Senior R&D Engineer Embedded, ADAS, Audio/Video Processing

mailto:*****.********@*****.*** FETHI MALAMANE Tel: 878-***-****

Signal Processing Algorithms - Research & Design.

Profile/Skills/Summary

Summary:

Experiences include Signal Processing Algorithms Research & Design, DSP Real-Time Embedded Development & Implementations in various DSP platforms/multi- Threaded architectures. Main interests in: solving Inverse Problems. Numerical Analysis. Estimation & Detection. Multivariate, FIR/IIR, Transverse & Lattices structures. Adaptive in Non-Stationary context & Noise canceling. Linear Algebra. Matrix & Tensor Analysis. Higher-Order Statistics. Experiences:

– A - (Ford): AUTOSAR -Embedded SW-Components Design Eng. (2018 – Dec.2023 1- Design & Development Embedded SW Components

2- Vector tools – CAN, Tests (Unity Tests, Ceedling) MVS / VS & GNU, Coverage reports,…. 3- Central Software (Autosar Embedded Group): Support (ECG/TCU/SYNC3): Resolve Assigned Issues (Jira/Jenkins/…); Test Bench / Platforms

– B - (Valeo, Autoliv): Automotive Safety Radar SP & DSP RT- Embedded systems. (2014 – Oct.2018) 1- SIL S/W development & testing for several safety features (Blocage, BSD, CA, Mitigation) – FFT Radix2/4 Vectorization/Optimization. 2- High-Resolution Array Signal Processing (ESPRIT-technique) for Range-Bearing estimation, DOA estimation. 3- Wide-band Radar SP development on TI’s EVE (Embedded Vision Engine) – UWB & NB Radar SP development on TI-DSP float.

– C - (Harris – Ericsson – Nortel – …) Signal Processing Algorithms & RT- Embedded S/W development – Telco., Radio-Comm., Wireless. (Sept.1995 – Apr.2014) 1- Speech Recognition–Audio (AMR, EVRC)–Voice & Modems–Digital Filtering (FIR, IIR, Multi-rate) Transverse & Lattice structures –Adaptive Algorithms for Tracking non-stationary signals and noise canceling Linear Estimation Adaptive Optimal/Sub-Optimal – Recursive/Non-Recursive Algorithms (Kalman, Fast Kalman, FApEST, FTF, N-LMS, RLS) 2- Identification Direct, Inverse & Prediction, In several applications: acoustical responses – Frequency Offset/Drift Estimate – Rupture Models – Equalization. 3- Blind demodulation of MIMO signals. BSS/ICA-based algorithm. –BSS implemented algebraically–Blind Initialized Kalman filter to track channel variations. 4- Modeling of a OFDM MODEM. Simulations emphasize on (BER–Num. of Carriers, Code Performance, Choice of Constellation) Parameters – Ch. Equalizer. 5- Implementation of a reference transmission chain – Channel modeling – Improvement of QPSK modulation – Introduction of BCH and Reed Salomon code – influence of the number of carriers in OFDM.

6- Land Mobile Radio Voice Encryption/Decryption. P25 TDMA FEC, PHY-MAC layers, RS-error correction IMBE coder/decoder. Golay/Hamming CODEC. 7- PID-Fast Control Loop in EDFA-Optical Automatic Gain and Power Control (40GHz - ULH line amplification). 8- Signal Image Processing: Feature/Contour Detection & Image Deconvolution/Restoration using Simulated Annealing-based Algorithm. Pattern recognition using MATLAB Neural Networks.

9- Modeling of Geophysical Electromagnetic Responses (EM-VLF). EM-VLF's 2-parameters model as solutions of Hankel transform of the elliptically polarized EM- VLF.

10- Blind Signals Separation of EM-VLF sensed mixtures using Independent Component Analysis (ICA) with a comparative study to Principal Component Analysis

(PCA). High-Order-Statistical Signal Array Processing. Education:

M.A.Sc. Polytechnic School of Montreal. Montreal, QC Canada

-Research Thesis: Blind Identification of VLF-Geophysical Electromagnetic Responses

(Modeling EM-VLF Geophysical anomalies. HANKEL transform. ICA / PCA. Blind Signal Separation) M.E.E. A / D.E.A (Pre-Doctorate). University Paris XI / E.S.E- Paris, France

-Research Thesis: Real-Time 1D-Discrete Deconvolution Algorithm

(Recursive 1D-discrete deconvolution Fast Kalman-based algorithm. Fixed-point implementation). ADDENDUM: DESCRIPTIONS OF SOME PROJECTS

(a) Blind identification of geophysical anomalies

– Modeling, Simulation and Synthesis of the geophysical EM-responses.

– These scattered EM-responses signals, are elliptically polarized. The wave-ellipse’s 2 parameters describing the signals (sources) are solutions of the:

– Hankel Transform

– and represented as Discrete Convolution.

– The scattered signals are observed via a sensors network.

– The observed data are the mixtures of the sources (contributions of these geophysical anomalies).

– The ICA (independent component analysis) is used to retrieve the canonical basis from the mixtures.

– A comparative study between the both implemented ICA and PCA (principal component analysis), was applied to real geophysical data. Very convincing results and consistent with the theoretical predictions of the performances of the ICA over PCA, for this type of processed VLF-EM signals.

(b) Recursive 1D-discrete Deconvolution Fast Kalman-based algorithm

– The deconvolution problem addresses the so-called “ill-posed” problems and the solution of this inverse problem, done mainly to retrieve the input signal.

(a) Simulation: Blind demodulation of MIMO signals.

– Application of BSS (blind signal separation), using the ICA (independent component analysis).

– Two methods were implemented:

– 1) BSS implemented algebraically, thus allowing rapid convergence of the algorithm.

– 2) To use of a blindly initialized Kalman filter to track channel variations. This method can be used for both instantaneous channels or time-varying convolutional channels.

(b) Simulation: Modeling of a OFDM MoDem.

The simulation was carried out to highlight the interests of an OFDM modulation

-- From a point of view of the Bit Error Rate

-- As well as that the parameters of the essential transmission chain (number of carriers, code performance, choice of constellation) to be adjusted depending on the channel and data to be transmitted.

With the following steps:

Implementation of a reference transmission chain & Channel modeling QPSK modulation – BCH & Reed Solomon

XDMA (FDMA, CDMA, TDMA, SDMA); AM. FSK, PSK, OFDM Modulations.

– Several projects in Modems GIII – ITU-Standard (V.21, V27ter, V.29 and V14)

– Acoustic / Echo cancellation

– Equalizer and decision device

Land Mobile radio.

– PHY and MAC -layers

– Voice Encryption/Decryption [Rijndael Algorithm (AES)] for Full & Half Audio Frames.

– Land mobile radio.

– Central Software (Autosar Embedded Group): Support (ECG/TCU/SYNC3): Resolve Assigned Issues (Jira/Jenkins/…); Test Bench / Platforms:

– Vector tools – CAN

– Development SW Components

– Tests (Unity Tests, Ceedling) MVS / VS & GNU

– Coverage reports.

– Automotive Radar Signal Processing Development and DSP implementation of safety features & Tracker/Detector integration.

– Code optimization in TI-EVE’s VCOP (vectoriel coprocessor).

– SIL SW Simulation & Testing of several ADAS modules.

– DSPs (TI-EVE + ADSP float + TI-F28x) platforms. – UWB (77 GHz) & NB (24GHz).

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