When you purchase through links in our articles, we may earn a small commission. Laptop docking stations may evolve into'AI docks' with video and apps Synaptics also says it will offer both USB4 chips as well as DisplayLink in the future. For better or worse, laptop docking stations have generally been "dumb" devices. Synaptics and its customers are hoping to change that. Right now, there are two main technologies that "compete" in the docking stations space: USB4 (which Intel puts its own spin on with its Thunderbolt 4 technology) and DisplayLink (a technology Synaptics bought in 2020).

Device-free human presence detection is a crucial technology for various applications, including home automation, security, and healthcare. While camera-based systems have traditionally been used for this purpose, they raise privacy concerns. To address this issue, recent research has explored the use of wireless channel state information (CSI) extracted from commercial WiFi access points (APs) to provide detailed channel characteristics. In this paper, we propose a device-free human presence detection system for multi-room scenarios using a time-selective conditional dual feature extract recurrent network (TCD-FERN). Our system is designed to capture significant time features on current human features using a dynamic and static data preprocessing technique. We extract both moving and spatial features of people and differentiate between line-of-sight (LoS) and non-line-of-sight (NLoS) cases. Subcarrier fusion is carried out in order to provide more objective variation of each sample while reducing the computational complexity. A voting scheme is further adopted to mitigate the feature attenuation problem caused by room partitions, with around 3% improvement of human presence detection accuracy. Experimental results have revealed the significant improvement of leveraging subcarrier fusion, dual-feature recurrent network, time selection and condition mechanisms. Compared to the existing works in open literature, our proposed TCD-FERN system can achieve above 97% of human presence detection accuracy for multi-room scenarios with the adoption of fewer WiFi APs.

In recent years, the demand for pervasive smart services and applications has increased rapidly. Device-free human detection through sensors or cameras has been widely adopted, but it comes with privacy issues as well as misdetection for motionless people. To address these drawbacks, channel state information (CSI) captured from commercialized Wi-Fi devices provides rich signal features for accurate detection. However, existing systems suffer from inaccurate classification under a non-line-of-sight (NLoS) and stationary scenario, such as when a person is standing still in a room corner. In this work, we propose a system called CRONOS (Colorization and Contrastive Learning Enhanced NLoS Human Presence Detection), which generates dynamic recurrence plots (RPs) and color-coded CSI ratios to distinguish mobile and stationary people from vacancy in a room, respectively. We also incorporate supervised contrastive learning to retrieve substantial representations, where consultation loss is formulated to differentiate the representative distances between dynamic and stationary cases. Furthermore, we propose a self-switched static feature enhanced classifier (S3FEC) to determine the utilization of either RPs or color-coded CSI ratios. Our comprehensive experimental results show that CRONOS outperforms existing systems that either apply machine learning or non-learning based methods, as well as non-CSI based features in open literature. CRONOS achieves the highest human presence detection accuracy in vacancy, mobility, line-of-sight (LoS), and NLoS scenarios.

Human presence detection in indoor environments using millimeter-wave frequency-modulated continuous-wave (FMCW) radar is challenging due to the presence of moving and stationary clutters in indoor places. This work proposes "HOOD" as a real-time robust human presence and out-of-distribution (OOD) detection method by exploiting 60 GHz short-range FMCW radar. We approach the presence detection application as an OOD detection problem and solve the two problems simultaneously using a single pipeline. Our solution relies on a reconstruction-based architecture and works with radar macro and micro range-Doppler images (RDIs). HOOD aims to accurately detect the "presence" of humans in the presence or absence of moving and stationary disturbers. Since it is also an OOD detector, it aims to detect moving or stationary clutters as OOD in humans' absence and predicts the current scene's output as "no presence." HOOD is an activity-free approach that performs well in different human scenarios. On our dataset collected with a 60 GHz short-range FMCW Radar, we achieve an average AUROC of 94.36%. Additionally, our extensive evaluations and experiments demonstrate that HOOD outperforms state-of-the-art (SOTA) OOD detection methods in terms of common OOD detection metrics. Our real-time experiments are available at: https://muskahya.github.io/HOOD

