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INTEL’STALKING CAR

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WI-FIAUDIO SYSTEM

Issue 1 / March 2014

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CONTENTS

4 TECH COLUMNBack to the Basics: What’s an FFT?

3

6 TECH ARTICLEAchieving Accurate & Faster Noise Characterization of Analog Circuits

FEATURED ARTICLEIntel’s New Talking Car Project12

18 COVER INTERVIEWInterview with Cees Links - CEO & Founder of

GreenPeak Technologies

BACK TO BASICS:What is an FFT?

The theory behind FFTs makes an assumption, which is that the time-domain signal being transformed into a frequency-domain spectrum is of infinite duration. Obviously this is not achievable, so the compromise between theory and practice is to view the time-domain signal as consisting of an infinite series of replicas of itself.

In using FFT on a time-domain signal, what’s really happening is that the signal is being separated out into its constituent frequency components, essentially diluting its spectral energy in some number of frequency bins corresponding to multiples of the frequency resolution Δf. The capture time, T, determines the frequency resolution of the FFT (Δf = 1/T). Meanwhile, the sampling period and record length set the maximum frequency span that can be obtained (fNyq = Δf*N/2).

All of the above could, of course, be worked out mathematically as a discrete Fourier transform. But to do so even on an eight-sample signal would involve 64 complex multiplications. A signal with 1024 samples balloons out to over 1 million multiplications.

Thus, an FFT operation on an N-point time-domain signal is comparable to passing the signal through

a comb filter consisting of a bank of N/2 filters. All of these filters have the same shape and width and are centered at N/2 discrete frequencies, meaning that there are N/2 frequency “bins.” The distance in hertz between the centering frequency of any two neighboring bins is always Δf.

The way your FFT turns out is dictated to a large extent by the “window” chosen for the operation (Table 1). The window type defines the bandwidth and shape of the bank of filters applied to the time-domain signal. The weighting functions imposed by these windows control not only the filter response shape, but also noise bandwidth and side-lobe levels. Ideally, the main lobe should be as narrow and flat as possible to effectively discriminate all spectral components; meanwhile, all side lobes should be infinitely attenuated.

You can think of choosing a window type along the lines of choosing a camera lens for a given photo. Some experimentation might be in order. As shown by the table, some windows will lend themselves better to certain signal types than others, with tradeoffs between leakage and frequency resolution.

In an earlier post, we discussed the basics of setting up a fast-Fourier transform (FFT) on an oscilloscope, and why you’d want to use an FFT to get a frequency-domain view of a time-domain signal in the first place. It might be a good idea to take a step back and dig into just what an FFT is (Figure 1).

Rectangular

Figure 1: An FFT of a 300-kHz square wave.

Table 1: FFT window types and their characteristics.

Window Type Applications & Limitations

Normally used when the signal is transient – completely contained in the time-domain window – or known to have a fundamental frequency component that is an integer multiple of the fundamental frequency of the window. Signals other than these types will show varying amounts of spectral leakage and scallop loss, corrected by selecting another type of window.

Reduce leakage and improve amplitude accuracy. However, frequency resolution is also reduced.

Reduce leakage and improve amplitude accuracy. However, frequency resolution is also reduced.

The window provides excellent amplitude accuracy with moderate reduction of leakage, but also at the loss of frequency resolution.

It reduces the leakage to a minimum, but again along with reduced frequency resolution.

Hanning (Von Hann)

Hamming

Flat Top

Blackman–Harris

David MaliniakTechnical Marketing Communication SpecialistTeledyne LeCroy

The Vision

As a research scientist with Intel’s User Experience Research Lab, Healey’s task is to foresee the future. “It’s my job to find out how computation is going to be used in the future,” Healey told EEWeb, “In particular, I’m working with embedded computation and wireless communication, which is a growing market and something that we believe to be the future of computing.” Healey’s primary focus is on how cars talk to each other, how they talk to infrastructure, how they talk to the cloud, and how cars will interact with the burgeoning Internet of Things.

The dynamic wireless exchange of data between vehicles and the surrounding infrastructure offers a significant opportunity for improving safety and enhancing the driving experience. But getting all vehicles involved in the conversation is easier said than done. As Healey explained, “In this road network, we have cars going at 65 MPH that are

trying to communicate with each other. They are privately owned, move quickly, and have no established infrastructure.” Without an established infrastructure in this network, how do you begin to even identify the targets at the other end of the wireless communication?

Spreading the Gossip

A considerable amount of research has been invested in vehicle-to-vehicle communication and to help circumvent the remaining technical and standardization hurdles, Intel has teamed with National Taiwan University (NTU) to establish the Connected Context Computing Center. This center sponsors a research group—headed by Professor Bob Wang—that has developed a prototype system that enables cars to share “gossip.” The idea is that inter-car gossip will give your car a peek into the other cars around you. The shared information will go beyond the simple communication of speed, location, or trajectory,

making driving down the street a more social and safer prospect.

The proposed system employs 2D LIDAR, stereo cameras, and dedicated short-range communication (DSRC) standard radios for vehicle-to-vehicle sensing. This combination, according to Healey, can enable autonomous driving with minimal cost and maximum computation ability. “The advantage of 2D LIDAR is that it is basically laser radar, so it can give you depth information,” Healey told EEWeb, “We are combining that with a computer vision system, which segments objects by color.” However, there are many examples where LIDAR can fail because of light bouncing off of other objects. If there is a group of people walking together, they might all be dressed differently and wearing different colors, but to LIDAR, they are a big solid object. The big goal for Healey’s team is to use these two complimentary things to try and get object segmentation.

To enable autonomous driving, the Intel-NTU group has developed two main algorithms that work in concert to communicate information about your vehicle’s location relative to other vehicles. The simultaneous location and mapping algorithm, or SLAM, is used to locate your car’s position. The motion object tracking, or MOT, is used to track other objects relative to your car. “If you are transmitting your GPS location, you have an estimate of their position from your computer vision,” Healey explained, “Or, you can improve this estimate if they’re transmitting their GPS data back.” In this model, your car will estimate your position and velocity and predict your future

position. If you send your information back, you can find out if the person is actually in a different location—essentially a live correction of the algorithm, which is akin to ballistics tracking.

A Scalable Vehicle-to-Vehicle Network

While the complete Intel-NTU system is ideal for vehicle-to-vehicle communication, it isn’t necessary that all cars be outfitted with the system. The system is designed to be scalable meaning that any car fitted with a simple receiver can take part in the vehicular chatter. Such a communication protocol could work two ways. One is the “car in front, car behind” scenario, where the car in front would broadcast a message that it is breaking really fast, just to the car behind it. All the car in

The shared information will go beyond the simple communication of speed, location, or trajectory, making driving down the street a more social and safer prospect.

