Robots equipped with legs have significant potential for real-world applications. Many industries, including those concerned with instruction, aid, security, and surveillance, have shown interest in legged robots. However, these robots are typically incredibly complicated and expensive to purchase. Iron Dog Mini is a low-cost, easily replicated, and modular quadruped robot built for training, security, and surveillance. To keep the price low and its upkeep simple, we designed our quadruped robot in a modular manner. We provide a comparative study of robotic manufacturing cost between our proposed robot and previously established robots. We were able to create a compact femur and tibia structure with sufficient load-bearing capacity. To improve stability and motion efficiency, we considered the novel Watt six-bar linkage mechanism. Using the SolidWorks modeling software, we analyzed the structural integrity of the robot’s components, considering their respective material properties. Furthermore, our research involved developing URDF data for our quadruped robot based on its CAD model. Its gait trajectory is planned using a 14-point Bezier curve. We demonstrate the operation of the simulation model and briefly discuss the robot’s kinematics. Computational methods are emphasized in this research, coupled with the simulation of kinematic and dynamic performances and analytical/numerical modeling.

Continuous post-endovascular aneurysm repair (EVAR) surveillance is critical for patients who have undergone a stenting procedure. Despite it being life-threatening monitoring, studies reported that patients gradually discontinue the post-EVAR surveillance due to the significant burden associated with CT scans, X-rays, etc. Considering the importance and necessity of post-EVAR surveillance, we introduce a self-resonating flexible tube implanted in a standard clinically approved stent that enables wireless sensing of blood pressure inside an aneurysm through inductive coupling. To enable blood pressure monitoring outside the body, we designed and fabricated a spiral-type antenna, specifically crafted to capture the inherent resonance frequency of the self-resonating stent tube. To showcase the wireless pressure monitoring capability, a 3D-printed elastic model of an abdominal aortic aneurysm (AAA) was prepared. Our quantitative study validated the pressure-sensing capability with adequate sensitivity (up to 687.5 Hz/mmHg) when tissue was located between the stent and the external antenna. The wireless sensing also presents a consistent linear shift in resonance frequency across all tested measuring distances as the applied pressure ranges from 60 to 140 mmHg. The proposed self-resonating stent offers promising insights for improving post-EVAR surveillance.

Images under low-light conditions suffer from noise, blurring, and low contrast, thus limiting the precise detection of objects. For this purpose, a novel method is introduced based on convolutional neural network (CNN) dual attention unit (DAU) and selective kernel feature synthesis (SKFS) that merges with the Retinex theory-based model for the enhancement of dark images under low-light conditions. The model mentioned in this paper is a multi-scale residual block made up of several essential components equivalent to an onward convolutional neural network with a VGG16 architecture and various Gaussian convolution kernels. In addition, backpropagation optimizes most of the parameters in this model, whereas the values in conventional models depend on an artificial environment. The model was constructed using simultaneous multi-resolution convolution and dual attention processes. We performed our experiment in the Tesla T4 GPU of Google Colab using the Customized Raw Image Dataset, College Image Dataset (CID), Extreme low-light denoising dataset (ELD), and ExDark dataset. In this approach, an extended set of features is set up to learn from several scales to incorporate contextual data. An extensive performance evaluation on the four above-mentioned standard image datasets showed that MSR-MIRNeT produced standard image enhancement and denoising results with a precision of 97.33%; additionally, the PSNR/SSIM result is 29.73/0.963 which is better than previously established models (MSR, MIRNet, etc.). Furthermore, the output of the proposed model (MSR-MIRNet) shows that this model can be implemented in medical image processing, such as detecting fine scars on pelvic bone segmentation imaging, enhancing contrast for tuberculosis analysis, and being beneficial for robotic visualization in dark environments.