A digital scan of Defense Secretary Pete Hegseth’s driver’s license is among more than 170 million identity documents that can now be purchased on the dark web. The scans, as reported by cybersecurity expert Brian Krebs on Tuesday, are linked to a new identity theft service known as Nexus. The service was discovered one day earlier when it was advertised on a Russian cybercrime forum. Download the Straight Arrow app today to get the stories that matter free from manipulation, bias or agenda.™ Point phone camera here Nexus claims to offer more than 153 million scans of drivers licenses from the U.S. and Canada, an additional 10 million ID cards, three million travel documents and international IDs, as well as roughly 579,000 medical IDs. Krebs, who reported gaining access to the service, says that an undetermined number of other forms of identification are also on the site. They include marijuana dispensary cards; “CDLs,” presumably a reference to commercial drivers licenses; and CAC cards, which Krebs said may be common access cards used by government employees to gain access to government buildings and secure spaces. The people behind Nexus — not to be confused with LexisNexis, the U.S. data analytics firm — claim that the documents are being obtained from an ongoing breach at a major identity verification company. “We have been continuously exfiltrating new data for over a year into our private database,” the group behind Nexus said in a post to the cybercrime forum. “Records are available to preview before purchase with pertinent information redacted. Customer photos are displayed if available.” Number of stolen IDs growing With the number of IDs available in Nexus increasing by nearly 400,000 in just 24 hours, Krebs said he believes Nexus’ claim that the breach is ongoing. Based on Krebs’ analysis of the
Sep 2, 2026 · via san.com
At any café in Oslo, a fellow patron wearing sleek frames could be silently identifying faces at the next table — logging presence, running features against a database — and no one would know. That specific discomfort is exactly what Norway’s government moved to address on August 25, 2026, when Digitalisation Minister Karianne Tung announced plans to tighten rules on camera-enabled wearables. This isn’t Scandinavian technophobia. It’s a direct question about who controls what gets built into the device sitting on someone’s face. What Norway Is Actually Proposing A targeted strike at specific AI features — not a blanket ban on smart glasses. Most legislation chases technology. Norway is trying to outrun it. Tung’s announcement doesn’t touch the core gadget appeal of products like Meta Ray-Bans or Snap Spectacles — it targets the surveillance capabilities underneath. “We can see that people’s private lives and privacy are coming under pressure from new technology, and I therefore want to regulate smart glasses and similar devices more strictly than today,” Tung said, according to a Norwegian government statement carried by RTE News. What’s on the table: - A potential ban on facial recognition in consumer wearables used in public spaces - An expert advisory group being established to recommend specific regulatory options - The Norwegian Consumer Council urging retailers to pause sales pending legal clarity - Both public and private sectors have been asked to review their own smart glasses guidelines Critically, the move is precautionary — Meta hasn’t officially activated facial recognition on its glasses yet, and Norway isn’t waiting. Tung has also flagged something worth sitting with: this isn’t only about glasses. “A clear trend that artificial intelligence is being combined with cameras and microphones built into things like glasses, earbuds, caps, and other items we use in everyday life,” she
Sep 2, 2026 · via gadgetreview.com
A mere five years after the government first started talking about it, new changes have finally been proposed for the Privacy Act. They could not be more overdue and urgent. “Pervert glasses” are selling out at Kmart, facial recognition technology is proliferating in our everyday environments, and microtargeting for advertising has filled our algorithms with nonsense and toxic junk. Privacy reform is hugely popular: 93% of Australians say protecting personal information is important to them, and 87% say they are more concerned about their privacy than they were five years ago. But it is practically impossible for us to take personal responsibility for our data footprint, especially in the age of AI. Tick-a-box consent is a broken model. Australians rate protecting our personal information as their number one priority for AI regulation. Granting strong legal protections over personal information is one of the most important and impactful ways to reshape technology in the interests of the many, not the few. Data-extractive business models give rise to all sorts of negative downstream consequences, such as extremist and misleading content, addictive algorithms and careless product design. Privacy law has the capacity to target reform at the source of the problem – the collection, use and storage of personal information. This is far more effective than playing whack-a-mole with the latest exploitative or harmful product built by some avaricious tech bro who has been encouraged by our permissive regulatory environment. Australia’s privacy laws remain woefully out of date, with the majority having been drafted four decades ago. This latest tranche of reform, if implemented, will be a highly significant improvement, bringing us closer to similar jurisdictions such as Europe and California. At the centre is a fair and reasonable test. This shifts the onus away from individuals to make impossible decisions about consenting