The system is designed to be scalable meaning that

any car fitted with a simple receiver can take part in

the vehicular chatter. Such a communication protocol

could work two ways.

““

“ “

Jennifer Healey pictured right.

TECH ARTICLEBuilding a Multi-source, Multi-Speaker

Home Audio System Using Wi-Fi26

BACK TO BASICS:What is an FFT?

The theory behind FFTs makes an assumption, which is that the time-domain signal being transformed into a frequency-domain spectrum is of infinite duration. Obviously this is not achievable, so the compromise between theory and practice is to view the time-domain signal as consisting of an infinite series of replicas of itself.

In using FFT on a time-domain signal, what’s really happening is that the signal is being separated out into its constituent frequency components, essentially diluting its spectral energy in some number of frequency bins corresponding to multiples of the frequency resolution Δf. The capture time, T, determines the frequency resolution of the FFT (Δf = 1/T). Meanwhile, the sampling period and record length set the maximum frequency span that can be obtained (fNyq = Δf*N/2).

All of the above could, of course, be worked out mathematically as a discrete Fourier transform. But to do so even on an eight-sample signal would involve 64 complex multiplications. A signal with 1024 samples balloons out to over 1 million multiplications.

Thus, an FFT operation on an N-point time-domain signal is comparable to passing the signal through

a comb filter consisting of a bank of N/2 filters. All of these filters have the same shape and width and are centered at N/2 discrete frequencies, meaning that there are N/2 frequency “bins.” The distance in hertz between the centering frequency of any two neighboring bins is always Δf.

The way your FFT turns out is dictated to a large extent by the “window” chosen for the operation (Table 1). The window type defines the bandwidth and shape of the bank of filters applied to the time-domain signal. The weighting functions imposed by these windows control not only the filter response shape, but also noise bandwidth and side-lobe levels. Ideally, the main lobe should be as narrow and flat as possible to effectively discriminate all spectral components; meanwhile, all side lobes should be infinitely attenuated.

You can think of choosing a window type along the lines of choosing a camera lens for a given photo. Some experimentation might be in order. As shown by the table, some windows will lend themselves better to certain signal types than others, with tradeoffs between leakage and frequency resolution.

In an earlier post, we discussed the basics of setting up a fast-Fourier transform (FFT) on an oscilloscope, and why you’d want to use an FFT to get a frequency-domain view of a time-domain signal in the first place. It might be a good idea to take a step back and dig into just what an FFT is (Figure 1).

Rectangular

Figure 1: An FFT of a 300-kHz square wave.

Table 1: FFT window types and their characteristics.

Window Type Applications & Limitations

Normally used when the signal is transient – completely contained in the time-domain window – or known to have a fundamental frequency component that is an integer multiple of the fundamental frequency of the window. Signals other than these types will show varying amounts of spectral leakage and scallop loss, corrected by selecting another type of window.

Reduce leakage and improve amplitude accuracy. However, frequency resolution is also reduced.

Reduce leakage and improve amplitude accuracy. However, frequency resolution is also reduced.

The window provides excellent amplitude accuracy with moderate reduction of leakage, but also at the loss of frequency resolution.

It reduces the leakage to a minimum, but again along with reduced frequency resolution.

Hanning (Von Hann)

Hamming

Flat Top

Blackman–Harris

David MaliniakTechnical Marketing Communication SpecialistTeledyne LeCroy

44

BACK TO BASICS:What is an FFT?

The theory behind FFTs makes an assumption, which is that the time-domain signal being transformed into a frequency-domain spectrum is of infinite duration. Obviously this is not achievable, so the compromise between theory and practice is to view the time-domain signal as consisting of an infinite series of replicas of itself.

In using FFT on a time-domain signal, what’s really happening is that the signal is being separated out into its constituent frequency components, essentially diluting its spectral energy in some number of frequency bins corresponding to multiples of the frequency resolution Δf. The capture time, T, determines the frequency resolution of the FFT (Δf = 1/T). Meanwhile, the sampling period and record length set the maximum frequency span that can be obtained (fNyq = Δf*N/2).

All of the above could, of course, be worked out mathematically as a discrete Fourier transform. But to do so even on an eight-sample signal would involve 64 complex multiplications. A signal with 1024 samples balloons out to over 1 million multiplications.

Thus, an FFT operation on an N-point time-domain signal is comparable to passing the signal through

a comb filter consisting of a bank of N/2 filters. All of these filters have the same shape and width and are centered at N/2 discrete frequencies, meaning that there are N/2 frequency “bins.” The distance in hertz between the centering frequency of any two neighboring bins is always Δf.

The way your FFT turns out is dictated to a large extent by the “window” chosen for the operation (Table 1). The window type defines the bandwidth and shape of the bank of filters applied to the time-domain signal. The weighting functions imposed by these windows control not only the filter response shape, but also noise bandwidth and side-lobe levels. Ideally, the main lobe should be as narrow and flat as possible to effectively discriminate all spectral components; meanwhile, all side lobes should be infinitely attenuated.

You can think of choosing a window type along the lines of choosing a camera lens for a given photo. Some experimentation might be in order. As shown by the table, some windows will lend themselves better to certain signal types than others, with tradeoffs between leakage and frequency resolution.

In an earlier post, we discussed the basics of setting up a fast-Fourier transform (FFT) on an oscilloscope, and why you’d want to use an FFT to get a frequency-domain view of a time-domain signal in the first place. It might be a good idea to take a step back and dig into just what an FFT is (Figure 1).

Rectangular

Figure 1: An FFT of a 300-kHz square wave.

Table 1: FFT window types and their characteristics.

Window Type Applications & Limitations

Normally used when the signal is transient – completely contained in the time-domain window – or known to have a fundamental frequency component that is an integer multiple of the fundamental frequency of the window. Signals other than these types will show varying amounts of spectral leakage and scallop loss, corrected by selecting another type of window.

Reduce leakage and improve amplitude accuracy. However, frequency resolution is also reduced.

Reduce leakage and improve amplitude accuracy. However, frequency resolution is also reduced.

The window provides excellent amplitude accuracy with moderate reduction of leakage, but also at the loss of frequency resolution.

It reduces the leakage to a minimum, but again along with reduced frequency resolution.

Hanning (Von Hann)

Hamming

Flat Top

Blackman–Harris

David MaliniakTechnical Marketing Communication SpecialistTeledyne LeCroy

What is an FFT?

5

TECH ARTICLE

5

BACK TO BASICS:What is an FFT?

The theory behind FFTs makes an assumption, which is that the time-domain signal being transformed into a frequency-domain spectrum is of infinite duration. Obviously this is not achievable, so the compromise between theory and practice is to view the time-domain signal as consisting of an infinite series of replicas of itself.

