Abbas Dandache

Bio: Abbas Dandache is an academic researcher from University of Lorraine. The author has contributed to research in topics: Field-programmable gate array & Throughput (business). The author has an hindex of 5, co-authored 23 publications receiving 123 citations.

Design and FPGA implementation of a wireless hyperchaotic communication system for secure real-time image transmission

Abstract: In this paper, we propose and demonstrate experimentally a new wireless digital encryption hyperchaotic communication system based on radio frequency (RF) communication protocols for secure real-time data or image transmission. A reconfigurable hardware architecture is developed to ensure the interconnection between two field programmable gate array development platforms through XBee RF modules. To ensure the synchronization and encryption of data between the transmitter and the receiver, a feedback masking hyperchaotic synchronization technique based on a dynamic feedback modulation has been implemented to digitally synchronize the encrypter hyperchaotic systems. The obtained experimental results show the relevance of the idea of combining XBee (Zigbee or Wireless Fidelity) protocol, known for its high noise immunity, to secure hyperchaotic communications. In fact, we have recovered the information data or image correctly after real-time encrypted data or image transmission tests at a maximum distance (indoor range) of more than 30 m and with maximum digital modulation rate of 625,000 baud allowing a wireless encrypted video transmission rate of 25 images per second with a spatial resolution of 128 × 128 pixels. The obtained performance of the communication system is suitable for secure data or image transmissions in wireless sensor networks.

Extracted haralick's texture features and morphological parameters from segmented multispectrale texture bio-images for classification of colon cancer cells

Abstract: The automatic recognition and classification of biomedical objects can enhance work efficiency while identifying new inter-relationships among biological features. In this paper two features types, Haralick's features based GLCM are applied for classification of cancer cell of textured images and morphological parameters based of cells detection. The objective in our work is the selection of the most discriminating parameters for cancer cells classification. In this work, a new approach aiming to detect and classify colon cancer cells is presented. Our detection approach was derived from the "Snake" method but using a progressive division of the dimensions of the image to achieve faster segmentation. Classification of three cell types was based on nine morphological parameters and five Haralick's features on probabilistic neural network. Three morphological parameters and three Haralick's features were used to assess the efficiency classifications models, including Benign Hyperplasia (BH), Intraepithelial Neoplasia (IN) that is a precursor state for cancer, and Carcinoma (Ca) that corresponds to abnormal tissue proliferation (cancer). Results showed that segmentation of microscopic images using this technique was of higher efficiency than the conventional Snake method. The time consumed during segmentation was decreased to more than 50%. The efficiency of this method resides in its ability to segment Ca type cells that was difficult through other segmentation procedures. Among the nine parameters morphology and five Haralick's features used to classify cells, only three morphologic parameters (Area, Xor convex and Solidity) and three Haralick's features (Correlation, Entropy and Contrast) were found to be effective to discriminate between the three types of cells. In addition, classification of unknown cells was possible using the morphology method. However, some IN cells were wrongly classified as BH cells due to their shapes that were similar to those of BH cells. On the other side, the classification based on three parameters (Correlation, Entropy and Contrast) were found to be effective to discriminate between the three types of cells without wrong. The results obtained using several images show the efficacy of our proposed method.

Extraction of Haralick Features from Segmented Texture Multispectral Bio-Images for Detection of Colon Cancer Cells

Abstract: The automatic recognition and classification of biomedical objects can enhance work efficiency while identifying new inter-relationships among biological features, in this paper Haralick's features based GLCM are applied for classification of cancer cell of textured bio-images. The objective of this work is the selection of the most discriminating parameters for cancer cells. A new approach aiming to detect and classify colon cancer cells is presented. Our detection approach was derived from the "Snake" method but using a progressive division of the dimensions of the image to achieve faster segmentation. The time consumed during segmentation decrease to more than 50%. The efficiency of this method resides in its ability to segment Carcinoma (Ca) type cells that was difficult through other segmentation procedures. Classification of three cell types was based on five Haralicks features, only three Haralicks features were used to assess the efficiency classifications models, including Benign Hyperplasia (BH), Intraepithelial Neoplasia (IN) that is a precursor state for cancer, and Ca that corresponds to abnormal tissue proliferation (cancer). The analysis results show that three parameters (correlation, entropy and contrast) were found to be effective to discriminate between the three types of cells. The results obtained show the efficacy of the method.

