Unlocking the capabilities of explainable fewshot learning in remote sensing

Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, fewshot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies. In this review, we provide an up to date overview of both existing and newly proposed fewshot classification techniques, along with appropriate datasets that are used for both satellite based and UAV based data. Our systematic approach demonstrates that fewshot learning can effectively adapt to the broader and more diverse perspectives that UAVbased platforms can provide. We also evaluate some SOTA fewshot approaches on a UAV disaster scene classification dataset, yielding promising results. We emphasize the importance of integrating XAI techniques like attention maps and prototype analysis to increase the transparency, accountability, and trustworthiness of fewshot models for remote sensing. Key challenges and future research directions are identified, including tailored fewshot methods for UAVs, extending to unseen tasks like segmentation, and developing optimized XAI techniques suited for fewshot remote sensing problems. This review aims to provide researchers and practitioners with an improved understanding of fewshot learnings capabilities and limitations in remote sensing, while highlighting open problems to guide future progress in efficient, reliable, and interpretable fewshot methods.Comment: Under review, once the paper is accepted, the copyright will be transferred to the corresponding journa

WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images

Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 15 times in terms of classification accuracy and $38.3\%$ in terms of computing efficiency as measured by Floating Point Operations per second (FLOPs). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency. Our code is available at \url{https://github.com/TanmDL/WATT-EffNet}.Comment: This paper is accepted in IEEE Trans. GRS

Microfluidic and micromachined/MEMS devices for separation, discrimination and detection of airborne particles for pollution monitoring

Most of the microfluidics-related literature describes devices handling liquids, with only a small part dealing with gas-based applications, and a much smaller number of papers are devoted to the separation and/or detection of airborne inorganic particles. This review is dedicated to this rather less known field which has become increasingly important in the last years due to the growing attention devoted to pollution monitoring and air quality assessment. After a brief introduction summarizing the main particulate matter (PM) classes and the need for their study, the paper reviews miniaturized devices and/or systems for separation, detection and quantitative assessment of PM concentration in air with portable and easy-to-use platforms. The PM separation methods are described first, followed by the key detection methods, namely optical (scattering) and electrical. The most important miniaturized reported realizations are analyzed, with special attention given to microfluidic and micromachined or micro-electro-mechanical systems (MEMS) chip-based implementations due to their inherent capability of being integrated in lab-on-chip (LOC) type of smart microsystems with increased functionalities that can be portable and are easy to use. The operating principles and (when available) key performance parameters of such devices are presented and compared, also highlighting their advantages and disadvantages. Finally, the most relevant conclusions are discussed in the last section.Published versio

Design of planar microcoil-based NMR probe ensuring high SNR

A microNMR probe for ex vivo applications may consist of at least one microcoil, which can be used as the oscillating magnetic field (MF) generator as well as receiver coil, and a sample holder, with a volume in the range of nanoliters to micro-liters, placed near the microcoil. The Signal-to-Noise ratio (SNR) of such a probe is, however, dependent not only on its design but also on the measurement setup, and the measured sample. This paper introduces a performance factor P independent of both the proton spin density in the sample and the external DC magnetic field, and which can thus assess the performance of the probe alone. First, two of the components of the P factor (inhomogeneity factor K and filling factor η) are defined and an approach to calculate their values for different probe variants from electromagnetic simulations is devised. A criterion based on dominant component of the magnetic field is then formulated to help designers optimize the sample volume which also affects the performance of the probe, in order to obtain the best SNR for a given planar microcoil. Finally, the P factor values are compared between different planar microcoils with different number of turns and conductor aspect ratios, and planar microcoils are also compared with conventional solenoids. These comparisons highlight which microcoil geometry-sample volume combination will ensure a high SNR under any external setup.Published versio

Fabrication of CNT-based planar micro-coils on silicon substrate

Micro-coils are the most popular inductor structures. It is desirable to have high aspect ratio (HAR) planar micro-coils where the 'planar' structure ensures less fabrication process steps compared to their 3-D counterparts, and HAR ensures that a lesser area is occupied by the micro-coil as well as low resistance due to larger conductor cross section. Such HAR planar micro-coils are generally realized through electrochemical-deposition (ECD) of metals, generally copper, inside blind HAR trenches in a substrate. However, such an ECD process into HAR trenches is very challenging, resulting into voids and the process is required to be optimized for different aspect ratios, which makes it time consuming and complex. In this paper, we present a novel way of realizing planar micro-coils in silicon using carbon nanotubes (CNTs). The issue of poor electrical conduction between two adjacent CNT bundle is resolved through graphene-deposition and this allows the electrical conduction along the length of the micro-coil (in the plane of the coil), which is necessary for a planar micro-coil structure. Planar micro-coils of an aspect ratio (AR) 3:1 is realized using such graphenic-CNTs and their electrical characterization is reported here. DC and low-frequency electrical characterization of these coils are performed, which shows the micro-coils have a high inductance value compared to the other micro-coils reported in the literature. The process of obtaining such planar micro-coils, using the CNT-graphenic heterostructure reported here, is less complex compared to the conventionally used ECD of Cu, and unlike ECD of Cu inside trenches of different AR will not require any optimization.Ministry of Education (MOE)This work was supported in part by the Ministry of Education (MoE) under Project MOE2010-T2-2-016 ARC4/11, in part by the office for space technology and industry (OSTIn), Singapore, under Project S14-1126-NRF OSTIn-SRP, and in part by the Department of Science & Technology (DST), Nanomission Goverment of India, under Grant SR/NM/NS-91/2016(G). The review of this paper was arranged by Associate Editor Z. Zhong. (Corresponding author: Zishan Ali.

