According to one embodiment, an acoustic sensor includes a base and a first strain sensing element. The base includes a support and a first film unit supported by the support. The first film unit is flexible. The first strain sensing element is provided on a first surface of the first film unit. The first strain sensing element includes a first magnetic layer, a second magnetic layer, and a first intermediate layer provided between the first magnetic layer and the second magnetic layer. An angle between a magnetization of the first magnetic layer and a magnetization of the second magnetic layer is variable by an acoustic wave. The acoustic wave is transmitted to a first film unit by a first transmitting material in contact with the first film unit.

Disclosed herein is a method and system for assessing one or more defects in a material. The method comprises extracting one or more assessment parameters from ultrasound waves propagated through a cross-section of the material. Further, the method identifies presence of one or more defects in the material by analyzing the one or more assessment parameters using a first machine learning model. Further, the method determines a position of each of one or more defects present in the material based on a graphical representation. Thereafter, the method determines a severity score and size of each of the one or more defects using a second machine learning model. Thus, an improved method and system for assessing one or more defects within a material by generating graphical representations of the one or more defects, thereby enhancing accuracy of defect detection is provided.

A fiber optic distributed acoustic sensing (DAS) system for detecting a red palm weevil (RPW) includes an optical fiber configured to be wrapped around a tree and a DAS box connected to the optical fiber. The DAS box includes a processing unit that is configured to receive a filtered Rayleigh signal reflected by the optical fiber, and run the filtered Rayleigh signal through a neural network system to determine a presence of the RPW in the tree.

A filler material is applied to a plurality of wood elements. The plurality of wood elements is bonded into a composite wood product, where the bonding includes delivering ultrasound energy to the plurality of wood elements. The ultrasound energy has a frequency within a frequency range of 10 kHz-20 MHz.

An integrated system for detecting a red palm weevil (RPW), farm fire, and soil moisture includes an optical fiber configured to be extending to a tree, and a distributed acoustic sensor (DAS) box connected to the optical fiber. The DAS box is configured to process first to third different optical signals reflected from the optical fiber, to determine a presence of the RPW from the first optical signal, a temperature at a location along the optical fiber from the second optical signals, and a moisture at a location around the tree from the third optical signal.

According to one embodiment, an acoustic sensor includes a base and a first strain sensing element. The base includes a support and a first film unit supported by the support. The first film unit is flexible. The first strain sensing element is provided on a first surface of the first film unit. The first strain sensing element includes a first magnetic layer, a second magnetic layer, and a first intermediate layer provided between the first magnetic layer and the second magnetic layer. An angle between a magnetization of the first magnetic layer and a magnetization of the second magnetic layer is variable by an acoustic wave. The acoustic wave is transmitted to a first film unit by a first transmitting material in contact with the first film unit.

Present disclosure discloses method and system for assessing health of a wood specimen. Method receives ultrasonic data for each of a plurality of alignments of a transmitter and associated receiver across a cross-section of one or more cross-sections along a length of the wood specimen. The ultrasonic data comprises a pulse velocity, a transit time and a distance travelled by an ultrasonic pulse between the transmitter and the associated receiver. Thereafter, method measures relative features of the wood specimen using the ultrasonic data. Subsequently, method identifies a condition of the cross-section of the wood specimen based on the relative features using a trained ML model. Upon identifying the condition of the cross-section to be defective, method determines a position of a defect in the cross-section of the wood specimen using the relative features and determines a severity of the defect using the trained ML model and the relative features.

The technology provides a device that measures an internal structural integrity and surface hardness of baseball bats having a bat clamp that secures the baseball bat at a handle end, a bat support that secures the baseball bat at a barrel end, and a rebound hammer that impacts the baseball bat to determine a coefficient of restitution value. The rebound hammer may indirectly impact the baseball bat through a force dispersing tool. The technology further provides a device that measures vibration propagation within baseball bats, having a bat clamp that secures the baseball bat at a handle end, a vibration sensor provided proximate to the handle end, and a vibration impact tool that impacts the baseball bat to transfer a force thereto.