Showing posts with the label Mobile Computing

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The Quick and Easy Way to Analyze Numpy Arrays

The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Limitations of Mobile Computing

What is Mobile Computing? Mobile computing ─ ability to use the technology to wirelessly connect to and use centrally located information and/or application software through the application of small, portable, and wireless computing and communication devices voice, data and multimedia communication standards Limitations Resource constraints: Battery Interference: the quality of service (QoS) Bandwidth: connection latency Dynamic changes in communication environment: variations in signal power within a region, thus link delays and connection losses Network Issues: discovery of the connection-service to destination and connection stability Interoperability issues: the varying protocol standards Security constraints: Protocols conserving privacy of communication