# smartphones Published articles for smartphones. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A low-carbon computing platform from your retired phones DevFeed: [A low-carbon computing platform from your retired phones]() Original publisher: [Read original article]() Published: 2026-06-12T17:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Green Software](), [cloud-computing](), [datacenter](), [Hardware](), [cpu](), [Google]() Tags: [climate-sustainability](), [cloud](), [cloud-computing](), [compute](), [datacenter](), [distributed-systems-parallel-computing](), [efficiency](), [energy](), [energy-efficiency](), [google](), [hardware](), [memory](), [performance](), [servers](), [smartphones](), [storage](), [sustainability]() ### AI overview Researchers at the University of California San Diego, with Google's support, are developing a low-carbon cloud computing platform from retired smartphones. A planned datacenter using 2,000 Pixel smartphones aims to provide low-cost computing while reducing the need for newly manufactured hardware and its associated emissions. ### Source excerpt Climate & Sustainability ## Towards passive heart health monitoring via smartphone camera DevFeed: [Towards passive heart health monitoring via smartphone camera]() Original publisher: [Read original article]() Published: 2026-06-04T19:47:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Monitoring](), [webcam](), [dataset](), [Deep learning](), [Google](), [Ground truth / benchmark quality]() Tags: [deep-learning](), [devices](), [google](), [health](), [health-bioscience](), [heart-rate-monitoring](), [human-computer-interaction-and-visualization](), [machine-intelligence](), [model](), [monitoring](), [publication](), [research](), [resource](), [smartphones]() ### AI overview Google Research presents PHRM, a research system that passively estimates heart rate and resting heart rate from facial video captured by a smartphone's front-facing camera during everyday use. The system applies deep learning to video recorded after face unlock events and reports accuracy comparable to electrocardiogram-derived ground truth and wearable trackers. The publication also releases a large, diverse smartphone-video dataset and the pre-trained PHRM-mini model for qualified researchers. ### Source excerpt Health & Bioscience ## Android expands AI-powered scam detection to Samsung devices and more regions DevFeed: [Android expands AI-powered scam detection to Samsung devices and more regions]() Original publisher: [Read original article]() Author: Edward Fernandez (noreply@blogger.com) Published: 2026-02-25T15:17:00Z Content type: release Language: en Sources: [Google Online Security]() Topics: [Android](), [Google AI](), [Google](), [Mobile]() Tags: [ai](), [android](), [android-security](), [devices](), [google](), [mobile](), [none](), [on-device-ai](), [protection](), [scams](), [smartphones]() ### AI overview Google describes Android's AI-powered scam protections, including Scam Detection warnings for suspicious calls. The article announces expansion of call Scam Detection to Samsung devices and additional regions, while noting existing availability on Google Pixel devices in several countries. ### Source excerpt Posted by Lyubov Farafonova, Product Manager, Phone by Google; Alberto Pastor Nieto, Sr. Product Manager Google Messages and RCS Spam and Abuse We've shared how Android's proactive, multi-layered scam defenses utilize Google AI to protect users around the world from over 10 billion suspected malicious calls and messages every month1. While that scale is significant, the true impact of these protections is best understood through the stories of the individuals they help keep safe every day. This includes people like Majik B., an IT professional in Sunnyvale, California. Despite his technical background, Majik recently found himself on a call that felt dangerously legitimate. While using his Pixel, he received a call that appeared to be from his bank. The number looked correct, the caller knew his name and his address, and the story about a "suspicious charge" made perfect sense. "I'm usually pretty careful about this stuff," Majik recalled, "but I stayed on the line longer than I normally would. Even knowing how these scams work, it was convincing in the moment." The turning point came when his phone displayed a Scam Detection warning during the call, which provided a critical moment to pause and reflect. Majik hung up, checked his bank app directly, and confirmed there was no fraudulent charge. For Majik, Scam Detection was the intervention he needed: "The warning is what made me pause and avoid a bad situation". While stories like Majik's show how our existing protections provide a robust shield against scams, our work isn't done. As scammers evolve their tactics and create more convincing and personalized threats, we're using the best of Google AI to stay one step ahead. A recent evaluation by Counterpoint Research found that