10 REAL LIFE APPLICATIONS OF IoT

The power of internet connectivity has now stepped beyond computers and smartphones. Every ‘smart’ device around us is now aiming to solve real world problems with digital interventions. These are the real life applications of IoT

Real Life Applications Of Iot

1. SMART TOOTHBRUSH

A smart toothbrush is embedded with high-quality sensors that capture data on your brushing frequency, missed spots, the pressure applied, and so on. You can track all of this crucial information on your smartphone and gain insights on how to improve your oral health.

2. FITNESS TRACKERS

Now, IoT-connected devices are here to help you stay fit. Fitness trackers help you track your daily activities such as your sleeping patterns, your heart rate, activity patterns, workout statistics, calories burned, and so on. Armed with this information, you can monitor and plan your fitness goals easily.

3. CHILD AND PET FINDER

The feeling of losing your loved ones, be it your kid or a four-legged member of your family, is terrifying. But, with an IoT-connected device that is connected to your smartphone, you can track their location in real-time. Thus, IoT enables you to stay in peace even when you are away from your loved ones.

4. INFANT MONITOR

There is good news for all the new parents out there! You can monitor your baby’s daily activities on your smartphone in real-time from anywhere in the world. Infant monitors give you information on your baby’s respiration statistics, sleeping positions, sleeping duration, body temperature, and so on.

5. SMART SHELVES

With sensors, cameras, and actuators embedded on shelves, retailers can get real-time updates on products, enabling them to replenish when needed. IoT also helps retailers get real-time alerts of misplaced products on their smart devices.

6. SMART GARDENING

No free time to water your plants? You can try smart gardening, instead. With sensors embedded in your garden, you can get information on the condition of the soil, temperature, humidity level, and suggestions for the right time to watering plants. All of these data points can be easily controlled and managed on your smartphone.

7. HEALTH MONITORING

Getting periodic information about the status of your kids’ or old parents’ health has become an essential requirement these days. Now, health-tracking devices help you to monitor their health in real-time. Sudden changes in temperature, blood pressure, heart rate, breathing, etc. are notified to you, thereby allowing you to take necessary actions on time.

8. SMART REFRIGERATOR

How cool it is to peek into your fridge without opening its door! This thought is no more a fantasy now! The sensors and cameras attached to new-age smart refrigerators allow you monitor spoiled food items. Additionally, it enables you to track leftover food without opening its door.

9. POLLUTION WARNINGS

Smart air monitors detect pollutants emitted from vehicles in a particular region, affecting the environment. Such real-time updates to government agencies help them take required steps quickly.

10. SMART SHOES

Smart shoes allow users to change the color of the shoe with one tap on their smartphone! With the tap of your heels, you can also send a text message to your friends, call a cab, monitor your steps, calories burned, and so on.

Honestly, we are already enjoying applications that we never thought of, a few years back. As the concerns around robust network connectivity, security, and privacy get resolved, we will see more groundbreaking applications disrupting our lives.
Source: allerin

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Recognizing deep-fake images using frequency analysis

They look convincingly real, and it is very challenging to distinguish them from real photos. However, researchers have found a new way to identify them.

These images (called deep-fake images) are created by machine learning algorithms and it is very challenging to distinguish them from real photos. Researchers have found a new method to effectively identify those images. They analyze the objects in the frequency domain and set up a signal processing technique.

Interaction of two algorithms results in new images

Deep-fake images - are generated with the help of computer models, so-called Generative Adversarial Networks, GANs for short. Two algorithms work together in these networks: the first algorithm creates random images based on certain input data. The second algorithm needs to decide whether the image is a fake or not. If the image is found to be a fake, the second algorithm gives the first algorithm the command to revise the image - until it no longer recognizes it as a fake.

Frequency analysis reveals typical artefacts in computer-generated images. Credit: © RUB, Marquard

Frequency analysis reveals typical artefacts

Nowadays, deep-fake images have been analysed using complex statistical methods. The research group chose a different approach by converting the images into the frequency domain using the discrete cosine transform. The generated image is then expressed as the sum of many different cosine functions. Natural images consist mainly of low-frequency functions.

The analysis has shown that images generated by GANs exhibit artefacts in the high-frequency range. For example, a typical grid structure emerges in the frequency representation of fake images. "Our experiments showed that these artefacts do not only occur in GAN generated images. They are a structural problem of all deep learning algorithms," explains Joel Frank from the Chair for Systems Security. "We assume that the artefacts described in our study will always tell us whether the image is a deep-fake image created by machine learning," adds Frank. "Frequency analysis is therefore an effective way to automatically recognise computer-generated images."

Source: Science Daily

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers. 

