Accurate lung cancer tumor staging is crucial for prognosis and treatment planning. However, it remains challenging for end-to-end deep learning approaches, as such approaches often overlook spatial and anatomical information that are central to the tumor-node-metastasis system. The tumor stage depends on multiple quantitative criteria, including the tumor size and its proximity to the nearest anatomical structures, and small variations can alter the staging outcome. We propose a medically grounded hybrid pipeline that performs staging by explicitly measuring the tumor's size and distance properties rather than treating it as a pure image classification task. Our method employs specialized encoder-decoder networks to precisely segment the lung and adjacent anatomy, including the lobes, tumor, mediastinum, and diaphragm. Subsequently, we extract the necessary tumor properties, i.e. measure the largest tumor dimension and calculate the distance between the tumor and neighboring anatomical structures by a quantitative analysis of the segmentation masks. Finally, we apply rule-based tumor staging aligned with the medical guidelines. This novel framework has been evaluated on the Lung-PET-CT-Dx dataset, demonstrating superior performance compared to traditional deep learning models, achieving an overall classification accuracy of 91.36%. We report the per-stage F1-scores of 0.93 (T1), 0.89 (T2), 0.96 (T3), and 0.90 (T4), a critical evaluation aspect often omitted in prior literature. To our knowledge, this is the first study that embeds explicit clinical context into tumor stage classification. Unlike standard convolutional neural networks that operate in an uninterpretable "black box" manner, our method offers both state-of-the-art performance and transparent decision support.

In this work, a large area, low cost and flexible polymer/paper-based double touch mode capacitive pressure sensor is demonstrated. Garage fabrication processes are used which only require cutting, taping and assembly of aluminum (Al) coated polyimide (PI) foil, PI tape and double-sided scotch tape. The presented pressure sensor operates in different pressure regions i.e. normal (0 to 7.5 kPa), transition (7.5 to 14.24 kPa), linear (14.24 to 54.9 kPa) and saturation (above 54.9 kPa). The advantages of the demonstrated double touch mode capacitive pressure sensors are low temperature drift, long linear range, high pressure sensitivity, precise pressure measurement and large die area. The linear output along with a high sensitivity range (14.24 to 54.9 kPa pressure range) of the sensor are utilized to wirelessly control the movement of a robotic arm with precise rotation and tilt movement capabilities.

Capacitive pressure sensors have several advantages in areas such as robotics, automation, aerospace, biomedical and consumer electronics. We present mathematical modelling, finite element analysis (FEA), fabrication and experimental characterization of ultra-low cost and paper-based, touch-mode, flexible capacitive pressure sensor element using Do-It-Yourself (DIY) technology. The pressure sensing element is utilized to design large-area electronics skin for low-cost prosthetic hands. The presented sensor is characterized in normal, transition, touch and saturation modes. The sensor has higher sensitivity and linearity in touch mode operation from 10 to 40 kPa of applied pressure compared to the normal (0 to 8 kPa), transition (8 to 10 kPa) and saturation mode (after 40 kPa) with response time of 15.85 ms. Advantages of the presented sensor are higher sensitivity, linear response, less diaphragm area, less von Mises stress at the clamped edges region, low temperature drift, robust structure and less separation gap for large pressure measurement compared to normal mode capacitive pressure sensors. The linear range of pressure change is utilized for controlling the position of a servo motor for precise movement in robotic arm using wireless communication, which can be utilized for designing skin-like structure for low-cost prosthetic hands.

Speaker protection algorithm is to leverage the playback signal properties to prevent over excursion while maintaining maximum loudness, especially for the mobile phone with tiny loudspeakers. This paper proposes efficient DL solutions to accurately model and predict the nonlinear excursion, which is challenging for conventional solutions. Firstly, we build the experiment and pre-processing pipeline, where the feedback current and voltage are sampled as input, and laser is employed to measure the excursion as ground truth. Secondly, one FFTNet model is proposed to explore the dominant low-frequency and other unknown harmonics, and compares to a baseline ConvNet model. In addition, BN re-estimation is designed to explore the online adaptation; and INT8 quantization based on AI Model efficiency toolkit (AIMET\footnote{AIMET is a product of Qualcomm Innovation Center, Inc.}) is applied to further reduce the complexity. The proposed algorithm is verified in two speakers and 3 typical deployment scenarios, and $>$99\% residual DC is less than 0.1 mm, much better than traditional solutions.

