DIGITAL LIFE

AI deepfakes are getting more realistic. CU scientists developed a better way to spot them

Deepfake threats to companies...Deepfakes – synthetic media where a person’s likeness is swapped for another – are not a new concept. Photographic and film manipulation, telephone voice decoders, and image adjustment have been with us for some time. However, the rise of generative artificial intelligence (GenAI) has moved everything much further forward, making tools available at low/no cost, enabling almost anyone with a smartphone and basic understanding to create AI-generated synthetic media. In the hands of bad actors, these technologies can increasingly be used to make deepfakes for fraud, deception, and defamation.  

Chief Executive Officers (CEOs), General Counsels and the heads of Information Security, Fraud, Risk, Marketing, all have good reason to be concerned about the rapid spread of deepfake technology. In this article, KPMG professionals seek to examine some of the potential threats of deepfakes and discuss how organizations can shore up their defenses to try to reduce the risks to their business.  

The rise of deepfakes exploits our natural tendency to trust visual and auditory content, posing significant risks. It's critical for organizations to enforce robust Trusted AI programs to help ensure safe and ethical AI use and secure deployment. Implementing a zero-trust architecture is equally essential, embedding verification across all operations with stringent controls and cutting-edge technology. This strategic approach is vital for maintaining integrity and trust in the digital age."...KPMG International

Child’s play or malicious weapon? The democratization of deepfake technology...Virtually anyone can make a deepfake, enabled by the accelerating democratization of GenAI, driven by open-source tools, user-friendly platforms, and decentralized access to deepfake technology. This can make it simple and low-cost (or potentially even free) to create audio, video, and text via advanced GenAI techniques, mostly based on large language models (LLM) that consume and classify content and reproduce it in new ways. It's already possible to go online and learn how to make a convincing deepfake, based on a mere three seconds of recorded audio of someone’s voice – using off-the-shelf, publicly available software. On top of this, there is an emergence of "deepfake-as-a-service" as a lucrative market on the dark web.

Attack surfaces are increasing, in part due to the hybrid work environment where many people are connecting with organizations remotely from homes, coffee shops, airports, gyms and other locations. The use of biometric data for authentication and authorization presents further opportunities to infiltrate organizations, via synthetic voices and images. According to one recent global study, 70 percent of people say they’re not confident they can identify a real versus a cloned voice.

This gets to the essence of the deepfake threat, which is the human propensity for trust. As a visual and communicative species, people often authenticate, and trust based on what can be seen and heard. But the knowledge that deepfakes are out there could seriously erode trust and necessitate new ways to verify authenticity.

The deepfake threat spectrum...Criminals or other malicious actors can use deepfakes in a number of ways that are potentially damaging, amplifying the costs of fraud, regulatory fines, and data breaches, and eroding trust in brand integrity: 

Fraudulent financial transactions...Cybercriminals could use deepfakes to impersonate senior executives during phone calls or video conferences (sometimes called “vishing”), convincing others that they carry authority. They could then acquire confidential information or even persuade individuals to transfer significant funds. Insurance companies could be targeted with deepfake generated images submitted with claims. With more companies moving toward automated claims processes, removing the human claims adjuster, such images may not come under as much scrutiny. And customers can be targeted by deepfakes posing as official company representatives and surrender personal financial details or even make payments to criminals.

Disinformation...Deepfakes videos or audio recordings can spread fraudulent or false and defamatory information about individuals and organizations, which could damage stakeholder, customer, and wider public trust. In a social media age, such content can go viral in seconds. For instance, by circulating deepfakes of executives announcing a company’s financial status, upcoming mergers, or product launches and marketing materials – or making derogatory remarks, or inaccurate political statements – criminals could profit from subsequent fluctuations in share prices, as well harming a company’s reputation. Such tactics could also be used by competitors to cause stock price volatility and deter investors – as well as by other nation states to undermine the economy. Similarly, malicious actors may try to damage companies’ reputation by spreading deepfakes about environmental harm, poor labor practices, faulty, dangerous products, or inappropriate behavior from executives. 

Enhanced social engineering attacks...By using deepfakes, bad actors can penetrate organizations by, for example, impersonating a Chief Technology Officer (CTO) to persuade staff to grant access to a core technology system – to steal confidential information or plant malware. This might be achieved through targeted “spear phishing” emails with a deepfake video attached.

Other deepfake risks...Many companies are also vulnerable to extortion from AI fabricated incriminating content, brand misuse, potentially leading to legal liabilities, fines, loss of trust and business. Remote hiring practices could open the door for either criminals or under-qualified candidates, using deepfakes to give synthetic identities a convincing face and voice – even going so far as to conduct interviews.

Hundreds of the nation’s smartest computer scientists gathered for a conference in Denver last fall, and Tuan Tran had a big test for them:

Could they tell the difference between the real Brad Pitt and an AI-generated version of him?

Tran, a doctoral candidate in computer science at CU Boulder, rolled the tape on a large projector. Only half of the audience could spot the deepfake.

“These deepfake generating models are becoming very strong,” Tran said.

To address this, Tran and a team of researchers at CU Boulder set out last year to build their own system for spotting fakes in video. It uses far less computing power to achieve the same results as the current detection models.

Their findings were presented at SIGCOMM, a flagship conference in computer networking and systems held in August in Denver.  

Bad actors have used AI-generated deepfakes to impersonate financial officers at major companies and convince employees to wire large amounts of money. One such deepfake scam in Hong Kong resulted in someone wiring $25 million to fraudsters. 

Millions of people remain vulnerable to such trickery when they hop onto video conferencing calls. Tran said Zoom, Apple FaceTime and Google Meet lack real-time deepfake detection. Microsoft Teams does too but the company recently announced an effort to start providing it. 

The commercially-available systems designed to flag imposters require a massive amount of computing power, making them hard to scale and too expensive for most users.

Tran’s team set out to determine whether deepfake detection could be simplified enough to bring to the masses. 

