Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. In digital electronics, a digital signal is represented as a pulse train, which is typically generated by the switching of a transistor.
Signal processing is an electrical engineering subfield that focuses on analyzing, modifying and synthesizing signals, such as sound, images, potential fields, seismic signals, altimetry processing, and scientific measurements. Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted signals, subjective video quality and to also detect or pinpoint components of interest in a measured signal.
John Edward Hopcroft is an American theoretical computer scientist. His textbooks on theory of computation and data structures are regarded as standards in their fields. He is the IBM Professor of Engineering and Applied Mathematics in Computer Science at Cornell University, Co-Director of the Center on Frontiers of Computing Studies at Peking University, and the Director of the John Hopcroft Center for Computer Science at Shanghai Jiao Tong University.
Standard Delay Format (SDF) is an IEEE standard for the representation and interpretation of timing data for use at any stage of an electronic design process. It finds wide applicability in design flows, and forms an efficient bridge between dynamic timing verification and static timing analysis.
In mathematics, a time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.
ISO/IEC/IEEE 12207Systems and software engineering – Software life cycle processes is an international standard for software lifecycle processes. First introduced in 1995, it aims to be a primary standard that defines all the processes required for developing and maintaining software systems, including the outcomes and/or activities of each process.
The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.
A System Requirements Specification (SyRS) (abbreviated SysRS to be distinct from a software requirements specification (SRS)) is a structured collection of information that embodies the requirements of a system.
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context of learning, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection. Given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice.
A software design description is a representation of a software design that is to be used for recording design information, addressing various design concerns, and communicating that information to the design’s stakeholders. An SDD usually accompanies an architecture diagram with pointers to detailed feature specifications of smaller pieces of the design. Practically, the description is required to coordinate a large team under a single vision, needs to be a stable reference, and outline all parts of the software and how they will work.
Process mining is a family of techniques relating the fields of data science and process management to support the analysis of operational processes based on event logs. The goal of process mining is to turn event data into insights and actions. Process mining is an integral part of data science, fueled by the availability of event data and the desire to improve processes. Process mining techniques use event data to show what people, machines, and organizations are really doing. Process mining provides novel insights that can be used to identify the execution paths taken by operational processes and address their performance and compliance problems.
Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.
Nexus or IEEE-ISTO 5001-2003 is a standard debugging interface for embedded systems.
Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data-gathering methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, etc.
In statistical signal processing, the goal of spectral density estimation (SDE) or simply spectral estimation is to estimate the spectral density of a signal from a sequence of time samples of the signal. Intuitively speaking, the spectral density characterizes the frequency content of the signal. One purpose of estimating the spectral density is to detect any periodicities in the data, by observing peaks at the frequencies corresponding to these periodicities.
Visual analytics is an outgrowth of the fields of information visualization and scientific visualization that focuses on analytical reasoning facilitated by interactive visual interfaces.
In computer science, in-memory processing (PIM) is a computer architecture for processing data stored in an in-memory database. In-memory processing improves the power usage and performance of moving data between the processor and the main memory. Older systems have been based on disk storage and relational databases using Structured Query Language, which are increasingly regarded as inadequate to meet business intelligence (BI) needs. Because stored data is accessed much more quickly when it is placed in random-access memory (RAM) or flash memory, in-memory processing allows data to be analyzed in real time, enabling faster reporting and decision-making in business.
Emotion recognition is the process of identifying human emotion. People vary widely in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively nascent research area. Generally, the technology works best if it uses multiple modalities in context. To date, the most work has been conducted on automating the recognition of facial expressions from video, spoken expressions from audio, written expressions from text, and physiology as measured by wearables.
