I am a very beginner in software and I am asking or a direction to proceed for research technologies to build my app. I am having just an idea for the app. I am trying to build something like zomato but different services. The idea of location based system is similar. I searched online and came to know about GIS systems. But while researching further, it seems I've to create a map all together. This feels redundant to build as we have api of google maps.
But can i use this api to build a system "ON" it????
Any tutorials or some direction in this direction would be helpful.
Also what is difference between GIS and gps based apps.
As you see, I am not very clear in the fundamentals of the GIS and GPS based apps
Thanks for the help
Regarding Android, you have almost all you need by combining the platform API and the comprehensive Google Maps Android API. Regarding the later, it's actually a matter of opting by convenience and possibly paying a licence fee to Google, versus developing your own solutions of aggregating free or cheaper services from elsewhere.
Most problems solved by apps are not the same problems solved by classical GIS software, since the former are more consumer-oriented (using public transportation, navigating a route, planning a trip, finding a nearby restaurant), and the later are more specialist-oriented, typically solving larger-scale and more technical issues (detecting regions with flood risk, monitoring deforestation, calculating volumes of terrain to be bulldozed, etc.)
You should not, IMO, be discouraged by the seemingly hard technical concepts of geography and map making. Your best bet is to have a clear vision of what actual problems you app should be solving, and study the geography topics gradually, as the need arises.
A bit of consideration on your question about GIS:
If it were created today, the GIS acronym would mean any software dealing with geographic data, be it a mobile app or a workstation software suite destined to specialized professional use.
But when it was created, the term meant almost exclusively the later sense, and so it has a lot of tradition and cultural legacy to it - which is of couse not always a good thing. Specifically (at least in my experience), it seems to me the jargon and concepts used by the classic GIS community are a bit impenetrable to the newcomer, specially if she comes from the software-development field instead of the geo-sciences field.
But geographic information availability has gone from scarcity to overwhelming abundance, and so have its enabling technologies: GPS satellites, mobile computing and mobile connectivity.
Related
The question is about using a chat-bot framework in a research study, where one would like to measure the improvement of a rule-based decision process over time.
For example, we would like to understand how to improve the process of medical condition identification (and treatment) using the minimal set of guided questions and patient interaction.
Medical condition can be formulated into a work-flow rules by doctors; possible technical approach for such study would be developing an app or web site that can be accessed by patients, where they can ask free text questions that a predefined rule-based chat-bot will address. During the study there will be a doctor monitoring the collected data and improving the rules and the possible responses (and also provide new responses when the workflow has reached a dead-end), we do plan to collect the conversations and apply machine learning to generate improved work-flow tree (and questions) over time, however the plan is to do any data analysis and processing offline, there is no intention of building a full product.
This is a low budget academy study, and the PHD student has good development skills and data science knowledge (python) and will be accompanied by a fellow student that will work on the engineering side. One of the conversational-AI options recommended for data scientists was RASA.
I invested the last few days reading and playing with several chat-bots solutions: RASA, Botpress, also looked at Dialogflow and read tons of comparison material which makes it more challenging.
From the sources on the internet it seems that RASA might be a better fit for data science projects, however it would be great to get a sense of the real learning curve and how fast one can expect to have a working bot, and the especially one that has to continuously update the rules.
Few things to clarify, We do have data to generate the questions and in touch with doctors to improve the quality, it seems that we need a way to introduce participants with multiple choices and provide answers (not just free text), being in the research side there is also no need to align with any specific big provider (i.e. Google, Amazon or Microsoft) unless it has a benefit, the important consideration are time, money and felxability, we would like to have a working approach in few weeks (and continuously improve it) the whole experiment will run for no more than 3-4 months. We do need to be able to extract all the data. We are not sure about which channel is best for such study WhatsApp? Website? Other? and what are the involved complexities?
Any thoughts about the challenges and considerations about dealing with chat-bots would be valuable.
