifetime while adhering to strict energy limits. By more skillfully choosing energy-efficient cluster heads, it has been proposed to employ a modified version of the LEACH protocol, which stands for low energy adaptive clustering hierarchy. This study investigates the Modified LEACH on three distinct networks. It will be very clear from the calculations and findings that the recommended improvement to LEACH increases throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

In the Department of Knee Surgery, measuring the range of motion (ROM) of the knee is a crucial physical examination done for patient evaluation, diagnosis, prognosis, and treatment. The most typical approach is to use a universal goniometer to complete this operation. The range of motion (ROM) can be measured using a variety of instruments, including goniometers, inclinometers, electrogoniometers, and triaxial electrogoniometers. Our proposed solution is to create a digital knee IoT-integrated range of motion measurement device that can measure ROM and all other related data, send the collected information to the cloud for further processing, and make inferences about the patient's progress, the disease type, the intensity of Stimulation Intensity, and many other things. The device has sensors that assess the knee's range of motion and should be accurate when compared to the gold standard. Without the help of a therapist, the system's advantages include the ability to determine knee range of motion (ROM). This work is particularly helpful in the treatment of knee injuries Functional Electrical Stimulation Therapy (FEST). [ABSTRACT FROM AUTHOR]

PhotoPlethysmogram (PPG) signals may be used to improve non-invasive cuff-less blood pressure monitoring, according to this article. Cardiovascular factors and blood flow are strongly correlated with the PPG signal's morphology. The height and breadth of the pulse have been taken into account for the assessment of blood pressure as a distinct characteristic of PhotoPlethysmoGram signals. An ECG machine and an inflated cuff can be used to measure pulse plethysmograms, but this new device just needs one. There are several drawbacks to using a typical blood pressure machine, such as the need for an expert, the inability to notice the korotkoff noises, and discomfort for the user. Furthermore, the presence of a cuff makes this very sensitive to artifacts. PAT and PTT, two of the most often used non-invasive procedures, need a large number of variables to accurately estimate blood pressure. Using the linear regression approach from a PhotoPlethysmography signal, this technology predicts the cardiovascular characteristic of a non-invasive cuff-less blood pressure measurement. LabVIEW GUI was used to collect real-world data, while MATLAB tools were used to analyse and model the signal. As a consequence of the research, it was shown that this method is easy to use, non-invasive, affordable, and reliable for assessing arterial pulse signals in relation to wave amplitude variations. When it comes to measuring blood pressure, a specific property of PhotoPlethysmoGraphy signals such as the pulse's height and breadth are evaluated. Blood pressure readings received from different monitors were cross-validated. As the systolic blood pressure rises, the simulated output shows that the pulse (systolic height) increases in inverse proportion to the increase in cardiac output, but this happens in a shorter period of time. Systolic blood pressure is related to the breadth and area of the pulse. Our simulation results reveal that PPG and blood pressure have a precise relationship. Inflection heights between 10 and a minimum of 2 have lower error percentages, according to the computation among the retrieved characteristics. [ABSTRACT FROM AUTHOR]

In this paper, we have designed an Ear will identify that person. Nowadays, Biometric identification has a few applications, particularly in the security framework. One of the mainstream biometric recognizable pieces of proof is individual distinguishes through ear because every individual has an extraordinary ear example. It doesn't change rapidly; it may take less time to change than other identification. In this, we have to capture the person's ear image, and then we have analyzed the ear image use of image processing and conventional neural network. To enhance the ear images for analysis, we use grayscale histogram features to make the photos fit for the study of ear patterns. We have also used the Feature Extraction technique for calculating the entropy of the image, with the help of entropy value, contrast, and energy. We can find whether the person is authenticated/ accessible or not. Our Approach (with ≥ 75% overlap) and time taken to identify in seconds are 1.77, and the detection rate is 96.35% (331 2D images). [ABSTRACT FROM AUTHOR]