About Rik

Myself Rik and I am founder of Riktronics. I study Electronics and Communication Engineering in IIE. My hobby is playing with electronics and making various projects, mainly about embedded systems. Love to do coding, and making tutorials about electronics/programming.
Contact me in any need at abhra0897@gmail.com
My blog : riktronics.wordpress.com

RoboBee – A Flying Microbot That Can Perform Search And Rescue Missions

Inspired by the biology of a bee, researchers at the Wyss Institute developed RoboBees, man-made microbots that could perform endless roles in agriculture or disaster relief. A RoboBee is about half the size of a paper clip, weighs less than one-tenth of a gram, and flies using materials that contract when an electric pulse is applied. Now, they progressed even further and designed a hybrid RoboBee that can fly, dive into water, swim, propel itself back out of the water, and safely land.

New, hybrid RoboBee can fly, dive into water, swim, propel itself back out of water, and safely land.
New, hybrid RoboBee can fly, dive into water, swim, propel itself back out of the water, and safely land.

New floating devices allow this multipurpose air-water microrobot to stabilize on the water’s surface before an internal combustion system ignites to propel it back into the air. This latest-generation RoboBee is 1000 times lighter than any previous aerial-to-aquatic robot. This can be used for numerous applications, from search-and-rescue operations to environmental monitoring and biological studies. Yufeng Chen, Ph.D. and a Postdoctoral Fellow at the Wyss Institute, said:

This is the first microrobot capable of repeatedly moving in and through complex environments

The researchers have faced numerous challenges to design a millimeter-sized robot that moves in and out of the water. The robot’s wing flapping speed will vary widely between the two mediums as water is 1000 times denser than air. If the flapping frequency is too low, the RoboBee can’t fly. If it’s too high, the wing will snap off in the water. So, it requires a precise balancing as well as a smart multimodal locomotive strategy to overcome this problem.

RoboBee has four buoyant outriggers and a central gas collection chamber. Once the RoboBee swims to the surface, an electrolytic plate in the chamber converts water into oxyhydrogen, a highly combustible gas fuel. The gas increases the robot’s buoyancy and pushes the wings out of the water. The outriggers stabilize the RoboBee on the water’s surface. Elizabeth Farrell Helbling, a graduate student in the Microrobotics Lab, said:

Because the RoboBee has a limited payload capacity, it cannot carry its own fuel, so we had to come up with a creative solution to exploit resources from the environment.

The research team hopes that in future research the RoboBee can fly immediately upon propulsion out of the water, which is currently not possible due to the lack of onboard sensors and limitations in the current motion-tracking system.

Atmel ATmega8 – A World-Famous Microcontroller Created By Two Annoyed Students

AVR is a family of microcontrollers developed by Atmel beginning in 1996. These are modified Harvard architecture 8-bit RISC single-chip microcontrollers. The Atmel AVR core combines a rich instruction set with 32 general purpose working registers. Atmel’s ATmega8 comes from the AVR line of microcontroller and it is a gem of the modern maker movement. It is used as the heart of the first generation of the Arduino board to be widely adopted by electronics hobbyists. Countless creative projects are designed with those cheap yet powerful chips.

ATmega8 was originally developed in the early 1990s by two students at the Norwegian University of Science and TechnologyAlf-Egil Bogen, and Vegard Wollan. Microcontrollers are different from microprocessors in terms of built-in memory and I/O peripherals. They typically have their own onboard program memory and RAM, rather than relying on external chips for these resources.

When Bogen and Wollan were in university, they faced trouble in following the steep learning curve of the complex instruction sets for microprocessors. Most of the processors used in those days were CISC (Complex instruction set computer) based. They wanted to design a RISC (reduced instruction set computer) based microcontroller with an aim in mind to create something that would be easy to program and relatively powerful. Bogen explained in a YouTube video,

I found them very hard to us. The learning curve to get to use them was hard; I found the development tools crappy. And also I saw that the performance of the products was not where I wanted it to be.