I am aware that game engines like Unity, Unreal, Cry Engine provide almost all the tools necessary to build an AAA title game. Its also the best choice if the game has a tight release data or if your new to game development. But since they are generalist game engines (meaning that they are made to fit multiple genre of games. Correct me if in wrong) for some games (next-gen or games which require a lot of performance), they might leave some performance on the table, something which could be accomplished by developing a custom engine.
This brings me to my question,
Do game developers (indie game developers, large teams or even companies) still build game engines from scratch to tailor fit a game or a game franchise?
Thank You!
when we talk about big companies like Ubisoft or rockstar they built their own engines and didn't use Unity or unreal
Rockstar uses "Rockstar Advanced Game Engine"
and Ubisoft uses "AnvilNext"
but why?
there are millions of reasons they do such a thing, I'm gonna say just 2 from #scremyCat
the support
and the license
Support: Highest degree of support and understanding - as they built it all, they understand all of its internals and can offer
complete support. E.g. A game needs X feature, they'll easily know
if they can implement it or not. Another benefit of this is not
having to wait on external entities, if there's a game breaking bug
in the engine they can get right on it, while a third party engine
depending on the licensing agreement this might not be possible
(though they would typically license the source code anyway).
License: Licensing - as an indie developer accepting that you might have to pay a small percentage of your revenue for licensing
the engine might not be as much of an issue seeing as the amount you
need to breakeven is unlikely going to be very high and chances are
you're already making when your revenue is at the levels needed to
pay a %, and your total revenue from a game isn't likely going to be
huge anyway so the amount in licensing fees you need to pay may seem
very reasonable. Meanwhile a AAA game will have a much higher
break-even target and their expected revenue is most definitely in
the tens to hundreds of millions, which now means they're paying a
large amount in licensing fees. Now it should be said they usually
get much better licensing deals to begin with than the indie dev
gets, but still they're paying huge amounts.
As for timeframe, it can take years to fully develop an engine of their scale. Often why you'll see them using the same version of the engine for a good cycle of games whilst working on the next version of the engine. And as for what's involved, a LOT. They need to handle every platform they'll be targeting, the rendering, the physics, the AI, the audio, the input, the file system access, the asset management pipeline, the tools, etc.
How are they better than current popular engines? They aren't necessarily (to other developers), but to them with their own reasoning for doing it they are. The simplest answer for how can they be better is that when you're creating your own engine from scratch you can do whatever you want.
It should also be said that developing your own engine isn't just limited to large game companies, a number of smaller developers also do this. The more popular reasons for this are typically because they enjoy it, and have some functionality they want that isn't available in existing options. E.g. While you can create many games with Unity or Unreal, there's plenty of things which just aren't feasible or might take considerable work to even make possible anyway. This can be a reason for a smaller dev to make their own engine.
Yes, they absolutely do. Nintendo is a good example.
I want to create a application which converts 2d-images/video into a 3d model. While researching on it i found out similar application like Trnio, Scann3D, Qlone,and few others(Though few of them provide poor output 3D model). I also find out about a technology launched by the microsoft research called mobileFusion which showed the same vision i was hoping for my application but these apps were non like that.
Creating a 3D modelling app is complex task, and achieving it to a high standard requires a lot of studying. To point you in the right direction, you most likely want to perform something called Structure-from-Motion(SfM) or Simultaneous Localization and Mapping (SLAM).
If you want to program this yourself OpenCV is a good place to start if you know C++ or Python. A typical pipeline involves; feature extraction and matching, camera pose estimation, triangulation and then optimised using a bundle adjustment. All pipelines for SfM and SLAM follow these general steps (with exceptions of course). All of these steps are possible is OpenCV although Googles Ceres Solver is an excellent open-source bundle adjustment. SfM generally goes onto dense matching which is where you get very dense point clouds which are good for creating meshes. A free open-source pipeline for this is OpenSfM. Another good source for tools is OpenMVG which has all of the tools you need to make a full pipeline.