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ROBOT CLEANERS USED IN LEEDS TRIALS

Robots are being trialled to see how effective they can be in disinfecting items and public areas in the fight against coronavirus.

The University of Leeds is using robots to help sanitise parts of the city centre and Leeds Bradford Airport. The schemes aim to reduce the risk of Covid-19 transfer to both cleaning staff and the public.

The robots being used in the city centre and the airport were originally designed to inspect, and conduct repairs on, infrastructure such as pipes and bridges.

They were developed by researchers in the Self Repairing Cities project, a consortium involving the University of Leeds and Birmingham, and University College London.

Adapted to combine navigation, computer vision and artificial intelligence, they find objects which need regular cleaning and then spray a mist of diluted alcohol over them.

 

Dr Bilal Kaddouh, assistant professor at the Leeds School of Mechanical Engineering, said the trials had gone well.

"The robots were able to identify the objects that they needed to clean, and they were able to manoeuvre in the public spaces.

"The robotic arms effectively delivered the disinfectant on to the target surfaces."

The next stage will be to have the robots work autonomously, he said.

 Source: BBC

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers

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3 Use Cases Of Artificial Intelligence In Business

With the rapid development of 4.0 Industrial Revolution, Artificial Intelligence was gradually applied in many fields from manufacturing, services, transportation to health and education. According to the study by Harvard Business Review, there are three main purposes for enterprises to apply AI: Robotic Process Automation, Cognitive Engagement and Cognitive Insight. 

Robotic Process Automation (RPA) is the most common need when businesses use AI with low investment costs and ease of implementation. These applications can automatically update the customer information and profiles, communicate with customers through a chatbot and provide basic guidance on standardized information and documents. Those help businesses in management and customer care.  

Cognitive Insight uses more complex algorithms incorporating machine learning to identify patterns from multiple data sets and extract meaning from them. The applications of Cognitive Insight are used in activities such as making prediction about purchasing options, detecting fraudulent transactions, automating advertisements in the direction of personalization, etc. Deep insights are accessed from user data, predicting customer behavior, and providing solutions with constantly updated and improved data. The goal is to improve the performance of tasks performed by machines that require a fast level of automation and massive data, beyond human capabilities.

Cognitive Engagement is a way of approaching customers by predicting analytics to provide insight into future customer behavior. Artificial Intelligence helps to provide maximum personalized content in interacting with customers quickly, right content, right audience and right time. As a result, businesses can formulate strategies and plans to increase interaction efficiency with customers and increase competitive advantage.

Depending on the specific needs and purposes, businesses can apply individually or combine the above applications for business activities in the most effective way.

Source: Toptal

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Machine Learning Is Fun!

What is machine learning?

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.

For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It’s the same algorithm but it’s fed different training data so it comes up with different classification logic.

This machine learning algorithm is a black box that can be re-used for lots of different classification problems

“Machine learning” is an umbrella term covering lots of these kinds of generic algorithms.

Two kinds of Machine Learning Algorithms

You can think of machine learning algorithms as falling into one of two main categories — supervised learning and unsupervised learning. The difference is simple but really important.

Supervised Learning

Let’s say you are a real estate agent. Your business is growing, so you hire a bunch of new trainee agents to help you out. But there’s a problem — you can glance at a house and have a pretty good idea of what a house is worth, but your trainees don’t have your experience so they don’t know how to price their houses.

To help your trainees (and maybe free yourself up for a vacation), you decide to write a little app that can estimate the value of a house in your area based on its size, neighborhood, etc and what similar houses have sold for.

So you write down every time someone sells a house in your city for 3 months. For each house, you write down a bunch of details — number of bedrooms, size in square feet, neighborhood, etc. But most importantly, you write down the final sale price:

Using that training data, we want to create a program that can estimate how much any other house in your area is worth:

This is called supervised learning. You knew how much each house sold for, so in other words, you knew the answer to the problem and could work backward from there to figure out the logic.

To build your app, you feed your training data about each house into your machine learning algorithm. The algorithm is trying to figure out what kind of math needs to be done to make the numbers work out.

This kind of like having the answer key to a math test with all the arithmetic symbols erased:

Oh no! A devious student erased the arithmetic symbols from the teacher’s answer key!

From this, can you figure out what kind of math problems was on the test? You know you are supposed to “do something” with the numbers on the left to get each answer on the right.

In supervised learning, you are letting the computer work out that relationship for you. And once you know what math was required to solve this specific set of problems, you could answer to any other problem of the same type!

Unsupervised Learning

Let’s go back to our original example with the real estate agent. What if you didn’t know the sale price for each house? Even if all you know is the size, location, etc of each house, it turns out you can still do some really cool stuff. This is called unsupervised learning.

