SKU: 88533854464

Sigma ROX 4.0 GPS Cycle Computer Sensor Set - Black

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Description

Sigma ROX 4.0 GPS Cycle Computer Sensor Set - BlackThe ROX 4. 0 has over 30 functions, offering all the data you need, such as altitude profile, speed, distance, and training time. The outstanding 2. 4 inch display is very easy to read, even when the bike is moving. A genuine highlight of ROX 4. 0 is the integrated air pressure sensor that uses barometric readings to determine inclines and declines. Providing you with reliable information about altitude and generating a precise record of your route.

The ROX 4.0 has over 30 functions, offering all the data you need, such as altitude profile, speed, distance, and training time. The outstanding 2.4 inch display is very easy to read, even when the bike is moving. A genuine highlight of ROX 4.0 is the integrated air pressure sensor that uses barometric readings to determine inclines and declines. Providing you with reliable information about altitude and generating a precise record of your route. ROX 4.0 shows navigation and can automatically connect with your e-bike and external sensors for measuring speed, heart rate, and cadence.

Large Display: The 2.4-inch-diagonal high-contrast display and extra-large numbers make it easy to read, even during a ride.

Red Circle: The innovative Red Circle on the upper right corner of the display is highlighted in various colours and offers innovative, interesting display of training data.

Everything At a Glance: The SIGMA RIDE app allows you to set your display view individually. You can set up to six views, each with three values. Current speed and time are shown continuously on the display.

Ready to go Any Time: The ROX 4.0 bike computer can be positioned directly on the handlebars in a few easy steps – entirely without cables or sensors. If you want, you can connect your Rox 4.0 to additional sensors for measuring speed, heart rate, and cadence. Your ROX uses GPS and GLONASS to determine your position within a few seconds of being switched on. The user-friendly operation with three buttons enables you to start a bike tour right away. You can use the SIGMA RIDE app to set your individual display views.

Perfectly Positioned: The Overclamp Butler enables you to perfectly align your ROX 4.0 bike computer, even if you have a short stem. The computer head can thus be positioned centrally above the stem at the level of the handlebars. This ensures that there is space and order on your handlebars and that you have a good overview of all values, even if you’re not riding a mountain bike. The Butler fits on handlebars of 31.8 and 35 mm diameters.

The Classic: The classical SIGMA GPS handlebar bracket can be fixed in place without tools in a few seconds with two cable ties. This gives you a choice between installation on your handlebars or on the stem, and the device fits handlebars of all sizes.

Up Where The Air Is Clear: A true highlight of the ROX 4.0 is the integrated air pressure sensor. It uses barometric readings to determine inclines and declines and provide you with reliable information about altitude, allowing a precise recording of your track. You can also call up a graphical altitude profile on the ROX 4.0. The home altitude at the beginning of the tour is determined automatically from GPS, or you can set altitude manually.

Pioneer: Connect your ROX 4.0 with your komoot app and leave your smart phone in your pocket. Receive turn-by-turn navigation from the komoot app on your ROX 4.0. It can be displayed either as an arrow on the display or inside the Red Circle. This circle on the display is highlighted in red and shows the distance to upcoming turn-offs as coloured bars that get shorter as the turn-offs get nearer. The number of metres to the next turn-off is also displayed next to the circle.

E-Bike-Ready: You ride an e-bike? No problem. The ROX 4.0 is e-bike-ready and can be easily connected to many e-bikes. Your GPS bike computer establishes a connection to the compatible e-bike system and shows you the current assist mode, battery status, and light and other data on the display. You can use this list to find out if your E-BIKE is compatible with the ROX 4.0.

Best Measurements: Do you need more data or even greater accuracy for your values? ANT+ and BLE enable you to connect external sensors such as speed, heart rate, and cadence sensors to your ROX 4.0. You can find accessories sets with various sensors for your ROX in our shop.

For Data Collectors: You can use the SIGMA RIDE app to collect your training data, transfer them to your smart phone via BLE, and quickly set your favourite display views on your smart phone. The most important data and statistics can be read from your smart phone in real time with the SIGMA RIDE app’s LIVE function and assessed conveniently.

Be There Live: Record your data with your bike computer and analyse them right away with the SIGMA RIDE app’s LIVE view. You can set your individual display views extremely easily while you’re riding.

Stay Tuned: Smart notifications connect you to the outside world even when you’re riding. Notifications of incoming calls and messages to your smart phone appear on your display.

In The Box

  • Bike computer
  • USB-C cable
  • Quick-start guide
  • R1 DUO Heatrate transmitter and chest strap
  • Overclamp Butler GPS Mount
  • DUO Magnetless Speed Sensor
  • DUO Magnetless Cadence Sensor
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    SKU: 88533854464

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    Walter Echo-Hawk, author of THE SEA OF GRASS.
    Boise, US
    ★★★★★ 5
    Native American history at its best!
    Format: Hardcover
    Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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    Reviewed in the United States on April 1, 2019
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    Whiting, US
    ★★★★★ 5
    Excellent book on ML
    Format: Paperback
    This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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    Reviewed in the United States on December 20, 2024
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    Richard Hackathorn
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    ★★★★★ 5
    Excellent Textbook for Hands-On Learning of ML
    Format: Kindle
    This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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    Reviewed in the United States on February 26, 2022
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    Amazon Customer
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    ★★★★★ 4
    Just learning it
    Format: Paperback
    Nice learning book just have to finish it
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    Reviewed in the United States on December 10, 2025
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    Chelsea, US
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