Infrared (IR) imaging sensors, designed to detect the wavelength range between 0.9 mu m and 14 mu m, offer unique advantages over daylight cameras in consumer, industrial, and defense applications. However, IR images lack natural color information and can be challenging for individuals without sensor-specific training to interpret. Consequently, transforming IR images into perceptually realistic color images represents a valuable research endeavor with significant commercial potential. Recently, various studies utilizing deep neural networks for colorizing single-mode (near-IR or thermal) infrared images have been reported. This article will apply a common neural network architecture to images captured with different imaging modes (near-IR, thermal IR, and low-light) for colorization and compare the results. These experiments will examine the influence of perceived wavelength on the colorization process.
This study proposes a speech compression method based on one-dimensional convolutional autoencoder and residual vector quantization. The proposed method offers different compression ratios at low bit rates. Speech quality evaluation metric (PESQ) was used to test the performance of the proposed method. Experimental results show that the proposed method achieves a PESQ value of 1.903 for 2.5 kbps and 2.24 for 5 kbps.
Obstacle avoidance motion planning is a basic problem in mobile robotics. In cases like in a factory, the environment map is known, and the planner’s main task is to find a feedback control law that steers the robot to the goal location without collision. This study presents a novel deterministic method that quickly finds a suitable path by covering the obstacle-free space from start to goal with overlapping polygonal regions guided by Voronoi diagrams. Using a reference governor ensures that the robot stays in these regions and safely moves to the goal. A simulation study confirms the computational gains of our method.
This study aims to compute the periodicities of solar and geomagnetic indices, such as the solar indice IMF-Bz, Dst, Kp, and AE, using the spectral analysis method. This approach offers a fresh perspective on the dynamics of Space Weather and its implications for satellite technology. The primary focus is on investigating the periodic changes in these indices through long-term, one-dimensional temporal Fourier Transform analysis, after the data has been smoothed by applying a sliding median filter. In a solar maximum year, such as 2001, the dominant period for the solar indice IMF-Bz aligns with the Sun’s rotation period, which is approximately 27 days. Additionally, the upper and lower harmonics of this cycle have been identified in indices that reflect levels of geomagnetic disturbance, occurring at intervals of approximately 1 month, 3 months, and 6 months, as well as shorter periods of 1 week, 2 weeks, and 3 weeks, respectively.
This study introduces a method for breast cancer detection using a microstrip patch antenna designed to exploit dielectric property contrasts between healthy and malignant tissues. Three-layered breast phantoms, mimicking human breast tissue, were developed and tested using the antenna for cancer detection. Utilizing a 28.8 x 28.8 mm(2) patch antenna on an FR4 structure, the study examines tumor impact through return loss values. Initial simulations on a multilayer breast phantom model, created with CST Microwave Studio, were conducted for tumor identification. Subsequently, realistic breast phantoms comprising skin, fat, and glandular tissues were fabricated to validate the antenna's performance. Return loss measurements on fabricated phantoms, conducted with a NanoVNA-F Network Analyzer, closely matched simulated results. The study proposes a promising method for breast cancer detection through valuable insights provided to researchers via the testing of the proposed antenna and realistic breast phantoms.