Even if you aren’t trying to predict an unknown number (like price), you can still do interesting things with machine learning

This is kind of like someone giving you a list of numbers on a sheet of paper and saying “I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”

So what could do with this data? For starters, you could have an algorithm that automatically identified different market segments in your data. Maybe you’d find out that home buyers in the neighborhood near the local college really like small houses with lots of bedrooms, but homebuyers in the suburbs prefer 3-bedroom houses with lots of square footage. Knowing about these different kinds of customers could help direct your marketing efforts.

Another cool thing you could do is automatically identify any outlier houses that were way different than everything else. Maybe those outlier houses are giant mansions and you can focus your best sales people on those areas because they have bigger commissions.

Supervised learning is what we’ll focus on for the rest of this post, but that’s not because unsupervised learning is any less useful or interesting. In fact, unsupervised learning is becoming increasingly important as the algorithms get better because it can be used without having to label the data with the correct answer.

Side note: There are lots of other types of machine learning algorithms. But this is a pretty good place to start.

Source: Medium.com

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Vietnam’s technology skills top two in Asia Pacific

Vietnamese technology skills second in Asia Pacific and 22nd in global, data from the Global Skills Index 2020 report.

In this report - which was published last week, the worldwide online learning platform Coursera benchmarked 10 industries for business and technology skills. Coursera said these are its most popular domains in terms of enrollments and "encapsulate the skills most crucial to the future of work."

Source: LifeSuccess

According to the report, Vietnam’s strongest skill in the technology domain is operating systems (specifically Android and iOS software development), followed by computer networking (blockchain and wireless networking), human-computer interaction (user interface and machine translation), security engineering (cyber attacks and cryptography) and then software engineering (software development and algorithms).

In terms of the technology domain, Vietnam outranked Japan and Australia to finish in second place. The nation scooped 22nd place in the global ranking. Whereas in the business domain, Vietnam ranked seventh in Asia Pacific and 33th in the world. 

Jeff Maggioncalda, CEO of Coursera, stated that in workforce recovery in a post-pandemic world depends on broad-base re-skilling. Educational institutions must lead this effort by proving learners with equal access to skills required for jobs of the future.

Coursera's data indicated every skill proficiency percent gained in a country’s average proficiency (across domains) is associated with a $600 increase in per capita GDP.

Source: VnExpress

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers.

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Vietnam to commercialize 5G in October

According to the Ministry of Information and Communications (MIC), Vietnam has made official plans to commercialize 5G Network in October using only domestically produced components.

On January 17, the first video call using a 5G connection on a gNobeB transceiver was successfully implemented, this means that Viet Nam has officially mastered 5G network technology.

Vietnam is among the few countries that have the capability to produce 5G equipment. The MIC is making arrangements to direct investment in research and production of 5G equipment this year. Together with that, Vietnam is also building a roadmap to remove 2G technology from 2022 to aid the establishment of 5G technology.

The ministry said telecom infrastructure has seen an important change in frequency infrastructure. Implementing 5G in Viet Nam has advantages as Vietnamese electronics and telecommunications businesses have produced lots of equipment and infrastructure, whereas before foreign manufacturers were relied upon.

This would be an important step for socio-economic development and ensuring national defense, he added. 

Source: Shutterstock/SunnyVMD

According to the ministry’s Department of Information and Technology, the development of the 5G network has been one of the key orientations to upgrade digital infrastructure, serving national digital transformation. 

Deputy Minister Phan Tam said the Government has launched the 'Make in Viet Nam' strategy to help the country escape the middle-income trap. The country aims to produce chipsets for 5G networks and the internet of things (IoT) equipment. Government officials have offered preferential policies for the production as it is a high-tech area.

Source: Ministry of Information and Communications

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers.

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WILL AI REPLACE PROGRAMMERS?

Software developers have plenty to keep them awake at night. Their top concern is no longer how to express the latest algorithm in their favorite language (C, C++,Erlang, Java, etc.). Instead, It’s being replaced by artificial intelligence(AI).

Here we take a look at the process for AI writing code and answer the question: willAI replace programmers?

The Future of AI Technology

In a survey conducted by Evans Data Corp, 550 software developers were asked about the most worrying aspects in their career. 29 percent said, “I and my development efforts are replaced by artificial intelligence.”

A team of researchers at the U.S. Department of Energy's Oak Ridge National Laboratory agrees. By 2040, machine learning and natural language processing technologies will be so advanced that they will be capable of writing better software code. And they’ll do it faster than the best human developers. 

Oxford University’s "The Future of Employment" study warns that software engineers may become computerized as machine learning advances. And software design choices will be optimized by algorithms.