Bogen
Alf-Egil Bogen – one of the creators of the AVR core

Computers, that are typically used on the day-to-day basis, use Von Neumann architecture. In this architecture, programs are loaded into the RAM first and then executed from the same. AVR uses the Harvard architecture, in which program memory and working RAM are kept separate, thus enables faster execution of instructions. The first prototype of AVR used ROM, which is not re-writeable, as the program memory. Later Atmel added easily programmable (and reprogrammable) flash memory to the processor core. The first commercial AVR chip, the AT90S8515, was released in 1996. Wollan says in a video,

instructions and stuff were things we were actually thinking of from the very beginning to make it efficient and easy to use from a high-level point of view

Wollen
Vegard Wollen – another creator of AVR

ICECool – An Intra-Chip Cooling System That Is More Efficient

In the Moore’s Law race to keep improving computer performance, the IT industry has turned upward, stacking chips like nano-sized 3D skyscrapers. But those stacks have their limits, due to overheating. Researchers from IBM have solved this problem by developing an intra-chip cooling system as a contribution to ICECool program research project by the DARPA (Defense Advanced Research Projects Agency).

ICECool - intra-chip cooling system by IBM
ICECool – intra-chip cooling system by IBM

Today, chips are typically cooled by fans which blow air through heatsinks that sit on top of the chips to carry away excess heat. Advanced water-cooling approaches, which are more effective than air-cooling approaches, replace the heatsink with a cold plate that is fixed on the top of the chip.  But this approach requires extra protection and proper insulation of the chip because of the electrical conductivity of water. Neither of these technologies can cool down the chip much efficiently. Here comes the ICECool that cools the chip down from the inside rather than just from the upper surface.

ICECool uses a nonconductive fluid to bring the fluid into the chip. This completely eliminates the need for a barrier between the chip and fluid. It not only delivers a lower device junction temperature, but also reduces system size, weight, and power consumption significantly. The tests performed on the IBM Power 7+ chips demonstrated junction temperature reduction by 25ᵒ C, and chip power usage reduction by 7 percent compared to traditional air cooling. This is clearly a great achievement when the operating cost is much smaller than the conventional cooling technologies.

IBM’s ICECool intra-chip cooling system solves the problem of cooling the 3D “skyscraper” chips by pumping a heat-extracting dielectric fluid right into microscopic gaps, some no thicker than a single strand of hair, between the chips at any level of the stack. Being nonconducting, the dielectric fluid used in ICECool can come into contact with electrical connections without causing any short circuit, so is not limited to one part of a chip or stack. Based on the tests with IBM Power Systems, ICECool technology could reduce the cooling energy for a traditional air-cooled data center by more than 90 percent.

WISP – Re-programmable Microcontroller That Runs On Energy Harvested From Radio Waves

A new research initiative between the University of Washington’s Sensor Lab and the Technical University of Delft in the Netherlands has created a microprocessor that can power itself through stray radio waves and receive programmable updates in the same fashion. While the RISC-derived 16-bit microcontroller CPU is very weak compared to modern standards, it’s much more powerful than any other device that’s powered by ambient energy in the environment with no battery required.

The WISP 5 - Microchips and sensors run from radio wave's energy
The WISP 5 – Microchips and sensors run from radio wave’s energy

This battery-free system is equipped with a sensor and a microchip, which can be powered entirely by radio waves harvested from the air and is up to 10 times faster than similar ambient-powered devices. Best of all, in contrast to similar devices, it can also download executables, allowing it be reprogrammed or upgraded to newer version of firmware whenever needed. This has significant implications for the Internet of Things development and for ambient computing as a whole.

The variety of handheld, portable technology, and wearable gadgets available today is truly amazing. In order to make devices even more compact and thinner, manufacturers typically try to shrink their designs as much as possible. Unfortunately, device size is ultimately limited by the batteries, all of which have a certain capacity before they dry out and must be recharged again. It is a challenge for engineers and designers to balance battery life with function and aesthetics.

