Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals

Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This could lead to a new breed of closed-loop responsive and personalised feedback, which we describe as Neuromorphic Neuromodulation. This can empower precise and adaptive modulation strategies by integrating neuromorphic AI as tightly as possible to the site of the sensors and stimulators. This paper presents a perspective on the potential of Neuromorphic Neuromodulation, emphasizing its capacity to revolutionize implantable brain-machine microsystems and significantly improve patient-specificity.Comment: 17 page

Applicability of Bevis formula at different height level and global weighted mean temperature model based on near-earth atmospheric temperature

Weighted mean temperature is a critical parameter in GNSS technology to retrieve precipitable water vapor (PWV). It is convenient to obtain high-accuracy Tm estimation near surface utilizing Bevis formula and surface temperature. However, some researches pointed out that the Bevis formula has large uncertainties in high-altitude regions. This paper researches the applicability of Bevis formula at different height levels and finds that the Bevis formula has relatively high precision when the altitude is low, while with altitude increasing, the precision decreases gradually. To solve the problem, this paper studies the relationship between Tm and atmospheric temperature of the near-earth space range (the height range between 0~10 km) and finds that they have high correlation on a global scale. Accordingly, this paper builds a global weighted mean temperature model based on near-earth atmospheric temperature. Validation results of the model show that this model can provide high-accuracy Tm estimation at any height level in the near-earth space range

Establishment and Evaluation of a New Meteorological Observation-Based Grid Model for Estimating Zenith Wet Delay in Ground-Based Global Navigation Satellite System (GNSS)

With the availability to high-accuracy a priori zenith wet delay (ZWD) data, the positioning efficiency of the precise point positioning (PPP) processing can be effectively improved, including accelerating the convergence time and improving the positioning precision, in ground-based Global Navigation Satellite System (GNSS) technology. Considering the limitations existing in the state-of-the-art ZWD models, this paper established and evaluated a new in-situ meteorological observation-based grid model for estimating ZWD named GridZWD using the radiosonde data and the European Centre for Medium-Range Weather Forecasts (ECWMF) data. The results show that ZWD has a strong correlation with the meteorological parameter water vapor pressure in continental and high-latitude regions. The root of mean square error (RMS) of 24.6 mm and 36.0 mm are achievable by the GridZWD model when evaluated with the ECWMF data and the radiosonde data, respectively. An accuracy improvement of approximately 10%~30% compared with the state-of-the-art models (e.g., the Saastamoinen, Hopfield and GPT2w models) can be found for the new built model

L-Unet: A Landslide Extraction Model Using Multi-Scale Feature Fusion and Attention Mechanism

At present, it is challenging to extract landslides from high-resolution remote-sensing images using deep learning. Because landslides are very complex, the accuracy of traditional extraction methods is low. To improve the efficiency and accuracy of landslide extraction, a new model is proposed based on the U-Net model to automatically extract landslides from remote-sensing images: L-Unet. The main innovations are as follows: (1) A multi-scale feature-fusion (MFF) module is added at the end of the U-Net encoding network to improve the model’s ability to extract multi-scale landslide information. (2) A residual attention network is added to the U-Net model to deepen the network and improve the model’s ability to represent landslide features. (3) The bilinear interpolation algorithm in the decoding network of the U-Net model is replaced by data-dependent upsampling (DUpsampling) to improve the quality of the feature maps. Experimental results showed that the precision, recall, MIoU and F1 values of the L-Unet model are 4.15%, 2.65%, 4.82% and 3.37% higher than that of the baseline U-Net model, respectively. It was proven that the new model can extract landslides accurately and effectively

L-Unet: A Landslide Extraction Model Using Multi-Scale Feature Fusion and Attention Mechanism

At present, it is challenging to extract landslides from high-resolution remote-sensing images using deep learning. Because landslides are very complex, the accuracy of traditional extraction methods is low. To improve the efficiency and accuracy of landslide extraction, a new model is proposed based on the U-Net model to automatically extract landslides from remote-sensing images: L-Unet. The main innovations are as follows: (1) A multi-scale feature-fusion (MFF) module is added at the end of the U-Net encoding network to improve the model’s ability to extract multi-scale landslide information. (2) A residual attention network is added to the U-Net model to deepen the network and improve the model’s ability to represent landslide features. (3) The bilinear interpolation algorithm in the decoding network of the U-Net model is replaced by data-dependent upsampling (DUpsampling) to improve the quality of the feature maps. Experimental results showed that the precision, recall, MIoU and F1 values of the L-Unet model are 4.15%, 2.65%, 4.82% and 3.37% higher than that of the baseline U-Net model, respectively. It was proven that the new model can extract landslides accurately and effectively