Applications of rolling diaphragm transmissions for medical and teleoperated robotics are of great interest, due to the low friction of rolling diaphragms combined with the power density and stiffness of hydraulic transmissions. However, the stiffness-enabling pressure preloads can form a tradeoff against bearing loading in some rolling diaphragm layouts, and transmission setup can be difficult. Utilization of cable drives compliment the rolling diaphragm transmission's advantages, but maintaining cable tension is crucial for optimal and consistent performance. In this paper, a coaxial opposed rolling diaphragm layout with cable drive and an electronic transmission control system are investigated, with a focus on system reliability and scalability. Mechanical features are proposed which enable force balancing, decoupling of transmission pressure from bearing loads, and maintenance of cable tension. Key considerations and procedures for automation of transmission setup, phasing, and operation are also presented. We also present an analysis of system stiffness to identify key compliance contributors, and conduct experiments to validate prototype design performance.

In his early twenties, Lee Nam-hyun was an avid swimmer. But in 2004 he broke his neck in a pool, which left him paralyzed from the shoulders down. Recovery from his injuries required years of rehabilitation. The accident also temporarily halted his lifelong passion for singing. Opera and K-pop songs are his favorites, and being able to sing again became one of his top goals in recovery.

Audeze is one of the only headphone makers devoted exclusively to using planar-magnetic (PM) drivers in its products. The company offers a wide range of models, from reference over-ear designs to gaming-specific cans to in-ear monitors, all of which are based on PM technology. I have little experience with planar-magnetic headphones. Many years ago, I reviewed the Stax SR-007 MK2 electrostatic headphones, which use a somewhat similar technology, but they require a special amplifier and are extremely expensive. But I've never spent any quality time with PM headphones.

The proliferation of portable, voice-enabled gadgets like Amazon's Tap speaker and Doppler Labs's smart, wireless earbuds enables us to play music, search the Web, and answer phone calls around the house and on the go using verbal commands. But because these devices are susceptible to damage from dirt and moisture and last only a few hours per battery charge, we don't use them as much as we might. Boston startup Vesper has devised a solution: miniature piezoelectric microphones that use a cantilever structure to harvest energy from sound and promise to be more durable and energy-efficient than conventional microphones. Vesper says that once its microphones are incorporated into gadgets, which will happen later this year, we should be able to use our voice-enabled devices outdoors and in inclement weather with less worry, for days on a single charge. To demonstrate its microphones' toughness, Vesper plunges them into beer and soda; envelops them in hot steam infused with cooking oil; subjects them to simulated dust storms; and drops them from heights onto hard surfaces (see videos above and below).

Whitney says the device has greater torque per weight (torque density) than highly geared servos or brushless motors coupled with harmonic drives. And more significant: To build an autonomous robot, you'd need a set of motors and a control system capable of replacing the human puppeteer who's manually driving the fluid actuators [below]. John P. Whitney: The original motivation was the same as for the MIT WAM arm and other impedance-based systems designed for human interaction: Using a lightweight high-performance transmission allows placing the drive motors in the body, instead of suffering the cascading inertia if they were placed at each joint. We are learning that many of the "analog" qualities of this system will pay dividends for autonomous "digital" operation; for example, the natural haptic properties of the system can be of equal service to an autonomous control system as they are to a human operator.

Stress is actually a bit of a buzzword. The initial definition was "the reaction to changes", which is why you get stressed also when good things happen, hence the distinction between good stress, eustress, and bad stress, distress. Since the 90's, stress has become a synonym of the everyday hustling, describing a life style. We focus on the original definition, evaluating the user autonomic balance that can be estimated from heart rate variability (HRV). The autonomic nervous system (the sympathetic and parasympathetic systems) has a direct influence over most of our internal organs.