In using FFT on a time-domain signal, what’s really happening is that the signal is being separated out into its constituent frequency components, essentially diluting its spectral energy in some number of frequency bins corresponding to multiples of the frequency resolution Δf. The capture time, T, determines the frequency resolution of the FFT (Δf = 1/T). Meanwhile, the sampling period and record length set the maximum frequency span that can be obtained (fNyq = Δf*N/2).

All of the above could, of course, be worked out mathematically as a discrete Fourier transform. But to do so even on an eight-sample signal would involve 64 complex multiplications. A signal with 1024 samples balloons out to over 1 million multiplications.

Thus, an FFT operation on an N-point time-domain signal is comparable to passing the signal through

a comb filter consisting of a bank of N/2 filters. All of these filters have the same shape and width and are centered at N/2 discrete frequencies, meaning that there are N/2 frequency “bins.” The distance in hertz between the centering frequency of any two neighboring bins is always Δf.

The way your FFT turns out is dictated to a large extent by the “window” chosen for the operation (Table 1). The window type defines the bandwidth and shape of the bank of filters applied to the time-domain signal. The weighting functions imposed by these windows control not only the filter response shape, but also noise bandwidth and side-lobe levels. Ideally, the main lobe should be as narrow and flat as possible to effectively discriminate all spectral components; meanwhile, all side lobes should be infinitely attenuated.

You can think of choosing a window type along the lines of choosing a camera lens for a given photo. Some experimentation might be in order. As shown by the table, some windows will lend themselves better to certain signal types than others, with tradeoffs between leakage and frequency resolution.

In an earlier post, we discussed the basics of setting up a fast-Fourier transform (FFT) on an oscilloscope, and why you’d want to use an FFT to get a frequency-domain view of a time-domain signal in the first place. It might be a good idea to take a step back and dig into just what an FFT is (Figure 1).

Rectangular

Figure 1: An FFT of a 300-kHz square wave.

Table 1: FFT window types and their characteristics.

Window Type Applications & Limitations

Normally used when the signal is transient – completely contained in the time-domain window – or known to have a fundamental frequency component that is an integer multiple of the fundamental frequency of the window. Signals other than these types will show varying amounts of spectral leakage and scallop loss, corrected by selecting another type of window.

Reduce leakage and improve amplitude accuracy. However, frequency resolution is also reduced.

Reduce leakage and improve amplitude accuracy. However, frequency resolution is also reduced.

The window provides excellent amplitude accuracy with moderate reduction of leakage, but also at the loss of frequency resolution.

It reduces the leakage to a minimum, but again along with reduced frequency resolution.

Hanning (Von Hann)

Hamming

Flat Top

Blackman–Harris

David MaliniakTechnical Marketing Communication SpecialistTeledyne LeCroy

66

We live in a noisy world. The noise can be self-induced, like an intrusive, buzzing alarm clock that gets us up to start the day. Or it can be natural noise, like chirping from a bird, or human-made noise from millions of vehicles on the road. One thing remains common: these unwanted and—in some cases wanted—noise sources have diminished our ability to hear clearly over time.

ACHIEVING ACCURATE AND FASTER NOISE CHARACTERIZATION OF ANALOG CIRCUITSBy Nebabie Kebebew, Cadence

7

TECH ARTICLE

7

ACHIEVING ACCURATE AND FASTER NOISE CHARACTERIZATION OF ANALOG CIRCUITS

8

Circuits that are used in our everyday electronic devices are no different. Their performance is directly impacted by noise, which ultimately can degrade the system-level performance of the design. This is especially pronounced with advanced technology nodes and analog and RF circuits that have noise-sensitive architectures, such as data converters, PLLs, and switching circuits. The device noise signals are inherently present in the device, or are internally produced and are exacerbated in circuits with fast switching signals. Electronic device noise comes in many flavors, including thermal, flicker, and shot noise. When designing high-precision analog and RF circuits, especially at 90nm and below, it’s vital to consider these noise sources to minimize soaring IC design development costs that can result from long simulation time and re-spins of chips. Hence, circuit noise characterization is a necessary step and a critical part of designing analog circuits.

Time-Domain and Frequency-Domain Noise Analyses

Time-domain, as in transient, noise analysis can be applied to any circuit. However, in many cases, it is not the most efficient way to verify noise behavior of a given analog circuit.

If the circuit has a stable DC operating point, like operational amplifiers, LNAs, and filters, the small-signal (frequency-domain) noise analysis is the most efficient way, and almost always outperforms transient noise analysis performance. For a wide class of periodic circuits, including VCOs, mixers, dividers, phase detectors, charge pumps, and switched-capacitor filters, Periodic Steady State (PSS) analysis followed by periodic noise analysis provides the same information as transient noise analysis in a significantly less simulation time.

However, for many other designs, there is no alternative to transient noise analysis. This includes non-periodic circuits, like sigma-delta modulators and fractional-N PLLs. It also include periodic circuits where PSS analysis becomes impractical

due to the large circuit size, or circuits with wide frequency range, like most PLLs and ADCs. Some periodic circuits require long random bit sequences for performance testing, which makes them effectively non-periodic.

Cadence’s Spectre® RF Simulation Option provides the analog designer a comprehensive set of analyses based on circuit characteristic and application type. The designer is able to simply handle the set up, drive the simulation, and perform noise measurements from a unified design environment, Cadence® Virtuoso® Analog Design Environment. Table 1 below summarizes typical circuit types and the recommended Spectre RF noise analysis approaches for accurate and faster noise characterization.

Table 1: Circuit type and Spectre RF noise analyses

“The designer is able to simply handle

the set up, drive the simulation, and perform

noise measurements from a unified design

environment”

8

9

TECH ARTICLE

9

Spectre RF Full Spectrum Periodic Noise Analysis In cases of analog circuits with large signal inputs, the noise from each of the devices in the circuit is treated as a small signal that depends on the nonlinear large signal performance of the circuit, making the noise analysis a two-step process. In the Spectre RF solution, the first step is handled by performing the PSS analysis, with a user-specified maximum frequency, to solve the large signal analysis in the time domain. The analysis is based on a production-proven shooting newton simulation engine. The second step entails running the periodic noise (Pnoise) analysis using the solution from PSS as the operating point and representing each noise source as a small signal.

