An Improved Spectral Subtraction Method for Eliminating Additive Noise in Condition Monitoring System Using Fiber Bragg Grating Sensors.

The additive noise in the condition monitoring system using fiber Bragg grating (FBG) sensors, including white Gaussian noise and multifrequency interference, has a significantly negative influence on the fault diagnosis of rotating machinery. Spectral subtraction (SS) is an effective method for handling white Gaussian noise. However, the SS method exhibits poor performance in eliminating multifrequency interference because estimating the noise spectrum accurately is difficult, and it significantly weakens the useful information components in measured signals. In this study, an improved spectral subtraction (ISS) method is proposed to enhance its denoising performance. In the ISS method, a reference noise signal measured by the same sensing system without working loads is considered the estimated noise, the same sliding window is used to divide the power spectrums of the measured and reference noise signals into multiple frequency bands, and the formula of spectral subtraction in the standard SS method is modified. A simulation analysis and an experiment are executed by using simulated signals and establishing a vibration test rig based on the FBG sensor, respectively. The statistical results demonstrate the effectiveness and feasibility of the ISS method in simultaneously eliminating white Gaussian noise and multifrequency interference while well maintaining the useful information components.

Bioinspiration and Biomimetic Art in Robotic Grippers.

The autonomous manipulation of objects by robotic grippers has made significant strides in enhancing both human daily life and various industries. Within a brief span, a multitude of research endeavours and gripper designs have emerged, drawing inspiration primarily from biological mechanisms. It is within this context that our study takes centre stage, with the aim of conducting a meticulous review of bioinspired grippers. This exploration involved a nuanced classification framework encompassing a range of parameters, including operating principles, material compositions, actuation methods, design intricacies, fabrication techniques, and the multifaceted applications into which these grippers seamlessly integrate. Our comprehensive investigation unveiled gripper designs that brim with a depth of intricacy, rendering them indispensable across a spectrum of real-world scenarios. These bioinspired grippers with a predominant emphasis on animal-inspired solutions have become pivotal tools that not only mirror nature's genius but also significantly enrich various domains through their versatility.

Universally Grasping Objects with Granular-Tendon Finger: Principle and Design.

Nowadays, achieving the stable grasping of objects in robotics requires an increased emphasis on soft interactions. This research introduces a novel gripper design to achieve a more universal object grasping. The key feature of this gripper design was a hybrid mechanism that leveraged the soft structure provided by multiple granular pouches attached to the finger skeletons. To evaluate the performance of the gripper, a series of experiments were conducted using fifteen distinct types of objects, including cylinders, U-shaped brackets, M3 bolts, tape, pyramids, big pyramids, oranges, cakes, coffee sachets, spheres, drink sachets, shelves, pulley gears, aluminium profiles, and flat brackets. Our experimental results demonstrated that our gripper design achieved high success rates in gripping objects weighing less than 210 g. One notable advantage of the granular-tendon gripper was its ability to generate soft interactions during the grasping process while having a skeleton support to provide strength. This characteristic enabled the gripper to adapt effectively to various objects, regardless of their shape and material properties. Consequently, this work presented a promising solution for manipulating a wide range of objects with both stability and soft interaction capabilities, regardless of their individual characteristics.

Flexible integrated sensor with asymmetric structure for simultaneously 3D tactile and thermal sensing.

The human body detects tactile stimuli through a combination of pressure force and temperature signals via various cutaneous receptors. The development of a multifunctional artificial tactile perception system has potential benefits for future robotic technologies, human-machine interfaces, artificial intelligence, and health monitoring devices. However, constructing systems beyond simple pressure sensing capabilities remains challenging. Here, we propose an artificial flexible and ultra-thin (50 µ m) skin system to simultaneously capture 3D tactile and thermal signals, which mimics the human tactile recognition process using customized sensor pairs and compact peripheral signal-converting circuits. The 3D tactile sensors have a flower-like asymmetric structure with 5-ports and 4 capacitive elements in pairs. Differential and average signals would reveal the curl and amplitude values of the fore field with a resolution of 0.18/mm. The resistive thermal sensors are fabricated with serpentine lines and possess stable heat-sensing performance (165 mV/°C) under shape deformation conditions. Real-time monitoring of the skin stimuli is displayed on the user interface and stored on mobile clients. This work offers broad capabilities relevant to practical applications ranging from assistant prosthetics to artificial electronic skins.

Independent Mobility Achieved through a Wireless Brain-Machine Interface.

Individuals with tetraplegia lack independent mobility, making them highly dependent on others to move from one place to another. Here, we describe how two macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex, and decoded to achieve brain control of the platform. We found that free-running brain control of the platform (which was not equipped with any machine intelligence) was fast and accurate, resembling the performance achieved using joystick control. The decoding algorithms can be trained in the absence of joystick movements, as would be required for use by tetraplegic individuals, demonstrating that the non-human primate model is a good pre-clinical model for developing such a cortically-controlled movement prosthetic. Interestingly, we found that the response properties of some neurons differed greatly depending on the mode of control (joystick or brain control), suggesting different roles for these neurons in encoding movement intention and movement execution. These results demonstrate that independent mobility can be achieved without first training on prescribed motor movements, opening the door for the implementation of this technology in persons with tetraplegia.