Android smartphones provide the most comprehensive AI-powered protections of any mobile platform. We are committed to building on this foundation by expanding our AI-powered protections to more users and devices, while rolling out new feature ## Unlocking health insights: Estimating advanced walking metrics with smartwatches DevFeed: [Unlocking health insights: Estimating advanced walking metrics with smartwatches]() Original publisher: [Read original article]() Published: 2026-01-15T22:56:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Deep learning](), [model architecture](), [data](), [Google]() Tags: [architecture](), [data](), [deep-learning](), [evaluation](), [google](), [health](), [health-bioscience](), [human-computer-interaction-and-visualization](), [model](), [performance](), [portable](), [smartphones](), [validation]() ### AI overview Google researchers report a large-scale validation study showing that consumer smartwatches can accurately estimate comprehensive spatio-temporal gait metrics, including walking speed, step length, and double support time. They describe a multi-output deep learning model using a temporal convolutional network and smartwatch inertial sensor data, with performance comparable to smartphone-based methods. ### Source excerpt Health & Bioscience ## How B2B Online Payment Works DevFeed: [How B2B Online Payment Works]() Original publisher: [Read original article]() Author: Aditeya Viju Govind Published: 2025-10-29T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Transactions](), [Software as a service](), [Software]() Tags: [ach](), [b2b](), [checkout](), [erp](), [gateway](), [invoicing](), [payments](), [pricing](), [saas](), [smartphones]() ### AI overview This tutorial explains how B2B online payments work, covering payment gateways, checkout, approvals, recurring billing, invoicing and ERP integrations, payment methods such as cards and ACH, and gateway types including integrated, standalone, mobile and cross-border systems. ### Source excerpt Understand how B2B online payments work step by step, from checkout and orchestration to gateway authorization and settlement timelines. ## postmarketOS in 2025-09: PCB v0.2, Tauchgang, immutable, cellbroadcastd, pmb 3.6.0, ppc64le, SM7150 overhaul DevFeed: [postmarketOS in 2025-09: PCB v0.2, Tauchgang, immutable, cellbroadcastd, pmb 3.6.0, ppc64le, SM7150 overhaul]() Original publisher: [Read original article]() Published: 2025-10-12T00:00:00Z Content type: article Language: en Sources: [postmarketOS Blog]() Topics: [Linux](), [Mobile](), [systemd](), [Kernel](), [Development](), [Qualcomm](), [engineering-culture](), [Rust]() Tags: [code-review](), [culture](), [kernel](), [linux](), [postmarketos](), [qualcomm](), [rust](), [smartphones](), [systemd]() ### AI overview A postmarketOS development update covers recent work on mobile Linux, including the Tauchgang project, systemd maintenance, Qualcomm SM7150 devices, kernel and U-Boot development, Rust musl support, community office hours, and code review guidelines. ### Source excerpt Welcome to the latest edition of what happened in the last (two) months of postmarketOS development! This time we kick it off with a photo of a Xiaomi Mi 9T running the new Tauchgang project, more on that below. It was taken at BTO7, one of the many events where we hung out lately. Others include XDC, Datenspuren (pmOS flashing stand, pmOS talk, Linux on smartphones talk), KDE Akademy 2025, FrOSCon 20 (FOSS on mobile stand, pmOS talk), WHY2025, and DEF CON 33. Thanks to all who attended and represented postmarketOS and Linux Mobile there! Organizational Jane stepped down as Trusted Contributor. She has done a lot of great things in Alpine (e.g. maintaining Phosh there for some time) and postmarketOS. Most notably she made the very first systemd proof-of-concept and played a huge part in getting it all the way to the v25.06 stable release of postmarketOS. Jane has been maintaining systemd in postmarketOS for all this time, ensuring new versions land, hunting down bugs, doing some of the musl-related patches upstreaming, adjusting other packaging to have it nicely work together, etc. Thank you very much! Aelin is our newest TC! She is maintaining Qualcomm SM7150 devices, doing Kernel development and U-Boot bringup, making more devices boot generic images and even improving musl support in the Rust compiler. Furthermore she runs a monthly mainlining meetup at the CCCAC that you should definitely check out if you are in the area. Welcome to the team! Pablo joined Clayton with setting up office hours, but with his own focus "on non-technical aspects and listen to people about their feedback and frustrations". If you want to talk about that, join his weekly Jitsi call with your browser. Thanks, Pablo! Clayton has been working on postmarketOS full-time for about 1.5 years! "But this story really starts a bit earlier, when I realized I couldn't keep splitting my attention between a job I loved and a side