“Some people thought this was impossible because of the system requirements and current architecture,” said Sangtae Ha, a computer science professor at CU Boulder who co-led the research team. “But by looking into the system from another perspective, you can actually solve the problem.”

Problem pixels...The team spent many hours in front of a whiteboard deep inside CU’s engineering center trying to crack the code and figure out a way to simplify deepfake detection. Sometimes they wouldn’t go home until 1 a.m.

They worked through fall break, Christmas and even the New Year. 

“The experience itself was very joyful, even when it was intense,” said Shirin Ebadi, a doctoral candidate in electrical and computer engineering, and a co-author. ”The most exciting part is doing something that’s never been done.”

She said existing deepfake detectors work well, but they need to see the actual picture to work. Video travels the Internet in compressed form, so before a detector can examine a face, it has to reconstruct every frame of every participant's video at full resolution. 

That takes a lot of computing power...The researchers discovered they didn’t need to reconstruct the video at all to spot deepfakes. The clues were already sitting in the compressed form of the video that travels over the network.

Imagine a video clip of a person speaking in front of a plain wall. Video compression doesn’t send every pixel of every frame. Instead, it describes the changes that occurred since the frame before. 

The wall barely changes in the video, so it’s covered by a few large blocks of pixels, while the face and its shifting expressions are carved into many smaller blocks to capture the detail. 

When someone's appearance changes abruptly, a compressor has to write down a lot of new information. The team measured how much of that new information lands on someone’s face and how widely it's spread. 

Crucially, the method reads these numbers directly from the compressed video without ever decompressing it to a higher resolution. Only when something looks suspicious does the detector ask the sender's device for a complete, fresh frame and use AI to check for a deepfake.

"We only had to look at data from one out of every hundred frames, instead of every frame," Ha said.

Scaling up...The team demonstrated that the detection system could spot deepfakes using 143 times less computing power than the existing models.  They tested it on a library of 1,000 online videos containing deepfakes, and reported near-perfect accuracy.

Eric Keller, an electrical and computer engineering professor at CU Boulder and a co-leader of the project, said the team wants to adapt the detection system so it can be scaled up and used for large-scale video conferencing.

The team is betting that as deepfakes become a bigger problem, the demand for new detection technology will only grow.

Researchers at the University of Colorado Boulder (CU Boulder) developed a lightweight deepfake detection system that analyzes compressed video data directly without needing to deconstruct or reconstruct full video frames.

How the new method works:

• No full reconstruction: Traditional detectors must decode and rebuild every frame at full resolution to examine a face, which requires massive computing power.

• Reading compression clues: The CU Boulder team realized they could read clues directly from the compressed data blocks (like MPEG or similar formats) already moving across the network.

• Tracking facial changes: Video compression uses small blocks of pixels for fast-moving or expressive areas like a face, and larger blocks for static areas like a plain wall. The system measures how much new compression data lands on a person's face and how it spreads.

• Targeted verification: Instead of checking every single frame, the system only looks at data from roughly 1 out of every 100 frames. It only requests a full, fresh frame to run deeper AI checks if something looks suspicious.

Key results and advantages:

• Lower compute load: The method uses 143 times less computing power than standard models.

• High accuracy: In tests on a library of 1,000 online videos containing deepfakes, the system achieved near-perfect accuracy.

• Scalability: Researchers aim to scale up the technology for real-time, large-scale video conferencing.

Details on the research were presented at the SIGCOMM computer networking and systems conference. You can read the university's announcement on the CU Boulder Today portal.

CU Boulder

 

FUJITSU

FMV Note U UX-L3: Fujitsu’s new 14-inch notebook weighing just 634 grams

Fujitsu has unveiled the FMV Note U UX-L3, the world's lightest 14-inch AI laptop with an 8C/8T Intel Core Ultra 7 355 CPU. The laptop offers up to 49 TOPS of AI compute power, a rugged carbon-fiber body, multiple ports, and MIL-STD 810H durability.

Fujitsu has unveiled the FMV Note U UX-L3, the lightest 14-inch laptop in the world according to the company. The laptop is rated for drop, shock, and pressure resistance to MIL-STD-810H standards due to its rugged, carbon fiber body. The laptop comes with an AI NPU and a slew of ports.

The UX-L3 weighs 634 grams (1.40 pounds) and has a rated runtime of 7.5 hours when playing videos (JEITA 3.0 testing standard) using its 31 Wh battery. Its revised charger is lighter than the prior model and now weighs in at 148 grams (0.33 pounds). The fast charger can add about 80% charge in an hour, says Fujitsu.

The laptop has been MIL-STD-810H tested against drops, vibration, shock, dust, humidity, and high and low temperatures. The durable body is designed to survive 200 kgf (441 lbf) of pressure on top of its closed lid.

The ultralight laptop comes with an 8C/8T Intel Core Ultra 7 355 CPU, with an integrated NPU providing up to 49 TOPS of AI compute power for hardware acceleration of AI services such as Microsoft Copilot in Windows, along with a fixed 16 GB of LPDDR5X-7467 RAM and 512 GB of PCIe SSD storage.

The Note U is fitted with a 14-inch 1,920 x 1,200 matte LCD display, and comes with two USB-A 3.2 Gen 1 ports, two USB-C 3.2 Gen 2x2 ports (DP Alt Mode, PD), a full-sized HDMI port, an Ethernet port, Wi-Fi 7, BT 5.4, a 3.5mm headphone jack, and a microSD card slot. 

Users can instantly log into Windows 11 using the Windows Hello fingerprint sensor or the Full HD webcam. The included AI-powered Noise Cancellation software can be trained to recognize the user's voice, then automatically filter out all other speakers as well as background noises from online video calls. Also included is Umore, an AI beautifying software that tracks users in 3D, then applies skin correction and virtual makeup.

The Fujitsu FMV Note U UX-L3 laptop has an undisclosed price that will likely be around 260,000 yen (~$1,700) when it goes on sale in Japan on November 13, 2026.

