Current Issue : April-June Volume : 2023 Issue Number : 2 Articles : 5 Articles
Adhesion assessments of an embedded interface in a multilayer system that contains a ductile layer are challenging. The occurrence of plastic deformation in the ductile layer often leads to additional complexity in analysis. In this study, an innovative “push-out” technique was devised to evaluate the interfacial toughness (Gin) of the embedded SiN/GaAs interface in a Au/SiN/GaAs multilayer system. Focus ion beam (FIB) milling was utilized to manufacture the miniaturized specimen and scratching with a conical indenter was used to apply load. This approach effectively minimized plastic deformation in the soft Au layer while inducing tensile stress to the embedded SiN/GaAs interface. As a result, the Au/SiN bilayer detached from the GaAs substrate with little plasticity. The energy associated with the interfacial delamination was derived from analyzing the load–displacement curves obtained from the scratching test. The Gin of the SiN/GaAs interface was calculated by means of energy analysis, and the average Gin was 4.86 ± 0.96 J m−2....
In the automotive field, the introduction of keyless access systems is revolutionizing car entry techniques currently dominated by a physical key. In this context, this paper investigates the possible use of smartphones to create a PEPS (Passive Entry Passive Start) system using the BLE (Bluetooth Low-Energy) Fingerprinting technique that allows, along with a connection to a low-cost BLE micro-controllers network, determining the driver’s position, either inside or outside the vehicle. Several issues have been taken into account to assure the reliability of the proposal; in particular, (i) spatial orientation of each microcontroller-based BLE node which ensures the best performance at 180° and 90° referred to as the BLE scanner and the advertiser, respectively; (ii) data filtering techniques based on Kalman Filter; and (iii) definition of new network topology, resulting from the merger of two standard network topologies. Particular attention has been paid to the selection of the appropriate measurement method capable of assuring the most reliable positioning results by means of the adoption of only six embedded BLE devices. This way, the global accuracy of the system reaches 98.5%, while minimum and maximum accuracy values relative to the individual zones equal, respectively, to 97.3% and 99.4% have been observed, thus confirming the capability of the proposed method of recognizing whether the driver is inside or outside the vehicle....
We propose a mode switch based on hybrid-core vertical directional couplers with an embedded graphene electrode to realize the switching function with low power consumption. We designed the device with Norland Optical Adhesive (NOA) material as the guide wave cores and epoxy polymer material as cladding to achieve a thermo-optic switching for the E11, E21 and E12 modes, where monolayer graphene served as electrode heaters. The device, with a length of 21 mm, had extinction ratios (ERs) of 20.5 dB, 10.4 dB and 15.7 dB for the E21, E12 and E11 modes, respectively, over the C-band. The power consumptions of three electric heaters were reduced to only 3.19 mW, 3.09 mW and 2.97 mW, respectively, and the response times were less than 495 μs, 486 μs and 498 μs. Additionally, we applied such a device into a mode division multiplexing (MDM) transmission system to achieve an application of gain equalization of few-mode amplification among guided modes. The differential modal gain (DMG) could be optimized from 5.39 dB to 0.92 dB over the C-band, together with the characteristic of polarization insensitivity. The proposed mode switch can be further developed to switch or manipulate the attenuation of the arbitrary guided mode arising in the few-mode waveguide....
This paper presented a new kind of salinity and temperature dual-parameter sensor based on a fiber ring laser (FRL) with tapered side-hole fiber (SHF) embedded in a Sagnac interferometer. The sensing structure is majorly composed of tapered SHF located in the middle of SHF inside the Sagnac interferometer loop structure. The influences of the SHF’s diameters of different tapered in the Sagnac interferometer loop on the FRL sensing system are studied. The presence of air holes in the SHF makes the cladding mode easier to excite, and the interaction between the cladding mode with its surroundings is enhanced, thus having higher salinity sensitivity. Besides, the unique advantages of high resolution, narrower linewidth, and high signal-to-noise ratio (SNR) of fiber laser make the measurement results more accurate. In this experiment, the SHF with different taper diameters was made, and it was found that reducing the diameter of the taper waist diameter could further improve the salinity sensitivity. When the waist diameter was 9.70 μm, the maximum salinity sensitivity of 0.2867 nm/‰was achieved. Temperature sensing experiments were also carried out. The maximum temperature sensitivity of the FRL sensing system was −0.3041 nm/◦C at the temperature range from 20 to 30 ◦C. The sensor has the characteristics of easy manufacture, good selectivity, and high sensitivity, proving the feasibility of simultaneous measurement of seawater salinity and temperature....
We use 250 billion microcontrollers daily in electronic devices that are capable of running machine learning models inside them. Unfortunately, most of these microcontrollers are highly constrained in terms of computational resources, such as memory usage or clock speed. These are exactly the same resources that play a key role in teaching and running a machine learning model with a basic computer. However, in a microcontroller environment, constrained resources make a critical difference. Therefore, a new paradigm known as tiny machine learning had to be created to meet the constrained requirements of the embedded devices. In this review, we discuss the resource optimization challenges of tiny machine learning and different methods, such as quantization, pruning, and clustering, that can be used to overcome these resource difficulties. Furthermore, we summarize the present state of tiny machine learning frameworks, libraries, development environments, and tools. The benchmarking of tiny machine learning devices is another thing to be concerned about; these same constraints of the microcontrollers and diversity of hardware and software turn to benchmark challenges that must be resolved before it is possible to measure performance differences reliably between embedded devices. We also discuss emerging techniques and approaches to boost and expand the tiny machine learning process and improve data privacy and security. In the end, we form a conclusion about tiny machine learning and its future development....
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