Lifeboat Foundation

The combined force of these disruptive technologies (AI and 5G) enables fast, secure, and ubiquitous connectivity of cost-efficient smart networks and IoT (Internet-of-Things) devices. This convergence point is essential to concepts like intelligent wireless edge.

5G and AI, the connected digital edge

Artificial intelligence and 5G are the two most critical elements that would empower futuristic innovations. These cutting-edge technologies are inherently synergistic. The rapid advancements of AI significantly improve the entire 5G ecosystem, its performance, and efficiency. Besides, 5G-connected devices’ proliferation helps drive unparalleled intelligence and new improvements in AI-based learning and inference. Moreover, the transformation of the connected, intelligent edge has commenced as on-device intelligence has garnered phenomenal traction. This transformation is critical to leveraging the full potential of 5G’s future. With these prospects, these technologies hold enough potential to transform every industry. Here’s how the combination of AI and 5G has been reshaping industries.

Boa, an open-source web server suitable for embedded applications that was discontinued since 2005 is now becoming a security threat because of the complex nature of how it was built into the internet of things (IoT) device supply chain. A recent report by tech major Microsoft said that hackers are exploiting vulnerabilities in the software to target organizations in the energy sector.

Microsoft researchers revealed in an analysis that a vulnerable open-source component in the Boa web server, is used widely in a range of routers and security cameras as well as popular software development kits (SDKs), a set of tools that allow developers to write or use an existing framework to develop applications for a given platform.

Despite the software being discontinued a nearly two decades ago, Microsoft reports that attackers are continuing their attempts to exploit the flaws of the Boa web servers which include a high-severity information disclosure bug (CVE-2021–33558) and another arbitrary file access flaw (CVE-2017–9833). An unauthenticated attacker could exploit these vulnerabilities to obtain user credentials and leverage them for remote code execution.

Earlier this year, we announced our vision to empower any developer to become a space developer through Azure. With over 90 million developers on GitHub, we have created a powerful ecosystem and we are focused on empowering the next generation of developers for space. Today, we are announcing a crucial step towards democratizing access to space development, with the preview release of Azure Orbital Space SDK (software development kit)—a secure hosting platform and application toolkit designed to enable developers to create, deploy, and operate applications on-orbit.

By bringing modern cloud-based applications to spacecrafts we not only increase the efficiency, value, and speed of insights from space data but also increase the value of that data through the optimization of ground communication.

Many of the fundamental technological improvements that have accelerated the growth of Internet of Things (IoT) in the past decade remain untapped by space development missions today. With the Azure Orbital Space SDK, we will help bring those improvements to space through modern agile software deployment, container-based development, use of higher-level languages, and cloud-managed networking. Extending the power of the Azure cloud into space means that spacecraft development will take less time, cost less, and bring more people into the space development ecosystem.

By Chuck Brooks

There are many other interesting trends to look out for in 2023. These trends will include the expansion of use of a Software Bill of Materials (SBOM), the integration of more 5G networks to bring down latency of data delivery, more Deep Fakes being used for fraud, low code for citizen coding, more computing at the edge, and the development of initial stages of the implementation of quantum technologies and algorithms.

When all is said and done, 2023 will face a boiling concoction of new and old cyber-threats. It will be an especially challenging year for all those involved trying to protect their data and for geopolitical stability.

Continue reading “A Boiling Cauldron: Cybersecurity Trends, Threats, And Predictions For 2023” »

To be a sheep is to blindly follow the crowd. But is a sheep’s sheepiness really enough to make an entire flock walk around in a circle non-stop for days on end?

That’s a mystery the internet has pondered for about a week now and solving it has proved more difficult than you’d expect.

Continue reading “Eerie Video of Bizarre Sheep Phenomenon Has The World Running in Circles” »

https://youtube.com/watch?v=WL3DQfQqCQg

Do you think the image of Elon Musk as a good human being should come to be questioned when he’s a major contractor of the war machine? What are your thoughts?