Unfortunately, readers outside Japan are faced with much heavier laptop choices. Even the smaller Surface Pro 12 tablet PC (sold here on Amazon) weighs a heavier 686 g (1.51 pounds) without a keyboard.

Fujitsu Client Computing (FCCL) has announced new models for its "FMV Note U" series of 14-inch laptops. The additions include an ultra-lightweight variant weighing approximately 634 grams, as well as a special model developed in collaboration with other companies.

The Note U series serves as the flagship for the newly unveiled FMV winter 2026 lineup, which encompasses five series and eleven models, with a phased rollout beginning on October 23. The company is also expanding its range of Copilot+ PCs.

According to FCCL, as of September 1, 2026, the ultra-light weight of approximately 634 grams applies to the "FMV UX-L3," "FMV WU6-L3," and "FMV WU5-L3" models. This makes the series the lightest 14-inch device in its category globally. Despite their lightweight design, the Note U devices meet the requirements of the MIL-STD-810H military standard.

Compared to the previous model, battery life during video playback—measured according to the JEITA 3.0 standard—has improved from 7.0 to 7.5 hours, representing a 107% increase. The U59-L3 model also features a battery that can be replaced without the use of tools.

Regarding processors, several platforms with integrated NPUs for Copilot+ functions are available. FCCL offers configurations featuring the Intel Core Ultra 7 355 (UX-L3)—with an option for the top-tier Core Ultra 9—as well as versions equipped with the AMD Ryzen AI 7 445 (U77-L3) and Ryzen AI 5 430 (U59-L3, U55-L3). In addition, the company offers a variant called the UQ-L3, featuring the Snapdragon X X1-26-100 processor. The hardware is complemented by updated software: FMV’s proprietary application, "Quick Capture," now includes text recognition and search functionality for screen recordings, while the "Reclip" program supports NPU processing.

All portable PC models are manufactured in Japan and come with a power adapter that weighs approximately 148 grams and features a flip-up plug. Concurrently, the company has launched its own accessories brand, "FMV Accessory."

Mobile connectivity options and shifting user behavior...The "FMV WU7-L3" model ensures continuous connectivity via KDDI’s "ConnectIN povo" service. Weighing approximately 870 grams and equipped with a 3-cell battery, the device integrates a 5G and 4G LTE module with a permanently installed eSIM.

Users have access to the "povo2.0" network, as well as over 100,000 "GigaZou Wi-Fi" hotspots, which can be used free of charge for three years across up to five devices. The plan carries no monthly fee and offers data packages of 100 GB, 500 GB, or 1 TB over a three-year period, supplemented by optional flat-rate passes: 6 hours for 250 yen or 24 hours for 330 yen.

This model update is also based on the results of a user survey conducted by FCCL. In the survey, approximately 85% of respondents stated that they chose the notebook primarily for its weight and portability. More than 20% expressed dissatisfaction with the battery life, while about 38.7% indicated that they use their notebook in different rooms throughout their homes. As a collaborative project, FCCL has introduced the "FMV Zero GODZILLA EDITION (FMV WU3G-L3)" to coincide with the release of the film "Godzilla-0.0." Weighing approximately 886 grams and housed in a magnesium chassis, the device meets MIL-STD-810H standards and can be configured with either an AMD Ryzen AI 7 445 or a Ryzen AI 5 430 processor.

The notebook comes equipped with 16GB or 32GB of RAM, SSD storage options ranging from 256GB to 2TB, and runs Windows 11 Home or Pro. Visually, it features a black Godzilla motif on the lid, stylized dorsal fins on the touchpad, a "G-0.0" logo on the Enter key, a limited-edition base, pre-installed wallpapers, and a cursor shaped like a footprint.

A compatible power adapter will also be available for separate purchase starting in late November. The device will make its public debut on November 3, 2026, at "Godzilla Fest 2026"—an event hosted by the production studio Toho at TAKANAWA GATEWAY CITY—coinciding with the theatrical release of the film, which is directed by Takashi Yamazaki.

Pre-orders via the company’s online shop, the FMV Store, opened at 1:00 PM. Standard shipments of the devices are set to begin in mid-November, with the Godzilla edition scheduled to ship starting in late November. Five variants of the "Note U" series will be available in physical retail stores beginning November 13, 2026, with prices starting at just over 260,000 yen (excluding tax).

mundophone

 

TECH

Data-driven operation of biogas plants: Generating electricity when wind and solar power are scarce

Under planned amendments to the German Renewable Energy Sources Act, biogas plants are expected to supply electricity primarily when wind and solar power generation is low and electricity prices are high. The Fraunhofer Institute for Interfacial Engineering and Biotechnology IGB demonstrates how existing plants can be operated in response to electricity prices using digital technologies. A specially developed data platform makes process data from biogas plants available for forecasts, soft sensors, and AI-supported operational optimization. The approach can also be applied to other process engineering facilities, including wastewater treatment and agricultural irrigation systems.

Biogas plants are considered the third pillar of renewable electricity generation in Germany. Unlike solar and wind power, electricity generation from biogas is largely independent of weather conditions and can generally be scheduled. With an amendment to the Renewable Energy Sources Act (EEG) planned for 2027, the regulatory framework is expected to place greater emphasis on flexible, market-oriented electricity generation: Biogas plants would supply electricity primarily when solar and wind generation is low. To support this shift, the number of operating hours eligible for remuneration is expected to be limited.

For operators of the nearly 10,000 biogas plants currently operating in Germany, the timing when electricity is fed into the grid from combined heat and power (CHP) units will increasingly determine the economic viability of their operations. Expanding gas storage capacity is likely to be economically viable for only some older plants. Fraunhofer IGB has developed a cost-effective digital approach to electricity-price-based operation: An “Edge Device” is connected to the existing plant control system, consolidates process and operational data, and makes these data available on-site for models and AI applications—without requiring changes to the existing control logic.