Sep 2, 2026 · via theguardian.com
The Department of Homeland Security’s Science and Technology Directorate tested facial biometric systems in May at the Progreso International Bridge to help U.S. Customs and Border Protection verify pedestrians leaving the United States for Mexico, according to the agency. Pilot tests facial recognition technology The pilot brought industry partners to the Texas border crossing to assess whether cameras and related systems could capture usable facial images of travelers walking out of the United States. CBP asked S&T in October 2025 to identify technology that could securely, efficiently and accurately confirm pedestrian departures using biometrics. CBP already uses biometric verification for other traveler-screening purposes at Progreso, as The Dallas Express previously reported. DHS selects three biometric systems S&T coordinated with federal and industry partners through Cooperative Research and Development Agreements, allowing vendors to cover their own costs while providing data for government decision-making. In early 2026, S&T invited six companies to the Maryland Test Facility for initial capability demonstrations. Those demonstrations led CBP and S&T to select three solutions for additional data collection: two from NEC National Security Systems and a joint offering from Paravision and AiFi. Before the field test, S&T built a replica of the pedestrian exit environment at the Maryland Test Facility so vendors could install and assess their prototypes in a controlled setting. The industry partners then arrived at the Progreso International Bridge in May for the operational assessment. Technology faces real-world conditions The partners mounted some cameras on poles at different heights to account for travelers of different statures. They used artificial intelligence and other technologies to construct a 3D digital environment of the capture zone, observe pedestrian movement and analyze whether facial images were suitable for matching in CBP’s Traveler Verification Service. The test accounted for variables including walking pace, hats, sunglasses and umbrellas. The
Sep 2, 2026 · via dallasexpress.com
The Denton County Sheriff’s Office has renewed an agreement with Clearview AI to continue using the company’s facial recognition technology in investigations. Denton County Commissioners approved a new agreement between the Denton County Sheriff’s Office and Clearview AI, a company that offers artificial intelligence-based facial recognition technology. The Sheriff’s Office has been using this company’s technology since Oct. 1, 2023, according to past Commissioners Court agendas. In the renewal, Denton County will pay almost three times as much over the course of the three-year contract since 2023. According to documents included with a Commissioners Court agenda from Sept. 12, 2023, the county agreed three payments over three years totaling $23,628. In documents included with the Aug. 25 agenda, county commissioners approved the renewal that will total $62,500 over the three-year contract. It’s not clear in the documents what services or technology are provided, or if there are changes in services in the renewal. The Denton Record-Chronicle made multiple attempts to contact the Denton County Sheriff’s Office but did not receive further information about the office’s use of the technology by Tuesday afternoon. When an officer uploads a photo of a person to Clearview AI, it searches more than 70 billion publicly accessible images to produce links that may contain similar faces, according to the company’s website. Clearview AI is currently available for use only by government agencies. The company claims to produce results that are at least 99% accurate for all demographics. The Fort Worth, Irving and Plano Police Departments were reportedly also using Clearview AI on a “trial basis” in 2020. The Denton Record-Chronicle attempted to contact all three Police Departments to confirm if they are still using the technology; none responded by Tuesday afternoon. The Dallas Police Department has also been using the technology since October 2024. The
Sep 1, 2026 · via dentonrc.com
Notting Hill Carnival: 121 arrests came from face scanning Almost a fifth of the 636 arrests began with live facial recognition, deployed within the carnival grounds for the first time at the weekend Previous Article Next Article
Sep 1, 2026 · via thetimes.com