With Spectre RF Pnoise analysis, a specific input noise is injected into the analog circuit model to represent an inherent noise source. This causes

Figure 2 : Spectre RF Simulation Option: noise output response for a switched capacitor filter

an output noise, in the frequency domain, with a number of frequency translations (called sidebands) each around an integer-multiple of the large signal frequency. In an ideal case, all the frequency translations would be taken into account and simulated to get an accurate reading of the circuit’s periodic noise. However, simulating all the noise sidebands has been computationally challenging – it requires a long simulation runtime due to the large number of noise translations. In cases where the circuits aren’t highly nonlinear, one can manage the computation load by simulating a small number of sidebands, such as 10 to 20 sidebands. This is possible since the contribution of the noise sidebands beyond the initial set of sidebands is minimal and can be ignored without compromising the accuracy of output noise. But this is not the case for broadband switching circuits such as divider circuits, switched capacitor circuits, etc., where the

1010

noise contribution from very high frequencies is significant. To obtain an accurate characterization of the output noise, a large number of the side bands, ranging in thousands to tens of thousands, needs to be taken into account and simulated. Simulation runtime is typically days to weeks, leading to a huge productivity hit in the IC design development process.

As a result, to meet market window demands, some analog design teams are forced to tapeout a design without knowing the actual noise performance, potentially compromising design robustness and quality. Others set the periodic noise analysis parameters to speed up simulation runtime, such as specifying a relatively small maximum sideband value (maxsideband), which limits the number of noise translations around the harmonics that will be considered for simulation. Setting the “maxsideband” parameter is fine for some circuits; however, for switching circuits, not all the aliasing is taken into account. The designer may not be aware of it until silicon testing in the lab, when functional failure is observed.

Cadence’s proprietary Spectre RF shooting newton-based full-spectrum Pnoise simulation removes the burden from the user, for most designs, of having to figure out an optimal “maxsideband,” and automatically speeds up Pnoise simulation. In contrast to the standard Spectre RF Pnoise simulation, the “maxsideband” is not always required and noise simulation time is dramatically improved, enabling up to 100X better performance compared to the standard Pnoise analysis and other, traditional methods of noise analysis. This allows for exhaustive noise simulation in minutes and hours, reducing the probability of chip respins.

Circuit Noise Measurement

Device noise can be measured in a number of ways, such as signal-to-noise ratio (SNR) and, ultimately, bit error rate (BER) in a complete system.

However, before assessing the SNR, the designer will want to measure the output noise response and generate a list of noise contributors in the circuit, as shown in Figure 2. Visibility of the noise contribution of each device towards the total noise is necessary to determine the major culprit of the noise source and contributor, and to identify where to potentially redesign the circuit.

Summary

Accurate noise characterization of analog circuits is a necessary part of the IC design process. This helps to both ensure that the given analog circuit meets all performance specifications and to avoid unwanted and catastrophic system failure. Equally important is the ability to perform noise simulation fast enough, with minimal manual user intervention. Tools such as Virtuoso Analog Design Environment and Spectre RF Simulation Option can help accelerate the noise characterization process for complex, high-precision analog circuits. ■

“Tools such as Virtuoso Analog Design

Environment and Spectre RF Simulation Option can help accelerate the noise characterization process

for complex, high-precision analog circuits.”

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11

TECH ARTICLE

11Copyright 2013, Silicon Frameworks, LLC PCBWeb.com

ep page master fp.indd 1 3/11/13 10:34:41 AM

1212

INTEL IS SPREADING THEGOSSIP

People die in car crashes on a daily basis—the equivalent of a plane full of people crashing every day of the year. Yet for better or worse, approximately 220 million people get into their cars each day, close the door, and with nothing more than a pair of eyes to guide them, venture down partially-seen roadways, in and amongst other metal giants, at super-human speeds. But Jennifer Healey, a research scientist at Intel, envisions a safer future. By employing gossip-based protocols in combination with onboard sensors, cars of the future will be able to see and communicate with one another, alert drivers of hazards, and even intervene when a collision seems imminent.

LET’S FACE IT; DRIVING IS DANGEROUS.

…AMONG CARS

13

TECH ARTICLE

13

INTEL IS SPREADING THEGOSSIP

People die in car crashes on a daily basis—the equivalent of a plane full of people crashing every day of the year. Yet for better or worse, approximately 220 million people get into their cars each day, close the door, and with nothing more than a pair of eyes to guide them, venture down partially-seen roadways, in and amongst other metal giants, at super-human speeds. But Jennifer Healey, a research scientist at Intel, envisions a safer future. By employing gossip-based protocols in combination with onboard sensors, cars of the future will be able to see and communicate with one another, alert drivers of hazards, and even intervene when a collision seems imminent.

LET’S FACE IT; DRIVING IS DANGEROUS.

…AMONG CARS

1414

The Vision

As a research scientist with Intel’s User Experience Research Lab, Healey’s task is to foresee the future. “It’s my job to find out how computation is going to be used in the future,” Healey told EEWeb, “In particular, I’m working with embedded computation and wireless communication, which is a growing market and something that we believe to be the future of computing.” Healey’s primary focus is on how cars talk to each other, how they talk to infrastructure, how they talk to the cloud, and how cars will interact with the burgeoning Internet of Things.

The dynamic wireless exchange of data between vehicles and the surrounding infrastructure offers a significant opportunity for improving safety and enhancing the driving experience. But getting all vehicles involved in the conversation is easier said than done. As Healey explained, “In this road network, we have cars going at 65 MPH that are

trying to communicate with each other. They are privately owned, move quickly, and have no established infrastructure.” Without an established infrastructure in this network, how do you begin to even identify the targets at the other end of the wireless communication?

Spreading the Gossip

A considerable amount of research has been invested in vehicle-to-vehicle communication and to help circumvent the remaining technical and standardization hurdles, Intel has teamed with National Taiwan University (NTU) to establish the Connected Context Computing Center. This center sponsors a research group—headed by Professor Bob Wang—that has developed a prototype system that enables cars to share “gossip.” The idea is that inter-car gossip will give your car a peek into the other cars around you. The shared information will go beyond the simple communication of speed, location, or trajectory,

making driving down the street a more social and safer prospect.

The proposed system employs 2D LIDAR, stereo cameras, and dedicated short-range communication (DSRC) standard radios for vehicle-to-vehicle sensing. This combination, according to Healey, can enable autonomous driving with minimal cost and maximum computation ability. “The advantage of 2D LIDAR is that it is basically laser radar, so it can give you depth information,” Healey told EEWeb, “We are combining that with a computer vision system, which segments objects by color.” However, there are many examples where LIDAR can fail because of light bouncing off of other objects. If there is a group of people walking together, they might all be dressed differently and wearing different colors, but to LIDAR, they are a big solid object. The big goal for Healey’s team is to use these two complimentary things to try and get object segmentation.

To enable autonomous driving, the Intel-NTU group has developed two main algorithms that work in concert to communicate information about your vehicle’s location relative to other vehicles. The simultaneous location and mapping algorithm, or SLAM, is used to locate your car’s position. The motion object tracking, or MOT, is used to track other objects relative to your car. “If you are transmitting your GPS location, you have an estimate of their position from your computer vision,” Healey explained, “Or, you can improve this estimate if they’re transmitting their GPS data back.” In this model, your car will estimate your position and velocity and predict your future

position. If you send your information back, you can find out if the person is actually in a different location—essentially a live correction of the algorithm, which is akin to ballistics tracking.