Biogas plants generate more data than they use...A biogas plant generates a significant amount of data: Sensors measure temperatures in the digester, the composition of the biogas, and the fill level of the gas storage tank. The plant control system tracks the rotational speeds, runtimes, and operating statuses of pumps and agitators. It also logs warnings and malfunctions. Other information is not measured directly but is equally crucial—such as which substrates are fed into the system and in what quantities.

However, the majority of this information is used solely for the immediate operation of the plant. “For long-term analyses, forecasts, or comprehensive optimization, the data are often not available in the required format,” explains Alexander Laug, a process engineer at Fraunhofer IGB. Measured values are often stored only for a limited time, data structures vary, and important contextual information—such as operating states or threshold values—is available only separately.

Optimizing the operation of a biogas plant requires the various data sets to be considered together. A temperature reading of “40 degrees,” for example, only gains meaning within the context of the plant: In which tank was it measured, and what setpoint applies there? Is the associated pump running, and is the plant in automatic or manual mode? Which substrates were added previously, and how is gas production developing?

A data platform in the control cabinet...The Edge Device brings these data together. “Our Edge Device is an industrial computer that is installed on-site. It adds a data processing and computing layer to the existing programmable logic controller (PLC),” explains Laug. The Edge Device retrieves process and operational data from the PLC, stores them over extended periods, and organizes them in a consistent structure. This creates a common data foundation that links current measurements, historical trends, and associated operating states. External information, such as electricity price forecasts and weather forecasts, can also be integrated.

Data processing takes place directly on-site. “Operational data do not necessarily have to be transferred to an external cloud,” Laug explains. Instead, operators decide for themselves which data leave the plant and which are processed exclusively locally. In doing so, Fraunhofer IGB relies on a technical architecture that is as open as possible. This allows the platform to be adapted to different plants and tasks and expanded with additional models or applications.

The Edge Device bridges conventional plant automation and data-driven plant operation. While the PLC remains responsible for reliable plant control, the Edge Device adds data storage, analytics, and computing capabilities for forecasting, optimization, and AI models. Multiple applications can build on the same data foundation. These may include electricity price and gas production forecasts, soft sensors, energy optimization, and predictive maintenance.

From data to decisions...The real added value arises when the aggregated data are transformed into concrete operational recommendations. The system takes into account not only the plant’s current status but also how it has evolved over time. “It can determine how gas production and gas composition have changed following the addition of a specific feedstock, how full the gas storage tank is, or how long pumps and other equipment have been running,” explains Laug. Not every application needs to intervene directly in the plant control system. Models can initially focus solely on analyzing data and generating recommendations for operational staff.

Generating electricity when it delivers the greatest value...For electricity-price-based operation, the data platform combines the current plant status with electricity price forecasts. “A high expected electricity price alone is not enough to make a decision—sufficient gas must also be available, and the plant must be technically capable of operating as planned,” says Laug.

Accordingly, the system considers expected gas production, the gas storage level, available combined heat and power (CHP) capacity, and technical operating constraints. Based on these inputs, the model proposes an operating schedule: When should biogas be stored? When should the CHP unit run? And when is electricity generation likely to yield the highest revenue at forecast market prices?

Feedstock also influences electricity generation...Digital optimization can begin even earlier in the process, because the amount of biogas available at a given time depends largely on the feedstock supplied to the digester. Substrates differ in their organic content, biogas yield potential, and cost. They also produce biogas at different rates. By combining these data with historical operating data, it becomes possible to assess how a particular substrate mix affects subsequent gas production and, in turn, electricity generation.

The data platform developed at Fraunhofer IGB makes process data from biogas plants usable: as a basis for forecasts, soft sensors, and AI-supported operational optimization. © Fraunhofer IGB

“An optimization model can calculate how an available substrate mix should be composed without exceeding the digester’s permissible load limits,” explains Laug. At the same time, substrate costs and expected gas production can be taken into account. The goal is not to produce as much gas as possible. What matters is when and how much gas is available, what it costs to produce, and how it can subsequently be used as effectively as possible. Operators can already test a simulation tool on the institute’s website that illustrates the effects of substrate mix and electricity prices on a biogas plant operating strategy optimized in line with electricity prices.

Applicable to other process engineering facilities...The principle underlying this data infrastructure can be applied to other process engineering facilities—from wastewater treatment plants to irrigation systems and drinking water supply systems. These facilities generate a large amount of measurement data and operational information, which are currently used primarily for real-time control. In wastewater treatment plants, for example, typical opportunities for optimization include more demand-driven aeration of the biological treatment stage and the monitoring of energy-intensive equipment.

Existing facilities therefore do not need to be completely rebuilt to make better use of their data. Laug sums it up: “The first step toward a smart facility is to bring existing information together in a reliable, understandable data foundation. Forecasts, soft sensors, and model-based operational optimization can then be built on that foundation.”

Data-driven operation of biogas plants allows them to scale up electricity generation flexibly when intermittent renewables like wind and solar power are scarce.

How data-driven biogas operations work:

• On-site edge computing: Industrial computers—such as the Edge Device developed by Fraunhofer IGB—sit alongside existing programmable logic controllers (PLCs) to aggregate, clean, and store operational data locally without forcing constant cloud transfers.

• Contextual data unification: Systems combine scattered data points—such as digester temperatures, pump runtimes, agitator statuses, feedstock types, and gas storage tank fill levels—into a single coherent framework.

• External forecasting integration: The platform integrates external data like real-time weather forecasts and electricity price forecasts to predict market demand and generation shortfalls.

• Automated scheduling: Combined heat and power (CHP) units are scheduled dynamically: biogas is stored during surplus wind and solar periods and combusted to generate power when prices peak or green energy drops.

Core benefits for the energy grid:

• Balancing intermittency: Biogas acts as a dispatchable, firm baseload renewable pillar that can ramp up or dial back independently of weather conditions or time of day.

• No large battery requirement: Storing biogas in tanks above the digester provides a built-in buffer, avoiding the high cost of utility-scale chemical battery storage.

• Maximized revenue: Plant operators optimize generation windows to sell electricity when market value is highest rather than running continuous, uncoordinated baseload output.

