IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC. Visit the SPS Job Submission Form.
he Signal Processing Society (SPS) has 12 Technical Committees and 3 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
The IEEE Signal Processing Society welcomes proposals from prospective organizers of 2018-2019 winter schools under the S3P (Seasonal Schools in Signal Processing) Program. S3P seasonal schools are typically either summer schools or winter schools, and are geared towards providing graduate students, early stage researchers, and practitioners with background on selected topics in signal processing.
Discrete-time rational transfer functions are often converted to parallel second-order sections due to better numerical performance compared to direct form infinite impulse response (IIR) implementations. This is usually done by performing partial fraction expansion over the original transfer function.
With the proliferation of Internet of Things (IoT) applications, billions of household appliances, phones, smart devices, security systems, environment sensors, vehicles, buildings, and other radio-connected devices will transmit data and communicate with each other or people, and it will be possible to constantly measure and track virtually everything.
Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with a focus on the most recent compressive sensing (CS)-enabled approaches.
Processing, storing, and communicating information that originates as an analog signal involves converting this information to bits. This conversion can be described by the combined effect of sampling and quantization, as shown in Figure 1. The digital representation is achieved by first sampling the analog signal to represent it by a set of discretetime samples and then quantizing these samples to a finite number of bits.
By bringing research into the curriculum, this article explores new opportunities to refresh some classic signal processing courses. Since 2015, we in the Electrical and Electronic Engineering (EEE) Department of Imperial College London, United Kingdom, have explored the extent to which the level of student engagement and learning can be enhanced by inviting the students to perform signal processing exercises on their own physiological data.
As the size and speed of many conventional electronics technologies begin reaching their practical limits, a growing number of researchers is turning their attention to photonics and related optical-based approaches to continue the push toward smaller, faster, cheaper, and more innovative computer and communication devices.
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