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Toshiba Launches Low Power Consumption Brushed DC Motor Driver IC With Popular Pin-assignment HSOP8 Package

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TOKYO–(BUSINESS WIRE)–Toshiba
Electronic Devices & Storage Corporation
(“Toshiba”) has
launched “TB67H450FNG,” the latest addition to its line-up of brushed DC
motor driver ICs. The new product with a maximum rating of 50V/3.5A[1]
drives motors with a wide range of operating voltage. It also offers a
small HSOP8 surface mount package with a popular pin-assignment that
enhances the product sourcing possibility. Mass production starts today.

The new IC can drive brushed motors with a power supply ranging from
4.5V to 44V. It supports wide range of applications that includes, USB
powered, battery powered, and industrial 12-36V devices. TB67H450FNG
also has 3.5A current driving capability that can be used in
applications such as robot vacuum cleaners, refrigerators and other home
appliances actuators, office equipment, ATM machines, and many others.

To meet demand for lower power consumption, Toshiba has also optimized
the TB67H450FNG standby current consumption with a new power supply
circuit that allows the stop mode to move into standby mode
automatically and to turn off the VCC regulator for internal circuit
operation. This helps OA equipment and home appliances to cut energy
consumption and improves the battery life of battery powered devices.

Housed in a small surface-mount type HSOP8 pin package, the IC achieves
space saving and yet good heat dissipation through the package thermal
pad design.

Key Features

  • Wide range of operating voltages: from 4.5V to 44V for large-current
    drive devices
  • Low standby current consumption: 1 μA (max) @VM=24V, Ta=25°C
  • Small 8-pin surface mount package HSOP8 with a popular pin-assignment
    and with bottom side E-pad to enhance thermal dissipation

Applications

Industrial equipment, including OA equipment and banking terminals; home
appliances, including robot vacuum cleaners; battery powered devices
(electronic locks and small household robots); and devices using 5V USB
power supplies

 

Main Specifications

Part number   TB67H450FNG
Supply voltage (operating range) 4.5V to 44V

Output voltage/current
(Absolute maximum rating)

50V/3.5A
Number of H-bridge channels 1ch
Motor to apply DC Brushed motor
Output on-resistance (upper + lower) 0.6Ω (typ.)@VM=24V, Ta=25℃
Safety function Over current detection, thermal shut down, and under voltage lockout
Package

HSOP8
(Size: 4.9mm×6.0mm)

Other features Current consumption in standby mode: 1 μA or less

Constant-current control

(constant-current PWM control)

Support forward/reverse/stop control

Stock Check & Purchase  

Buy
Online

 

Note:
[1] Actual driven motor current depends on the use
environment and such factors as ambient temperature and power supply
voltage.

For more information about the new product, please visit:
https://toshiba.semicon-storage.com/ap-en/product/linear/motordriver/detail.TB67H450FNG.html

To check the availability of the new product at online distributors,
please visit:
https://toshiba.semicon-storage.com/ap-en/buy/stockcheck.TB67H450FNG.html

*Company names, product names, and service names may be trademarks of
their respective companies.

Customer Inquiries:
System Devices Marketing Dept.II
Tel:
+81-3-3457-3332
https://toshiba.semicon-storage.com/ap-en/contact.html

Information in this document, including product prices and
specifications, content of services and contact information, is current
on the date of the announcement but is subject to change without prior
notice.

About Toshiba Electronic Devices & Storage Corporation

Toshiba Electronic Devices & Storage Corporation combines the vigor of a
new company with the wisdom of experience. Since becoming an independent
company in July 2017, we have taken our place among the leading general
devices companies, and offer our customers and business partners
outstanding solutions in discrete semiconductors, system LSIs and HDD.

Our 22,000 employees around the world share a determination to maximize
the value of our products, and emphasize close collaboration with
customers to promote co-creation of value and new markets. We look
forward to building on annual sales now surpassing 800-billion yen (US$7
billion) and to contributing to a better future for people everywhere.
Find
out more about us at https://toshiba.semicon-storage.com/ap-en/top.html

Contacts

Media Inquiries:
Toshiba Electronic Devices & Storage
Corporation
Digital Marketing Department
Chiaki Nagasawa
Tel:
+81-3-3457-4963
semicon-NR-mailbox@ml.toshiba.co.jp

Cannabis

Strainprint™ Technologies Welcomes Organic Medical Cannabis Producer to Growing List of Subscribers

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Strainprint™ Technologies Ltd, the leader in cannabis data and analytics is pleased to expand their nation-wide coverage with the addition of Stewart Farms.

