MWC 2018. (write up)

Those thinking 2018. MWC lacked sparkles, obviously didn’t see dazzling 3D Hypervsn(TM).

AI

All mobile processor manufacturers have added, next to graphical processors, Artificial intelligence (Neural networks) engine to their ARM processors.

  • ARM added Mali
  • MediaTek (in P60) added  APU
  • Qualcomm added Adreno

There was no information on Huawei, and Apple AI processors – but they doubtlessly exist.

Israeli stand

An amazing inference chip, working entirely on optical principle was proposed by CogniFiber. (Each pixel has its own fiber of, say 30um, out of say, 600x400px. Thresholds of neural network are probably implemented as amplifiers for undersea fibers – no slowing down of light. Claim is: about 1000x less power and a few orders of magnitude speed up.)

Israeli syte.ai has a mobile app which can take camera photo to identify clothes (brand) on the person one envies and finds the price and where to buy it.

Another company has a ‘self-driving’ system for the passenger – one gets all the information about shops, restaurants, hotels, etc… on the camera photo, while being a co-driver.

SDR/URLLC

Fraunhofer institute demonstrated shortened TTI implemented in USRP.

CellXica from UK developed their own SDR platform and base station. (Thanx Prashant for tapping my shoulder.)

Other

SoftBank robotics brought childlike robots Nao and Pepper which were popular with women.

Memory

Intel True VR(TM) needs 2TB per hour (6 pairs of camera capture of 360degrees.)

Cars

Audi A8 has about 50x ARM processors.

(Ridiculous) concept car on Mercedes stand.

Graphene

Graphene corner was in Hall 8 just as the last year, and the monetisation remains the problem. Among other applications:

  • Transparent graphene film over window’s surface. (Maybe in future we will have each window as a TV?)
  • All kind of sensors
  • Thermal conductor (better than copper)

Braincom-project.eu has increased the number of brain implanted electrodes from few hundred, to, using graphene, few tens of thousands of electrodes.

Russian stand

On quite modest Russian stand one could have seen Sailfish OS – alternative to iOS, Android and Windows – used by government. (It’s a Linux flavour.)

Term 3

Path planning, concentrations and systems

Path planning

  • Search:
    Discrete path planning and solving algorithms (A*).
  • Prediction:
    Sensor fusion used to predict other objects behaviour.
  • Trajectory generation in C++:
    Project: Path planning

Drive a car down a highway with other cars using one’s own path planner.

Advanced deep learning

  • Fully connected convolutional networks
  • Scene understanding
  • Inference performance
  • Project: Semantic segmentation

 

Autonomous vehicle architecture

  • Introduction to ROS (Robot operating system):
    Architectural overview of ROS framework and setting up the environment.
  • Packages & Catkin workspaces:
    ROS workspaces structure, essential command line tools and software package management
  • Writing ROS nodes:
    Python and C++.
  • Project: System integration project

Running the code on Carla, Udacity’s autonomous vehicle.

Inaugural class of Udacity Self-driving nanodegree graduation celebration with Sebastian Thrun.

MWC 2017. (write up)

  1. Meeting with Small ePC vendors, possible participants of Small cell forum Private ePC plugfest (in the role of Small cell forum Interoperability chair).

  2. Status of DNN implementations
    (Note: companies maybe didn’t bring the best they have, otherwise, it’s bad):
    NXP had brought a range of radar chips, Lidar and ultrasonic chips. Sensor fusion is done in “blue box” powered by 4x ARM cores. (Implementation doesn’t seem perfect: Lidar doesn’t recognise all the people, and point cloud has artefacts, while camera recognises people where there are none.)
    Vodafone had Huawei‘s demonstrator for 5G (and possibly autonomous driving) running car simulation in PlayStation 4 (it seems Huawei doesn’t like x86 technology – I wouldn’t be surprised if Huawei and Sony announce partnership – for microprocessor/GPU/sensors, as Huawei is cut-off from top US tech.) Car simulation was not good/ convincing – if autonomous driving algorithms were used (and simulation had road-works/ cyclists/ pedestrians…).
    I observed a simulation similar to Huawei’s on Orange stand.
    HP Enterprise seemed to have car simulator similar to what we used, based on Unity engine. (Again, the driving skills of algorithm/ human were suboptimal.) HP Enterprise doesn’t seem to have a device for SMEs (a mistake, I think – they should be called HP for big Enterprise)
    Qualcomm had on their stand SnapDragon with DNN support through Adreno (GPU that Qcomm bought from AMD some time ago) manned by a Chinese person. A lonely stand, except for one more Chinese visitor – so the conversation was in Chinese. Ni hao. Not impressive (particularly in the light what other companies think about DNN on SnapDragon).
    SQREAM (from Israel) had SQL developed for GPUs. Very good solution. They mentioned (and the similar strategy will be used by all other companies) that they can swap AMD for Nvidia, but will never maintain two separate product lines (for AMD and Nvidia GPUs). Claim was that AMD improved a lot, but they stick with Nvidia. (And this is likely how AMD/ Nvidia “war” will end-up.)
    From Israeli stand, I got some indications how quick DNNs in mobile deployment, generally are.


