Новини світу мікро- та наноелектроніки

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This is the Microchip MCP41xxx digital potentiometer data sheet that includes (on page 15, their Figure 4-4) an interesting application circuit comprising a Dpot controlled amplifier with pseudologarithmic gain settings. However, as explained in the Microchip text, the gains implemented by this circuit start changing radically as the control setting of the pot approaches 0 or 256. As Microchip puts it: “As the wiper approaches either terminal, the step size in the gain calculation increases dramatically. This circuit is recommended for gains between 0.1 and 10 V/V.”

That’s good advice. Unfortunately, following it would effectively throw away some 48 of the 256 8-bit pot settings, amounting to a loss of nearly 20% of available resolution. The simple modification shown in Figure 1 gets rid of that limitation.

Figure 1 Two fixed resistors are added to bound the gain range to the recommended limits while keeping full 8-bit resolution.

Wow the engineering world with your unique design: Design Ideas Submission Guide

This results in the gain vs code red curve of Figure 2.

Figure 2 Somewhat improved pseudologarithmic gain curve from the simple modification shown in Figure 1.

However, despite this improvement, the key term remains pseudologarithmic. It still isn’t a real log function and, in fact, isn’t quantitatively even that close, deviating by almost a factor of two in places. Can we do better? Yes!

The simple (software) trick is to prepare a 257-byte logarithmic lookup table that translates the 0.1 to 10.0 gain range settings to the Dpot codes needed to logarithmically generate those gains.

Let’s call the table index variable J. Then for a 257-byte table of (abs) gains G from 0.1 to 10.0 inclusive,

J(G) = (128 LOG10(abs(G)) + 128)
…examples…
J(0.1) = 0,
J(0.5) = 89,
J(1.0) = 128,
J(10.0) = 256,
etc.

Inspection of the gain expression in Figure 1 reveals that the Dpot decimal code N required for (abs) gain G is:

N(G) = (284.4G – 28.4)/(G + 1)
…thus…
N(.1)  = (28.4 – 28.4)/(.1 + 1) = 0/1.1 = 0,
N(.5)  = (142 – 28.4)/(.5 + 1) = 114/1.5 = 76,
N(1.0) = (284.4 – 28.4)/(1 + 1) = (256)/2 = 128,
N(10.0) = (2844 – 28.4)/(10 + 1) = 2816/11 = 256,
etc.

Figure 3 summarizes the resulting relationship between G, J, and N. 

Figure 3 The Dpot settings [N(J)] versus log table indices [J(G)], summarizing the relationship between G, J, and N.

The table of log gains can be found in this excel sheet. The net result, with as good log conformity as 8 bits will allow, is exhibited as Figure 4’s lovely green line.

Figure 4 The absolute gain [Gabs = 10(J/128 -1)] versus decimal code (J).

Stephen Woodward’s relationship with EDN’s DI column goes back quite a long way. Over 100 submissions have been accepted since his first contribution back in 1974.

Related Content

• Keep Dpot pseudologarithmic gain control on a leash
• Synthesize precision Dpot resistances that aren’t in the catalog
• Reducing error of digital potentiometers
• Adjust op-amp gain from -30 dB to +60 dB with one linear pot
• Op-amp wipes out DPOT wiper resistance

The post Dpot pseudolog + log lookup table = actual logarithmic gain appeared first on EDN.

Meaning of data transformation

Data transformation is the process of converting data from one format or structure into another format or structure.  For instance, converting a raw dataset into a well arranged, scientifically analysed, vetted, and a user-friendly format.

As the aim of data transformation is to present the data into a very user-friendly format, it invariably involves converting dataset from one format of file into another format. For instance, CSV (comma separated values), excel spreadsheet, XML (extensible markup language), etc.

It involves conversion of both the format and/ or structure of a data set into a format or structure that is congruent to the requirements of the target system.

Aim of data transformation

The aim behind executing any data transformation process is to ensure that the available data is scientifically arranged, well-analysed, vetted from reliable sources and as per the internationally accepted standards, and presented in a user-friendly format.

This ensures that the decision making based on the available data is rational, logical, scientific, and correct to the best of knowledge. Hence, it aids in analyses and developing insights. Besides, further analyses of the data available after executing the process of data transformation brings to fore some of the hitherto unexplored facts or dimensions about any topic.