A Scalable Vehicle-to-Vehicle Network

While the complete Intel-NTU system is ideal for vehicle-to-vehicle communication, it isn’t necessary that all cars be outfitted with the system. The system is designed to be scalable meaning that any car fitted with a simple receiver can take part in the vehicular chatter. Such a communication protocol could work two ways. One is the “car in front, car behind” scenario, where the car in front would broadcast a message that it is breaking really fast, just to the car behind it. All the car in

The shared information will go beyond the simple communication of speed, location, or trajectory, making driving down the street a more social and safer prospect.

The system is designed to be scalable meaning that

any car fitted with a simple receiver can take part in

the vehicular chatter. Such a communication protocol

could work two ways.

“

““ “

Jennifer Healey pictured right.

15

TECH ARTICLE

15

The Vision

As a research scientist with Intel’s User Experience Research Lab, Healey’s task is to foresee the future. “It’s my job to find out how computation is going to be used in the future,” Healey told EEWeb, “In particular, I’m working with embedded computation and wireless communication, which is a growing market and something that we believe to be the future of computing.” Healey’s primary focus is on how cars talk to each other, how they talk to infrastructure, how they talk to the cloud, and how cars will interact with the burgeoning Internet of Things.

The dynamic wireless exchange of data between vehicles and the surrounding infrastructure offers a significant opportunity for improving safety and enhancing the driving experience. But getting all vehicles involved in the conversation is easier said than done. As Healey explained, “In this road network, we have cars going at 65 MPH that are

trying to communicate with each other. They are privately owned, move quickly, and have no established infrastructure.” Without an established infrastructure in this network, how do you begin to even identify the targets at the other end of the wireless communication?

Spreading the Gossip

A considerable amount of research has been invested in vehicle-to-vehicle communication and to help circumvent the remaining technical and standardization hurdles, Intel has teamed with National Taiwan University (NTU) to establish the Connected Context Computing Center. This center sponsors a research group—headed by Professor Bob Wang—that has developed a prototype system that enables cars to share “gossip.” The idea is that inter-car gossip will give your car a peek into the other cars around you. The shared information will go beyond the simple communication of speed, location, or trajectory,

making driving down the street a more social and safer prospect.

The proposed system employs 2D LIDAR, stereo cameras, and dedicated short-range communication (DSRC) standard radios for vehicle-to-vehicle sensing. This combination, according to Healey, can enable autonomous driving with minimal cost and maximum computation ability. “The advantage of 2D LIDAR is that it is basically laser radar, so it can give you depth information,” Healey told EEWeb, “We are combining that with a computer vision system, which segments objects by color.” However, there are many examples where LIDAR can fail because of light bouncing off of other objects. If there is a group of people walking together, they might all be dressed differently and wearing different colors, but to LIDAR, they are a big solid object. The big goal for Healey’s team is to use these two complimentary things to try and get object segmentation.

To enable autonomous driving, the Intel-NTU group has developed two main algorithms that work in concert to communicate information about your vehicle’s location relative to other vehicles. The simultaneous location and mapping algorithm, or SLAM, is used to locate your car’s position. The motion object tracking, or MOT, is used to track other objects relative to your car. “If you are transmitting your GPS location, you have an estimate of their position from your computer vision,” Healey explained, “Or, you can improve this estimate if they’re transmitting their GPS data back.” In this model, your car will estimate your position and velocity and predict your future

position. If you send your information back, you can find out if the person is actually in a different location—essentially a live correction of the algorithm, which is akin to ballistics tracking.

A Scalable Vehicle-to-Vehicle Network

While the complete Intel-NTU system is ideal for vehicle-to-vehicle communication, it isn’t necessary that all cars be outfitted with the system. The system is designed to be scalable meaning that any car fitted with a simple receiver can take part in the vehicular chatter. Such a communication protocol could work two ways. One is the “car in front, car behind” scenario, where the car in front would broadcast a message that it is breaking really fast, just to the car behind it. All the car in

The shared information will go beyond the simple communication of speed, location, or trajectory, making driving down the street a more social and safer prospect.

The system is designed to be scalable meaning that

any car fitted with a simple receiver can take part in

the vehicular chatter. Such a communication protocol

could work two ways.

“

““ “

Jennifer Healey pictured right.

1616

As collision avoidance systems are refined, noticeable benefits will only be seen once there is a critical mass of adopters. But while it can take many years to turn over a nation’s vehicle fleet, pressure from the government could expedite the process. The U.S. government recently announced their plan to require auto makers to equip new vehicles with collision avoidance systems starting in 2017. In the meantime, the Intel-NTU group will continue refining their technology as they “leverage what humans are good at with what computers are good at” to ensure safer future for all drivers.

front really needs to broadcast this message is a radio transmitter. The car behind simply needs a radio receiver and an algorithm that’s doing the difference of velocity to inform the driver that the car in front is slowing down really fast and I need to break.

Drivers equipped with the complete Intel-NTU system can be alerted if a car is breaking quickly in front of them by taking advantage of the system’s built-in tracking technology. This means that communication with the car in front is not necessary. “Assuming that the car in front wasn’t broadcasting its speed,” Healey explained, “If the car in back had stereo cameras and LIDAR, it could use the stereo cameras for object detection. The object detection would allow recognition that the object is a car. The 2D LIDAR could track the depth, and so infer the front car’s velocity.” Based on the sensor data from that car, the car could warn the driver that there will be a collision.

Next Steps

The current generation of the Intel-NTU system relies on the driver to respond to warnings communicated by the device. However, by sending information to a human and then relying on them to interpret and react to the information can take too long, which can have disastrous repercussions. Rather, the Intel-NTU group is working towards a collision-avoidance system that takes advantage of the stereo cameras, the LIDAR, and decision making algorithms to ensure your car is smart enough to respond when you are not.

“The algorithms can definitely be made robust enough so that the car can make a better decision than you can with your two eyes,” Healey explained. These cameras allow the human to have a much wider parallax view in front of you than what your eyes can provide. Humans can only process information from these two visual sensors—your eyes.

“The problem is that I don’t have eyes in the back of my head, so I can’t see what is going on behind me. Computers are not limited in that way. My vision is that, instead, the car will just automatically respond—it will break if a car gets too close, etc..,” said Healey.

Rather, the Intel-NTU group is working towards a collision-avoidance system

that takes advantage of the stereo cameras, the

LIDAR, and decision making algorithms to ensure your

car is smart enough to respond when you are not.