The project of radio wave-driven microcontroller is dubbed WISP, or Wireless Identification and Sensing Platform. RFID (CRFID) technology is an example of  WISP. In particular, WISP is capable of being powered passively by converting radio frequencies emitted by conventional RFID (radio frequency identification) readers into electrical power. The project’s latest accomplishment is the addition of Wisent (short for “wirelessly sent”), a faster and more reliable downstream communication-oriented protocol for CRFIDs that can tolerate fluctuations in operating power.

The WISP is constructed out of an open source, open architecture EPC Class 1 Generation 2 RFID tag that incorporates a fully programmable 16-bit microcontroller, in addition to any add-on sensors. It differs from ordinary RFID tags as it is programmable, and can be multi-functional. The team writes in their research paper,

The novelty of Wisent is its ability to change adaptively the frame length sent by the reader, based on the length throttling mechanism, to minimize the transfer times at varying channel conditions. Wisent enables wireless CRFID reprogramming, demonstrating the world’s first wirelessly reprogrammable CRFID.

Researchers Developed Low Cost Battery From Graphite Waste

Lithium-ion batteries are flammable and the price of the raw material is increasing. Scientists and engineers have been trying to find out a safe yet efficient alternative to the Lithium-ion technology. The researchers of Empa and ETH Zürich have discovered promising approaches as to how we might produce powerful batteries out of waste graphite and scrap metal.

Kostiantyn Kravchyk and Maksym Kovalenko, the two chief researchers of the Empa’s Laboratory for Thin Films and Photovoltaics, led the research group. Their ambitious goal is to make a battery out of the most common elements in the Earth’s crust – such as graphite or aluminum. These metals offer a high degree of safety, even if the anode is made of pure metal. This also enables the assembly of the batteries in a very simple and inexpensive way.

In typical lithium-ion battery design, the negative electrode or anode is made from graphite. This new design, however, uses graphite as the positive electrode or cathode. In order to make such batteries run, the liquid electrolyte needs to consist of special ions that form a kind of melt and do not crystallize at room temperature. The metal ions move back and forth between the cathode and the anode in this “cold melt”, encased in a thick covering of chloride ions.

Alternatively, large but lightweight and metal-free organic anions could be used. But, this raises some questions which cannot be solved easily – where are these “large” ions supposed to go when the battery is charged? What could be a suited cathode material? In comparison, the cathode of the lithium-ion battery is made of a metal oxide which can easily absorb the small lithium cations during charging. This does not work for such large organic ions.

To solve the problem, Kovalenko’s team came up with a unique and tricky solution: the researchers turned the principle of the lithium-ion battery upside down. In Kovalenko’s battery, the graphite is used as a cathode; i.e., the positive pole. The thick anions are deposited in the intermediate spaces in the graphite. While searching for the “right” graphite, they found that waste graphite produced in steel production (known as kish graphite) works the best as a cathode material. Natural graphite is suitable when it is in the form of coarse flakes and not too finely ground.

Researchers Developed New Efficient, Thin, and Flexible Cooling Device

Engineers and scientists from the UCLA Henry Samueli School of Engineering and Applied Science and SRI International, California, have created a thin flexible device that could keep smartphones and laptop computers cool and prevent overheating. The component is based on the electrocaloric effect – a phenomenon where the temperature of material changes when an electric field is applied to it. The research has been published in Science.

Thin, flexible cooling device
Thin, flexible cooling device

The system’s flexibility also allows it to be used in wearable electronics, robotic systems, and new types of personalized cooling systems. It is the first demonstration of a solid-state cooling device based on the electrocaloric effect. The method devised by UCLA and SRI researchers is very energy-efficient. It uses a thin polymer film that transfers heat from the heat source – a battery or a processor – to a heat sink, and alternates contact between the two by switching on and off the electric voltage.

Because the polymer film is very flexible, the system can be used in devices with complex shapes or moving surfaces. Body tracking wearable devices can easily accommodate this flexible cooling device. Such cooling pad could keep a person comfortable in a hot office and thus lower the electricity consumption for air conditioning. Or it could be placed in a shoe to keep a runner comfortable while running in the sun. It’s like a personal air conditioner.