By Alan MacLeod / MintPress News

Continue reading “Elon Musk Is Not a Renegade Outsider—He’s a Massive Pentagon Contractor” »

Large-capacity wireless data transmission systems are demanded along with the development of multimedia services, video-based interactions, and cloud computing in the era of big data. Compared with radio-frequency communication systems, free-space optical (FSO) signal transmission technology has the merits of high data rate, great flexibility, less power consumption, high security, and large license-free bandwidths [1– 3], which has been widely applied in terrestrial transmission [4], last mile solutions [5], ground-to-satellite optical communication [6], disaster recovery [7], and so on. To date, up to 10 Gbit/s FSO communication system has been realized for transmission distance over 1,000 km of star-ground or inter-star communications [8], and 208 Gbit/s terrestrial communication is also reported at 55 m transmission distance [9]. Wavelength-division multiplexing (WDM) technology is commonly employed to improve data transmission capacity in fiber communication systems, which would be more effective in FSO communication systems benefitting from very weak non-linear cross talk between different frequency channels in free space. Based on a simulation platform, a WDM FSO communication system could boost the signal transmission capacity to 1.28 Tbit/s by modulating 32 optical channels with dual-polarization 16 quadrature amplitude modulation signals [10]. To date, beyond 10 Tbit/s FSO communication systems have been experimentally demonstrated recently using WDM technology [11,12]. However, a WDM communication system becomes power-hungry and bulky with the increase of transmission channels while traditional distributed feedback lasers are used as optical carriers. In addition, more rigorous requirement is imposed on the frequency tolerance of carrier lasers to avoid channel overlap with the decrease of channel frequency interval.

The invention of microresonator-based optical frequency combs provides novel integrated optical laser sources with the natural characteristic of equi-spaced frequency intervals which can overcome the challenge of massive parallel carrier generation [13 – 19]. In particular, the spontaneously organized solitons in continuous-wave (CW)-driven microresonators provide a route to low-noise ultra-short pulses with a repetition rate from 10 GHz to beyond terahertz. Soliton microcombs (SMCs) are typical stable laser sources where the double balances of non-linearity and dispersion as well as dissipation and gain are reached in microcavities. Meanwhile, the linewidth of the comb lines is similar with the pump laser, which enables low power consumption and costs multiwavelength narrow-linewidth carriers for a wide range of applications. Through designing the scale of microresonators, the repetition rate of SMCs could be compatible with dense wavelength-division multiplexing (DWDM) communication standard. To date, several experiments have demonstrated the potential capacity for ultra-high-speed fiber communication systems using SMCs as multiwavelength laser sources [20 – 30]. For instance, a coherent fiber communication system has improved the transmission capacity up to 55 Tbit/s using single bright SMCs as optical carriers and a local oscillator [20]. And dark solitons and soliton crystals are also employed as multiwavelength laser sources for WDM communication systems [27 – 30]. However, few studies have carried out massive parallel FSO communication systems using the integrated SMCs as laser sources.

In this paper, we experimentally demonstrate a massive parallel FSO communication system using an SMC as a multiple optical carrier generator. 102 comb lines are modulated by 10 Gbit/s differential phase shift keying (DPSK) signals to boost the FSO transmission rate up to beyond 1 Tbit/s. The transmitter and receiver terminals are installed in two buildings at a distance of ∼1 km, respectively. Using a CW laser as reference, the influence of optical signal-to-noise ratios (OSNRs) on the bit error rate (BER) performance is experimentally analyzed. Our results show an effective solution for large-capacity spatial signal transmission using an integrated SMC source which has potential applications in future satellite-to-ground communication systems.

Made using windows movie maker and the internet and kampfbereit because I was bored.

Before a machine-learning model can complete a task, such as identifying cancer in medical images, the model must be trained. Training image classification models typically involves showing the model millions of example images gathered into a massive dataset.

However, using real image data can raise practical and ethical concerns: The images could run afoul of copyright laws, violate people’s privacy, or be biased against a certain racial or ethnic group. To avoid these pitfalls, researchers can use image generation programs to create synthetic data for model training. But these techniques are limited because expert knowledge is often needed to hand-design an image generation program that can create effective training data.

Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere took a different approach. Instead of designing customized image generation programs for a particular training task, they gathered a dataset of 21,000 publicly available programs from the internet. Then they used this large collection of basic image generation programs to train a computer vision model.