This press release is provided by GlobeNewswire and is published as received. VANCOUVER, British Columbia, Sept. 01, 2026 (GLOBE NEWSWIRE) -- SPARC AI Inc. (CSE: SPAI; OTCQB: SPAIF; Frankfurt: 5OV0) (“SPARC AI” or the “Company”) a defense technology company delivering positioning for drones in GPS-denied environments, today announced the successful completion of a series of flight tests in which the Overwatch Positioning Network accurately calculated target locations while the drone’s camera was physically blindfolded and covered. With the camera lens fully obscured for the duration of the tests, the aircraft flew a structured test pattern and the Overwatch API returned target coordinates computed entirely from flight telemetry. The results confirm a deliberate design principle of the Overwatch architecture: the positioning solve uses no image recognition software and no pre-loaded reference imagery of any kind. Most alternative approaches to GPS-denied positioning and targeting rely on computer vision with matching camera imagery against stored maps or terrain features. Those systems degrade or fail at night, in fog, smoke, or dust, over water, snow, desert, and featureless terrain, and they require imagery of the operating area to be collected and loaded in advance. Overwatch is telemetry-only and none of these limitations apply. The successful blindfold tests demonstrate that Overwatch performs identically in zero-visibility conditions, and that the onboard camera serves purely as an operator aiming aid and not as a sensor input to the positioning calculation. The vision-free architecture also underpins Overwatch’s integration model. The system requires no hardware or software installed on the aircraft. Its inputs are telemetry that every commercial flight controller already produces, and its output is standard latitude, longitude, and time — indistinguishable to an autopilot from a conventional GPS fix. This means any drone OEM can integrate the SPARC AI API into an existing workflow without redesigning the
Sep 1, 2026 · via bnnbloomberg.ca
Proximity and Decision-Making: Redefining Machine Vision in Industry 4.0 Key Highlights - Manufacturers are shifting focus from scale to proximity, producing closer to demand and responding faster to emerging needs. - Effective machine vision systems should be designed around the specific decision they support, not just defect detection. - Identifying the earliest reliable signal of a problem allows for earlier intervention, reducing waste and preventing defects. - Pattern recognition and contextual data enhance understanding of process variations, enabling proactive maintenance and quality control. - The ultimate goal is to connect visual insights directly to operational decisions, reducing response times and increasing manufacturing agility. For much of industrial history, manufacturers won by mastering scale. They built larger plants, longer production runs, standardized processes, and global supply chains designed to lower unit cost and increase consistency. That logic still matters. But it is no longer the only source of advantage. The next advantage comes from proximity. By proximity, I mean the ability to move value closer to the moment and location at which a need emerges. In manufacturing, that might mean producing closer to the customer, configuring products later, responding faster to demand, or correcting a process before a small variation becomes a larger quality problem. Machine vision sits at the center of this shift. Too often, vision systems are specified around a narrow question: Can the system detect the defect? That is an essential starting point, but it is no longer enough. One of my first clients in my early days at McKinsey was a company producing inexpensive image-capture devices that manufacturers could affordably embed throughout the production flow. Even then, the promise was clear: when manufacturers could see more of what was happening inside the operation, they could make better decisions. Those technologies have advanced dramatically since, and companies are
Sep 1, 2026 · via vision-systems.com
Windows sometimes feels like it's lacking advanced features when compared to other operating systems, but the truth is that Windows has a lot of fancy power tools at its disposal. The problem, however, is that they're often buried in menus or just not immediately obvious—you have to know they're there, or you'll never use them. So, here are a few such features that you'll probably want to start using immediately and wish you'd known about years ago. Stop losing things you copied with Clipboard History Keep your copied items handy A virtual clipboard is a place that stores all the stuff you've recently copied. It's something you've likely seen and used before within your phone's keyboard, but what many don't realize is that Windows has this feature as well, and it's easier to access than you might think. To access Clipboard History, simply press Windows+V. It's disabled by default, but Windows will ask you if you'd like to enable the feature, which you definitely should. Alternatively, enable it in Settings > System > Clipboard. There's an option in there to sync Clipboard History across your devices if you wish. Once it's active, the clipboard will save the 25 most recently copied items. If you have certain templates that you copy and paste from a document all the time, you can pin your most-used ones in the clipboard so they always stay there, even after restarting your PC. I have several such items, and this trick has saved me hours of needless copy-pasting over the past couple of years. Turn one monitor into several with Virtual Desktops Make one desktop feel like several Have you always wished you had more than one monitor but just didn't have the space for it? With Virtual Desktops, you can create multiple workspaces on the