Data transformation is only change in the format of the data and not the content of the data

An important feature of data transformation is that it only involves conversion of data from one format to another. It does not change anything in the content of the data.

Who are the people involved in the process of data transformation?

Majorly, the data engineers, data analysts, and data scientists collaborate amongst each other to execute the process of data transformation.

Processes involved in data transformation

Data transformation is executed by accomplishing three processes. They are as follows:

First, data integration.

Second, data migration.

Third, warehousing.

Phases of data transformation

Data transformation is accomplished over five phases. They are as follows:

First, data discovery.

Second, data mapping.

Third, code generation.

Fourth, code execution.

Fifth, data review.

Classification of the process of data transformation

The process of data transformation is classified into four types. They are as follows:

First, constructive data transformation. In this type, data is copied, replicated, or added.

Second, destructive data transformation. In this type, data pertaining to fields or records is deleted.

Third, structural data transformation. In this type, columns in data is combined, moved or renamed.

Fourth, aesthetic data transformation. In this type, data pertaining to certain values are standardized.

Significance of data transformation

First, data transformation is a critical stage of both the ETL (Extract, Transform, Load) and ELT (extract, load, transform) processes.

The difference between the ETL approach and the ELT approach is that the ETL approach uses a fixed criteria to sort data from multiple sources before compiling it a central place. On the other hand, the ELT approach aggregates data as it is from the beginning and then transforms it later depending upon the requirements of the case and analytics.

Second, data transformation is an important aspect of big data analytics. Hence, it is of immense importance in today’s age of big data, an age when the data is already huge in volume and is rapidly growing in gargantuan proportions.

Common life examples of data transformation

Data transformation is undertaken in various applications in our life. Few such examples are as follows:

First, converting file from CSV format to XML format.

Second, conversion of speech into text by means of speech conversion software.

The post Data transformation- Meaning, Aim, Processes involved, Phases, Classification, and Significance appeared first on ELE Times.

Fraunhofer Institute for Applied Solid State Physics (IAF) of Freiburg, Germany has used metal-organic chemical vapor deposition (MOCVD) to fabricate and characterize aluminum yttrium nitride (AlYN), enabling the development of new, diverse applications...

The companies enable an interoperable coherent optics ecosystem that can help streamline the development of data center interconnect and metro transport systems

The latest data center architectures and increased traffic are driving higher bandwidth requirements between data centers. To address this challenge, system developers must streamline the development of a new generation of 1.2 Tbps (1.2T) transport solutions across a wide range of client configurations. This requires that today’s terabit-scale Ethernet PHY devices and coherent optical modules interoperate with each other in Data Center Interconnect (DCI) and metro transport networks. Microchip Technology today announces that it has worked with Acacia to demonstrate the fourth generation of interoperability between Microchip’s META-DX2 Ethernet PHY family and Acacia’s Coherent Interconnect Module 8 (CIM 8).

The two companies’ interoperable devices enable low-power, bandwidth-optimized, scalable solutions for pluggable optics in DCI and transport networks. They deliver three key benefits as they jointly enable high-capacity, multi-rate muxponders for optical transport platforms:

• Optimized DCI bandwidth: The META-DX2 family, through its META-DX2+ PHY, uses its unique Lambda Splitting feature to split 400 GbE or 800 GbE clients across multiple wavelengths driven by the CIM 8 modules. This maximizes the capacity between data centers in rate configurations such as 3×800 GbE over 2×1.2 Tbps waves or 5×400 GbE over 2×1.0 Tbps waves.
• Reduced design risk: Microchip and Acacia have jointly verified successful SerDes interoperation at up to 112G per lane for Ethernet and OTN clients, which reduces design validation and system qualification requirements.
• Better support for full bandwidth, multi-rate operation: The META-DX2+ crosspoint and gearbox functions enable 100 GbE to 800 GbE client modules to connect with full bandwidth to CIM 8 modules.

