Developing a breast cancer screening method is very important to facilitate early breast cancer detection and treatment. Building a screening method using medical imaging modality that does not cause body tissue damage (non-invasive) and does not involve physical touch is challenging. Thermography, a non-invasive and non-contact cancer screening method, can detect tumors at an early stage even under precancerous conditions by observing temperature distribution in both breasts. The thermograms obtained on thermography can be interpreted using deep learning models such as convolutional neural networks (CNNs). CNNs can automatically classify breast thermograms into categories such as normal and abnormal. Despite their demostrated utility, CNNs have not been widely used in breast thermogram classification. In this study, we aimed to summarize the current work and progress in breast cancer detection based on thermography and CNNs. We first discuss of breast thermography potential in early breast cancer detection, providing an overview of the availability of breast thermal datasets together with publicly accessible. We also discuss characteristics of breast thermograms and the differences between healthy and cancerous thermographic patterns. Breast thermogram classification using a CNN model is described step by step including a simulation example illustrating feature learning. We cover most research related to the implementation of deep neural networks for breast thermogram classification and propose future research directions for developing representative datasets, feeding the segmented image, assigning a good kernel, and building a lightweight CNN model to improve CNN performance.

The amount of solar energy could be acquired optimally by proper tracking of sun movement in a sun tracker. A sun tracker is a device which tracks the sun as it moves on its path through the sky during the day, exposing the solar cells to an increased amount of sunlight and hence producing more electricity. In this paper, we compare the performance and optimization of two types of sun tracker based on tetrahedron geometry using PID and Fuzzy logic algorithm. These two sun trackers are built identically and experimental data acquisition of servo movement and load of solar cell performed side by side. PID and fuzzy logic algorithm are used to control the servo movement of the dual-axis sun tracker. We record the load received by solar cell which attached to both sun tracker and compare the result. The study shows that fuzzy logic algorithm controller performs better than PID controller which can be seen from the amount of load received by solar cell.

The Ring Oscillator Physical Unclonable Function (RO-PUF) is a hardware security innovation that creates a secure and distinct identifier by utilizing the special physical properties of ring oscillators. Their unique response, low hardware overhead, and difficulty of reproduction are some of the security benefits that make them valuable in safe authentication systems. Numerous developments, such as temperature adjustment methods, aging mitigation, and better architecture and layout, have been created to increase its security, dependability, and efficiency. However, achieving the sacrifice metric makes it challenging to implement with additional complex circuits. This work focuses on stability improvement in terms of the reliability of the RO-PUF in enhanced challenge and response (CRP) by exploiting existing on-chip hard processors. This work establishes only ROs and their counters inside the chip. The built-in microprocessor performs the remaining process using the intermediary process of a Q factor and new frequency mapping. As a result, the reliability improves significantly to 95.8% compared to previous methods. The proper use of resources due to the limitation of on-chip resources has been emphasized by considering that a hard processor exists inside the new FPGA chip.

One alternative for environmentally friendly vehicles is wind-powered bikes. This vehicle can reduce air pollution and can be convenient in big cities. The proposed bike is equipped with a distance sensor to sense the safe proximity to nearby obstacles. If the obstacles are below a reasonable distance, the mounted buzzer is active as the warning. This bike uses the wind generated by the 8045 hp dc rotation. The pulse-width (PWM) modulation is used to control a dc engine rotational speed with a fixed value of 155 = low and 255 = high. Fuzzy Logic Controller is used to increasing system effectivity to processes the input from the proximity sensor. The sufficient distance between the bike and the obstacles ahead, right and left, is set to be 1 meter. The PID controller ensures that the dc motor rotations are smooth and steady to ensure that the PWM value is still positive. In order to maintain a stable state Kp = 60, Ki = 4.2, and Kd = 2.9 are set as the appropriate settings for the used PID constants.