Sep 1, 2026 · via howtogeek.com
Au10tix, Neo bring biometric identity verification to Australian gaming kiosks Au10tix has integrated its identity verification platform into self-service kiosks from Australian automation provider Neo, bringing biometric matching, liveness detection, document authentication, age assurance and fraud checks to gaming, wagering and hospitality venues. The system supports customer-initiated identity verification at self-service terminals. The integration automates customer onboarding, registration and card replacement, reducing identity verification workflows by up to tenfold compared with manual processing. Customers scan an identity document and capture a live facial image. The system verifies the document, performs liveness detection and matches the live face biometric with the identity document portrait before completing the identity check. “As more customer journeys move to self-service, identity verification has to move with them,” says Yair Tal, chief executive officer of Au10tix. Card replacement and reissuance are key applications that can be handled at a self-service terminal. The companies estimate that the card reissuance process will be reduced to minutes. The companies say they are currently supporting live deployments at Australian gaming and hospitality venues. They did not disclose the number of customers, terminals, or transactions using the integration. Neo has been supplying self-service technology to wagering and gaming operators since 1991. Its portfolio includes terminals for account registration, wagering, card payments, and identity-related services. It has worked with technology provider Circle to supply more than 95 self-service kiosks across three properties operated by The Star Entertainment Group. Australian gambling operators using biometric verification must also comply with privacy requirements. Biometric facial information may be considered sensitive information under Australia’s Privacy Act, and guidance from the Office of the Australian Information Commissioner says organizations generally require consent to collect it. The announcement does not confirm if facial images are deleted after the transaction or retained for later authentication. Australia is the first
Sep 1, 2026 · via biometricupdate.com
Figures Abstract Background Artificial Intelligence (AI) models for mammography classification is prone to shortcut learning because diagnostically relevant evidence is typically sparse, localized, and easily dominated by non-lesion background context. This study aimed to develop a mammography-specific framework that integrates lesion-focused evidence with global image representations to improve classification performance and provide more clinically interpretable decision support. Methods We propose LENS, a hybrid CNN-Transformer family designed specifically for mammography. LENS combines a lightweight multi-scale convolutional neural network (CNN) backbone with an alternating local-global Transformer encoder. In addition to global image representations, a weakly supervised lesion-aware branch identifies and aggregates suspicious regional evidence to support image-level prediction. LENS was evaluated on the large-scale VinDrMammo dataset for the three-class mammography task and compared to advanced architectures, including ConvNeXt, DINOv2, GMIC, and Swin Transformer under a unified experimental protocol. Results LENS-Base achieved the highest overall Accuracy of 85.5%, Macro F1-score of 79.6%, and Matthews correlation coefficient of 0.65. Quantitative localization evaluation results indicated that the selected regional evidence frequently overlapped with annotated abnormalities. Furthermore, these promising results come with fewer parameters and lower FLOPs. Conclusion LENS integrates local lesion-related features with global anatomical context to achieve improved class-balanced mammography classification while providing quantitatively supported lesion-focused evidence. These findings suggest that LENS is a promising framework for AI-assisted mammography screening. Citation: Hoang DQ, Cao VK, Nguyen TN, Nguyen NS (2026) LENS: A mammography-specific hybrid CNN-Transformer with lesion-aware evidence modeling. PLoS One 21(9): e0350720. https://doi.org/10.1371/journal.pone.0350720 Editor: Fahad Farhan Almutairi, King Abdulaziz University, SAUDI ARABIA Received: May 16, 2026; Accepted: July 30, 2026; Published: September 1, 2026 Copyright: © 2026 Hoang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are
Sep 1, 2026 · via journals.plos.org