Low-cost method and biochip for measuring the trans-epithelial electrical eesistance (TEER) of esophageal epithelium

Trans-epithelial electrical resistance (TEER) is a good indicator of the barrier integrity of epithelial tissues and is often employed in biomedical research as an effective tool to assess ion transport and permeability of tight junctions. The Ussing chamber is the gold standard for measuring TEER of tissue specimens, but it has major drawbacks: it is a macroscopic method that requires a careful and labor intensive sample mounting protocol, allows a very limited viability for the mounted sample, has large parasitic components and low throughput as it cannot perform multiple simultaneous measurements, and this sophisticated and delicate apparatus has a relatively high cost. This paper demonstrates a low-cost home-made "sandwich ring" method which was used to measure the TEER of tissue specimens effectively. This method inspired the subsequent design of a biochip fabricated using standard soft lithography and laser engraving technologies, with which the TEER of pig epithelial tissues was measured. Moreover, it was possible to temporarily preserve the tissue specimens for days in the biochip and monitor the TEER continuously. Tissue responses after exposure tests to media of various pH values were also successfully recorded using the biochip. All these demonstrate that this biochip could be an effective, cheaper, and easier to use Ussing chamber substitute that may have relevant applications in clinical practice.Ministry of Education (MOE)Published versionThis research was supported by the Ministry of Education, Singapore, under its Academic ResearchFund Tier 1, Project ID: 2017-T1-002-080, grant/project number RG 151/17

Fabrication of silicon-embedded low resistance high-aspect ratio planar copper microcoils

Low resistance is an important requirement for microcoils which act as a signal receiver to ensure low thermal noise during signal detection. High-aspect ratio (HAR) planar microcoils entrenched in blind silicon trenches have features that make them more attractive than their traditional counterparts employing electroplating through a patterned thick polymer or achieved through silicon vias. However, challenges met in fabrication of such coils have not been discussed in detail until now. This paper reports the realization of such HAR microcoils embedded in Si blind trenches, fabricated with a single lithography step by first etching blind trenches in the silicon substrate with an aspect ratio of almost 3∶1 and then filling them up using copper electroplating. The electroplating was followed by chemical wet etching as a faster way of removing excess copper than traditional chemical mechanical polishing. Electrical resistance was further reduced by annealing the microcoils. The process steps and challenges faced in the realization of such structures are reported here followed by their electrical characterization. The obtained electrical resistances are then compared with those of other similar microcoils embedded in blind vias.MOE (Min. of Education, S’pore)Published versio

Design and fabrication of Poly(dimethylsiloxane) arrayed waveguide grating

We have designed, fabricated and characterized poly(dimethylsiloxane) (PDMS) arrayed waveguide grating (AWG) with four-channel output for operation in the visible light wavelength range. The PDMS AWG was realized based on the single-mode PDMS rib waveguide. The device was designed for 1 nm channel spacing with the wavelength ranging from 639 to 644 nm. The measured insertion loss is 11.4 dB at the peak transmission spectrum and the adjacent crosstalk is less than -16 dB. The AWG device occupies an area of 7.5 x 15 mm(2). PDMS AWG has the potential for integration with microfluidics in a monolithic PDMS lab-on-a-chip device for visible light spectroscopy applications. (C) 2010 Optical Society of Americ

WATT-EffNet: a lightweight and accurate model for classifying aerial disaster images

Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 15 times in terms of classification accuracy and 38.3% in terms of computing efficiency as measured by Floating Point Operations per second (FLOPs). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency. Our code is available at \url{https://github.com/TanmDL/WATT-EffNet}.Civil Aviation Authority of Singapore (CAAS)Nanyang Technological UniversityThis work was supported by the Civil Aviation Authority of Singapore and Nanyang Technological University (NTU) in collaboration with the Air Traffic Management Research Institute

Electrochemical DNA-nano biosensor for the detection of cervical cancer-causing HPV-16 using ultrasmall Fe3O4-Au core-shell nanoparticles

This paper reports a label-free biosensor for detecting human papillomavirus type 16 (HPV-16). For this purpose, the surface of the screen-printed carbon electrodes (SPCEs) was coated with Fe3O4-Au core-shell nanoparticles (NPs) using a green and facile eco-friendly method. The modified surfaces of the electrodes were then functionalized with thiolated single-strand DNA (ssDNA) probe human papillomavirus (HPV) DNA sequences. Next, the hybridization events with the immobilized probe DNA were monitored by cyclic voltammetry (CV) and differential pulse voltammetry (DPV) using [Fe(CN)6]3‐/4− as the redox indicator. Our results demonstrate that the modified electrodes could distinguish the redox current signals of [Fe(CN)6] 3−/4− due to the absence/presence of the immobilized probe DNA. Furthermore, quantitative estimations of the concentration of the probe DNA were also possible. Optimal performance was obtained for probe DNA concentrations between 1 and 10 μM. The best performance of our HPV biosensor was obtained for probe DNA concentration of 5 μM, for which the limit of detection and sensitivity of our developed sensor resulted to be 0.1 nM and 2.4 μA/nM, respectively