““

As collision avoidance systems are refined, noticeable benefits will only be seen once there is a critical mass of adopters. But while it can take many years to turn over a nation’s vehicle fleet, pressure from the government could expedite the process. The U.S. government recently announced their plan to require auto makers to equip new vehicles with collision avoidance systems starting in 2017. In the meantime, the Intel-NTU group will continue refining their technology as they “leverage what humans are good at with what computers are good at” to ensure safer future for all drivers.

front really needs to broadcast this message is a radio transmitter. The car behind simply needs a radio receiver and an algorithm that’s doing the difference of velocity to inform the driver that the car in front is slowing down really fast and I need to break.

Drivers equipped with the complete Intel-NTU system can be alerted if a car is breaking quickly in front of them by taking advantage of the system’s built-in tracking technology. This means that communication with the car in front is not necessary. “Assuming that the car in front wasn’t broadcasting its speed,” Healey explained, “If the car in back had stereo cameras and LIDAR, it could use the stereo cameras for object detection. The object detection would allow recognition that the object is a car. The 2D LIDAR could track the depth, and so infer the front car’s velocity.” Based on the sensor data from that car, the car could warn the driver that there will be a collision.

Next Steps

The current generation of the Intel-NTU system relies on the driver to respond to warnings communicated by the device. However, by sending information to a human and then relying on them to interpret and react to the information can take too long, which can have disastrous repercussions. Rather, the Intel-NTU group is working towards a collision-avoidance system that takes advantage of the stereo cameras, the LIDAR, and decision making algorithms to ensure your car is smart enough to respond when you are not.

“The algorithms can definitely be made robust enough so that the car can make a better decision than you can with your two eyes,” Healey explained. These cameras allow the human to have a much wider parallax view in front of you than what your eyes can provide. Humans can only process information from these two visual sensors—your eyes.

“The problem is that I don’t have eyes in the back of my head, so I can’t see what is going on behind me. Computers are not limited in that way. My vision is that, instead, the car will just automatically respond—it will break if a car gets too close, etc..,” said Healey.

Rather, the Intel-NTU group is working towards a collision-avoidance system

that takes advantage of the stereo cameras, the

LIDAR, and decision making algorithms to ensure your

car is smart enough to respond when you are not.

““

©2013 Intersil Americas LLC. All rights reserved.

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INTERVIEW WITH CEES LINKSCEO & Founder of GreenPeak Technologies

18

GreenPeak is a leader in RF communication technology for “smart home” applications. The company offers innovative, ultra low-power wireless data communication controller chips for smart home applications such as lighting, heating and HVAC, security, and many more. With the rise of the Internet of Things, having a network of devices with reliable connectivity is becoming essential for the future of “smart” product development.

We spoke with Cees Links, CEO & Founder of GreenPeak Technologies as well as one of the co-founders of the Wi-Fi protocol, about how developing Wi-Fi has given his new venture a competitive edge and his vision for the smart homes of the future.

GreenPeak:Shaping Smart Homes of the Future

COVER INTERVIEW

19

20

What motivated you to start GreenPeak Technologies?

I am actually one of the inventors of Wi-Fi. The whole idea for Wi-Fi came about at a McDonald’s in the Netherlands where we managed to link our cordless phones together. I spent about a decade from 1990 until 2000 pursuing this idea, and eventually it became made a reality. Back then, nobody believed that Wi-Fi would be a working, reliable resource with any useful application, until 2000 when Apple implemented this technology in their Airport and iBook products.

Since then, whenever my son has a friend visiting, the first question they ask is always, “What is your Wi-Fi password here?” I like asking these guys if they knew about life before Wi-Fi. They always look at me saying, “Life before Wi-Fi? How and what would that have been like?”

Wi-Fi became a reality only after a decade’s worth of effort. We couldn’t have believed that it would have the global effect that it does today. It’s very exciting to have been part of the basic development from my original idea to Wi-Fi becoming standardized and, eventually, a worldwide success.

In 2005, I had the idea for a way to have a low-power form of Wi-Fi that would connect all the other devices in our homes to the internet. There has really been a breakthrough in the industry in terms of going from an idea to an opportunity for technology differentiation in 2010 than in 2005. The greatest challenge in the industry is starting a company, making it unique, and differentiating your product from other market offerings. We had some unique ideas and the drive to succeed, so we decided to start GreenPeak Technologies.

How did your initial idea develop into what it is today?

We have found a way to connect all these devices to a central box, gateway, or router, in such a way that the energy consumption would be very low, and that the battery life would exceed the life of the sensor. Let’s say you have a hundred devices in your house and the battery life in each is about one year─you end up changing two batteries per week, which is not a feasible solution. The battery life needs to be longer than the expected life of the product. We originally thought about energy harvesting, so that it would not need batteries at all. However,

“ The greatest challenge in the industry is starting a company, making it unique, and differentiating your product from other market offerings. We had some unique ideas and the drive to succeed, so we decided to start GreenPeak Technologies.”

COVER INTERVIEW

21

we found out that energy harvesting is very expensive and that batteries are very cheap. So, we ended up with the idea that as long as you don’t have to change the batteries, then the customer would be happy.

When we started the company, there were a few initial challenges. The first challenge with this kind of technology is dealing with the ZigBee protocol. It’s really interesting because nowadays, everybody knows Wi-Fi.

A decade ago, when we first created Wi-Fi, we struggled to envision where and how it would be implemented in the market. With ZigBee, it was pretty much the same, but now we have a lot of experience and we felt more confident about it.

“Back then, nobody believed that Wi-Fi would

be a working, reliable resource with any useful

application, until 2000 when Apple implemented

this technology in their Airport and iBook

products.”

2222

“ It is always a challenge to start a chip company that is doing communication standards. If you have no standards, there is no good communication, but if there is a standard, you need to differentiate yourself.”

23

TECH ARTICLE

23

We asked ourselves where we can start with ZigBee and we established that “Smart Homes” are the right place to start.

In Smart Homes, we found out that operators are the key in driving ZigBee as the standard. It was for this simple reason that operators need the opportunity to market in-home services like security, energy management, and healthy living. They are looking for something that will increment their portfolio to subscribers and consumers. Operators said that there is a need for them to standardize on technology that can easily install these devices, and that is why they have selected ZigBee.

Who came up with the ZigBee standard and when did you decide to embrace that?

The whole activity on ZigBee started around the year 2000, based on a spinout of IEEE 802.11. IEEE 802.11 is the official name for the Wi-Fi standard wireless specifications. The spinout of Wi-Fi became 802.15.4. IEEE 802.15.4 then became the foundation of what we know as ZigBee today.

When you started GreenPeak technologies, was the ZigBee standard finalized?

Yes, the ZigBee standard was finalized. I think there are still new chapters on the Wi-Fi book and there are also new chapters on the ZigBee book that come out every year.

Can you tell us about GreenPeak’s core products?