© Fraunhofer IGB

 

TECH

MANO330: Compact SBC with up to 32 GB RAM and Twin Lake CPUs now available for purchase

Axiomtek has introduced a new SBC dubbed the MANO330. Equipped with quad-core Intel Twin Lake CPUs paired with up to 32 GB of RAM, the SBC offers a healthy array of connectivity options, including a SIM slot.

The Axiomtek MANO330 SBC was launched a few months ago, and it appears that the product is now finally available for purchase. Equipped with a decent Intel CPU and a plethora of I/O options. The SBC measures just 17 by 17 cm, or roughly 6.7 by 6.7 inches.

To be more specific, the SBC is equipped with either an Intel N97 or Intel N150 CPU. The 12 W N97 features a four-core setup consisting of Crestmont efficiency cores clocked at up to 3.6 GHz. The 6 W N150 also consists of four Gracemont efficiency cores clocked at up to 3.6 GHz. 

Axiomtek’s MANO330 is a coming-soon Thin Mini-ITX single-board computer designed around Intel Processor N97 and N150 options, with DDR5 memory, multiple display interfaces, dual LAN, and M.2 expansion for compact embedded systems.

According to the product listing, the MANO330 supports DDR5-4800 SO-DIMM memory with up to 32GB capacity. Expansion options include an M.2 Key B 3042/3052 slot, an M.2 Key E 2230 slot, and an M.2 Key M 2280 slot. These provide support for storage, wireless connectivity, and cellular modules depending on the system configuration.

The board is also listed with dual LAN ports, USB Type-C support, USB 3.2 connectivity, and triple-view display capability. Display interfaces mentioned on the product page include HDMI, VGA, and LVDS, suggesting support for embedded display applications as well as external monitor connections.

According to synthetic benchmarks, the N97 should be substantially more powerful than the N150 by around 17% or so. Both the CPUs, while potent, are unlikely to be powerful enough for intensive workloads.

That said, the amount of compute on offer should be enough for many industrial applications, such as usage in POS terminals, kiosks and digital signage, industrial machine control panels, and the like.

As for memory and storage, the N97 variant can be equipped with up to 32 GB of DDR5-4800 RAM, whereas the N150 is officially rated for only up to 16 GB. Storage, on the other hand, is handled by a single M.2 2280 slot as well as a SATA 3.0 port.

The I/O offered is pretty good, which is to be expected from an SBC aimed at industrial use:

Triple USB 3.2 Gen 1

Single USB 3.2 3.2 Gen 1 Type-C with DisplayPort

2.5G Ethernet, 1G Ethernet

HDMI, VGA, LVDS/eDP

Dual USB 2.0 (internal)

6x RS-232 (internal, of which two support RS-422/485) 

Other notable features of the SBC include TPM 2.0 and 8-bit GPIO. A single M.2 2030, M.2 3042/3052, as well as a nano SIM slot are also present for further expansion.

Axiomtek, a world-renowned leader relentlessly devoted to the research, development, and manufacture of innovative, highly efficient, and reliable industrial computer products, proudly introduces the MANO330, a compact Thin Mini-ITX motherboard powered by the Intel® Processor N97 or N150 and DDR5 memory. Designed for space-constrained systems that require flexible connectivity and expansion, the MANO330 combines multi-display capability, dual LAN connectivity, versatile M.2 expansion, and industrial I/O in a compact platform. With a wide 12–24 VDC power input, the MANO330 provides system developers with a flexible foundation for smart manufacturing and industrial automation, digital signage, smart retail, and POS applications.

Compact performance for space-constrained systems...The MANO330 combines a compact Thin Mini-ITX design with Intel® Processor N97 or N150 and DDR5-4800 memory support. The N97 supports up to 32GB of memory, while the N150 supports up to 16GB. Measuring just 170 x 170 mm, the MANO330 provides the essential computing performance and I/O capabilities needed for embedded systems, helping developers build space-efficient solutions for a wide range of industrial and commercial applications.

Rich visuals and reliable network connectivity...Designed for information-rich applications, the MANO330 supports multiple independent display outputs through HDMI, VGA, LVDS/eDP interface, and USB Type-C with DisplayPort support. This display flexibility allows system developers to configure multiple screens for applications such as digital signage, retail displays, and industrial monitoring. The board also integrates 2.5GbE and GbE LAN, providing reliable network connectivity for connected systems and industrial applications.

Flexible expansion to fit different applications...The MANO330 provides flexible expansion options for storage and wireless connectivity. Its M.2 Key M 2280 slot supports SATA/NVMe storage, while M.2 Key E 2230 supports wireless connectivity. The M.2 Key B 3042/3052 interface provides an additional expansion option and is paired with a Nano SIM slot for wireless applications. These options allow customers to configure storage and connectivity according to their application requirements, helping simplify system development and integration.

Built-In I/O for easier system integration...The MANO330 integrates commonly used industrial interfaces directly on the board, including RS-232, RS-422/485, USB, SATA, and GPIO, helping reduce the need for additional interface hardware. Two RS-232 ports support jumper-selectable RS-422 or RS-485, while four additional RS-232 ports provide further serial connectivity for industrial devices. Combined with the board's 12–24 VDC power input, these built-in interfaces provide system developers with greater flexibility and help simplify system integration.

“Today’s embedded systems need to balance compact design with flexibility for different application requirements. With versatile display, connectivity, expansion, and industrial I/O options, the MANO330 gives system developers greater freedom to configure and integrate solutions for a wide range of industrial and commercial applications”— Tina Tu, Product Manager at Axiomtek

Pricing and availability...The Axiomtek MANO330 SBC is already available for purchase, although its pricing details are not disclosed to the public.

Interested customers can always request a quote directly from the company. Like most other enterprise products, final pricing is likely to depend on order volume.