project that had grown into an obsession. Once I finally admitted that ## Implementing a Kalman Filter in Postgres to Smooth GPS Data DevFeed: [Implementing a Kalman Filter in Postgres to Smooth GPS Data]() Original publisher: [Read original article]() Author: Thorsten Rieß Published: 2025-09-26T16:47:33Z Content type: tutorial Language: en Sources: [Blog -- Neon Docs]() Topics: [Algorithm](), [Database](), [SQL](), [Code](), [data](), [Open Source]() Tags: [algorithm](), [analytics](), [code](), [community](), [data](), [database](), [datasets](), [devices](), [errors](), [gps](), [iot](), [open](), [postgres](), [smartphones](), [sql](), [visualization]() ### AI overview This tutorial explains how to implement a Kalman Filter in Postgres to smooth noisy GPS data. It describes the filter's prediction and update steps, uncertainty modeling, and the challenges of maintaining state across SQL rows, while highlighting the benefits of in-database processing for large datasets. ### Source excerpt Modern GPS datasets are notoriously noisy: satellites drift, buildings scatter signals, and consumer devices introduce frequent errors. When working with millions of position samples from vehicles, smartphones, or IoT devices, this noise makes analysis unreliable. Routes jump, tr... ## Arm & ExecuTorch 0.7: Bringing Generative AI to the masses DevFeed: [Arm & ExecuTorch 0.7: Bringing Generative AI to the masses]() Original publisher: [Read original article]() Author: EricSondhi; Gian Marco Iodice Published: 2025-08-13T14:55:10Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Arm](), [Generative AI](), [Artificial Intelligence](), [Large Language Model](), [AI Inference](), [cpu](), [Mobile](), [Raspberry Pi](), [Latency]() Tags: [ai](), [ai-accelerator](), [android](), [arm](), [cpu](), [cross-platform](), [developers](), [edge-ai](), [generative](), [generative-ai](), [large-language-models-llms](), [latency](), [llama-cpp](), [mediapipe](), [memory](), [onnx](), [performance](), [raspberry-pi](), [smartphones]() ### AI overview Arm's KleidiAI will be enabled by default in the upcoming ExecuTorch 0.7 beta, providing automatic AI acceleration through ExecuTorch and XNNPack on current and older Arm-based devices. The article explains how this can improve model startup time, latency, and memory efficiency, making Generative AI and large language models more practical on mobile devices and Raspberry Pi 5. It also describes how the Arm SDOT instruction accelerates low-precision matrix multiplication used by LLMs. ### Source excerpt With Arm's recent SME2 announcement, the role of Arm KleidiAI is increasingly clear as Arm's AI accelerator layer powering the next wave of AI. By embedding into widely-used Edge AI frameworks like XNNPack, MediaPipe, MNN, ONNX Runtime, and even llama.cpp, KleidiAI has delivered substantial performance improvements with no code changes required by developers. ## The Humane AI Pin and the Future of Computer Interfaces DevFeed: [The Humane AI Pin and the Future of Computer Interfaces]() Original publisher: [Read original article]() Published: 2024-04-22T00:00:00Z Content type: opinion Language: en Sources: [Radek Pazdera]() Topics: [interfaces](), [Computing]() Tags: [computer](), [evolution](), [interfaces](), [smartphones](), [tech-industry]() ### AI overview This commentary examines the Humane AI Pin and broader attempts to develop computer interfaces beyond conventional smartphones. It compares foldable phones, spatial computing, voice assistants, screenless devices, touchscreens, speech interfaces, vision, and LLMs, while questioning whether newer interfaces can replace smartphones. ### Source excerpt Are smartphones indisruptable? ## On-device fetal ultrasound assessment with TensorFlow Lite DevFeed: [On-device fetal ultrasound assessment with TensorFlow Lite]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-06-20T16:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Lite](), [Google](), [Artificial Intelligence](), [Inference](), [Mobile](), [Protocol (disambiguation)]() Tags: [ai](), [devices](), [google](), [health](), [healthcare](), [mobile](), [on-device](), [open-source](), [predictions](), [protocol](), [smartphones](), [tensorflow-lite]() ### AI overview This article explains how TensorFlow Lite and Google Research AI models are being used with portable, smartphone-integrated ultrasound devices to help health workers assess gestational age and fetal presentation in under-resourced settings. The models analyze ultrasound video collected through a blind sweep protocol and are intended to expand access to clinically useful prenatal screening. ### Source excerpt Posted by Angelica Willis and Akib Uddin, Health AI Team, Google Research How researchers at Google are working to expand global access to maternal healthcare with the help of AI TensorFlow Lite* is an open-source framework to run machine learning models on mobile and edge devices. It's popular for use cases ranging from image classification, object