A fast and configurable architecture for Discrete Wavelet Packet Transform

Abstract: Discrete Wavelet Packet Transformation (DWPT) has been gaining much popularity in recent communication systems as a core transmission function, both as signal analysis or denoising technique. This trend is largely due to the mathematical properties of DWPT, and particularly to its excellent locality in the time-frequency domain. Unfortunately, DWPT is also a rather demanding operation. For instance, the much known tree algorithm developed by Mallat requires large amounts of operative and storage resources. In this paper, we are proposing a novel architecture for DWPT, aiming to provide an effective performance trade-off. High throughput is achieved using only a relatively limited amount of hardware, thanks to a clever sharing of hardware resources between the low-pass and high-pass filters in the Mallat-tree algorithm, and an efficient use of the different throughput rates in different places of the architecture. The design has been modeled in VHDL at the RTL level, and synthesized using Altera Quartus II targeting an Altera Cyclone IV FPGA. The architecture is fully configurable at synthesis according to the tree depth (number of tree levels), the order of the filters and the filter coefficient quantization. Furthermore, the tree depth and filters order has little impact (only due to place and route variations) on throughput. The results obtained so far compare favorably to previous works.

VHDL-AMS electro-thermal modeling of a lithium-ion battery

Abstract: Li-ion rechargeable batteries present an useful energy, low battery effect, and slow Self-discharge loss of charge when not in use. In this paper, we describe a VHDL-AMS model describing coupled electrical and thermal behaviors of a Li-Ion cell. This model relies on the experimental extraction of real characteristic parameters by using an efficient and low-cost test bench. Therefore, it allows to predict the realistic voltage variations by taking into account the state of charge and heating of the Li-ion cell, in order to design a battery system according the required capacity and electric power for the current electrical applications.

A novel no-equilibrium hyperchaotic multi-wing system via introducing memristor

Abstract: Summary In this paper, a new multi-wing chaotic attractor is constructed. Based on the proposed multi-wing system, the paper presents a novel method to generate hyperchaotic multi-wing attractors. By introducing a flux-controlled memristor into the proposed multi-wing system, hyperchaotic multi-wing attractor is observed in new memristive system. At the same time, the new memristive system has no equilibrium. The phase portraits and Lyapunov exponents are used to analyze the dynamic behaviors of the no-equilibrium memristive system. Moreover, we analyze the influence on multi-wing system when adding the memristor in different position. The electronic circuit is realized by using off-the-shelf components. Copyright © 2017 John Wiley & Sons, Ltd.

Statistical analysis of Haralick texture features to discriminate lung abnormalities

Abstract: The Haralick texture features are a well-known mathematical method to detect the lung abnormalities and give the opportunity to the physician to localize the abnormality tissue type, either lung tumor or pulmonary edema. In this paper, statistical evaluation of the different features will represent the reported performance of the proposed method. Thirty-seven patients CT datasets with either lung tumor or pulmonary edema were included in this study. The CT images are first preprocessed for noise reduction and image enhancement, followed by segmentation techniques to segment the lungs, and finally Haralick texture features to detect the type of the abnormality within the lungs. In spite of the presence of low contrast and high noise in images, the proposed algorithms introduce promising results in detecting the abnormality of lungs in most of the patients in comparison with the normal and suggest that some of the features are significantly recommended than others.

Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.

Abstract: The aim of this study was to investigate the potential of texture analysis, applied to dynamic contrast-enhanced MRI (DCE-MRI), to predict the clinical and pathological response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) before NAC is started. Fifty-eight patients with LABC were classified on the basis of their clinical response according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines after four cycles of NAC, and according to their pathological response after surgery. T1 -weighted DCE-MRI with a temporal resolution of 1 min was acquired on a 3-T Siemens Trio scanner using a dedicated four-channel breast coil before the onset of treatment. Each lesion was segmented semi-automatically using the 2-min post-contrast subtracted image. Sixteen texture features were obtained at each non-subtracted post-contrast time point using a gray level co-occurrence matrix. Appropriate statistical analyses were performed and false discovery rate-based q values were reported to correct for multiple comparisons. Statistically significant results were found at 1-3 min post-contrast for various texture features for the prediction of both the clinical and pathological response. In particular, eight texture features were found to be statistically significant at 2 min post-contrast, the most significant feature yielding an area under the curve (AUC) of 0.77 for response prediction for stable disease versus complete responders after four cycles of NAC. In addition, four texture features were found to be significant at the same time point, with an AUC of 0.69 for response prediction using the most significant feature for classification based on the pathological response. Our results suggest that texture analysis could provide clinicians with additional information to increase the accuracy of prediction of an individual response before NAC is started.