“This interoperability extends a long-established partnership with Acacia to help accelerate and optimize the build-out of cloud computing and AI-ready optical networks while reducing development risk for our customers,” said Maher Fahmi, vice president for Microchip’s communications business unit. “Our META-DX2 is the first solution of its kind to integrate 1.6T of encryption, port aggregation and Lambda Splitting into the most compact 112G PAM4 device in the market.”

“With Acacia’s CIM 8 coherent modules verified to interoperate with Microchip’s META-DX2 devices, we see this as a robust solution that reduces system time-to-market,” said Markus Weber, senior director DSP product line management of Acacia. “The compact size and power efficiency of our CIM 8 coherent modules were designed to help network operators deploy and scale capacity of high-bandwidth DWDM connectivity between data centers and throughout transport networks.”

The post Microchip and Acacia Collaborate to Enable Optimized Terabit-Scale Data Center Interconnect Systems appeared first on ELE Times.

Welcome to my where’s Waldo themed workbench also it’s Wednesday in New zealand so i’d say this counts. List of stuff to find: • DSSTC H-bridge(50 points) • tesla coil secondary(10 points) • multimeter(1 point because its easy to find) • drill battery(5 points) • Variac(20 points) • shunt resistor (you win instantly and gain the achievement: how tf?) Comment you’re score try and beat my high-score of 0 (I have no idea where anything is anymore lol) submitted by /u/jjiscool_264 [link] [comments]

Infineon Technologies AG has announced the expansion of its Bluetooth portfolio by eight new products in the AIROC CYW20829 Bluetooth Low Energy 5.4 microcontroller (MCU) family, featuring Systems-on-Chip (SoCs) and modules optimized for industrial, consumer, and automotive use cases. The high integration of the CYW20829 product family allows designers to reduce bill-of-material (BOM) cost and device footprint in a wide variety of applications, including PC accessories, low-energy audio, wearables, solar micro inverters, asset trackers, health and lifestyle, home automation and others. Product designers also benefit from Infineon’s rich development infrastructure and commitment to robust security, with support for secure boot and execution environments and cryptography acceleration to safeguard sensitive data.

The latest automotive part in the product family, the AIROC CYW89829 Bluetooth Low-Energy MCU, is ideal for car access and wireless battery management systems (wBMS) applications, due to its robust RF performance, long range and latest Bluetooth 5.4 features including PAwR (Periodic Advertising with Responses). The dual ARM Cortex core design of the chip family features separate application and Bluetooth Low Energy subsystems that deliver full featured support for Bluetooth 5.4, low-power, 10 dBm output power without a PA, integrated flash, CAN FD, crypto accelerators, high security including Root of Trust (RoT), and is PSA level 1 ready.

“Infineon offers one of the industry’s broadest portfolios of IoT solutions. Our Bluetooth solutions offer robust connectivity and the latest features,” said Shantanu Bhalerao, Vice President of the Bluetooth Product Line, Infineon Technologies. “Our automotive AIROC CYW89829 Bluetooth LE MCU, and versatile AIROC Bluetooth CYW20829 LE MCU deliver ultra-low power and a high degree of integration for a better user experience across various applications in automotive, industrial, and consumer markets.”

Infineon has been working with customers to design with current products in the CYW20829 family and has received positive reviews:

“The Infineon CYW20829 is the leading Bluetooth part in the market, which has passed the latest Bluetooth 5.4 certification,” said Kevin Wang, CEO of ITON. “CYW20829 has very good RF performance, supports PAwR and LE Audio. These features bring more possibilities in consumer and industrial markets.”

“CYW20829, with perfect RF performance, flexible API, and good long-range features, provides a good solution for commercial lighting, industrial IoT, and more,” said Cai Yi, CEO of Pairlink.

“Earlier this year, the Bluetooth SIG released version 5.4 of the specification with new features: Periodic Advertising with Responses and Encrypted Advertising Data. These features implemented on Infineon’s CYW20829 chips allow Addverb to develop a secure monitoring and controlling system for a fleet of wireless robots in the industrial warehouse, satisfying safety requirements,” said Tapan Pattnayak, Chief Scientist at Addverb, a global leader in robotics.

The post Infineon expands its Bluetooth portfolio with eight new parts, including the AIROC CYW89829 Bluetooth LE MCU for automotive applications appeared first on ELE Times.

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