GreenPeak Technologies is a chip company. We have a portfolio of low-power ZigBee radio chips that go into central home control boxes, set top boxes, and routers as well as remote controls. For operators, these devices are the top applications right now. Infrared is disappearing from remote controls─so you don’t have to point and shoot anymore. By using ZigBee, you can hide the central box behind the television or inside a cabinet instead of having it next to the television. That was the starting point and from there on, we started integrating our chips in thermostats, security devices, door locks, lights, sensors and so on. We went from zero to a million chips per week, and it is still accelerating very rapidly.

How do your ZigBee products compete against other ZigBee products in the market?

It is always a challenge to start a chip company that is doing communication standards. If you have no standards, there is no good communication, but if there is a standard, you need to differentiate yourself. Our starting point was exceeding the battery life of the product. Our battery life is better than of our competitors’. We are also the only one to have a single chip to control a battery-free light switch. We have a fully operational ZigBee environment with a light switch where the energy is generated by flipping the switch. In these light switches. There is no need for a battery at all. Having ultra-low power devices was the cornerstone of our company when we started it. We have developed special architectures in our chips and that is another point of differentiation.

Another advantage we have is that we knew how Wi-Fi was developed. The challenge was that ZigBee is in the same frequency band as Wi-Fi--the 2.4 GHz ISM band. It is the same frequency band worldwide. Whatever place you go, you can bring your laptop, turn it on, and get on a network on your laptop, PC, or smartphone--they are all certified. We wanted a 2.4 GHz solution, but we did not want any nuisance from Wi-Fi and vice versa.

“We currently have this smartphone trend and

everything is smartphone-oriented, but maybe the next wave will be having

a Smart Home, where we can also reach into

our home with our Smart Phone to control and manage our homes.”

2424

Because of this, we have implemented some features that made the architecture better by eliminating interference from other devices that use the same radio spectrum.

We can have a single chip in the same box. It is a Wi-Fi chip in a box in the same frequency band, yet we are in a peaceful co-existence with each other, without problems of interference, which is very important for operators.

Our range is the best in industry tests because we have a concept called antenna diversity. Every chip can support two antennas simultaneously and we select the best among those antennas for a specific place in the house. If you have two antennas, one will always have good reception, to ensure a market leading on range and reliability.

Our ZigBee product transmits throughout your house just like Wi-Fi. In the future, we see two networks in our homes, a content network Wi-Fi and a ZigBee network for sensor control and for all the other devices that need ultra-long battery life and just have a small data packages that they want to share once in a while.

What is your vision of a “Smart Home?”

My vision for the Smart Home starts with my smartphone. With my phone, I can change the thermostat at home and turn on the lights from a remote location. When I come home, the house is warm and the lights are turned on. That is my Smart Home, but it is only one example. I would say the possibilities for a Smart Home are infinite. Today, almost every home has around 10 Wi-Fi devices, and a few safety devices. In the Smart Home, wireless devices, both WiFi and ZigBee, would include laptops, PCs, smartphones and game stations as well as your safety devices like your lights, door locks, security sensors, and thermostats.

Another example of the Smart Home would be internal monitoring. For awhile, every time I went into my bathroom in my home, I could hear the gas meter running, which was strange. I knew something was wrong because it ran every time the toilet flushed. I decided to go to the basement and I found that the basement was completely flooded. Turns out it had been there for three days and the heater was warming the water.

“ The Smart Home is the next opportunity for device makers, software developers, and app developers. On top of that, we think that a Smart Home is actually the first chapter of what we call The Internet of Things (IoT).”

25

TECH ARTICLE

25

Utilities companies couldn’t care less. The more energy you consume, the more electricity, the more gas, and the more water you’re using--they are fine with it. They’ll be the last to give you a warning. They can’t even tell you that your consumption is a little bit out of line and that you should check on it. Having a Smart Home gives you an option to prevent problems like this. We currently have this smartphone trend and everything is smartphone-oriented. But maybe the next wave will be having a Smart Home, where we can also reach into our home with our smartphone to control and manage our homes.

The difficulty there is in trying to standardize on Smart Homes with the phone. The problem about trying to get it into your home is now you need to have a platform you can work with. We need something like an ecosystem or a dashboard app to plug into your home in a meaningful way.

That is happening now with the operators who are driving these standards. This is not so much something for the Apples or Googles of the world, since they are way too content-focused. What we are talking about is a lot of devices. Smart Homes are not simple. The fact that they don’t exist yet points everything into that direction.

People currently use their smartphones to manage their photos, music, and more. There is now a strong is a demand from consumers to reach out to their house and control it using their SmartPhones. They want to have the ability to connect to their home system, lights, heater, security system, and even connect with whoever is situated at home.

What do you think is needed to drive the adoption of the Smart Home?

It is interesting because we have had Wi-Fi available since 1991, and the real breakthrough came when Apple brought it to market with the AirPort and iBook. It is an interesting question─you are talking about the trigger point on how new technologies get adopted. One key is that there are good product companies who can develop nifty marketing concepts to bring these new solutions to market. We might be waiting for this for a little while, but

we see communications companies like Verizon and AT&T already offering a range of Smart Home application packages.

Operators are first offering typical vertical applications. They offer security, energy management, home monitoring, and very specific health care aid applications. These are very specific solutions for specific problems.

What we really consider as a Smart Home is a step further. An example is if you have a motion sensor in a room. The motion sensor today is part of a security system or energy management system. The security system will sense if there is motion and an alarm is turned on and some alert will go out. The motion sensor can also be part of an energy management system. If somebody walks into the room, the light turns on and the heat kicks into action. . What we don’t have today is one sensor connected to the Internet that says IF the alarm is turned on, and if somebody walks into the room then I need to send out an alert. If the alarm is not on, then the same sensor manages climate and environmental related functions. In Smart Homes, there will be sensors and applications in the background that basically monitor your house and understand all the necessary exceptions.

What is the most exciting aspect of the potential Smart Home?

The Smart Home is the next opportunity for device makers, software developers, and app developers. On top of that, we think that a Smart Home is actually the first chapter of what we call The Internet of Things (IoT). Just like how Wi-Fi started with the consumer in Apple AirPort and iBook and now you see it everywhere, we think that once the sensor and the technology concepts are well understood, it will become industry hardened.

I envision this to go out into any industry─into building automation, into agriculture, into cattle management, into logistics, and even into retail. We think that a Smart Home, even as big as it can be, it is just phase one of the internet of things, which will again completely change the way we communicate with each other, as well as with our environment. ■

2626

By Dimitris Leonardos, VP Engineering, Econais Inc.

HOW TO CREATE A

MULTI-SPEAKERMULTI-SOURCE

HOME AUDIO PLAYER

USING WI-FI

27

TECH ARTICLE

27

When talking about audio, we usually think about a speaker stereo system or a set of stereo headsets. In these cases, music is playing in a single room or close to the device hosting the music.

