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Devices: Orange Pi Zero, Avalue, RTL-SDR

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Linux
Hardware
  • Orange Pi Zero LTS SBC Launched for $8.49 and Up

    You can now buy Orange Pi Zero LTS Arm Linux SBC for $8.49 and up. The tiny board is ideal for headless applications with WiFI and Ethernet connectivity.

  • Toughened up embedded PC can run 8th or 9th Gen Coffee Lake CPUs

    Avalue’s rugged “EPS-CFS” computer runs Linux or Win 10 on Intel 8th or 9th Gen Coffee Lake CPUs up to an octa-core Core i7-9700TE, and supplies up to 32GB GB DDR4, 2x SATA bays, 2x GbE, 2x HDMI, and 4x USB 3.2 ports.

    Avalue announced an embedded computer with Intel’s 8th Gen Coffee Lake T-series or the new, but similarly 14nm-fabricated, 9th Gen Coffee Lake Refresh TE-series chips. The EPS-CFS computer, which is built around Avalue’s 3.5-inch ECM-CFS SBC, joins other 9th Gen-ready products including Kontron’s COMe-cWL6 (E2S) and Congatec’s Conga-TS370 COM Express modules.

  • RTL-SDR: Seven Years Later

    When I wrote that article in 2012, the RTL-SDR project and its community were still in their infancy. It took some real digging to find out which TV tuners based on the Realtek RTL2832U were supported, what adapters you needed to connect more capable antennas, and how to compile all the software necessary to get them listening outside of their advertised frequency range. It wasn’t exactly the most user-friendly experience, and when it was all said and done, you were left largely to your own devices. If you didn’t know how to create your own receivers in GNU Radio, there wasn’t a whole lot you could do other than eavesdrop on hams or tune into local FM broadcasts.

    Nearly a decade later, things have changed dramatically. The RTL-SDR hardware and software has itself improved enormously, but perhaps more importantly, the success of the project has kicked off something of a revolution in the software defined radio (SDR) world. Prior to 2012, SDRs were certainly not unobtainable, but they were considerably more expensive. Back then, the most comparable device on the market would have been the FUNcube dongle, a nearly $200 USD receiver that was actually designed for receiving data from CubeSats. Anything cheaper than that was likely to be a kit, and often operated within a narrower range of frequencies.

Orange Pi Zero2

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More in Tux Machines

Security Leftovers

  • NSA Researchers Talk Development, Release of Ghidra SRE Tool

    The National Security Agency released its classified Ghidra software reverse-engineering (SRE) tool as open source to the cybersecurity community on April 4. NSA researchers Brian Knighton and Chris Delikat shared how Ghidra was built and the process of releasing it at Black Hat 2019. Ghidra is a framework developed by the NSA’s Research Directorate for the agency’s cybersecurity mission. It’s designed to analyze malicious code to give security pros a better understanding of potential vulnerabilities in their networks and systems.

  • Linux Is Being Hit with Zero-Day Exploits/ Zero-Day Attacks [Ed: This is not news. If you have a system that is unpatched for months, despite many warnings, it is a risk, no matter the OS/kernel.]

    It was once the popular opinion that Linux was immune to zero-day exploits. However, even before the Equifax exploit, vulnerabilities were found in Linux distributions like Fedora and Ubuntu. In particular, back in 2016, a security researcher discovered that you could exploit a Linux system by playing a specific music file. Then, in 2017, a group of attackers used Struckshock vulnerability to carry on the attack on Equifax. These zero-day attacks are Advanced Persistent Attacks that exploit recently discovered vulnerabilities. Read on to learn more about what are zero-day exploits and how they can affect a Linux system.

  • Intel, Google, Microsoft, and Others Launch Confidential Computing Consortium for Data Security

    Major tech companies including Alibaba, Arm, Baidu, IBM, Intel, Google Cloud, Microsoft, and Red Hat today announced intent to form the Confidential Computing Consortium to improve security for data in use.

  • Intel, Google, Microsoft, and others launch Confidential Computing Consortium for data security

    Major tech companies including Alibaba, Arm, Baidu, IBM, Intel, Google Cloud, Microsoft, and Red Hat today announced intent to form the Confidential Computing Consortium to improve security for data in use. Established by the Linux Foundation, the organization plans to bring together hardware vendors, developers, open source experts, and others to promote the use of confidential computing, advance common open source standards, and better protect data. “Confidential computing focuses on securing data in use. Current approaches to securing data often address data at rest (storage) and in transit (network), but encrypting data in use is possibly the most challenging step to providing a fully encrypted lifecycle for sensitive data,” the Linux Foundation said today in a joint statement. “Confidential computing will enable encrypted data to be processed in memory without exposing it to the rest of the system and reduce exposure for sensitive data and provide greater control and transparency for users.”

Linux-driven modules to showcase new MediaTek AIoT SoCs

Innocomm is prepping an “SB30 SoM” with the new quad -A35 MediaTek i300 followed by an “SB50 SoM” with an AI-equipped, octa-core -A73 and -A53 MediaTek i500. Both modules ship with Linux/Android evaluation kits. Innocomm, which has produced NXP-based compute modules such as the i.MX8M Mini driven WB15 and i.MX8M powered WB10, will soon try on some MediaTek SoCs for size. First up is an SB30 SoM due to launch in October that will run Linux or Android on MediaTek’s 1.5GHz, quad-core, Cortex-A35 based MediaTek i300 (MT8362) SoC. In November, the company plans to introduce an SB50 SoM based on the MediaTek i500 (MT8385). Read more

Devices: Raspberry Pi and More

Programming: Rust and Python Miscellany

  • This Week in Rust 300
  • Python String Interpolation with the Percent (%) Operator

    There are a number of different ways to format strings in Python, one of which is done using the % operator, which is known as the string formatting (or interpolation) operator. In this article we'll show you how to use this operator to construct strings with a template string and variables containing your data.

  • Your Guide to the CPython Source Code

    Are there certain parts of Python that just seem magic? Like how are dictionaries so much faster than looping over a list to find an item. How does a generator remember the state of the variables each time it yields a value and why do you never have to allocate memory like other languages? It turns out, CPython, the most popular Python runtime is written in human-readable C and Python code. This tutorial will walk you through the CPython source code. You’ll cover all the concepts behind the internals of CPython, how they work and visual explanations as you go.

  • Python 3.8 support in PyCharm

    The release of Python 3.8 brought new features to the Python coding realm. The language is evolving according to its community’s needs by addressing cases where new syntax or logic become necessary. From new ways of assigning expressions to restriction of usage of function declarations, calls, and variable assignations, this latest release presents new options to code. Of course, PyCharm couldn’t get behind, so we now support some of the major features coming with this new version. This article will walk you through the features currently supported by our latest PyCharm release. To try them out, get the latest version of PyCharm and download the current beta release of Python 3.8 from here. From there you will just need to switch to Python 3.8 as your interpreter in PyCharm (if you’re not sure how to switch the interpreter, jump into our documentation for help).

  • Python Arrays in a Nutshell

    Python arrays are homogenous data structure. They are used to store multiple items but allow only the same type of data. They are available in Python by importing the array module. Lists, a built-in type in Python, are also capable of storing multiple values. But they are different from arrays because they are not bound to any specific type. So, to summarize, arrays are not fundamental type, but lists are internal to Python. An array accepts values of one kind while lists are independent of the data type.