SLAM is similar to SfM, however, has more of a focus on real-time application and less on absolute accuracy. Applications for this is more centred around robotics where a robot wants to know where it is relative to its environment, but it not so concerned on absolute accuracy. The top SLAM algorithms are ORB-SLAM and LSD-SLAM. Both are open-source and free for you to implement into your own software.
So really it depends what you want... SfM for high accuracy, SLAM for real-time. If you want a good 3D model I would recommend using existing algorithms as they are very good.
The best commercial software in my opinion... Agisoft Photoscan. If you can make anything half as good as this i'd be very impressed. To answer your question what resources will you require. In my opinion, python/c++ skills, the ability to google well and a spare time to read up on photogrammetry and SfM properly.
Does needing just a single word voice recognition reduce the complexity of the task enough to be able to fully perform voice recognition processing offline, on an iOS or Android smartphone? (E.g., could a reasonably accurate counter for the number of times that a single, pre-programmed word was spoken while the microphone is active be developed to work offline on a standard iOS or Android smartphone?).
I've found plenty of tools and examples capturing voice and sending it to an online service (e.g., the Google cloud voice-to-text), but does the single-word focus reduce the complexity enough for the recognition to be doable offline today? If so, do you have any libraries to suggest or where would you start?
Cloud services are good for various reasons relating to your question:
It makes deployment of new versions of the algorithm (which happen much more frequently than most people realize) a lot easier
It allows the developer to collect your data and use it in future algorithm development (or whatever they please)
From a practical standpoint, most deployed models (at least the effective ones) can be quite large and take up quite a bit of space on a mobile device.
In addition to the above, I don't think that the singular word focus changes much, if anything. The model has to not just account for words, but also for the different ways those words can be said (volume, tone, accents, inflection, etc, etc).
So what you are asking can be done but there's also good reasons why it's on the cloud.
I am trying to use semantic technologies in IOT. From the last two months I am doing literature survey and during this time I came to know some of the tools required like (protege, Apache Jena). Now along with reading papers I want to play with semantic techniques like annotation, linking data etc so that I can get the better understanding of the concepts involved. For the same I have put the roadmap as:
Collect data manually (using sensors) or use some data set already on the web.
Annotate the dataset and possibly use ontology (not sure)
Apply open linking data principles
I am not sure whether this road map is correct or not. I am asking for suggestions in following points
Is this roadmap correct?
How should I approach for steps 2 and 3. In other words which tools should I use for these steps?
Hope you guys can help me in finding a proper way for handling this issue. Thanks
Semantics and IoT (or semantic sensor web [1]) is a hot topic. Congratulations that you choose a interesting and worth pursuing research topic.
In my opinion, your three steps approach looks good. I would recommend you to do a quick prototype so you can learn the possible challenges early.
In addition to the implementation technologies (Portege, etc.), there are some important works might be useful for you:
Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE). [2] It is an important work for sharing and exchanging sensor observation data. Many large organizations (NOAA, NASA, NRCan, AAFC, ESA, etc.) have adopted this standard. This standard has defined a conceptual data model/ontology (O&M, ISO 19156). Note: this is a very comprehensive standard, hence it's very BIG and can be time consuming to read. I recommend to read #2 mentioned below.
OGC SensorThings API (http://ogc-iot.github.io/ogc-iot-api/), a IoT cloud API standard based on the OGC SWE. This might be most relevant to you. It is a light-weight protocol of the SWE family, and designed specifically for IoT. Some early research work has been done to use JSON-LD to annotate SensorThings.
W3C Spatial Data on Web (http://www.w3.org/2015/spatial/wiki/Main_Page). It is an on-going joint work between W3C and OGC. Part of the goal is to mature SSN (Semantic Sensor Network) ontology. Once it's ready, the new SSN can be used to annotate SensorThings API for example. A work worth to monitor.
[1] Sheth, Amit, Cory Henson, and Satya S. Sahoo. "Semantic sensor web." Internet Computing, IEEE 12.4 (2008): 78-83.
[2] Bröring, Arne, et al. "New generation sensor web enablement." Sensors 11.3 (2011): 2652-2699.