I'm looking for a way to get unix time in CHICKEN Scheme, or any other timestamp for that matter, which I can compare like it's a number with other timestamps.
I want this because I'm trying to emulate (runtime) which is defined in the book I'm reading (SICP) as "the time since starting the interpreter", but because we are just looking for differences between these stamps at different points, it doesn't really matter.
I could go into more detail if that helps: I'm trying to do exercise 1.22.
There's (current-seconds) from the chicken module, which gives you the current UNIX timestamp.
With (current-milliseconds), which is also from the chicken module, you can get the number of milliseconds that have passed since the process was started.
Of course you're building this in the exercise yourself, but in real world cases you probably want to just use the (time) special form, which gives you detailed information about the running time of an algorithm, including GC information.
Related
I want to convert a sound from Mic to binary and match it from the database(a type of voice identification program but don't getting idea how to get sound from Mic directly so that i can convert it to binary?Also it is possible or not. Please guide me )
See this:
http://www.dotnetspider.com/resources/4967-How-record-voice-from-microphone.aspx
You're not going to be able to identify voices by doing a binary comparison on sound data. The binary of a particular sound will not be identical to an imitation of that sound unless it is literally the same file because of minor variations in just about everything. You'll need to do some signals processing to do a fuzzy comparison of the data. You can read about signal processing on wikipedia.
You will probably find it easier to use a third party library to process the sound for you. Something like this might be a good start.
You're looking at two very distinct problems here.
The first is pretty technical: Getting sound from the microphone into a digital waveform. How you do this exactly depends on the OS and API you're using (on Windows, you're probably looking at DirectX audio or, if available, ASIO). Typically, this is how you'd proceed:
Set up a recording buffer for the microphone, with suitable parameters (number of channels, physical input on the sound card, sample rate, bit depth, buffer size)
Start the recording. This usually involves pointing the sound library to a callback function to process the recorded buffer.
In the callback, read the buffer, convert it to a suitable format, and append it to the audio file of your choice. (You could also record to RAM only, but longer recordings may exceed available storage).
Store the recorded audio in a suitable database field (some kind of binary blob)
This is the easy part though; the harder part is matching a chunk of audio data against other chunks. A naïve approach would be to try and find exact matches, but that won't help you much, because the chance that you find one is practically zero - recording equipment, even the best, introduces a bit of random noise, and recording setups vary slightly whether you want to or not, so even if you'd have someone say something twice, perfectly identical, you'd still see differences in the recorded audio.
What you need to do, then, is find certain typical characteristics of the waveform. Things you could look for are:
Overall amplitude shape
Base frequencies
Selected harmonics (formants)
Extracting these is non-trivial and involves pretty severe math; and then you'll have to condense them into some sort of fingerprint, and find a way to compare them with some fuzziness (so that a near-match is good enough, rather than requiring exact matches). Finding the right parameters and comparison algorithms isn't easy, and it takes a lot of tweaking and testing; your best bet is to go find a library that does this for you.
I'm trying to re-create this conquerclub (Risk-like) game:
http://conquerclub.barrycarter.info/ONEOFF/7460216.html
In other words, I want to know who owned each territory at each point
in time, and how many troops they had on that territory. My primary
source of information is the Game Log. Notes:
% It's not in the Game Log, but all territories start w/ 3 troops.
% Since we know the territory owners at the end of the game, and the
Game Log mentions all owner changes, determining territory owners at
any point in time is easy.
% The challenge is to find the number of troops on a territory at a
given time.
% The Game Log gives information on troop deployment, reinforcement,
and conquest.
% However, the Game Log is incomplete. Suppose territory X attacks
territory Y unsuccessfully, but both territories lose troops in the
process. The Game Log will not mention this.
% It's probably not possible (in general) to find the exact number
of troops on a territory at a given time, so I'm looking for a range.
% I tried feeding the data to Mathematica as a series of
inequalities, but as the manual warns, the computation time increases
exponentially with the number of inequalities. Even with a fairly
small number of inequalities, it hangs. Plus, I'm not convinced
Mathematica is the right tool here.
% Any thoughts? Another example is:
http://conquerclub.barrycarter.info/ONEOFF/7562013.html
% I know about http://userscripts.org/scripts/show/83035 but that only tracks \
owners, not number of troops.
You could make use of Prolog's constraint programming (specifically, CLP/FD). It would require you to encode all rules in Prolog, which might be a non-trivial task. However Prolog would be able then to show you all possible valid (legal in terms of encoded rules) ways of playing such game, or just show ranges of possible values.
Also, while CLP/FD in Prolog sometimes is quite fast, it might be difficult to use it to make solving your problem quickly. Most free solvers have many quirks.
Again, I think this is a nontrivial task, and even greater if you haven't programmed in Prolog earlier. But I am pretty sure this would give you answers you seek.
I know there are some scheduling problems out there that are NP-hard/NP-complete ... however, none of them are stated in such a way to show this situation is also NP.
