Deep Multimodal Fusion for Generalizable Person Re-identification

Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a competitive performance on a specific target domain. However, when Re-ID models are directly deployed in a new domain without target samples, they always suffer from considerable performance degradation and poor domain generalization. To address this challenge, in this paper, we propose DMF, a Deep Multimodal Fusion network for the general scenarios on person re-identification task, where rich semantic knowledge is introduced to assist in feature representation learning during the pre-training stage. On top of it, a multimodal fusion strategy is introduced to translate the data of different modalities into the same feature space, which can significantly boost generalization capability of Re-ID model. In the fine-tuning stage, a realistic dataset is adopted to fine-tine the pre-trained model for distribution alignment with real-world. Comprehensive experiments on benchmarks demonstrate that our proposed method can significantly outperform previous domain generalization or meta-learning methods. Our source code will also be publicly available at https://github.com/JeremyXSC/DMF

Modern Potentiometric Biosensing Based on Non-Equilibrium Measurement Techniques

Modern potentiometric sensors based on polymeric membrane ion-selective electrodes (ISEs) have achieved new breakthroughs in sensitivity, selectivity, and stability and have extended applications in environmental surveillance, medical diagnostics, and industrial analysis. Moreover, nonclassical potentiometry shows promise for many applications and opens up new opportunities for potentiometric biosensing. Here, we aim to provide a concept to summarize advances over the past decade in the development of potentiometric biosensors with polymeric membrane ISEs. This Concept article articulates sensing mechanisms based on non-equilibrium measurement techniques. In particular, we emphasize new trends in potentiometric biosensing based on attractive dynamic approaches. Representative examples are selected to illustrate key applications under zero-current conditions and stimulus-controlled modes. More importantly, fruitful information obtained from non-equilibrium measurements with dynamic responses can be useful for artificial intelligence (AI). The combination of ISEs with advanced AI techniques for effective data processing is also discussed. We hope that this Concept will illustrate the great possibilities offered by non-equilibrium measurement techniques and AI in potentiometric biosensing and encourage further innovations in this exciting field. The introduction of bioreceptors, new sensing concepts, and a deeper theoretical understanding of the potentiometric responses of ISEs encourages innovations in potentiometric biosensing applications. Dynamic responses based on non-equilibrium measurement techniques under zero-current conditions and stimulus-controlled modes offer fruitful information that can be automatically analyzed by artificial intelligence technology.imag

Deep Learning-Enhanced Potentiometric Aptasensing with Magneto-Controlled Sensors

Bioelectronic sensors that report charge changes of a biomolecule upon target binding enable direct and sensitive analyte detection but remain a major challenge for potentiometric measurement, mainly due to Debye Length limitations and the need for molecular-level platforms. Here, we report on a magneto-controlled potentiometric method to directly and sensitively measure the target-binding induced charge change of DNA aptamers assembled on magnetic beads using a polymeric membrane potentiometric ion sensor. The potentiometric responses of the negatively charged aptamer, serving as a receptor and reporter, were dynamically controlled and modulated by applying a magnetic field. Based on a potentiometric array, this non-equilibrium measurement technique combined with deep learning algorithms allows for rapidly and reliably classifying and quantifying diverse small molecules using antibiotics as models. This potentiometric strategy opens new modalities for sensing applications

Adaptive Control of Flapping-Wing Micro Aerial Vehicle with Coupled Dynamics and Unknown Model Parameters

With the complex aerodynamics, the accurate system model of the flapping-wing micro aerial vehicle required for precise control is hard to acquire, meanwhile, due to the unique control strategy, the coupling between the actuators also brings a great challenge to the control of the vehicle. In this paper, we establish a theoretical model of the vehicle. Based on this model, we propose a multiaxial adaptive controller with the reference generator for the attitude and altitude control using the backstepping design method, the stability of this controller is proved by the Lyapunov function. Moreover, a control allocation algorithm is proposed to coordinate the different actuators such that they together produce the desired virtual control efforts. In addition, we detail the lightweight design of the flapping-wing micro aerial vehicle with altitude and attitude sensing onboard. Then, the effectiveness of the proposed control scheme is verified by the simulation and the flight test with multi-axis simultaneous control conducted on this lightweight vehicle. The experimental results show that the controller can maintain hovering flight and ensure the convergence of the adaptive parameters even when the unilateral thrust of the vehicle is not enough due to manufacturing and assembly errors. This work provides an idea for us to explore how insects maintain stable flight in the face of changes in their model parameters

