Sami Aldalahmeh (@sami_aldalahmah)

Dr. Sami Ahmed Aldalahmeh is an assistant professor in the department of communication and computer engineering at Al-Zaytoonah University of Jordan since 2013. He received his PhD degree from University of Leeds, UK, in 2013. He was awarded his MSc degree in Communications Signal Processing from King's College London, UK, in 2007 with distinction and top of his class. He earned his BSc degree in Electrical Engineering from the University of Jordan, Jordan, in 2006. His research interests include distributed detection and estimation, wireless sensor networks, stochastic geometry models for wireless networks, delay and throughput in wireless sensor networks, and sensor signal processing.

Engineering the Statistics

Sami Aldalahmeh ● March 26, 2012●2 comments

Statistical analysis can get messy fast when theory and MATLAB simulations refuse to agree. This post shares a graduate student’s hard-earned shortcuts for taming random variables, from deriving a CDF or moments to using Gaussian or Gamma approximations, and falling back on Chernoff bounds when the exact PDF stays out of reach.

Why is Fourier transform broken

Sami Aldalahmeh ● October 4, 2011●2 comments

Many engineers know the Gibbs phenomenon without grasping its root cause. This post shows that the problem comes from using the incomplete metric space of continuous functions, C[a,b], for Fourier series, and explains how switching to Lp spaces resolves convergence in the mean but allows functions to differ on sets of measure zero. It also reminds readers that Fourier analysis gives no time localization, so be mindful of its limits.

More free Ebooks

Sami Aldalahmeh ● September 13, 2011●2 comments

A handy roundup of free engineering ebooks and journals, with a focus on where DSPRelated readers might actually find useful material. The post points to InTechOpen, highlights several MATLAB books, and even calls out a numerical methods title plus some sensor fusion content. If you like browsing for practical references, this is a good place to start.

ICASSP 2011 conference lectures online (for free)

Sami Aldalahmeh ● July 5, 2011

For the first time, the oral sessions of ICASSP 2011 were recorded and posted online for free, giving engineers worldwide easy access to the conference. The talks span speech and communication signal processing, plus eclectic topics like bio-inspired methods, where Prof. Sayed uses a distributed LMS model to reproduce group predator and prey behavior. Expect some theoretical material, but many presentations are practical and inspiring for DSP practitioners.

FREE Peer-reviewed IEEE signal processing courses

Sami Aldalahmeh ● April 26, 2011●1 comment

IEEE Signal Processing Society is offering a small set of free, peer-reviewed courses covering topics like wavelets, speech analysis, and statistical detection. The post points to these endorsed materials as a useful way to browse vetted DSP learning resources without paying for formal coursework.

DSP Algorithm Implementation: A Comprehensive Approach

Sami Aldalahmeh ● April 13, 2011●6 comments

This post lays out a practical pathway for taking DSP algorithms from high level simulation to production hardware, comparing GPP, DSP, FPGA and ASIC platforms. It presents a stepwise methodology starting with nested loop programs, then exposing parallelism with data flow graphs, using SystemC transaction level modeling to bridge to Verilog or VHDL, and explains why that flow speeds design and simulation.

We are famous!!

Sami Aldalahmeh ● December 8, 2010●2 comments

A quick bit of DSPRelated pride, the IEEE Signal Processing eNewsletter mentioned the site’s blog section in a roundup of social media resources for DSP. The post shares the moment the author heard the news and points readers to the original mention. It is short, but it captures a nice sign that the community is paying attention.

State Space Representation and the State of Engineering Thinking

Sami Aldalahmeh ● November 23, 2010●2 comments

State space is common in control, but it shows up much less often in signal processing. This post argues that the difference is really about engineering priorities: for many DSP problems, transfer functions are enough, while state space becomes valuable when internal behavior matters, like filter scaling or Kalman filtering. It is a short, practical look at why engineers choose one model over the other.

Knowledge Mine for Embedded Systems

Sami Aldalahmeh ● June 25, 2010●1 comment

A little-known interactive portal makes learning embedded systems surprisingly practical and visual. The site is organized into four main areas: embedded systems design, design lifecycle, design methods, and design tools. Each section uses clickable system block diagrams so you can jump from a block, for example a MAC unit, to a focused page with detailed explanations. It’s a handy, ready reference for DSP and embedded engineers.

Hidden Linear Algebra in DSP

Sami Aldalahmeh ● June 17, 2010●5 comments

Linear algebra is hiding in plain sight inside many DSP techniques, not just abstract theory. By treating linear systems as matrix operators y = A x you reveal Toeplitz structure in LTI systems, connect to covariance matrices, and gain geometric intuition via eigenvalues and eigenvectors. This matrix viewpoint complements convolution-based thinking and offers practical tools for filter and channel analysis.