If you have a set of tasks constrained to a startAfter, startBy, and duration all trying to use a single resource ... can you resolve a schedule or identify that it cannot be resolved without an exhaustive search?
If the answer is "sorry pal, but this is NP-complete" what would be the best heuristic(s?) to use and are there ways to decrease the time it takes to a) resolve a schedule and b) to identify an unresolvable schedule.
I've implemented (in prolog) a basic conflict resolution goal through recursion that implements a "smallest window first" heuristic. This actually finds solutions rather quickly, but is exceptionally slow at finding invalid schedules. Is there a way to overcome this?
Yay for compound questions!
The hardest part of most scheduling problems in real life is getting hold of a reliability and complete set of constraints. If we take the example of creating a university timetable:
Professor A will not get up in the morning, he is on a lot of committees, but no-one will tell the timetable office about this sort of constraint
Department 1 needs the timetable by the start of term, however, Department 2 that uses the same rooms is unwilling to decide on the courses that will be run until after all the students have arrived
Etc
Then you need a schedule system that can cope with changes, so when one constraint is changed at the last minute you don’t have to change the complete timetable.
All of the above is normally ignored in research papers about scheduling systems. As to NP completeness of a given scheduling problem, in real life you don’t care as even if it is not NP complete you are unlikely to even be able to define what the “best solution” is, so good enough is good enough.
See http://www.asap.cs.nott.ac.uk/watt/resources/university.html for a list of papers that may help get you started; there are still many PHDs to be had in scheduling software.
There are often good approximation algorithms for NP-hard/complete optimization problems like scheduling. You might skim the course notes by Ahmed Abu Safia on Approximation Algorithms for scheduling or various papers.
In a sense, all public key cryptography is done with "less hard" problems like factoring partially because NP-hard problems offer up too many easy cases. It's the same NP-completeness that makes them "morally hard" which also gives them too many easy problems, which often fall within some error bound of optimal.
There is a deeper theory of hardness of approximation that discusses the limitations of approximation algorithms though.
You can use dynamic programming to solve some of these things. Greedy algorithms also come to mind. Scheduling theory is both deep and beautiful but those two I find will solve most of the problems I've faced. Perhaps I've been lucky.
What do you mean with startBy?
With startAfter and if there is only one resource, then a fast solution could be to use topological sorting. The example algorithm runs in linear time, but does not include the error case if the graph contains cycles.
Here's one that isn't.
Schedule a set of jobs i= 1,2...n on a single machine which each take time t(i) so that the average waiting time is minimized.
Solution: Sort in increasing order of t(i). O(n log n)
Good list here
Consider the scheduling problem that is in the class P:
Input: list of activities which include the start time and finish time.
Sort by finish time.
Select the first N elements of this sorted list to find the maximum amount of activities you can schedule in a given time.
You can add caveats like: all activities must end at 5pm, well in this case as you work through the list, stop once you reach an activity which ends after this time.
Say you have a conference room and meetings take place at arbitrary impromptu times. You would like to keep an audio record of all meetings. In order to make it as easy to use as possible, no action would be required on the part of meeting attenders, they just know that when they have a meeting in a specific room they will have a record of it.
Obviously just recording nonstop would be inefficient as it would be a waste of data storage and a pain to sift through.
I figure there are two basic ways to go about it.
Recording simply starts and stops according to sound level thresholds.
Recording is continuous, but split into X minute blocks. Blocks found to contain no content are discarded.
I like the second way better because I feel there is less risk for losing data because of late starts, or triggers failing.
I would like to implement in Python, and on Windows if possible.
Implementation suggestions?
Bonus considerations that probably deserve their own questions:
best audio format and compression for this purpose
any way of determining how many speakers are present, assuming identification is unrealistic
This is one of those projects where the path is going to be defined more about what's on hand for ready reuse.
You'll probably find it easier to continuously record and saving the data off in chunks (for example, hour long pieces).
Format is going to be dependent on what you in the form of recording tools and audio processing library. You may even find that you use two. One format, like PCM encoded WAV for recording and processing, but compressed MP3 for storage.
Once you have an audio stream, you'll need to access it in a PCM form (list of amplitude values). A simple averaging approach will probably be good enough to detect when there is a conversation. Typical tuning attributes:
* Average energy level to trigger
* Amount of time you need to be at the energy level or below to identify stop and start (I recommend two different values)
* Size of analysis window for averaging
As for number of participants, unless you find a library that does this, I don't see an easy solution. I've used speech recognition engines before and also done a reasonable amount of audio processing and I haven't seen any 'easy' ways to do this. If you were to look, search out universities doing speech analysis research. You may find some prototypes you can modify to give your software some clues.
I think you'll have difficulty doing this entirely in Python. You're talking about doing frequency/amplitude analysis of MP3 files. You would have to open up the file and look for a volume threshold, then cut out the portions that go below that threshold. Figuring out how many speakers are present would require very advanced signal processing.
A cursory Google search turned up nothing for me. You might have better luck looking for an off-the-shelf solution.