Magneto-controlled potentiometric assay for E. coli based on cleavage of peptide by outer-membrane protease T

Rapid, sensitive and reliable Escherichia coli (E. coli) detection and identification are critically important to protect public health. Here, we describe a magneto-controlled potentiometric assay for specific detection of E. coli cells by making use of the E. coli outer-membrane protease T (OmpT). OmpT is an endopepti-dase that specifically cleaves peptide at dibasic sites. A rationally designed peptide serving as both OmpT substrate and potentiometric signal reporter was immobilized on magnetic beads. The rapid accumu-lation and extraction of peptide-functionalized magnetic beads on a polymeric membrane doped with an ion exchanger can be achieved using a magnetic force. The magnetic-field-assisted extraction of the peptide into the polymeric membrane ion-sensitive sensor, as confirmed by Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy, can lead to a rapid, stable and reproducible potential change. OmpT is capable of cleaving the positively charged peptide on the magnetic beads, thus resulting in charge density change. The change in charge density and subsequently the potential change can be readily detected and used for quantification of E. coli at levels down to 5.0 x 10(-3) CFU mL(-1). This work provides a versatile, rapid and reliable potentiometric method for E. coli detection. (C) 2021 Elsevier Ltd. All rights reserved

Rapid Antibiotic Screening Based on Bacteria Apoptosis Using Potentiometric Sensor Array

Phenotypic antimicrobial susceptibility testing that identifies the phenotypic feature differences of bacteria after incubation with antibiotics enables reliable antibiotics screening but is restricted by bacterial proliferation rate. Here, a magneto-controlled potentiometric sensors array was developed for rapid antibiotic screening based on direct detection of apoptotic bacteria. Phosphatidylserine (PS)-binding peptide serving as bioreceptor and signal transducer was immobilized on magnetic beads (MBs-peptide), and could selectively capture apoptotic bacteria killed by antibiotics. Apoptotic bacteria binding-induced charge density change of MBs-peptide resulted in a potential change on a magneto-controlled polymeric membrane potentiometric sensor. Based on the apoptotic bacteria detection, antimicrobial activities of micrograms per milliliter antibiotics could be evaluated within 1.5 h, which were hardly achieved by current methods. In addition, the antibacterial ability of different antibiotics could be evaluated simultaneously using a potentiometric sensors array. This approach enables sensitive, general, and time-saving antibiotic screening, and may open up a new path for antibiotic susceptibility testing. © 2023, The Authors. All rights reserved

Robust Sampled-data H∞ Control Of Flapping Wing Micro Aerial Vehicles With Parameter Uncertainties and Actuator Saturation

Robust control is essential to the flapping wing micro aerial vehicle (FWMAV) due to model uncertainties, environmental or self changes during flight, such as the change of drag coefficient caused by airflow and the asymmetric effect caused by wing damage. This paper proposes a robust H∞ controller synthesis scheme for parameter-varying FWMAV systems with sampling measurement and control input saturation. A linear parameter varying (LPV) model is established to characterize the nonlinear FWMAV model with uncertainties. We introduce an input delay approach to transform the sampled-data system into a continuous system with time delay. A nonconvex optimization with bilinear matrix inequalities (BMI) is established to synthesize the proposed robust output-feedback controller with the PID structure, ensuring the H∞ performance of the closed loop. A Lyapunov function is proposed to ensure the asymptotic stability of the closed loop system. The BMI problem is restricted furthermore and transformed to a convex optimization problem with linear matrix inequalities (LMI) constraints, making the method computationally practical. The numerical simulations show that the proposed controller possesses superior performance and strong robustness in the FWMAV compared with four other controllers.</p