What happens if we need to have different music to every room in the house? With the development of the handheld devices (like smartphones and tablets), there is a need for flexible and high quality sound anywhere in the house (or in the building) and to any speaker or headset available.

2828

How can we have music coming from different sources (smartphone, PC, stereo system/home theatre, etc.) and direct it dynamically to a speaker, to headset or use two active speakers to create a Wi-Fi stereo audio system?

Currently, traditional speakers with cables do not provide the solution to this problem. It is not possible to send music to speakers without using the audio system that drives them through cables. Even most modern Wireless Speakers Systems are not a real solution as most of them provide a Wireless Speaker Connection as cable replacement.

Instead, by utilizing Wi-Fi as the communications medium, and by using the WiSmart platform and extending the capabilities with the necessary audio codec subsystem, it is possible to develop a solution capable of transferring music between any potential source (smartphone, PC, tablet, etc.), to any active speaker or headset in the home.

For existing Active Speakers, a single WiSmart device (WiSAudio) can be used to receive the stream, decode it and drive two speakers connected to it with wires, thus creating a stereo set of speakers that can deliver excellent sound quality and is able to receive the music stream from any compatible source.

The single or stereo Active Speakers configuration can be used in more than one room, and through the existing Wi-Fi connection to deliver high quality multi-room music. There are many benefits of this approach, but the most important of them are:

RangeThe solution utilizes the range of the Wi-Fi con-nection (25-40m indoors, up to 400m outdoors).

ConnectivityWi-Fi is the most developed and the widest spread technology in the last few years. Almost every house has a Wi-Fi router, all the Notebooks and all the smartphones have Wi-Fi connectivity. The use of existing infrastructure is a benefit of this approach.

High Quality SoundThe Wi-Fi bandwidth enables the solution to deliver WAV level high quality sound.

Multi-RoomUtilizing the Wi-Fi infrastructure, it is easy to have multi-room configurations, with different music to every room from the same or different sources. That means that while the PC streams music to the bedroom and to living room’s speakers, the smartphone could stream music to kitchen speaker.

There is a Bluetooth approach to the problem but has a few disadvantages:

• Very limited range (approximately 10m).

• Not suitable for multi-room configuration.

• Not able to provide High Quality WAV audio.

• Cannot have more than one different streams of music from the same device to different headsets or speakers.

The WiSAudio decodes the following audio formats:

• Ogg Vorbis

• MP3 (MPEG 1 & 2 audio layer III (CBR + VBR + ABR))

• MP1/MP2 (layers I & II optional)

• MPEG4/ 2 AAC-LC(+PNS),HE-AAC v2 (Level 3) (SBR + PS)

• WMA 4.0/4.1/7/8/9 all profiles (5-384 kbps)

• General MIDI 1 / SP-MIDI format 0 files

• FLAC

• WAV (PCM + IMA

• ADPCM)

29

TECH ARTICLE

29

Above: Three devices connected to WiSAudio simultaneously. The smartphone is controlling the audio and the two tablets are receiving streaming video from the WiSAudio.

Above: WiSAudio Wireless Wi-Fi Audio Transmission to Amplified Speakers. High quality audio streams from a laptop, cell phone or other device are transmitted via Wi-Fi to the WiSAudio board and are then fed to an amplifier and speakers.

3030

The Econais WiSAudio streaming audio solution is DLNA compatible. This enables it to communicate with all other DLNA/UPnP compatible devices. All modern PC operating systems are compliant to DLNA/UPnP, most modern smart devices, smart televisions, and there are many free players for the smartphones and tablets that can use the DLNA/UPnP devices to play music.

Easy Configuration

The WiSAudio can be easily configured by any device that has Wi-Fi connection and a web browser. The WiSAudio has a built-in configuration web server from where user can select what to do with each speaker and how to connect to the home network.

Low Power Consumption & Low Average TX Power

The power consumption of the WiSAudio is low, as its role is only to receive data.

The transmissions from the WiSAudio keep the connectivity during the playback and are limited to a few bytes for every Kbyte of received data. This is very important especially for the headset approach which:

a.) Is battery powered and power save is important.

b.) Is close to the head of the user, and minimizing the RF transmissions is important.

The use of hardware codec gives superior sound and extended audio format support, without the need for the MCU to do heavy decoding operations, which is very beneficial for the power consumption.

The WiSAudio can be used for creating Wi-Fi Active speakers and Wi-Fi stereo headsets. It is a DLNA compatible device, supporting Multi-room & Multiple Speaker on the same network.

DLNA mode operates as a normal DLNA renderer device. In this mode any other network device that supports DLNA can send music to WiSAudio.Examples of such devices are PCs with modern

Operating Systems (i.e. MS Windows 7, Linux or Apple Mac OS X) and a majority of Android and iOS smartphones and tablets. Even if a device does not natively support the DLNA standard, there are numerous free DLNA compliant players and applications that can be installed on them to send the music to the WiSAudio. DLNA implementation for the WiSAudio is widely supported. Using DLNA it is possible to have different songs playing in different WiSAudio speakers in different rooms of the house.

The music streams for WiSAudio speakers in the different rooms of the house could originate either from different sources (some from a PC, some from a smartphone or from a tablet), or all of them could originate from the same source (i.e. a notebook or desktop).

The problem with DLNA arises when we need to use 2 or more WiSAudio speakers that should reproduce synchronized sound of a specific source. The DLNA is does not have yet the synchronized playback feature. The configuration of each speaker can be done either through the built-in configuration web pages of the WiSAudio or by optional hardware buttons LCD screen panel on the speaker.

“Econais is a module manufacturer and solutions

company building ultra-low power connectivity

solutions to address the expanding Internet of Things (IoT) market.”

31

TECH ARTICLE

31

WiSAudio device with WiSmart module, audio codec, and audio line jacks (IN/OUT)

WiSAudio connected to Amplifier and Wi-Fi connections to smart devices and laptop containing media library (smartphone is remote control for audio from laptop library)

Summary

A Smart Wi-Fi audio system created with WiSmart modules, an audio system with excellent sound quality for a house or building offers many advantages. The most important benefits of an implementation are:

1. Wide range of audio formats supported on HW level

2. Higher quality audio compared to Bluetooth

3. DLNA support

4. Multi-room solution

5. Headset suitable solution

6. Flawless sound quality

7. Easy configuration

8. Low power consumption makes it suitable for battery operation

9. Extended connection range comparing to Bluetooth

Econais’ easy-to-use modules and software enable customers to leverage the globally installed base of Wi-Fi access points and smartphones to create connected products for healthcare, smart energy, audio, consumer and control/monitoring in industrial, commercial and residential markets.

http://www.Econais.com

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