I/O Raspberry Pi Expansion Card

The RIO is an I/O expansion card intended for use with the Raspberry Pi SBC. The card stacks on top of a Raspberry Pi to create a powerful embedded control and navigation computer in a small 20-mm × 65-mm × 85-mm footprint. The RIO is well suited for applications requiring real-world interfacing, such as robotics, industrial and home automation, and data acquisition and control.

RoboteqThe RIO adds 13 inputs that can be configured as digital inputs, 0-to-5-V analog inputs with 12-bit resolution, or pulse inputs capable of pulse width, duty cycle, or frequency capture. Eight digital outputs are provided to drive loads up to 1 A each at up to 24 V.
The RIO includes a 32-bit ARM Cortex M4 microcontroller that processes and buffers the I/O and creates a seamless communication with the Raspberry Pi. The RIO processor can be user-programmed with a simple BASIC-like programming language, enabling it to perform logic, conditioning, and other I/O processing in real time. On the Linux side, RIO comes with drivers and a function library to quickly configure and access the I/O and to exchange data with the Raspberry Pi.

The RIO features several communication interfaces, including an RS-232 serial port to connect to standard serial devices, a TTL serial port to connect to Arduino and other microcontrollers that aren’t equipped with a RS-232 transceiver, and a CAN bus interface.
The RIO is available in two versions. The RIO-BASIC costs $85 and the RIO-AHRS costs $175.

Roboteq, Inc.
www.roboteq.com

Natural Human-Computer Interaction

Recent innovations in both hardware and software have brought on a new wave of interaction techniques that depart from mice and keyboards. The widespread adoption of smartphones and tablets with capacitive touchscreens shows people’s preference to directly manipulate virtual objects with their hands.

Going beyond touch-only interaction, the Microsoft Kinect sensor enables users to play

This shows the hand tracking result from Kinect data. The red regions are our tracking results and the green lines are the skeleton tracking results from the Kinect SDK (based on data from the ChAirGest corpus: https://project.eia-fr.ch/chairgest/Pages/Overview.aspx).

This shows the hand tracking result from Kinect data. The red regions are our tracking results and the green lines are the skeleton tracking results from the Kinect SDK (based on data from the ChAirGest corpus: https://project.eia-fr.ch/chairgest/Pages/Overview.aspx).

games with their entire body. More recently, Leap Motion’s new compact sensor, consisting of two cameras and three infrared LEDs, has opened up the possibility of accurate fingertip tracking. With Project Glass, Google is pioneering new technology in the wearable human-computer interface. Other new additions to wearable technology include Samsung’s Galaxy Gear Smartwatch and Apple’s rumored iWatch.

A natural interface reduces the learning curve, or the amount of time and energy a person requires to complete a particular task. Instead of a user learning to communicate with a machine through a programming language, the machine is now learning to understand the user.

Hardware advancements have led to our clunky computer boxes becoming miniaturized, stylish sci-fi-like phones and watches. Along with these shrinking computers come ever-smaller sensors that enable a once keyboard-constrained computer to listen, see, and feel. These developments pave the way to natural human-computer interfaces.
If sensors are like eyes and ears, software would be analogous to our brains.

Understanding human speech and gestures in real time is a challenging task for natural human-computer interaction. At a higher level, both speech and gesture recognition require similar processing pipelines that include data streaming from sensors, feature extraction, and pattern recognition of a time series of feature vectors. One of the main differences between the two is feature representation because speech involves audio data while gestures involve video data.

For gesture recognition, the first main step is locating the user’s hand. Popular libraries for doing this include Microsoft’s Kinect SDK or PrimeSense’s NITE library. However, these libraries only give the coordinates of the hands as points, so the actual hand shapes cannot be evaluated.

Fingertip tracking using a Kinect sensor. The green dots are the tracked fingertips.

Our team at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory has developed methods that use a combination of skin-color and motion detection to compute a probability map of gesture salience location. The gesture salience computation takes into consideration the amount of movement and the closeness of movement to the observer (i.e., the sensor).

We can use the probability map to find the most likely area of the gesturing hands. For each time frame, after extracting the depth data for the entire hand, we compute a histogram of oriented gradients to represent the hand shape as a more compact feature descriptor. The final feature vector for a time frame includes 3-D position, velocity, and hand acceleration as well as the hand shape descriptor. We also apply principal component analysis to reduce the feature vector’s final dimension.