All said, hobbyists who are new to the world of SBCs can check out something like the ARM-based Radxa Rock 5C Lite with an 8-core SoC and 8 GB of RAM, currently available for $261 on Amazon.

mundophone

 

DIGITAL LIFE

How can you tell if an app is listening to you? Here’s how to block this feature

Smartphones incorporate a wide range of sensors and features that enable the use of increasingly sophisticated applications. Microphones, cameras, location services, voice recognition, and virtual assistants are among the tools that millions of people use daily.

Many apps utilize certain phone features to optimize the user experience, learn more about the user, and provide personalized services. However, this same complexity raises privacy concerns.

Key takeaways...Your phone isn’t constantly spying, but it can listen in specific cases. Voice assistants activate only after wake words, but apps with microphone permissions may access audio if misused.

Targeted ads are usually driven by data tracking, not eavesdropping. Search history, location data, and app activity are more commonly used to predict interests than actual voice recordings.

Malware and spyware are the real privacy threats. Malicious apps or advanced tools can hijack your microphone and camera to monitor activity without consent.

You can detect and stop unauthorized listening. Checking microphone access logs, watching indicator lights, restricting permissions, and running security scans help identify and remove suspicious activity.

Think your phone is spying on you? You might be right, especially if you find that your phone is acting strange and it ticks the boxes we're going to discuss in this article. In fact, given the current cybersecurity landscape and all the stories coming up about mobile phone safety, it’s not far-fetched to wonder: Is my phone listening to me? 

You’ve likely heard stories of people swearing that their phones are eavesdropping on their conversations. The most common one is the oddly specific ad that pops up right after you casually mention a product near your smartphone. 

But if anything truly malicious is happening, such as your private conversations being recorded or your data stolen, it’s usually the work of malware, not your phone’s built-in features. 

Still, it’s important to understand the difference and know when to be concerned. We asked our Bitdefender cybersecurity experts these same questions, and here’s what they revealed about why your phone might be listening and how you can stop it. 

How does your phone listen to you? Unless you’re a high-profile government official, political critic, or human rights activist, it’s highly unlikely that you’re the target of 24/7 surveillance or that anyone hacked your phone or deliberately spoofed your microphone to spy on your private conversations. 

That being said, smartphones do listen in certain contexts, mostly through features designed to make your experience more convenient. Let’s take a closer look at the most common ways this happens (and when you should actually be concerned). 

Through voice assistants...When you call out to Siri, Alexa, or Google Assistant, they instantly respond and help you handle everyday tasks like setting reminders, playing music, or managing your schedule. You’re not alone in this, as about half of U.S. adults use virtual assistant apps regularly.  

If you’ve not changed your settings, these assistants will constantly listen in the background for what’s known as “wake words.” For Siri, it’s “Hey Siri,” while Google responds to “Okay Google.” To detect these triggers, your device uses AI to passively listen and process background sounds. This also helps them learn how you speak, recognize patterns, and adapt to different accents. 

Apple and Google explicitly state that their assistants only start recording after hearing the wake words and that they do not save your recordings or audio by default. But if you opt in to allow saved audio, that data may be used to improve its speech recognition technology. 

Via third-party apps...Now, this is where you actually have cause for concern. When you install certain apps on your phone and casually grant them permissions, you might unknowingly be giving malicious actors access to your microphone. If those apps are harmful or run by shady developers, they can use that access to spy on your personal data and sell the information to advertisers. 

Some apps don’t need to be outright malicious, as they might just quietly collect more data than they should. Every “Allow microphone access” or “Allow background activity” click gives them more room to listen in. 

Through spyware like Pegasus...If you’re a high-profile journalist, activist, or politician, you or your family members could be targeted with sophisticated tools like Pegasus, a surveillance software known for hijacking a phone’s camera and microphone for data theft and other cybercrimes. 

Pegasus can be installed in several ways: by clicking on a phishing link, leaving your phone unattended long enough for someone to install it directly, or through zero-day exploits that don’t even require you to do anything. Once you’re targeted, you’re instantly compromised. 

One of the most common questions is whether an app might be listening to conversations through the device's microphone. Although modern operating systems include increasingly stringent security controls, periodically reviewing permissions remains a best practice. Both Android and iPhone offer tools to identify which apps have access to the microphone and to restrict that access when it is not needed.

Here are some useful steps to learn how to block this feature:

1. Check which apps have access to the microphone...The first step is to access your phone’s privacy settings and see which apps have permission to use the microphone. Many people are surprised to discover how many programs have requested this access over time. This check can help you identify apps you may have forgotten about or no longer use.

2. Evaluate whether the permission makes sense...Not all apps need access to audio. Video-calling tools, voice recorders, and virtual assistants require the microphone to function properly. However, other apps may request permissions that do not seem related to their primary purpose. Identifying these instances helps reduce unnecessary risks.

3. Disable unnecessary permissions...Once you have identified the apps, it is a good idea to revoke microphone access for those that do not require it. This change can usually be made with just a few taps in your device’s settings and does not necessarily require you to uninstall the app. 4. Grant permission only while the app is in use

Many systems allow you to choose between different access levels. A particularly useful option is to authorize microphone access only when the app is open. This reduces the possibility of unnecessary access occurring in the background.

5. Pay attention to privacy indicators...Both Android and iPhone display visual indicators when the microphone is in use. Observing these indicators helps detect unexpected behavior. If they appear when no app should be recording audio, it is worth investigating which program is active.

6. Keep your operating system up to date...Security updates often fix vulnerabilities and improve privacy controls. Keeping your phone updated reduces risks and provides access to the latest tools for managing sensitive permissions.

7. Review recently installed apps...If your concerns arose after downloading a new app, that is a good place to start your investigation. Checking an app’s permissions, the developer’s reputation, and reviews from other users provides valuable information.

8. Download apps only from official stores...Official app stores implement security controls that help reduce the presence of malware. Using unknown sources significantly increases the risk of installing apps that request excessive permissions.

9. Remove suspicious apps...If an app exhibits strange behavior, requests excessive permissions, or raises questions about its functionality, the simplest solution is usually to uninstall it and look for a more reliable alternative.