The tendency of flexible electronics to overheat remains a major challenge for engineers. The cooling systems in larger devices like air conditioners and refrigerators, which use vapor compression, are just too large for mobile electronics. The new cooling device produces a specific cooling power of 2.8 watts per gram and a COP of 13. This is more efficient and compact than the existing surface-mountable solid-state cooling technologies, opening a path to using the technology for a variety of practical applications.

Roy Kornbluh, an SRI research engineer, said,

The development of practical efficient cooling systems that do not use chemical coolants that are potent greenhouse gases is becoming even more important as developing nations increase their use of air conditioning.

Get Sensor Data From Arduino To Smartphone Via Bluetooth

Hariharan Mathavan at allaboutcircuits.com designed a project on using Bluetooth to communicate with an Arduino. Bluetooth is one of the most popular wireless communication technologies because of its low power consumption, low cost and a light stack but provides a good range. In this project, data from a DHT-11 sensor is collected by an Arduino and then transmitted to a smartphone via Bluetooth.

Required Parts

  • An Arduino. Any model can be used, but all code and schematics in this article will be for the Uno.
  • An Android Smartphone that has Bluetooth.
  • HC-05 Bluetooth Module
  • Android Studio (To develop the required Android app)
  • USB cable for programming and powering the Arduino
  • DHT-11 temperature and humidity sensor

Connecting The Bluetooth Module

To use the HC-05 Bluetooth module, simply connect the VCC to the 5V output on the Arduino, GND to Ground, RX to TX pin of the Arduino, and TX to RX pin of the Arduino. If the module is being used for the first time, you’ll want to change the name, passcode etc. To do this the module should be set to command mode. Connect the Key pin to any pin on the Arduino and set it to high to allow the module to be programmed.

Circuit to connect HC-05 with Arduino
Circuit to connect HC-05 with Arduino

To program the module, a set of commands known as AT commands are used. Here are some of them:

AT Check connection status.
AT+NAME =”ModuleName” Set a name for the device
AT+ADDR Check MAC Address
AT+UART Check Baudrate
AT+UART=”9600″ Sets Baudrate to 9600
AT+PSWD Check Default Passcode
AT+PSWD=”1234″ Sets Passcode to 1234

The Arduino code to send data using Bluetooth module:

//If youre not using a BTBee connect set the pin connected to the KEY pin high
#include <SoftwareSerial.h>
SoftwareSerial BTSerial(4,5); 
void setup() {
 String setName = String("AT+NAME=MyBTBee\r\n"); //Setting name as 'MyBTBee'
 Serial.begin(9600);
 BTSerial.begin(38400);
 BTSerial.print("AT\r\n"); //Check Status
 delay(500);
 while (BTSerial.available()) {
 Serial.write(BTSerial.read());
 }
 BTSerial.print(setName); //Send Command to change the name
 delay(500);
 while (BTSerial.available()) {
 Serial.write(BTSerial.read());
 }}
void loop() {}

Connecting The DHT-11 Sensor

To use the DHT-11, the DHT library by Adafruit is used. Go here to download the library. When the letter “t” is received, the temperature, humidity, and heat index will be transmitted back via Bluetooth.

circuit to connect DHT-11 with Arduino
circuit to connect DHT-11 with Arduino

The code used to read data from the DHT sensor, process it and send it via Bluetooth:

#include "DHT.h"
#define DHTPIN 2 
#define DHTTYPE DHT11 
DHT dht(DHTPIN, DHTTYPE);
void setup() {
 Serial.begin(9600);
 dht.begin();}

void loop()
{ char c; 
if(Serial.available()) 
 { 
 c = Serial.read(); 
 if(c=='t')
 readSensor();
 }}
void readSensor() {
 float h = dht.readHumidity();
 float t = dht.readTemperature();
 if (isnan(h) || isnan(t)) {
 Serial.println("Failed to read from DHT sensor!");
 return;
 }
 float hic = dht.computeHeatIndex(t, h, false);
 Serial.print("Humidity: ");
 Serial.print(h);
 Serial.print(" %\t");
 Serial.print("Temperature: ");
 Serial.print(t);
 Serial.print(" *C ");
 Serial.print("Heat index: ");
 Serial.print(hic);
 Serial.print(" *C ");
}