In recent years, indoor human presence detection based on supervised learning (SL) and channel state information (CSI) has attracted much attention. However, existing studies that rely on spatial information of CSI are susceptible to environmental changes which degrade prediction accuracy. Moreover, SL-based methods require time-consuming data labeling for retraining models. Therefore, it is imperative to design a continuously monitored model using a semi-supervised learning (SSL) based scheme. In this paper, we conceive a bifold teacher-student (BTS) learning approach for indoor human presence detection in an adjoining two-room scenario. The proposed SSL-based primal-dual teacher-student network intelligently learns spatial and temporal features from labeled and unlabeled CSI datasets. Additionally, the enhanced penalized loss function leverages entropy and distance measures to distinguish drifted data, i.e., features of new datasets affected by time-varying effects and altered from the original distribution. Experimental results demonstrate that the proposed BTS system sustains asymptotic accuracy after retraining the model with unlabeled data. Furthermore, BTS outperforms existing SSL-based models in terms of the highest detection accuracy while achieving the asymptotic performance of SL-based methods.

The Lenovo ThinkPad X1 Nano is just the kind of powerful, light, and long-lasting laptop you'll want to take with you on post-pandemic business trips--and it's handy even now just because it's so easy to take all over the house. It also performs right there in the ballpark with other 11th-gen Tiger Lake competitors, and at a hair under two pounds, it weighs less than almost all of them. Equipped with an IR camera for facial recognition, a presence-detecting radar, a 2K display with Dolby Vision HDR, and a premium keyboard, the X1 Nano covers the most bases for corporate users, and we haven't mentioned the superlative battery life yet. But with only two available ports (Thunderbolt 4, at least), you'll need to invest in a USB-C hub to connect legacy accessories. Lenovo offers nine versions of the ThinkPad X1 NanoRemove non-product link on its retail website.

This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. Through judicious pre-processing of the estimated CSI followed by deep learning, reliable presence detection can be achieved. Several challenges in passive RF sensing are addressed. With presence detection, how to collect training data with human presence can have a significant impact on the performance. This is in contrast to activity detection when a specific motion pattern is of interest. A second challenge is that RF signals are complex-valued. Handling complex-valued input in deep learning requires careful data representation and network architecture design. Finally, human presence affects CSI variation along multiple dimensions; such variation, however, is often masked by system impediments such as timing or frequency offset. Addressing these challenges, the proposed learning system uses pre-processing to preserve human motion induced channel variation while insulating against other impairments. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection. Extensive experiments are conducted. Using off-the-shelf WiFi devices, the proposed deep learning based RF sensing achieves near perfect presence detection during multiple extended periods of test and exhibits superior performance compared with leading edge passive infrared sensors. The learning based passive RF sensing thus provides a viable and promising alternative for presence or occupancy detection.

Evaluation kits from ams support eye tracking, presence detection and object recognition in virtual reality headsets, smart lighting and home and building automation products. The Raspberry Pi and Arduino-based NanEyeC evaluation kits are based around the ams NanEyeC miniature image sensor. The NanEyeC camera, image sensor is supplied as a 1.0 x 1.0mm surface-mount module. It produces 100kpixel resolution up to 58 frames per second and can be used for video applications where the camera needs to be accommodated in an extremely small space, such as eye tracking in virtual reality (VR) headsets. It can also be applied in user presence detection, to support automatic power on/off controls in home and building automation (HABA) applications such as air conditioning, home robotics, appliances and smart lighting.

Lattice Semiconductor announced key performance and design flow enhancements for its Lattice sensAI solutions stack. The Lattice sensAI stack provides a comprehensive hardware and software solution for implementing low power (1mW-1W), always-on artificial intelligence (AI) functionality in smart devices operating at the edge. IHS forecasts 40 billion devices will be operating at the network edge by 2025. For reasons including latency, network bandwidth limitations, and data privacy, OEMs designing always-on edge devices want to minimize sending data to the cloud for analytics. Lattice sensAI enables such OEMs to seamlessly update their existing designs with low power AI inferencing optimized for their application requirements.