As an aside- there may be legal complications to having a recorder running 24/7 without letting people know.
Most languages have some sort of date function where you really don't have to do any programming to get any date information you just get it from the date function/object. I am curious what goes on behind the scenes to make this happen?
Every computer has a system clock which keeps track of date and time. On the lowest level, date and time information it retrieved from there. Above that add timezone information, etc. from the operating system and you got a Date object or something similar.
Depending on your language/environment Date objects can either perform date calculation themselves or you have to use other functions to achieve that. Those ensure that leap years get handled correctly and no invalid date can be created.
But maybe I got your question wrong.
Typically a computer is storing a count of how many of a certain unit of time has gone by since a specific time and date in the past. In Unix systems, for example, this could be the number of seconds since the Unix Epoch, which is midnight, Jan 1st 1970 GMT. In Windows, this is the number of 100 ns intervals since 1601-01-0 (thanks Johannes Rössel). Or it could be something as simple as number of seconds since the computer was powered on.
So from the number of units that have gone by since that time/date, an OS can calculate the number of years, months, days, etc that have gone by. Of course all sorts of fun stuff like leap years and leap seconds have to be taken into account for this to occur.
Systems such as NTP (Network Time Protocol) can be used to synchronize a computer's internal count to atomic clocks via an NTP server over a network. To do this, they NTP takes into account the round trip time and learns the sorts of errors the link to the NTP server.
Date and time information is provided usually by operating system, so it's a system call. Operating system deals with realtime clock mounted on computer mainboard and powered by small battery (which lasts for years).
Well ... Most computers contain a "real-time clock", which counts time on the human scale of seconds, minutes etc. Traditionally, there is a small battery on the motherboard, that lets the chip either remember the time, or even keep counting it, even when the rest of the computer is powered off.
Many computers today use services like the network time protocol to periodically query a centralized high-precision clock, to set the current time. In this way, even if the battery is removed (or just fails), the computer will still know what time and date it is, and be able to update (to correct for errors in the real-time chip's time-keeping) that information as often as necessary.
Aside from the realtime clock, date calculations are mostly a software library function.
Dates are rather irregular and so behind the scenes a mixture of approximations, corrections and lookup-tables are used.
The representation of a date can vary as well, usually some (arbitrary) startdate is used. A common system, also used by astronomers are the Julian day numbers (not to be confused with the Julian calendar). Dates can be stored as seconds-since-start or as days-since-start (the latter is usually a floating point). Here are some more algorithms.
A surprising amount of surprisingly complicated code is required for date parsing, computation, creation etc.
For example, in Java, dates are computed, modified, stored etc via the Date, Calendar, and specifically and typically, the Gregorian Calendar implementation of Calendar. (You can download the SDK/JDK and look at the source for yourself.)
In short, what I took from a quick perusal of the source is: Date handling is non-trivial and not something you want to attempt on your own. Find a good library if at all possible, else you will almost certainly be reinventing the square wheel.
Your computer has a system clock and the BIOS has a timer function that can be updated from your OS. Languages only take the information from there and some can update it too.
Buy any of these books on Calendrical Calculations. They'll fill you in on how the date libraries work under the hood.
The date/time is often stored in terms of times since a certain date. For example ticks (100 nanosecond intervals) since January 1, 0001. It is also usueally stored in reference to UTC. The underlying methods in the OS, database, framework, application, etc. can then convert these to a more usable representation. Back in the day, systems would store component parts of the date, day, month, year, etc as a part of the data structure, but we learned our lesson with the Y2K mess that this probably isn't the best approach.
Most replies have been to do with how the current date is obtained. i.e. from system clock and so on.
If you want to know how it is stored and used there are many different implementations and it depends on the system.
I believe a common one is the use of a 64 bit signed integer in T-sql the 01/01/1970 is 0 so negative numbers are pre 1970 and positive on from that each increment adding 100 th of a second (think it's a 100th would need to check).
Why 01/01/1970 you may ask this is because the gregorian calendar is on a 400 year cycle. 01/01/1970 being the closes start of a cycle to the current date.
This is because "Every year that is exactly divisible by four is a leap year, except for years that are exactly divisible by 100; the centurial years that are exactly divisible by 400 are still leap years. For example, the year 1900 is not a leap year; the year 2000 is a leap year." Makes it very complicated I believe the 400 year cycle coincides with the days of the week repeating as well but would nee dto check. Basically it's very complicated.
Internally it is incredibly difficult to write the datetime library accounting for all these variations such as leap years, the fact there is no year zero..... Not to mention UTC, GMT UT1 times.
We had occasion when debugging a client problem to look at how SQL stores datetimes... fairly interesting and makes pretty good sense once you see it.
SQL uses 2 4 byte integers...
The first 4 bytes are the date in days since Jan. 1st, 1753. I believe the maximum year is supposed to be 9999, which doesn't exactly line up to the number of available integers in 4 bytes, but there you go.
The second 4 bytes are the time in milliseconds since midnight.