This Popular Video Doorbell Feature Is Leading To Lawsuits - Here's How To Disable It If you own a video doorbell, then you may want to look at what features you have turned on. That's because both Ring and Google are currently facing a lawsuit as a result of a facial-recognition feature built into security cameras and video doorbells they offer for purchase. The feature, which is called Familiar Faces on Ring devices and Familiar Face Detection on Google devices, is designed to recognize people you have saved in your device settings. While the feature sounds useful, the big problem here is that a new proposed class action lawsuit filed against Amazon in June claims that Ring has been collecting facial recognition information from anyone passing in front of those cameras. (via CNET) Because people often aren't aware of the fact that their faces are being recorded, the data has been gathered without their consent — which is a huge invasion of privacy. Some places, such as Illinois and Texas, don't allow features like Familiar Faces to be enabled even if the devices support them. The fact that these video cameras are almost always watching is just one of the many uncomfortable truths about using Ring cameras. Additionally, this is far from the first time we've seen Ring getting on people's bad sides. Earlier this year, we saw people getting rid of their Ring cameras. How to turn Familiar Faces off Ring devices have the feature turned off by default. However, if you have turned it on or just want to be sure you haven't enabled it, open the Ring app, tap the menu button, and go to Pro Features. From there, select Manage People under Familiar Faces, then tap the gear-shaped icon in the upper-right corner of the Familiar
Sep 1, 2026 · via bgr.com
Police to start using live facial recognition in county It is being used later this week. Lancashire Police is to start using live facial recognition technology in Blackpool this week. It will be deployed for the first time on Friday during the Illuminations Switch-On event, to help identify people wanted for serious offences. Ch Supt Chris Hardy, of Lancashire Police, said: “LFR compares faces captured on a live camera feed against a predetermined, secure watchlist of individuals wanted for serious offences, subject to court orders, or who pose a risk to the public or to themselves. “When the system identifies a possible match, a police officer will compare the image on screen with the person in view and decide whether to speak to them. This decision will always be made by an officer, not the technology. “Officers will always explain why they have engaged with someone and provide an information leaflet with contact details. “I want to be clear with people – we take your privacy very seriously - if you are not on a watchlist we will never store your biometric data from passing through the LFR zone of recognition. It is immediately and automatically deleted, ensuring privacy is protected. “Any further images are deleted within 24 hours after each day of deployment and CCTV footage is deleted within 31 days, the same as standard public cameras.”
Sep 1, 2026 · via planetradio.co.uk
Abstract Generating novel and functional molecules is an essential task in drug discovery, particularly in addressing the critical challenges of antibiotic resistance and the scarcity of effective treatments for major diseases such as cancer. Three-dimensional (3D) structure design can directly reflect a molecule’s biological function, while its complexity and topology irregularity make de novo 3D molecule generation highly difficult under valid geometric constraints. Conditional or controllable design is a promising solution for this challenging task in a more accurate and quick expectation manner. In this study, we propose TDmol-a Text-guided De novo 3D molecule generation approach based on a multimodal diffusion model. TDmol designs a new two-stage modality alignment contrastive deep learning pipeline to extract the cross-modality shared knowledge and unique features from different modalities, including molecular textual descriptions and molecular 2D/3D structures, enabling conditional and controllable molecule generation. To the best of our knowledge, TDmol is the first to realize text-3D modality alignment for conditional molecule generation. Experimental results demonstrate that TDmol achieves a significant performance enhancement for generating valid and reliable 3D molecular structures. This work highlights the potential of multimodal foundation models in digital medicine, offering a scalable and generalizable framework for 3D molecule generation that can be adapted across diverse clinical settings. A user-friendly webserver of TDmol has been deployed for academic use at http://www.csbio.sjtu.edu.cn/bioinf/TDMol. Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 62573293, 62473257), and the Science and Technology Commission of Shanghai Municipality (No. 24ZR1435300, 24510714300). Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use,
Sep 1, 2026 · via nature.com