Privacy begins with permissions...Most apps only use the microphone when the user grants permission. Nevertheless, regularly reviewing your privacy settings remains a best practice for digital security.

Knowing which apps have access to your audio—and limiting those permissions when they are not needed—helps you maintain greater control over your personal information.

Your phone does not constantly record your everyday conversations for advertisers, as major operating systems (iOS and Android) and independent research show no proof of continuous secret audio streaming. However, apps may access your microphone or make it feel like they are listening due to specific reasons:

Why it feels like your phone is listening:

• Voice assistants: Built-in assistants like Siri, Google Assistant, or Alexa stay locally active on a tiny chip to listen exclusively for wake words like "Hey Siri" or "OK Google". They only transmit audio to the cloud after hearing that trigger.

• Active features in use: If you open an app like a camera, social media recorder, voice memo tool, or chat app, it legitimately requests microphone access to perform functions you asked for (such as recording a video or sending a voice message).

• Aggressive tracking & coincidence: Instead of listening to your voice, tech platforms build hyper-detailed profiles of you using your search history, location data, website visits, app activity, and data shared by your friends. When you see an ad for something you just spoke about, it is usually the result of smart predictive algorithms and confirmation bias rather than active eavesdropping.

• Malware or spyware: In rare cases, unauthorized or malicious software can compromise a device and hijack hardware permissions.

How to check and stop app listening:

• Look for privacy indicators: Modern iOS and Android devices show a green or orange dot at the top of your screen whenever an app actively uses your microphone or camera.

Review microphone permissions:

• On iPhone: Go to Settings > Privacy & Security > Microphone and toggle off access for any app that does not need it.

• On Android: Go to Settings > Apps > Permission manager > Microphone or check individual app settings to deny unnecessary mic access.

        • Disable Voice Assistants: Turn off "Hey Siri" or "Hey Google" in your device or assistant settings if you do not use voice triggers

 

mundophone

TECH

Building brain-inspired semiconductor chips for smarter, greener data centres

With artificial intelligence (AI) data centres consuming electricity at a staggering and growing rate, identifying a sustainable computing solution to offset these massive community and environmental disruptors is proving critical.

Researchers from the University of Ottawa, Columbia University and Hanyang University believe building brain-inspired computing systems could provide the answer to this global conundrum, with small, fast, energy-efficient and scalable artificial neurons able to support the large-scale computing systems that are now regularly dotting our landscapes.

“The materials and architectures described in this review represent a fundamentally different way to process information. By operating more like a human brain than a traditional processor, these emerging technologies will lead to physical devices that are dramatically more energy-efficient, faster, and adaptive,” explained lead author Dr. Kannan Udaya Mohanan, a Postdoctoral Fellow in Professor Chang-Hyun Kim’s research group at uOttawa.

Building scalable brain-like systems...Professor Kim and his team performed a sweeping, systematic review of years of global advancements in electronic materials, device physics, and circuit integration to chart a new path for "neuromorphic" systems – systems that physically mimic the human brain's processing. Rather than focusing on a single experiment, they evaluated the entire landscape of emerging hardware neurons, distilling complex material science into concrete, real-world technological pathways to help researchers build scalable, brain-like computing systems.

These brain-inspired hardware findings offer a comprehensive, forward-looking roadmap for the scientific community just as the federal government provides new frameworks for data centre developments in Canada.

“Traditional computing is hitting a bottleneck known as the "memory wall," where transferring data between memory and processors causes massive delays and energy loss. We identified the most promising post-CMOS materials and device architectures that can act as artificial neurons, proving that next-generation circuits can process information faster and with a fraction of the energy used by current mainstream chips,” said Chang-Hyun Kim, an Associate Professor in the School of Electrical Engineering and Computer Science and head of the Semiconductor and Microelectronics Laboratory at uOttawa.

Enabling greener technology...Post-CMOS refers to emerging materials and device architectures that move beyond traditional chips. While these brain-inspired technologies can drastically reduce the energy footprint of modern data centres, they offer an equally powerful advantage for personal technology. This is because these novel materials are often lower cost, highly scalable, and support ultra-thin, lightweight and flexible form factors.

“Beyond green AI data centres, this hardware will directly enable smarter, low-power wearable technology for personalized healthcare and seamless human-machine interfaces,” added Kim.

The development of brain-inspired semiconductor chips, known as neuromorphic chips, has emerged as the primary solution for creating smarter and more sustainable data centers. As the adoption of artificial intelligence grows exponentially, traditional computing architecture faces what is known as the "memory wall" bottleneck—a situation where the constant transfer of data between the processor and memory leads to sluggish performance and massive energy waste. In contrast, the human brain is capable of performing incredibly complex tasks while operating with extreme efficiency, consuming only about 20 watts of power.

These new technologies overcome this barrier by unifying processing and storage within a single component, mimicking the behavior of biological neurons and synapses.

Key advancements in neuromorphic technology:

• Post-CMOS materials: Scientists at the University of Ottawa (uOttawa) have identified new materials and circuit architectures capable of functioning as artificial neurons, requiring only a fraction of the electrical power currently consumed.

• Hafnium memristors: Research from the University of Cambridge has led to the development of a stable form of hafnium oxide that operates with currents one million times lower than previous alternatives, with the potential to reduce energy consumption in AI applications by up to 70%.

• Extreme efficiency: Chips based on materials physics and spiking neural networks can process temporal data directly within the hardware, achieving up to 2,000 times greater energy efficiency for specific tasks.

• Biological computing ("wetware"): In an even more radical approach, partnerships involving the startup Cortical Labs have created systems that integrate living, lab-grown human neurons with traditional silicon chips, giving rise to the concept of "biological data centers." Impact beyond data centers... While the immediate focus is on mitigating the sustainability crisis facing large-scale global servers, brain-inspired chips are highly scalable, lightweight, and low-cost. This will enable local, decentralized processing—or "edge computing"—bringing advanced intelligence directly to wearable health devices, smart sensors, and human-machine interfaces, without any reliance on cloud computing.