Stewart Farms is a late-stage applicant headquartered in Alberta that is building a 100,000 sq. ft. vertical aquaponics farm in Saint StephensNew Brunswick. They will be utilizing automation, vertical farming, and land-based aquaculture to produce medical grade organic cannabis for both recreational and medical markets in Canada. All of their products will be free of herbicides, pesticides and synthetic nutrients. At full capacity, they will reach more than 10,000 kgs per year of organically farmed cannabis and more than 200,000 kgs of organic tilapia.

Stewart Farms’ long term vision is to deliver tools and products to support  future customers and patients during their health and wellness journeys. “Through our partnership with Strainprint we gain direct access to their incomparable data and analytics tools. This will aid us in educating a wider audience on the medical benefits and best practices of cannabis-based medicine,” said Tanner Stewart, Co-founder & CEO of Stewart Farms. “We know our customers are looking to consume more than dried cannabis. We know Canadians of all legal ages are trying to sort through their personal engagement with cannabis as a medicine. What we want to know, on an ongoing basis, is what is and is not working for people. Strainprint’s patient-led data services will give us the insight needed to create custom products and further understand the benefits of our existing products. Finally, our goal is to partner with companies and teams that truly have people’s best interest at heart. Caring about a patient’s success is what makes up the core of the Strainprint team. We couldn’t be happier to move forward with them at our side.”

Stewart Farms will access the Strainprint Analytics web platform, the most sophisticated cannabis analytics platform available to improve product development. Strainprint Analytics is built on top of the largest and most granular scientific data set of its kind in the world, with more than 1.3 million anonymized, patient reported medical cannabis outcomes and more than 65 million data points on strain efficacy.

“We’re thrilled to provide our real-time, crowd sourced data to an environmentally conscious company that uses innovative, cutting edge farming technology to produce top-tier medical cannabis,” said Strainprint CEO, Andrew Muroff. “Our organizations are equally committed to improving lives using cannabis therapy, and our shared core motivation is to provide guidance and support to help cannabis patients achieve their health goals.”

 

SOURCE Strainprint Technologies Ltd.

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Field Trip Ventures Inc. Retains KCSA Strategic Communications as Public Relations Counsel

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Greenery Map, the world’s first and only cannabis search engine to allow users to search for cannabis products based on their desired mood, medicinal use, and method of consumption, is now offering users – both businesses and consumers – the opportunity to register and connect to the app in an entirely mobile way. Previously, businesses were required to access GreeneryMap.com from a desktop in order to manage accounts and connect to the inventory API system, but that road block has been removed, allowing for an entirely mobile experience through the Greenery Map app.

App users turn to Greenery Map to help them decide on the strain of Cannabis and product that is right for them depending on their desired mood or medicinal effect. Following the matching, consumers are given a detailed history and description of the strain so that they can make educated purchases, cutting down on time that budtenders need to spend with patrons and speeding up the purchase process. Consumers can then see all of the ways to purchase said product, including the nearby dispensaries that have it in stock, online options, and even delivery options through the app. Greenery Map fully integrates with a dispensary’s inventory API system to update in real-time and give consumers real-time information on dispensary stock for the item they are looking for. The Greenery Map system also provides cannabis businesses in both the B2B and B2C sectors a private database to share and sell information and products. For Cannabis businesses, Greenery Map also offers the opportunity to open bank and merchant accounts through their partnership with PayHouse Consulting Group.

 

SOURCE Greenery Map

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WeBank, IBM and Other Organizations Jointly Held the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019

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Once a concept, AI is now ushering in a key stage of application. What’s the solution to the data silos among businesses? Given the enhanced regulation on data at home and abroad, what’s the solution to data privacy and security concerns? What’s the status quo of Federated Machine Learning and how to establish an ecosystem for FML in the future?

WeBank, IBM and other organizations jointly held the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19) in conjunction with the 28th International Joint Conference on Artificial Intelligence (IJCAI-19) on Aug. 12, 2019, to further discussion on these issues.

President of IJCAI, Chair of FML Steering Committee, Chief AI Officer of WeBank Professor Qiang Yang delivered opening remarks at the workshop. Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google presented keynote addresses. In the panel discussion, top scholars from WeBank, Bar-Ilan University, IBM, Squirrel AI, Google, Huawei, Clustar, Sinovation Ventures and many other renowned enterprises and universities shared and discussed their findings and experience in FML as an emerging AI technology.