detection, speech recognition, natural language tasks, and more. From helping parents of deaf children learn sign language, to predicting air quality, projects using TensorFlow Lite are demonstrating how on-device ML could directly and positively impact lives by making these socially beneficial applications of AI more accessible, globally. In this post, we describe how TensorFlow Lite is being used to help develop ultrasound tools in under-resourced settings. Motivation According to the WHO, complications from pregnancy and childbirth contribute to roughly 287,000 maternal deaths and 2.4 million neonatal deaths worldwide each year. As many as 95% of these deaths occur in under-resourced settings and many are preventable if detected early. Obstetric diagnostics, such as determining gestational age and fetal presentation, are important indicators in planning prenatal care, monitoring the health of the birthing parent and fetus, and determining when intervention is required. Many of these factors are traditionally determined by ultrasound. Advancements in sensor technology have made ultrasound devices more affordable and portable, integrating directly with smartphones. However, ultrasound requires years of training and experience, and, in many rural or underserved regions, there is a shortage of trained ultrasonography experts, making it difficult for people to access care. Due to this global lack of availability, it has been estimated that as many as two-thirds of pregnant people in these settings do not receive ultrasound screening during pregnancy. Expanding access by enabling non-experts Google Research is building AI m ## Proposed Europe/Oslo timezone alias change could discard pre-1970 data and break Joda-Time behavior DevFeed: [Proposed Europe/Oslo timezone alias change could discard pre-1970 data and break Joda-Time behavior]() Original publisher: [Read original article]() Author: Stephen Colebourne (noreply@blogger.com) Published: 2021-09-25T00:55:00Z Content type: opinion Language: en Sources: [Stephen Colebourne]() Topics: [DateTime](), [Programming](), [systems](), [Operating system]() Tags: [blog](), [jdk](), [joda](), [operating-systems](), [smartphones](), [systems](), [timezone]() ### AI overview The article criticizes a proposed change to the IANA timezone database that would make Europe/Oslo an alias of Europe/Berlin because their post-1970 data matches. It argues that this could replace researched pre-1970 Oslo data with Berlin data and cause Joda-Time tests and timezone identifier handling to fail. ### Source excerpt The last time I wrote about the timezone database on this blog, the database was under threat from a lawsuit. Fortunately that lawsuit went away relatively quickly as the company involved got the message that their action was a big mistake. Unfortunately this time the mess is internal. Paul Eggert is the project lead of the timezone database hosted at IANA, a position referred to as the TZ Coordinator. He is an expert in the field, having been involved in documenting timezone data for decades. Unfortunately, he is currently ignoring all objections to an action only he seems intent on making to solve an invented problem that only he sees as important. The database is the world's principle source of timezone information. The data is included in everything from operating systems to smartphones to programming language development kits such as the JDK. While you may never have heard of it, the sheer pervasiveness of the data makes the potential impact of change or damage pretty huge. The timezone database contains information about how clocks have varies in each region around the world. The mandate of the project is to record this information from 1970 onwards. Of course, computers being what they are, a function that returns the timezone for a given date can be passed in a pre-1970 date as well as a post-1970 one. For this, and reasons of completeness, the timezone database contains pre-1970 data as well as post-1970 data. If you go to your JDK or operating system and ask for the timezone offset for 1920-01-01 for the ID "Europe/Oslo" or "Europe/Berlin" you will get an answer: DateTimeZone oslo = DateTimeZone.forID("Europe/Oslo"); System.out.println(oslo.getOffset(new DateTime(1948, 6, 1, 12, 0))); //prints 3600000 DateTimeZone berlin = DateTimeZone.forID("Europe/Berlin"); System.out.println(berlin.getOffset(new DateTime(1948, 6, 1, 12, 0))); //prints 7200000 The proposed change is to downgrade "Europe/Oslo" to be merely an alias for "Europe/Berlin". The rationale is th ## 3rd Party Integrations in ESP RainMaker DevFeed: [3rd Party Integrations in ESP RainMaker]() Original publisher: [Read original article]() Author: John Lee Published: 2020-06-30T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Esp Rainmaker](), [Internet of things](), [Google](), [Code]() Tags: [alexa](), [blog](), [code](), [devices](), [esp-rainmaker](), [espressif](), [github](), [google-assistant](), [google-home](), [integrations](), [iot](), [rainmaker](), [smart-home](), [smartphones](), [voice]() ### AI