Software development, particularly in safety-critical industries, needs to ensure high code quality that delivers on functional requirements.

So, if AI is developing code, the code should be error- and issue-free. This also includes AI in software testing, as it should be able to detect coding errors “with a reliability that humans are unlikely to match.”

Is AI Writing Code Possible?

AI can write code.

In 2015, Andrej Karpathy ran a project that used Recurrent Neural Networks to generate code. He took GitHub’s Linux repository (all the source files and headers files), combined it into one giant document (it was more than 400 MB of C code), and trained the RNN with this code.

AI generated code — including functions and function declarations — overnight. It had parameters, variables, loops, and correct indents. Brackets were opened and later closed. It even had comments.

However, the AI produced code had syntactic errors. It didn’t keep track of variable names.  Sometimes variables were declared but never used. Other times variables were used but not defined. The second function in the code example compares "tty == tty".

The project is available on GitHub. It uses the Torch7 deep learning library. Here is the whole output file produced by Karpathy’s exercise.

So, Will AI Replace Programmers?

AI won’t replace programmers. But AI might write code one day.

Of course, it will take time before AI will be able to create actual, production-worthy code that spans more than a few lines.

Here’s how AI will impact software development in the near future.

AI Will Improve

It will become effective at helping developers understand their options. And it will then let the human decide how to optimize for circumstances beyond AI’s understanding.

AI Will Become a Coding Partner

Software developers will use AI as a coding pair to write better software.

But Programmers Will Remain Important

The true value of a programmer is not knowing how to build it. The value is in knowing what to build.

It will take even longer before AI learns how to interpret the business value of each feature and advise you what to develop first. There will always be a role for the human programmer.

What If AI Writes Reliable Code?

That’s a big "if". Most humans can’t write reliable code. And AI is just an application that analyzes vast amounts of human written code. So, it’s unlikely that AI will write reliable code.

So, AI isn’t the answer to improving code quality.

Source: Perforce

 About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers

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HOW IOT WORKS – 4 MAIN COMPONENTS OF IOT SYSTEM

How IoT Works?

Just like Internet has changed the way we work & communicate with each other, by connecting us through Internet, IoT also aims to take this connectivity to another level by connecting multiple devices at a time to the internet thereby facilitating man to machine and machine to machine interactions.

Here, 4 fundamental components of IoT system, which tells us how IoT works.

i. Sensors/Devices

First, sensors or devices help in collecting very minute data from the surrounding environment. All of this collected data can have various degrees of complexities ranging from a simple temperature monitoring sensor or a complex full video feed. A device can have multiple sensors that can bundle together to do more than just sense things.

For example, our phone is a device that has multiple sensors such as GPS, accelerometer, camera but our phone does not simply sense things.

The most rudimentary step will always remain to pick and collect data from the surrounding environment be it a standalone sensor or multiple devices.

ii. Connectivity

Next, that collected data is sent to a cloud infrastructure but it needs a medium for transport.

The sensors can be connected to the cloud through various mediums of communication and transports such as cellular networks, satellite networks, Wi-Fi, Bluetooth, wide-area networks (WAN), low power wide area network and many more.

Every option we choose has some specifications and trade-offs between power consumption, range, and bandwidth. So, choosing the best connectivity option in the IOT system is important.

iii. Data Processing

Once the data is collected and it gets to the cloud, the software performs processing on the acquired data.

This can range from something very simple, such as checking that the temperature reading on devices such as AC or heaters is within an acceptable range. It can sometimes also be very complex, such as identifying objects (such as intruders in your house) using computer vision on video. But there might be a situation when a user interaction is required, example- what if when the temperature is too high or if there is an intruder in your house? That’s where the user comes into the picture.

iv. User Interface

Next, the information made available to the end-user in some way. This can achieve by triggering alarms on their phones or notifying through texts or emails.

Also, a user sometimes might also have an interface through which they can actively check in on their IoT system. For example, a user has a camera installed in his house, he might want to check the video recordings and all the feeds through a web server.

However, it’s not always this easy and a one-way street. Depending on the IoT application and complexity of the system, the user may also be able to perform an action that may backfire and affect the system. For example, if a user detects some changes in the refrigerator, the user can remotely adjust the temperature via their phone.

There are also cases where some actions perform automatically. By establishing and implementing some predefined rules, the entire IOT system can adjust the settings automatically and no human has to be physically present. Also in case if any intruders are sensed, the system can generate an alert not only to the owner of the house but to the concerned authorities.

Conclusion

People who came up with this idea, have also realized that this IoT ecosystem is not limited to a particular field but has business applications in areas of home automation, vehicle automation, factory line automation, medical, retail, healthcare and more.

Source: DATAFLAIR TEAM

 About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers

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