A 3-D model of pointing gestures using a Kinect sensor. The top left video shows background subtraction, arm segmentation, and fingertip tracking. The top right video shows the raw depth-mapped data. The bottom left video shows the 3D model with the white plane as the tabletop, the green line as the arm, and the small red dot as the fingertip.

The next step in the gesture-recognition pipeline is to classify the feature vector sequence into different gestures. Many machine-learning methods have been used to solve this problem. A popular one is called the hidden Markov model (HMM), which is commonly used to model sequence data. It was earlier used in speech recognition with great success.

There are two steps in gesture classification. First, we need to obtain training data to learn the models for different gestures. Then, during recognition, we find the most likely model that can produce the given observed feature vectors. New developments in the area involve some variations in the HMM, such as using hierarchical HMM for real-time inference or using discriminative training to increase the recognition accuracy.

Ying Yin

Ying Yin is a PhD candidate and a Research Assistant at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory. Originally from Suzhou, China, Ying received her BASc in Computer Engineering from the University of British Columbia in Vancouver, Canada, in 2008 and an MS in Computer Science from MIT in 2010. Her research focuses on applying machine learning and computer vision methods to multimodal human-computer interaction. Ying is also interested in web and mobile application development. She has won awards in web and mobile programming competitions at MIT.

Currently, the newest development in speech recognition at the industry scale is a method called deep learning. Earlier machine-learning methods require careful selection of feature vectors. The goal of deep learning is automatic discovery of powerful features from raw input data. So far, it has shown promising results in speech recognition. It can possibly be applied to gesture recognition to see whether it can further improve accuracy.

As component form factors shrink, sensor resolutions grow, and recognition algorithms become more accurate, natural human-computer interaction will become more and more ubiquitous in our everyday life.

Dual-Channel Waveform Generators

B&K Precision 4053 Waveform Generator

B&K Precision 4053 Waveform Generator

The 4050 Series is a new line of four dual-channel function/arbitrary waveform generators. The instruments can generate 5-to-50-MHz waveforms for applications requiring stable and precise sine, square, triangle, and pulse waveforms with modulation and arbitrary waveform capabilities.

All models provide a main output voltage that can be vary from 0 to 10 VPP into 50 Ω and a secondary output that can vary from 0 to 3 VPP into 50 Ω. The generators feature a 3.5” color LCD, a rotary control knob, and a numeric keypad with dedicated waveform keys and output buttons.

The 4050 Series provides users with 48 built-in arbitrary waveforms. Using the included waveform editing software via the standard USB interface on the rear, users can create and load up to 10 custom 16-kpt waveforms. For general-purpose interface bus (GPIB) connectivity, an optional USB-to-GPIB adapter is available.

The generators offer a variety of modulation schemes for modulated signal applications including amplitude and frequency modulation (AM/FM), double sideband amplitude modulation (DSB-AM), amplitude and frequency shift keying (ASK/FSK), phase modulation (PM), and pulse-width modulation (PWM). Additional standard features include a linear and logarithmic sweep function, a built-in counter, sync output, a trigger I/O terminal, and a USB host port on the front panel to save and recall instrument settings and waveforms. A standard external 10-MHz reference clock input is provided to synchronize the instrument to another generator.

The 4052 (5-MHz) costs $499, the 4053 (10 MHz) costs $599, the 4054 (25 MHz) costs $850, and the 4055 (50 MHz) costs $1,050. Note: B&K Precision is offering 10% off MSRP through November 30, 2013. See website for details.

B&K Precision Corp.
www.bkprecision.com

Data Acquisition Instrument

The DI-145 USB data acquisition instrument features four ±100-V analog channels and two dedicated digital inputs. The included DATAQ WinDaq data acquisition software (DAS) enables you to display and record data to a PC hard drive in real time. Once recorded, data can be played back, analyzed, or exported to an array of data acquisition and spreadsheet formats.

DATAQ also provides access to the DI-145 data protocol, which enables access to the DI-145 on any Windows, Linux, or MAC OS. In addition, .NET control is available to Windows users who wish to use a third-party programming language (e.g., Microsoft’s Visual Basic or National Instruments’s LabVIEW) to interface with the DI-145.