Abstract Recent advancements in 3D Gaussian-based scene rendering have demonstrated significant potential for efficient neural scene representation. However, accurately capturing geometric boundaries and fine details in complex scenes during optimization remains challenging. Specifically, two key issues persist: (1) the blurring of edge details in high-contrast objects and (2) the loss of texture details in small, distant objects with sparse point cloud distributions. To address these challenges, we propose a method that introduces two key improvements: an anchor re-growing module that dynamically increases neural Gaussian density in high-gradient regions using edge-aware optimization, and the Segment Anything Model (SAM) for generating accurate object segmentation masks to guide segmentation-based loss computation. Our collaborative optimization strategy significantly enhances boundary clarity and texture fidelity in 3D Gaussian rendering, particularly in complex scenes. Evaluated on multiple datasets, our method demonstrates substantial improvements in rendering quality, achieving a 0.29 improvement in PSNR on the Tanks&Temples dataset compared to the baseline method. Project page: https://github.com/Mazycity57/Edge-GS Data availability No datasets were generated or analysed during the current study. References - Azinović, D., Martin-Brualla, R., Goldman, D.B., Nießner, M., Thies, J.: Neural RGB-D surface reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6290–6301. (2022) - Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5470–5479. (2022) - Berger, M., Tagliasacchi, A., Seversky, L.M., Alliez, P., Guennebaud, G., Levine, J.A., Sharf, A., Silva, C.T.: A survey of surface reconstruction from point clouds. Computer Gr. Forum 36, 301–329 (2017) Wiley Online Library - Chen, H., Li, C., Lee, G.H.: Neusg: neural implicit surface reconstruction with 3D gaussian splatting guidance. Preprint at arXiv:2312.00846 (2023) - Chen, Y., Lee, G.H.: Dogaussian: Distributed-oriented gaussian splatting for large-scale
Sep 1, 2026 · via link.springer.com
This article was originally published on Algorithm Watch on June 27, 2026. This edited version is republished on Global Voices under a Creative Commons Attribution 4.0 International license (CC BY 4.0). Over the past two years, the Georgian government has built a comprehensive face recognition enforcement system, procured by a Moscow-based company with ties to the Federal Security Service (FSB), and its impact on demonstrators is troubling. In mid-March 2025, Nino, a Georgian in her 40s, received a call from the Tbilisi City Court, which summoned her to a hearing for blocking a road in the Georgian capital city two and a half months earlier. The call took her by surprise. Nino had protested against the alleged rigging of the last elections as well as the suspension of EU integration talks. She had known for a long time that the protest site surrounding the Georgian Parliament was covered with surveillance cameras, but had not considered them to be so effective at identifying her. At the hearing, Nino saw footage of herself at the protest; she was accused of initiating a blockade of the road. The system had identified her through face recognition software and processed her as an unlawful protester — the imposed fine amounted to GEL 5,000 (around EUR 1,620 or USD 1,878). Five months later, Nino’s bank accounts were frozen. She was forced to set up a crowdfunding campaign to pay off the fine. Like many others in Tbilisi, Nino could not afford the risk of receiving any more fines and largely stopped protesting. Her case resonated with many other Georgians who, since 2025, have stopped attending demonstrations against the ruling Georgian Dream party, in power since 2012 and dominated behind the scenes by its founder Bidzina Ivanishvili, a billionaire whose fortune was built in post-Soviet Russia. The
Sep 1, 2026 · via globalvoices.org
I am standing in front of a camera that’s streaming to a giant video screen. The image recognition AI running on the camera puts a big green box around me. “PERSON,” it says. Artist Simon Weckert hands me his newest creation, a button-down shirt with flowery, blurry globs of green and pink. I put the shirt in front of me. The box and the word “PERSON” disappears. I pull the shirt away. The box pops back up. I put the shirt in front of me. It disappears. This is “digital camouflage,” and it has confused the algorithm. Weckert designed digital camouflage as a response to the proliferation of AI-powered surveillance cameras that detect people, vehicles, animals, bicycles, and other objects in their field of view. Many of these AI-powered cameras can detect when a person is lying down, “anomalous behavior” such as people loitering, people fighting, or an abandoned package, which can lead to a police response. These cameras are already relatively commonplace around the world, but Weckert created it because police recently deployed these types of cameras outside of Kotbusser Tor, a popular subway stop in Berlin. They are the first police-run object recognition surveillance cameras in the city. “Obviously people don’t like it because it means that AI is tracking the movements and behaviors of people. It’s one thing to have somebody behind the camera watching you, but now we have AI doing this kind of analysis,” Weckert told me. “It can detect if somebody’s laying on the ground so that means homeless people could be detected and police get triggered.”