In Situ Growth of NiSe₂-MoSe₂ Heterostructures on Graphene Nanosheets as High-Performance Electrocatalyst for Hydrogen Evolution Reaction

Developing highly efficient and stable electrocatalysts for hydrogen evolution reaction (HER) is regarded as a crucial way to reduce energy loss in water splitting. Herein, NiSe2/MoSe2 heterostructures grown on graphene nanosheets (NiSe2-MoSe2 HTs/G) have been in situ synthesized by a simple hydrothermal reaction. As an electrocatalyst for HER, NiSe2-MoSe2 HTs/G delivers superior performance with a low Tafel slope of 65 mV dec−1, a small overpotential of 144 mV at 10 mA cm−2, and long-term stability up to 24 h. The superior performance for HER can be mainly ascribed to the synergistic effects of NiSe2-MoSe2 heterostructures, which can facilitate the rapid electron transfer from the electrode to the exposed MoSe2 edges to take part in the HER reaction, thus boosting the HER kinetics. Moreover, the graphene matrix with high conductivity can not only improve the overall conductivity of the composite but also greatly increase the exposed active sites, therefore further promoting the HER performance. This study provides a simple route for fabricating bimetallic selenides-based heterostructures on graphene as an efficient and stable electrocatalyst for HER

Depth and thermal sensor fusion to enhance 3D thermographic reconstruction

Three-dimensional (3D) geometrical models with incorporated surface temperature data provide important information for various applications such as medical imaging, energy auditing, and intelligent robots. In this paper we present a robust method for mobile and real-time 3D thermographic reconstruction through depth and thermal sensor fusion. A multimodal imaging device consisting of a thermal camera and a RGB-D sensor is calibrated geometrically and used for data capturing. Based on the underlying principle that temperature information remains robust against illumination and viewpoint changes, we present a thermal-guided iterative closest point (T-ICP) methodology to facilitate reliable 3D thermal scanning applications. The pose of sensing device is initially estimated using correspondences found through maximizing the thermal consistency between consecutive infrared images. The coarse pose estimate is further refined by finding the motion parameters that minimize a combined geometric and thermographic loss function. Experimental results demonstrate that complimentary information captured by multimodal sensors can be utilized to improve performance of 3D thermographic reconstruction. Through e ective fusion of thermal and depth data, the proposed approach generates more accurate 3D thermal models using significantly less scanning data

On-the-fly extrinsic calibration of multimodal sensing system for fast 3D thermographic scanning

The fusion of three-dimensional (3D) geometrical and two-dimensional (2D) thermal information provides a promising method for characterizing temperature distribution of 3D objects, extending infrared imaging from 2D to 3D to support various thermal inspection applications. In this paper, we present an effective on-the-fly calibration approach for accurate alignment of depth and thermal data to facilitate dynamic and fast-speed 3D thermal scanning tasks. For each pair of depth and thermal frames, we estimate their relative pose by minimizing the objective function that measures the temperature consistency between a 2D infrared image and the reference 3D thermographic model. Our proposed frame-to-model mapping scheme can be seamlessly integrated into a generic 3D thermographic reconstruction framework. Through graphics-processing-unit-based acceleration, our method requires less than 10 ms to generate a pair of well-aligned depth and thermal images without hardware synchronization and improves the robustness of the system against significant camera motion

C–H Alkynylation of <i>N</i>‑Methylisoquinolone by Rhodium or Gold Catalysis: Theoretical Studies on the Mechanism, Regioselectivity, and Role of TIPS-EBX

The Rh/Au-catalyzed regioselective C-8/C-4 alkynylation of <i>N</i>-methylisoquinolone has been theoretically investigated with the aid of density functional theory (DFT) calculations. The versatile function of substrate 1-[(triisopropylsilyl)­ethynyl]-1,2-benziodoxol-3­(1<i>H</i>)-one (TIPS-EBX) is explored in this study. In the [Cp*RhCl₂]₂-catalyzed reaction, TIPS-EBX is acted as the Brønsted base to go through the self-assisted deprotonation mechanism to produce the C8-alkynylation product solely. The obvious regioselectivity could be attributed to the electron effects. In contrast, due to the steric effects, the C4-alkynylation product becomes the major product by employing AuCl as the catalyst. In this reaction, the iodine­(III) center in the TIPS-EBX moiety could be employed as a strong Lewis acid to activate the alkyne moiety efficiently