This workshop received 40 papers, of which 12 were presented during the workshop, 19 presented via poster. Awards include Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award, Best Presentation Award. Selected high quality papers will be invited for publication in a special issue in the IEEE Intelligent Systems journal. All these attracted numerous scholars to engage in discussions and join efforts for building the FML ecosystem.

Experts from IBM and Google Share Groundbreaking Findings with a Focus on the Theory and Application of FML

Privacy and security are becoming a key concern in our digital age. On 25th May last year, the implementation of General Data Protection Regulation (GDPR) by the EU, the toughest Act on data privacy protection, stressed that user data collection must be open and transparent. A series of laws and regulations from China and overseas also pose new challenges to the traditional way of handling data and model for cooperation. Seeking ways for AI to adapt to this new reality became top priority, a demand that led to this workshop on FML.

A wealth of solutions and breakthroughs were shared by Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google in their speech on FML.

Besides how FML can help tackle challenges in the business world, Dr. Shahrokh Daijavad also shared the concept of Fusion AI, which means to train models on widely distributed data sets, but fuse them to produce one equivalent to what centralized training would yield. “Unlike traditional machine learning, in Fusion AI, model parameters are shared and data is not transferred, which makes Fusion AI model better than models that moving data centrally.” Given the widely distributed data, the development of Fusion AI and FML became ever important and imminent.

“FML enables machine learning engineers and data scientists to work productively with decentralized data with privacy by default,” said Dr. Jakub Konečný from Google. He also shared with us how FML works and its use cases at Google. In the case of Gboard, as on-device data is privacy sensitive or large or is more relevant than server-side proxy data, and labels can be inferred naturally via user interaction, the application of Federated RNN compared to prior n-gram model can increase the accuracy of next-word prediction by 24%, and the click rate of prediction strip by 10%.

Major Figure Panelists Discuss the Way Ahead for FML

The moderator of the panel discussion, AI Principal Scientist of WeBank Dr. Lixin Fan joined panelists including Professor Benny Pinkas from Bar-Ilan University, Dr. Shahrokh Daijavad of IBM Academy of Technology, Chief Architect of Squirrel AI Dr. Richard Tong, Research Scientist of Google Dr. Jakub Konečný, Dr. Baofeng Zhang from CTO Office of CBG Software in Huawei, Executive VP of Clustar Dr. Junxue Zhang, VP of AI Institute in Sinovation Ventures Dr. Ji Feng and other experts in a host of in-depth exchanges with attendees, to shed light on the way ahead for FML.

Experts shared thoughts in the panel discussion on questions including but not limited to: How to meet the security and compliance requirements? Is there a way to extend the value of data while observing user privacy and data security? Given the classic trade-off between data regulation and development of AI, how to achieve the long-term goal of establishing a stable and win-win business ecosystem?

List of Award-Winners

Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award and Best Presentation Award selected by all attendees were announced at the closing of the workshop.

Best Theory Paper Award: 
Preserving User Privacy for Machine Learning: Local Differential Privacy or Federated Machine Learning? By Huadi Zheng, Haibo Hu & Ziyang Han;

Best Application Paper Award: 
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. By Yiqiang ChenJindong WangChaohui YuWen Gao & Xin Qin;

Best Student Paper Award: 
Quantifying the Performance of Federated Transfer Learning. By Qinghe JingWeiyan Wang, Junxue Zhang, Han Tian & Kai Chen;

Best Presentation Award: 
Federated Generative Privacy. By Aleksei Triastcyn and Boi Faltings.

President of IJCAI, Chief AI Officer of WeBank Professor Qiang Yang, Chief Architect of Squirrel AI Dr. Richard TongandVP of AI Institute in Sinovation Ventures Dr. Ji Feng presented the awards.

“The mission of this International Federated Machine Learning Workshop is to facilitate further understanding in the academia, business community as well as legal and regulatory institutions by promoting the establishment of FML ecosystem in the hope that more businesses will join and build a platform for students aspired to work in FML to find research teams that suit them,” said Professor Qiang Yang.

Held Aug. 10-16, 2019 in Macao, China, IJCAI-19 is one of the leading International Academic Conference on AI, attracting over 3000 AI research personnel and experts. The 1st International Workshop on Federated Machine Learning (FML’19) was a highlight for experts joining this event. Visionaries in the academia and industrial sector expressed the willingness to be part of the effort for academic research, application of FML in the future, and the development and boom of AI ecosystem.

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