Developing The Android App

The flow diagram of the Android app is illustrated below,

Flow diagram of the Android app
Flow diagram of the Android app

As this app will be using the onboard Bluetooth adapter, it will have to be mentioned in the Manifest.

uses-permission android:name="android.permission.BLUETOOTH"

Use the following code to test if Bluetooth adapter is present or not,

BluetoothAdapter bluetoothAdapter=BluetoothAdapter.getDefaultAdapter();
if (bluetoothAdapter == null) {
Toast.makeText(getApplicationContext(),"Device doesnt Support Bluetooth",Toast.LENGTH_SHORT).show();
}

The following part of the code deals with reading the data,

int byteCount = inputStream.available();
 if(byteCount > 0)
 {
 byte[] rawBytes = new byte[byteCount];
 inputStream.read(rawBytes);
 final String string=new String(rawBytes,"UTF-8");
 handler.post(new Runnable() {
 public void run()
 {
 textView.append(string);
 }
 });
 }

To send data, pass the String to the OutputStream.

outputStream.write(string.getBytes());

The complete source code of the Android application can be downloaded from here.

Testing

Power up the Arduino and turn on the Bluetooth from your mobile. Pair with the HC-05 module by providing the correct passcode – 0000 is the default one. Now, when “t” is sent to the Arduino, it replies with the Temperature, Humidity, and Heat Index.

the application screen
the application screen

Intel Introduces Loihi – A Self Learning Processor That Mimics Brain Functions

Intel has developed a first-of-its-kind self-learning neuromorphic chip – codenamed Loihi. It mimics the animal brain functions by learning to operate based on various modes of feedback from the environment. Unlike convolutional neural network (CNN) and other deep learning processors, Intel’s Loihi uses an asynchronous spiking model to mimic neuron and synapse behavior in a much closer analog to animal brain behavior.

loihi - Intel's self-learning chip
Loihi – Intel’s self-learning chip

Machine learning models based on CNN use large training sets to set up recognition of objects and events. This extremely energy-efficient chip, which uses the data to learn and make inferences, gets smarter over time and does not need to be trained in the traditional way. The Loihi chip includes digital circuits that mimic the brain’s basic mechanics, making machine learning faster and more efficient while requiring much lower computing power.

The chip offers highly flexible on-chip learning and combines training and inference on a single chip. This allows machines to be autonomous and to adapt in real time instead of waiting for the next update from the cloud. Compared to convolutional neural networks and deep learning neural networks, the Loihi test chip uses many fewer resources on the same task. Researchers have demonstrated learning at a rate that is a 1 million times improvement compared with other typical neural network devices.

The self-learning capabilities prototyped by this test chip have huge potential to improve automotive and industrial applications as well as personal robotics – any application that would benefit from the autonomous operation and continuous learning in an unstructured environment. For example, recognizing the movement of a car or bike for an autonomous vehicle. More importantly, it is up to 1,000 times more energy-efficient than general purpose computing.

Features

  • Fully asynchronous neuromorphic many core mesh.
  • Each neuron capable of communicating with thousands of other neurons.
  • Each neuromorphic core includes a learning engine that can be programmed to adapt network parameters during operation.
  • Fabrication on Intel’s 14 nm process technology.
  • A total of 130,000 neurons and 130 million synapses.
  • Development and testing of several algorithms with high algorithmic efficiency for problems including path planning, constraint satisfaction, sparse coding, dictionary learning, and dynamic pattern learning and adaptation.