overview This article explains how ESP RainMaker integrates with Alexa and Google Voice Assistant. It describes how RainMaker device types and parameters map to the assistants' device formats and capabilities, then outlines the code and setup steps for controlling supported devices. ### Source excerpt IoT, in general, has come a long away. During the very early stage, there was browser based control and monitoring, which, though more convenient than non-IoT devices, had a very narrow market. With the advent of Smartphones, IoT devices became much more easier to use and mass adoption became a possibility. However, the real push was given by the advent of various eco-systems and Voice assistants like Siri, Alexa and Google Assistant. ## Aiming for a 10 year life-cycle for smartphones DevFeed: [Aiming for a 10 year life-cycle for smartphones]() Original publisher: [Read original article]() Published: 2017-05-26T00:00:00Z Content type: article Language: en Sources: [postmarketOS Blog]() Topics: [Linux](), [Operating system](), [Android](), [Security](), [Vulnerabilities](), [Computing]() Tags: [alpine-linux](), [android](), [architecture](), [linux](), [linux-distribution](), [patches](), [postmarketos](), [security](), [smartphones](), [vulnerabilities]() ### AI overview An introduction to postmarketOS, an Alpine Linux-based, touch-optimized operating system designed to run on smartphones. It proposes sharing most packages across devices and upstreaming small device-specific patches to improve maintainability and long-term security updates. ### Source excerpt Introduction post to postmarketOS, a touch-optimized, pre-configured Alpine Linux with own packages, that can be installed on smartphones. (Not usable for most people yet!) Minimalistic Linux distributions run fine on ten year old PCs. It is 2017. Pick an average PC from 2007 and install a minimal Linux based operating system. You will be able to do basic computing tasks (eg. surfing the web, reading E-Mails, listening to music, chatting) just like on an expensive modern PC. You will even get security updates, so your old computer is protected, just like as a new one. Why are Android/Linux phones different? Androids architecture is based on forking (one might as well say copy-pasting) the entire code-base for each and every device and Android version. And then working on that independent, basically instantly incompatible version. Especially adding device-specific drivers plays an important role. This workflow makes it next to impossible to patch all Android devices with security updates in time or at all (Stagefright vulnerabilities for example rendered one billion devices vulnerable). And even if the vendor provides updates, it will only be for a limited time and then you must buy a new device to get security updates or the latest Android version. How convenient! Alright, so there is the LineageOS community, which provides weekly updates for an impressive number of smartphones. They provide a practical solution today, and we are very grateful for that. However, such Android based projects will always run behind Google and the phone industry, fixing only symptoms but never the root-cause. This is just the tip of the iceberg. Android has way more problems, read Cascardo's GNU on Smartphones (part II) for more nightmares. We can fix this as a community. Here is the solution: Bend an existing Linux distribution to run on smartphones. Apply all necessary changes as small patches and upstream them, where it makes sense. Of course we are not the only ones, that came to th ## Response to Thorium questions DevFeed: [Response to Thorium questions]() Original publisher: [Read original article]() Published: 2014-01-06T00:00:00Z Content type: opinion Language: en Sources: [Front Page on ReactOS Website]() Topics: [Operating system](), [Cloud](), [Linux](), [Windows](), [Network]() Tags: [cloud](), [free](), [funding](), [linux](), [network](), [open-source](), [os](), [react](), [reactos](), [smartphones](), [win32](), [winapi](), [windows]() ### AI overview The article responds to questions about Thorium's purpose and the funding needed to develop it. It describes a proposed environment that synchronizes applications, files, and desktop state across systems and devices through a network connection, then discusses the need for a base operating system, with Linux and Windows presented as possible choices. ### Source excerpt Quite a few people that first look at Thorium ask questions about what exactly it is for and what purpose does it serve. These are not unreasonable questions to ask and the answers are sometimes difficult to parse due to the complexity of Thorium itself. The second set of questions relates to why we believe Thorium is achievable with the amount of funding requested. Let's go over both sets and hopefully they will convince you we aren't completely out of our minds.