Sep 1, 2026 · via 404media.co
Without image processing: Drone autonomously hangs onto branches with a gripper A drone from TU Delft manages without image recognition to grasp branches of a tree and land on them. It feels the branches. A research team from Delft University of Technology (TU Delft) has developed a drone that can hang onto branches with a gripper to pause during a mission. The gripping function is triggered by tactile sensors, so no visual recognition systems need to be used, which would not function reliably in cluttered treetops anyway. When monitoring ecosystems, drones sometimes need to remain in one place to collect data. However, hovering is noisy and consumes a lot of energy. In such cases, it is better if the drone can switch off its motors and remain in a waiting position. This saves energy, and the battery lasts longer, as the scientists write in the study “Aerial tactile perching via an anthropomorphic hand with embodied soft tactile receptors,” which was published in Nature npj Robotics. The researchers therefore want to let the drone land on a branch without using complicated landing systems that rely on visual recognition and, by calculating the flight path and landing, would again consume a lot of energy. The TU Delft drone is intended to hang onto a branch with a simple tactile gripper, switch off its motors, and then collect data. Empfohlener redaktioneller Inhalt Mit Ihrer Zustimmung wird hier ein externes YouTube-Video (Google Ireland Limited) geladen. Ich bin damit einverstanden, dass mir externe Inhalte angezeigt werden. Damit können personenbezogene Daten an Drittplattformen (Google Ireland Limited) übermittelt werden. Mehr dazu in unserer Datenschutzerklärung. In cluttered treetops, visual systems with cameras for landing calculations would be overwhelmed, the scientists write. Foliage and small branches restrict visibility, impairing landing calculations. Such systems are therefore not practical. Branch recognition
Sep 1, 2026 · via heise.de
House intel committee calls for international travelers to undergo biometric screening to stop another 9/11 See more of our coverage in your search results. Add The New York Post on Google WASHINGTON — The House intelligence committee is urging the adoption of European-style border control booths that collect biometric information — including facial scans — on foreigners coming or going from the US to better safeguard against terrorism. The panel urged the change in a 66-page report drafted by Reps. Elise Stefanik (R-NY) and Josh Gottheimer (D-NJ), focused on implementing 9/11 Commission recommendations ahead of the 25th anniversary of the attacks. “Similar to the Schengen Area in Europe, foreign nationals traveling to the United States should pass control booths at points of entry at admission and departure, enabling the U.S. to track who is in the country, for how long, why, and when they leave,” the panel said. “Technological advancements make collecting biometric data at points of entry relatively quick and easy. As much as practically possible, U.S. borders should be secured to ensure foreign nationals seeking land exits to pass through land points of entry so CBP can collect biometrics and build profiles.” The committee said that land borders are particularly insecure in terms of scanning the faces of people entering from Canada or Mexico — and that airports do not currently scan outbound passengers’ faces, making it more difficult to verify that someone left the country. The 9/11 Commission in 2004 called for authorities to “quickly complete a biometric entry-exit screening system, one that also speeds qualified travelers” — however, it has not been done quickly. At least two — and possibly eight — of the 19 hijackers in the 9/11 attacks had phony or manipulated passports, the commission found. In the aftermath of the devastating attacks, commissioners
Sep 1, 2026 · via nypost.com
From disease-linked breath molecules to food spoilage and environmental pollutants, researchers are rethinking how sensors recognize the subtle molecular signatures hidden within complex odors. Paper: “Smelltronics” - From Gas to Smell Sensing. AI-generated abstract conceptual image created using ChatGPT/OpenAI A review recently published in the journal Advanced Materials examined recent trends in smell and odor-sensing studies, collectively referred to as “smelltronics”. The authors describe smelltronics as a materials-focused approach that bridges conventional gas sensing and biological olfaction by targeting larger, information-rich volatile organic compounds (VOCs) and designing sensing interfaces that can distinguish subtle differences in molecular structure, rather than relying primarily on downstream pattern recognition. Conceptual overview of smelltronics. The domain of chemical sensing is undergoing a transformation from traditional gas sensing, which predominantly focuses on highly volatile gases, to smelltronics, which seeks to detect and differentiate complex VOCs that convey specific odor information. To achieve precise odor identification, smelltronics relies on the development of three hierarchies: (1) diverse sensing materials engineered for specific interactions, (2) devices that transduce physicochemical events into digitized signals, and (3) systems that integrate sensor arrays with information science. Engineering Materials for Odor Recognition Noble metals, including platinum, gold, and palladium, can be incorporated into metal oxide chemiresistors to enhance their sensitivity to hydrogen, nitrogen dioxide, carbon monoxide, and other small gases. Tin dioxide nanowires have been functionalized using palladium, gold, or platinum nanoparticles and integrated into chemiresistors. Octadecylphosphonic acid (ODPA) has been used to modify zinc oxide nanowire-based chemiresistors to accelerate sensor recovery during nonanal gas sensing. Results showed that the ODPA-modified zinc oxide chemiresistor detected nonanal with higher sensitivity than the unmodified chemiresistor and did so reversibly. Cysteine was used to functionalize gold nanorods or nanoparticles, which were then used as plasmonic aggregative colorants. Gold nanoparticles with cysteine display color changes when exposed
Sep 1, 2026 · via azonano.com