Would you like to explore any specific aspect of this technology further? I can provide details on how memristors function, explain the differences between spiking neural networks (SNNs) and traditional AI, or share data regarding the implementation of systems incorporating living neurons ("wetware").

University of Ottawa, Columbia University and Hanyang University

 

TECH

Hot and cold climate extremes push UK’s energy system to its limits, new report reveals

The arrival of cooler weather following a summer of heatwaves, is a timely reminder that the UK electricity system must be resilient to the effects of temperature extremes at both ends of the thermometer.

The Royal Meteorological Society has released a new report that highlights how recent weather conditions have influenced electricity generation, demand and the infrastructure that connects them.

Authored by experts from the society, the Met Office and multiple UK universities, the State of the Climate for the UK Energy Sector Report 2025-26 shows that more energy is being provided by weather-dependent renewable sources. But high and low temperatures will continue to put the energy grid’s capacity and infrastructure to the test.

Data from the National Energy System Operator (Neso) shows that renewable energy formed a higher percentage of overall energy generated in the UK over the reporting period (April 2025 – March 2026) than any other year on record. The proportion coming from the combination of solar and wind increased, providing more than 38% of the nation’s electricity. The proportion of gas-powered generation reduced to 25%.

However, the report highlights how extreme weather tests the resilience of the grid. The conditions that lead to very high and low temperatures create different challenges through increasing electricity demand, limiting generation and damaging network infrastructure. The UK’s energy network is having to adapt to cope with increasingly extreme temperatures. In cold conditions, the network is put under strain due to increased heating demand. This requires more energy at short notice, increasing reliance on non-renewable sources.

For those responsible for balancing the electricity system, including Neso, these weather extremes can create operational challenges. This makes it harder to match energy supply with demand.

In early January 2026, strong northerly winds brought cold Arctic air, snow and ice into large parts of the UK. Initially these winds provided up to 20 gigawatts (GW) of power towards the system, a significant proportion of the demand.

But as the freeze reached its peak on the evening of January 6, the wind died down and wind generation fell to less than a quarter of the levels seen in the preceding days. Simultaneously, electricity demand peaked, with the increase in heating needs seeing demand peak an additional 5GW higher than the previous day.

Electricity supply and demand have to be kept in balance in real time. When demand rises or available generation falls, the energy system operator can maintain that balance by using additional generators to increase their output, for example. On this occasion, gas-powered electrical generation filled the gap by providing more than 25GW to the system at it’s peak.

This meant the demand was comfortably met, but this was an expensive strategy that meant the operator was paying up to £700 per megawatt-hour (MWh) for this generation. This was due to the higher cost of turning to gas generation, and the need for additional generation at short notice which costs more than generation that can be planned further in advance. This compares to the usual rate of just over £100 per MWh for the rest of January.

At the other end of the spectrum, hot temperatures are increasingly becoming more of a concern for the sector.

The summer of 2025 was the warmest on record at the time, with temperatures 1.51°C higher than average. During warmer temperatures, the network operator often limits the amount of power that transmission lines carry around the country to prevent components overheating. This reduces the risk of lines expanding more with heat which would make them sag and potentially short out on nearby vegetation or structures beneath.

Nevertheless, several faults in the network were attributed to heat causing infrastructure damage, leading to power cuts across parts of England, especially during two periods of heatwaves that occurred at the end of June and start of July.

In Europe, where temperatures also spiked, nuclear power plants were forced to limit output or shut down altogether because the water used to cool them with, taken from nearby rivers, was too warm. Increased temperatures and below-average rainfall also increased fire risks. The largest area lost to wildfire burning in the UK since satellite records began included a “mega-fire” in Scotland. Several of these fires led to localised power cuts across the country.

Preparing for future extremes...The UK’s new grid upgrade plan from the government, which aims to expand and modernise the British grid system to allow for connections to new generation sources and upgrades of current infrastructure, needs to consider the consequences of extreme temperatures. This includes ensuring that upgraded infrastructure can withstand the extreme heat we are now seeing more regularly in UK summers, as well as ensuring that capacity for transmission of electricity from generation sources to population centres is increased.

Delays to electricity grid upgrades risk higher consumer bills as the operator is required to pay wind farms to stop generating because electricity produced cannot be be carried to where it is needed, replacing it with more local gas-powered generation instead.

The resilience of the UK grid has stood up well to the challenges it faced from the weather in the past year. But maintaining that resilience requires continued investment and adaptation as the climate, energy needs of the nation and profile of energy generation all continue to change.

The Royal Meteorological Society’s annual report has revealed that extreme heat and cold are pushing the UK’s energy system to its limits. The document highlights the severe impact these temperature fluctuations have on energy infrastructure and underscores the urgent need to modernize the sector.

Key impacts on the energy system:

• Transmission constraints due to heat: During heatwaves, the grid operator must limit the amount of electricity flowing through transmission lines to prevent components from overheating. Heat causes cables to expand and sag, creating a risk of short circuits should they come into contact with vegetation.

• Localized blackouts and fires: The summer of 2025 saw temperatures 1.51°C above average. Extreme heat caused physical damage to infrastructure and triggered wildfires—including a "mega-fire" in Scotland—resulting in localized power outages across parts of England.

• Challenges for European nuclear power generation: Extreme heat also forced nuclear power plants in continental Europe to reduce output or shut down entirely, as the river water used for cooling had become too warm.

• Higher costs for consumers: Due to delays in upgrading the transmission grid, the operator is frequently forced to pay wind farms to halt clean energy production—since the power cannot be transported—and replace it with energy from local gas-fired plants, which drives up electricity bills.

Adaptation plans and requirements: The British government’s new grid upgrade plan faces the challenge of ensuring that modernized infrastructure can withstand frequent extreme heat, while also providing greater capacity to transport electricity from new generation sources to urban centers. The regulatory body Ofgem has also launched investigations to assess the performance of the market and the electrical system—managed by the NESO—during recent heat spikes.

Royal Meteorological Society