Virtual reality (VR) environments have been widely applied in cognitive assessment and intervention research; however, most existing studies on cognitive load rely on fixed-difficulty paradigms that are insufficient for addressing Alzheimer’s disease (AD)-specific deficits, limit ecological validity, and risk cognitive overload and interpretive bias. To overcome these limitations, we proposed and validated a multimodal VR method with adaptive adjustment capabilities, grounded in the affective–cognitive impairments of AD patients. In this study, we further introduce a closed-loop adaptive VR method that dynamically modulates task parameters across trials in accordance with users’ real-time cognitive states. Eye-tracking and electroencephalography (EEG) modules are incorporated to enable synchronous acquisition and integration of multimodal physiological signals, supporting real-time cognitive load modeling and adaptive task modulation. The task battery combines emotion recognition and executive function evaluation with multi-level visual and auditory interference to effectively elicit AD-related cognitive deficits. Experimental results demonstrate that the proposed method effectively maintains participants within an optimal cognitive load range, mitigating risks of overload and underload while significantly improving EEG signal reliability and behavioral validity. Our findings provide strong empirical evidence for the effectiveness of adaptive VR approaches in individualized cognitive assessment and intervention for AD, and lay a solid foundation for the broader application of immersive VR technology in neuropsychological research and practice.

This paper presents a fully integrated continuous-scalable conversion-ratio (CSCR) switched-capacitor voltage regulator (SCVR), capable of supporting a wide range of voltage conversion ratios and high load current for SOCs used in computing and other applications. This design can achieve both step-down and step-up voltage conversion and has phase-merged turbo modes to deliver higher output current. It can autonomously and dynamically transition between step-down and step-up modes based on conversion ratio and between regular and turbo modes depending on load current. This SCVR is designed using Intel’s 3nm FinFET technology and is capable of an output voltage range of 0.5V-1.2V from an input of 1.1V. Furthermore, the input voltage can be in the range 1.1-1.3V for the same output voltage range. It can deliver up to 8A of load current at a 1GHz flying capacitor switching frequency and exhibits a peak efficiency of 88%. This design minimizes the added die area for the converter by implementing the flying capacitors over the load domain and achieves a maximum active area power density of 14W/mm$^{2}$. The converter uses a fast asynchronous voltage droop detector, in combination with auto-transition to turbo modes, to achieve excellent transient response-an output voltage droop of 37.5mV was measured for a fast (100ps) load current step from 0.1A to 6A. It also features configurable maximum switching frequency reduction and clock interleaving to achieve an output voltage ripple of 20mV.

Due to the complexity and diversity of indoor environment objects and interference occlusions, the accuracy of multi-object target detection based on 3D point cloud is limited. To address this issue, we present a multi-target detection method based on adaptive feature adjustment (AFA) of 3D point cloud. First, our method preprocesses the dataset and constructs a backbone module. Afterwards, our method uses an improved PointNet[Formula: see text] network for feature adaptive learning, where an AFA module is added to learn the influence relationship between point pairs. The proposed method then establishes the relationship between contexts in the local point set area and extracts the feature of point cloud. Using the idea of Hough voting, our method can generate some votes close to the particle. Using these votes to generate proposal, the proposed method adds CBAM attention mechanisms to both modules of voting and proposal, which can fuse the feature information of the channel and expand the receptive field in space. Our method can enhance the important features and weaken the unimportant features, making the extracted features more directional and enhancing the expressiveness of the network. Finally, the generated results are visualized to complete the multi-target detection of 3D point cloud. To verify the effectiveness of our proposed method, two large datasets with real 3D scanning, scanNet2 and SunRGB-D, are used for training the network. The experimental results show that the proposed method can improve the effectiveness of point cloud target detection in indoor scenes, getting a higher detection accuracy.

Hough voting based on PointNet++ [1] is effective against 3D object detection, which has been verified by VoteNet [2], H3DNet [3], etc. However, we find there is still room for improvements in two aspects. The first is that most existing methods ignores the particular significance of different format inputs and geometric primitives for predicting object proposals. The second is that the feature extracted by PointNet++ overlooks contextual information about each object. In this paper, to tackle the above issues, we introduce MCGNet to learn multi-level geometric-aware and scale-aware contextual information for 3D object detection. Specifically, our network mainly consists of the baseline module based on H3DNet, geometric-aware module, and context-aware module. The baseline module feeding with four-types inputs (Point, Edge, Surface, and Line) concentrates on extracting diversified geometric primitives, i.e., BB centers, BB face centers, and BB edge centers. The geometric-aware module is proposed to learn the different contributions among the four-types feature maps and the three geometric primitives. The context-aware module aims to establish long-range dependencies features for either four-types feature maps or three geometric primitives. Extensive experiments on two large datasets with real 3D scans, SUN RGB-D and ScanNet datasets, demonstrate that our method is effective against 3D object detection.