The four ±10-V fixed differential channels are protected from transient spikes up to ±150 V peak (±75 V, continuous). A 10-bit ADC provides 19.5-mV resolution across the full-scale measurement range. Digital inputs are protected up to ±30 VDC/peak AC. The digital inputs enable you to use a switch closure or TTL signal to remotely insert event marks or record data to disk.

The DI-145 measures 1.53” × 2.625” × 5.5” (3.89 cm × 6.67 cm × 13.97 cm) and weighs 3.6 oz. The data acquisition instrument costs $29 and includes a mini screwdriver, a USB cable, WinDaq/Lite DAS, access to the data protocol, and .NET control.

DATAQ Instruments, Inc.
www.dataq.com

DIY Solar-Powered, Gas-Detecting Mobile Robot

German engineer Jens Altenburg’s solar-powered hidden observing vehicle system (SOPHECLES) is an innovative gas-detecting mobile robot. When the Texas Instruments MSP430-based mobile robot detects noxious gas, it transmits a notification alert to a PC, Altenburg explains in his article, “SOPHOCLES: A Solar-Powered MSP430 Robot.”  The MCU controls an on-board CMOS camera and can wirelessly transmit images to the “Robot Control Center” user interface.

Take a look at the complete SOPHOCLES design. The CMOS camera is located on top of the robot. Radio modem is hidden behind the camera so only the antenna is visible. A flexible cable connects the camera with the MSP430 microcontroller.

Altenburg writes:

The MSP430 microcontroller controls SOPHOCLES. Why did I need an MSP430? There are lots of other micros, some of which have more power than the MSP430, but the word “power” shows you the right way. SOPHOCLES is the first robot (with the exception of space robots like Sojourner and Lunakhod) that I know of that’s powered by a single lithium battery and a solar cell for long missions.

The SOPHOCLES includes a transceiver, sensors, power supply, motor
drivers, and an MSP430. Some block functions (i.e., the motor driver or radio modems) are represented by software modules.

How is this possible? The magic mantra is, “Save power, save power, save power.” In this case, the most important feature of the MSP430 is its low power consumption. It needs less than 1 mA in Operating mode and even less in Sleep mode because the main function of the robot is sleeping (my main function, too). From time to time the robot wakes up, checks the sensor, takes pictures of its surroundings, and then falls back to sleep. Nice job, not only for robots, I think.

The power for the active time comes from the solar cell. High-efficiency cells provide electric energy for a minimum of approximately two minutes of active time per hour. Good lighting conditions (e.g., direct sunlight or a light beam from a lamp) activate the robot permanently. The robot needs only about 25 mA for actions such as driving its wheel, communicating via radio, or takes pictures with its built in camera. Isn’t that impossible? No! …

The robot has two power sources. One source is a 3-V lithium battery with a 600-mAh capacity. The battery supplies the CPU in Sleep mode, during which all other loads are turned off. The other source of power comes from a solar cell. The solar cell charges a special 2.2-F capacitor. A step-up converter changes the unregulated input voltage into 5-V main power. The LTC3401 changes the voltage with an efficiency of about 96% …

Because of the changing light conditions, a step-up voltage converter is needed for generating stabilized VCC voltage. The LTC3401 is a high-efficiency converter that starts up from an input voltage as low as 1 V.

If the input voltage increases to about 3.5 V (at the capacitor), the robot will wake up, changing into Standby mode. Now the robot can work.

The approximate lifetime with a full-charged capacitor depends on its tasks. With maximum activity, the charging is used after one or two minutes and then the robot goes into Sleep mode. Under poor conditions (e.g., low light for a long time), the robot has an Emergency mode, during which the robot charges the capacitor from its lithium cell. Therefore, the robot has a chance to leave the bad area or contact the PC…

The control software runs on a normal PC, and all you need is a small radio box to get the signals from the robot.

The Robot Control Center serves as an interface to control the robot. Its main feature is to display the transmitted pictures and measurement values of the sensors.

Various buttons and throttles give you full control of the robot when power is available or sunlight hits the solar cells. In addition, it’s easy to make short slide shows from the pictures captured by the robot. Each session can be saved on a disk and played in the Robot Control Center…

The entire article appears in Circuit Cellar 147 2002. Type “solarrobot”  to access the password-protected article.