  3. SDR
    NI (National instruments) had a very nice 5G setup.
    LimeSDR, Octasic, …


  4. I was introduced to James Tagg, the director of Penrose Institute in La Jolla (Sir Roger Penrose is the best physicist in the world, in my opinion). James is involved with two fascinating projects:

    To predict when NN can fail (not clear if this is a computable problem) – Anyone who trained NN knows it can fail unpredictably – solution to this problem will help DNN deployments tremendously.
    To prove professor Penrose’s objective reality of quantum physics, by gravity induced collapse of quantum wave functions through an experiment.
    How unlikely to meet such a person at an event like MWC. (Thank you James to introducing me to James, and thank you James for being so kind and having time for the chat.)


  5. Other
    I spoke with Spanish satellite defence contractor, on the combination of Phased array antennae with Luneburg sphere.
    I met with Jean-Francois Lacasse of Cavium (who I first met through Small cell forum plugfest in Paris 2014) and had a nice chat – how things are turning great for multi-core ARM Cavium server. (He’s involved with LimeSDR and Ubuntu, and will chair Open Cellular)
    I visited 3D mapping companies (possibly to integrate their maps in Unity game engine). (LuxCarta ladies were especially nice, and gifted me with all sorts of gadgets.)

    In general, it seems more hype than money. Time for change…
    ( Hall 1: people who bought their way in.
    Hall 2: companies that GSMA seriously believe contribute to industry and are making money
    Hall 3: old companies, old money
    Hall 4: conference rooms
    Hall 5-7: small companies, and pavillions per country (Irish one is always the one with the best beer))

Term 2

Sensor Fusion

Sensors: Learn how lidar and radar work.

  • Kalman Filter:
    Use probability distributions to fuse lidar and radar data together.
  • C++ Tutorial:
    Review the key C++ concepts for implementing the Term 2 projects.
  • Kalman Filter in C++:
    Build high performance filters.
  • Project: Pedestrian Tracking

Fuse sanitized lidar and radar data together to track a pedestrian.

  • Unscented Kalman Filter:
    Fuse sensor nonlinear measurements.
  • Project: Pedestrian Tracking

Fuse real-world lidar and radar data together to track a pedestrian.

Localization

  • Motion: Study
    how motion and probability affect your belief about where you are in the world.
  • Markov Localization:
    Use a Bayesian filter to localize the vehicle.
  • Bayesian Filter:
    Implement a Bayesian filter for localization.
  • Egomotion:
    Estimate the position of the car over time given different sensor data.
  • Sampling for Localization:
    Use a particle filter to localize the vehicle.
  • Particle Filter:
    Implement a particle filter in C++.
  • Project: Kidnapped Vehicle

Implement a particle filter to take real-world data and localize a lost vehicle.

Control

  • Stability:
    Investigate the properties of stable and unstable systems.
  • Open-Loop Control:
    Implement a controller in which the actuation is independent of the controller output.
  • Closed-Loop Control:
    Implement a controller in which the actuation is dependent on the controller output.
  • PID Controller:
    Implement a Proportional-Integral-Derivative controller.
  • Linear Quadratic
    Regulator: Optimize the PID controller using a quadratic system of equations.
  • Project: Lane-Keeping

Implement a controller to overcome disturbances and keep a simulated vehicle in its path.

  • Vehicle control
    Kinematic and dynamic vehicle models.
  • Model predictive control
    Frame the control problem as an optimization problem over time horizons.
  • Project: Model predictive control

Drive the vehicle around the track even with the additional latency between commands!