The issue of image attribute migration is one of the hot research topics in the field of computer vision, which has received extensive research interest. However, current unsupervised image attribute migration models using symmetric generative adversarial network structure do not work well on datasets with large geometric variations, where the results lack diversity and are of low quality. To address these problems, we present an image attribute migration model based on decoupling and adaptive layer instance normalization. First, a codec structure based on a decoupled representation is constructed as the generator, and an adaptive layer instance normalization operation is used in the decoder. Then, the iterations of the model are constrained by various improved loss functions. We conducted controlled experiments and compared the results of our method with other methods using several datasets with large geometric variations. The experimental results demonstrate that the proposed method can achieve high quality and diverse image attribute migration.

This paper introduces the utilization of two different input-referred offset voltage correction methods applied to multiple amplifiers within a front-end sensing circuit of a buck regulator for the first time. The multi-amplifier system under investigation contains an instrumentation amplifier consisting of three folded cascode stages and an additional amplifier configured as a unity-gain buffer for a reference voltage. The first method in this work alleviates voltage offsets in this 4-amplifier system based on a shared auxiliary amplifier correction circuit that switches between different target amplifiers; whereas the second method applies a chopping-based auto-zero procedure to cancel the input-referred offset voltage of the same amplifiers. Since the instrumentation amplifier is designed for feedback sensing in integrated power management applications, it has a relatively high bandwidth requirement. For this reason, the chopping technique does not involve a low-pass or band-pass filter. Instead, a successive approximation register (SAR) analog-to-digital converter is used to sense the output. Measurements of the amplifiers fabricated in a 130nm CMOS technology demonstrate that the auxiliary auto-zero offset cancellation method leads to lower input-referred offset voltage standard deviation ($\sigma = 1.31\,\,\mu \text{V}$) compared to the chopping technique ($\sigma = 184.67\,\,\mu \text{V}$), and that the die area requirement and power consumption with the auxiliary amplifier-based offset cancellation (0.105 mm2, 1.32 mW) are lower than with the chopping method (0.25 mm2, 1.72 mW).

This paper introduces an accurate frequency regulation method based on a constant on-time (COT) buck regulator designed for point-of-load (POL) regulation with a fast load transient requirement and wide load range. Traditional COT regulators suffer from frequency shift due to load variations. The proposed frequency adjustment circuit helps the COT control engine to reach its target frequency of operation by adjusting the on-time pulse under varying conditions. This circuit includes a phase detector and a voltage-to-current converter block. The phase error between the target clock signal and the PWM is directly used to adjust the width of the on-time pulse. This will fine-tune the bandwidth of the frequency regulation loop, which can help to improve the transient performance of the COT as well. The fabricated chip has been tested under 20A/$\mu \text{s}$load transient speed with a 10A load step condition. The measured silicon performance shows that the error of the switching frequency is less than 0.16% in the 400kHz to 1MHz range during steady-state operation.

OBJECTIVE: Complexional inspection as a traditional methed in traditional Chinese medicine (TCM) has been widely used in disease treatment. The work in this paper compare the colorimetrie parameters of normal tongue tip color under different illuminants A, B, C, E and D65, and provide experimental basis for the standardization research of the complexional inspection. METHODS: In this study, 35 normal undergraduate students were taken as the research subjects. The data of tongue tip color were collected based on the visible reflection spectrum under the revised equal-energy white light (standard illuminant E), and the 380–780nm spectrum curve were obtained. Then the visible reflection spectrum curves under different illuminants were computed based on the spectrum distribution of the standard illuminants A, B, C, E and D65. The CIE XYZ tristimulus values of normal tongue tip color was calculated and the colorimetrie parameters on normal tongue tip color under different standard illuminants were obtained and analyzed, such as CIE 1964 chromaticity coordinates, dominant wavelength and RGB values. RESULTS: The results showed that there are significant differences among the visible reflection spectrum curves, CIE 1964 chromaticity coordinates and RGB values under the standard illuminants A, B, C, E and D6S. However, with reference to the characteristics of the different standard illuminants, the dominant wavelengths of the normal tongue tip color are consistent. CONCLUSION: The application of visible reflection spectrum is a standard way to collect colorimetrie data for inspection of the complexion.