Role Of Vision Processing With Artificial Neural Networks In Autonomous Driving

In next 10 years, the automotive industry will bring more change than we have seen in the last 50, due to technological advancement. One of the largest changes will be the move to autonomous vehicles, usually known as the self-driving car. Scientists from many universities are striving to implement vision processing with the artificial neural network to provide driver assistance in self-driving cars.

vision processing
Vision processing using convolutional artificial neural networks

Vision processing, as well as artificial neural networks, have been around for many years. Convolutional artificial neural networks (CNN) are sets of algorithms that extract meaningful information from sensor input. CNN’s are very computationally efficient at analyzing a scene. They are also able to identify objects as cars, people, animals, road signs, road junctions, road marking etc. enabling them to determine the relevant reality of the scene. As this system runs in real-time, the decision can be made as soon as the sensing part is complete.

One of the major steps in visual environment understanding for automotive applications is key points tracking and estimating ego-motion and environment structure subsequently from the trajectories of these key points. A propagation based tracking (PBT) method is popularly used to obtain the 2D trajectories from a sequence of images in a monocular camera setup.

The inputs from one or all of the sensors like LIDAR, RADAR, camera, IR, etc. are evaluated and decisions are taken accordingly. For example, if a car in the front suddenly brakes, the onboard computer would instantly verify the distance and calculate the speed with help of the existing sensors. Then it would apply the brakes faster than any human would be able to do. This method helps to prevent an accident with 90% efficiency.

The use of vision processing with CNN is rapidly increasing in automotive applications to enable camera-based autonomous driving. This technology sets a new driving standard. With this technology in our hand, fewer accidents, fewer fatalities, and less pollution are experienced. Vision processing in autonomous driving also enables efficient journeys, reduced crowding, car sharing, and packing cars in more tightly via vehicle to vehicle communication.

Researchers Developed Hybrid 3D Printing Method To Make Flexible Wearable Devices

Wearable electronic devices that intend to track and measure the body’s movements must be soft enough to flex and stretch to accommodate every body-movement. But, integrating rigid electronics on skin-like flexible materials has proven to be challenging. Clearly, Such components cannot stretch like soft materials can, and this mismatch frequently causes wearable devices to fail. Recently scientists solved this problem by developing a new method called hybrid 3D printing.

Making wearble devices using Hybrid 3D Printing method
Making wearable devices using Hybrid 3D Printing method

A collaboration between the Wyss Institute, Harvard’s John A. Paulson School of Engineering and Applied Sciences, and the Air Force Research Laboratory, has resulted in developing hybrid 3D printing method. It combines soft, electrically conductive inks, and matrix materials with rigid electronics into a uniformly stretchable device. Alex Valentine, a Staff Engineer at the Wyss Institute says,

With this technique, we can print the electronic sensor directly onto the material, digitally pick-and-place electronic components, and print the conductive interconnects that complete the electronic circuitry required to ‘read’ the sensor’s data signal in one fell swoop.

To make the circuits and the flexible layers, the researchers use thermoplastic polyurethane (TPU), both pure and with silver flakes. The method is quite easy to understand. As both the substrate and the electrodes contain TPU, they firmly adhere to one another while they are co-printed layer-by-layer. After the solvent evaporates completely, both of the inks harden, forming an integrated system that is both flexible and stretchable.

As the ink and substrate are 3D-printed, the scientists have complete control over where and how the conductive features are patterned. Thus they can design circuits to create soft electronic devices of nearly every size and shape. The hybrid 3D printing method enables development of flexible, durable wearable devices that move with the body.

A ring that is made using flexible conductingmaterial
A ring that is made using flexible conducting materials

Conductive materials exhibit changes in their electrical conductivity when stretched. Soft sensors, that detect movements, are made of those materials and are coupled with a programmable microcontroller to process those data. The microcontroller also transmits the data to communicate in a human-understandable way. As a proof-of-concept, the team created two devices – a wearable device that indicates how much the wearer’s arm is bending and a pressure sensor in the shape of a person’s left foot.

Watch the video to know about them,