Khalil Hachicha

Empirical Method Based on Neural Networks for Analog Power Modeling

We introduce an empirical method for power consumption modeling of analog components at system level. The principal step of this method uses neural networks to approximate the mathematical curve of the power consumption as a function of the inputs and parameters of the analog component. For a node of a wireless sensors network, we found an average error of 1.53% with a maximum error of 3.06% between our estimation and the measured power consumption. This novel method is suitable for Platform-Based Design and has three key features for architecture exploration purposes. Firstly, the method is generic as it can be applied to any analog component in any modeling and simulation environment. Secondly, the method is suitable for the total (analog and digital) power consumption estimation of a heterogeneous system. Thirdly, the method provides an online estimation of the instantaneous power consumption of analog blocks.

An Emulation-Based Method for Lifetime Estimation of Wireless Sensor Networks

Lifetime estimation in Wireless Sensor Networks (WSN) is crucial to ensure that the network will last long enough (low maintenance cost) while not being over-dimensioned (low initial cost). Existing solutions have at least one of the two following limitations: (1) they are based on theoretical models or high-level protocol implementations, overlooking low-level (e.g., hardware, driver, etc.) constraints which we find have a significant impact on lifetime, and (2) they use an ideal battery model which over-estimates lifetime due to its constant voltage and its inability to model the non-linear properties of real batteries. We introduce a method for WSN lifetime estimation that operates on compiled firmware images and models the complex behavior of batteries. We use the MSPSim/Cooja node emulator and network simulator to run the application in a cycle-accurate manner and log all component states. We then feed the log into our lifetime estimation framework, which models the nodes and their batteries based on both technical and experimental specifications. In a case study of a Contiki RPL/6LoWPAN application, we identify and resolve several low-level implementation issues, thereby increasing the predicted network lifetime from 134 to 484 days. We compare our battery model to the ideal battery model and to the lifetime estimation based on the radio duty cycle, and find that there is an average over-estimation of 36% and 76% respectively.

Architecture of an intelligent beacon for wireless sensor networks

In this paper, we introduce the architecture of an intelligent beacon for wireless sensor networks. This beacon acquires images of a scene and detects motion, thanks to the real-time execution of a Markov motion detection algorithm. When some motion is detected, neural networks are applied in real-time to the acquired images in order to trigger some alarm. Finally, when some alarm is triggered, video of the scene compressed with the MMJPEG2000 algorithm are sent on a wireless network, a long-range communication by satellite for example. The beacon is implemented on a platform including a microcontroller, a DSP, an FPGA and several dedicated modules.

Mask motion adaptive medical image coding

We combine a mask motion detection algorithm with both the WAAVES adaptive compression algorithm (resulting into MMWaaves) and a JPEG2000 coder (resulting into MMJPEG2000) for the compression of medical images sequences. Several images were compressed using Waaves, MMWaaves, and MMJPEG2000 to observe which tool provided the best visual quality while maintaining a high compression ratio. Compared to Waaves, the MMWaaves achieved compression gains up to 40% for CT scans and 50 % for MRI. In addition, the SSIM values attributed to the compressed images were between 0.96 and 0.988 while the PSNR values were higher than 42. In addition MMWaaves attained a superior performance than MMJPEG2000.

Noise-robustness improvement of the H.264 video coder

When sequence noise increases, the prediction process in the H.264 advanced video coding (AVC) encoder becomes less efficient. The residue resulting from the difference between the matching macroblocks in the frame coded in predictive mode and the best reference frame increases dramatically. To solve this problem, we propose to embed in the H.264/AVC encoder a motion detection technique based on the Markov random fields algorithm, which relies on robust moving pixel segmentation and reduces the luminance variation that results more often from noise rather than motion. This algorithm needs three reference frames to detect real motion between matching macroblocks, and it allows a decrease in bit rate while maintaining the compressed sequence quality.

Software radio FM broadcast receiver for audio indexing applications

Broadcast radio is a rich but underexploited source of multimedia content. To make this available to users, it will be indispensable to develop new types of navigators capable of searching the large quantities of information contained in the radio bands. The article introduces a prototype of a new software radio enabled broadcast media navigator implemented on an FPGA, which is able to demodulate simultaneously all channel in the FM band and perform audio indexing upon them, ultimately using a Graphics Processing Unit.

SmartEEG: A multimodal tool for EEG signals

In this paper we present an hardware realisation for an image coder used in the SmartEEG project. This collaborative project has the aim of the conception of a multimodal tool for EEG signal to allow transmission of a complete examination of a patient. We show that we can expect good performance on a FPGA board for the time consuming part of this tool that is the image coder.

FPGA vs DSP: A throughput and power efficiency comparison for Hierarchical Enumerative Coding

A comparative study of Hierarchical Enumerative Coding (HENUC) for FPGA and DSP implementation is presented. HENUC is a lossless fixed-point entropy coding algorithm employed by a wavelet-based image encoder, which provides good compression performance for the locally stationary image data. It has been implemented in our previous work on an Alteras 40nm Stratix IV EP4SGX230 FPGA as a hardware IP accelerator in a Nios II based system. In this paper, we implemented it on a Texas Instrumentss (TI) 40nm Integra C6A816x/AM389x DSP. We present experimental results regarding the execution time, resource utilization and core power consumption of the two implementations and we evaluate their throughput and power efficiency. Our results show that a highly parallelized FPGA implementation at 100MHz is 12.3× faster than a highly tuned DSP implementation running at 1.5 GHz and consumes 2.4× less power, they also show that the proposed algorithm is more suitable for hardware implementation.

Medical images compression with clinical diagnostic quality using logarithmic DWT

Diagnostic quality is an essential requirement in the medical images compression field to avoid misdiagnosis by radiologists. In this paper, a novel study on using the logarithm in medical images compression is presented. Two novel compression schemes are proposed to improve the image quality. The proposed compression schemes relies on discrete wavelet transform (DWT). The first approach is based on the logarithmic number system (LNS) arithmetic. The second approach (Log-DWT) is a hybrid of LNS and Linear arithmetic. Both schemes compromise between the computation speed and precision. Both approaches show a significant improvement in the image quality in addition to providing better compression rate compared to the classical approach which does not include any logarithmic operations. The structural similarity index (SSIM) was used to assess the two approaches in terms of the image quality. The performance has been evaluated for the proposed approaches and has been compared to the classical approach.

Synchronizing physiological data and video in a telemedicine application: A multimedia approach

Several medical examinations require that multiple modalities of exams are stored in a synchronized manner. For instance, an EEG exam needs that several physiological signals along with video of the exam are acquired synchronously to aid the neurophysiologists to perform their diagnostics. Furthermore support for telemedicine for such exams have become important in recent years. The existing EDF standard that is used for physiological signals makes it difficult to provide integrated support of adding video and compressed component data, however due to widespread use of EDF standard in the domain, cross compatibility with EDF standard for physiological data is also essential. We present in this work a novel idea to solve these issues. Our approach uses standard multimedia containers in which physiological data are embedded alongside video and audio. This paper provides our analyses of the state of the art of multimedia containers EDF, AVI, ASF, MPEG and MKV and their potentials for a telemedicine application and outlines how MKV stands out as an interesting option in this regard, allowing also capability of compression of physiological data if needed.