An embedded testbed architecture to evaluate autonomous car driving

The progress in autonomous car driving or also called Advanced Driving Assistance Systems (ADAS) has recently received an increasing attention in car industries. Thus, many autonomous car models are recently developed and some are even commercialized. However, they are still not ready to enter our daily life since they lack efficiency and reliability. In this sense, to obtain efficient and reliable systems, the evaluation process plays an important role. Nowadays, the evaluation is strongly related to the number of kilometers of drive. However, in order to make a comprehensive evaluation of such systems, in addition to the number of kilometers, each component should also be tested. In this study, we focused on developing a system that is able to evaluate each component of ADAS. We defined a versatile architecture that simplifies the evaluation of different types of ADAS. Several evaluation scenarios are shown and discussed.     

Retrieval of High Resolution Satellite Images Using Texture Features

In this research,a content-based image retrieval(CBIR)system for high resolution satellite images has been developed by using texture features.The proposed approach uses the local binary pattern(LBP)texture feature and a block based scheme.The query and database images are divided into equally sized blocks,from which LBP histograms are extracted.The block histograms are then compared by using the Chi-square distance.Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images(HRSI)retrieval.     

An efficient cooperative exploration strategy for wireless sensor network

Wireless sensor networks (WSNs) are used in several applications such as healthcare devices, aerospace systems, automobile industry, security monitoring. However, WSNs have several challenges to improve the efficiency, robustness, failure tolerance and reliability of these sensors. Thus, cooperation between sensors is an important deal that increases sensor trust. Cooperative WSNs can be used to optimize the exploration of an unknown area in a distributed way. In this paper, the distributed Markovian model strategy that is used due to their past state-dependent reasoning. Moreover, the exploration strategy depends totally on the wireless communication protocol. Hence, in this paper, we propose an efficient cooperative strategy based on cognitive radio and software-defined radio which are promising technologies that increase spectral utilization and optimize the use of radio resources. We implement a distributed exploration strategy (DES) in mobile robots, and several experiments have been performed to localize targets while avoiding obstacles. Experiments were performed with several exploration robots. A comparison with another exploration strategy shows that DES improves the robots exploration.     

In Silico and In Vitro Screening of 50 Curcumin Compounds as EGFR and NF-κB Inhibitors

The improvement of cancer chemotherapy remains a major challenge, and thus new drugs are urgently required to develop new treatment regimes. Curcumin, a polyphenolic antioxidant derived from the rhizome of turmeric (Curcuma longa L.), has undergone extensive preclinical investigations and, thereby, displayed remarkable efficacy in vitro and in vivo against cancer and other disorders. However, pharmacological limitations of curcumin stimulated the synthesis of numerous novel curcumin analogs, which need to be evaluated for their therapeutic potential. In the present study, we calculated the binding affinities of 50 curcumin derivatives to known cancer-related target proteins of curcumin, i.e., epidermal growth factor receptor (EGFR) and nuclear factor κB (NF-κB) by using a molecular docking approach. The binding energies for EGFR were in a range of −12.12 (±0.21) to −7.34 (±0.07) kcal/mol and those for NF-κB ranged from −12.97 (±0.47) to −6.24 (±0.06) kcal/mol, indicating similar binding affinities of the curcumin compounds for both target proteins. The predicted receptor-ligand binding constants for EGFR and curcumin derivatives were in a range of 0.00013 (±0.00006) to 3.45 (±0.10) µM and for NF-κB in a range of 0.0004 (±0.0003) to 10.05 (±4.03) µM, indicating that the receptor-ligand binding was more stable for EGFR than for NF-κB. Twenty out of 50 curcumin compounds showed binding energies to NF-κB smaller than −10 kcal/mol, while curcumin as a lead compound revealed free binding energies of >−10 kcal/mol. Comparable data were obtained for EGFR: 15 out of 50 curcumin compounds were bound to EGFR with free binding energies of <−10 kcal/mol, while the binding affinity of curcumin itself was >−10 kcal/mol. This indicates that the derivatization of curcumin may indeed be a promising strategy to improve targe specificity and to obtain more effective anticancer drug candidates. The in silico results have been exemplarily validated using microscale thermophoresis. The bioactivity has been further investigated by using resazurin cell viability assay, lactate dehydrogenase assay, flow cytometric measurement of reactive oxygen species, and annexin V/propidium iodide assay. In conclusion, molecular docking represents a valuable approach to facilitate and speed up the identification of novel targeted curcumin-based drugs to treat cancer.     

An accurate time-to-digital converter based on a self-timed ring oscillator for on-the-fly time measurement

This paper proposes a new architecture of a time-to-digital converter (TDC) based on a self-timed ring (STR) oscillator with sub-gate delay resolution. The proposed TDC can virtually achieve as fine as desired time resolution by simply increasing its number of stages thanks to the STR unique features. Exploiting the phase difference between events propagating in the same STR without collision, this TDC benefit from a uniform phase distribution. Thus, under certain conditions, a regular time base can be generated and a compact readout algorithm can be applied. Moreover, the proposed technique allows on-the-fly time measurement on fast non-periodic signals. As a proof-of-concept, an STR-based TDC with only 9-stages has been simulated using 28 nm FDSOI technology. A time resolution of 8.9 ps has been achieved. Without using calibration, the measured DNL and INL are 0.44 and 0.40 LSB, respectively. Simulation results point out the advantage of this TDC in terms of measurement accuracy and state the limit of the on-the-fly measurement according to the dependency between the jitter and the time resolution.     

Hybrid method combining superpixel,supervised learning,and random walk for glioma segmentation

Currently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions, particularly glioma. Glioma is one of the most common tumors, and one of the most difficult to detect because of its shape, irregularities, and ambiguous limits. The segmentation of these tumors is one of the most crucial steps for their classification and surgical planning. This article presents a new, accurate, and automatic approach for the precise segmentation of early gliomas (benign tumors), combining the random walk (RW) algorithm and the simple linear iterative clustering algorithm. The study was carried out in four steps. The first step consisted of decomposing the image into superpixels to obtain an initial outline of the tumor. The superpixels were generated using the SLIC algorithm. In the second step, for each superpixel, a set of statistical and multifractal characteristics were calculated (gray‐level co‐occurrence matrix, multifractal detrending moving average). In the third step, the superpixels were classified using a supervised random forest (RF) type classier into healthy or tumorous brain tissue. In the final step, the contour of the detected tumor was enhanced using the customized RW algorithm. The proposed method was evaluated using the Brain Tumor Image Segmentation Challenge 2013 database. The results obtained are competitive compared to other existing methods.     

Escherichia coli of poultry food origin as reservoir of sulphonamide resistance genes and integrons

The antimicrobial resistance phenotype and genotype, the flanking regions of sulphonamide resistance genes and the integrons were analyzed in 166 Escherichia coli isolates recovered from poultry meat in Tunisia. High percentages of resistance were detected to ampicillin, streptomycin, nalidixic acid, sulphonamide and tetracycline (66-95%), and lower percentages to gentamicin, amoxicillin-clavulanic acid and cefoxitin (1-4%). The bla_TEM, tet(A)/tet(B), aph(3′)-Ia, aac(6′)-Ib-cr, aac(3)-II and cmlA genes were identified in 92, 82, 29, 2, 2 and 7 isolates, respectively. Class 1 and/or class 2 integrons were detected in 52% of E. coli isolates and five different gene cassette arrangements were identified in the variable regions of class 1 integrons, which included antimicrobial resistance determinants. Sixty-eight isolates contained the sul1 gene and 37 of them presented this gene into a class 1 integron structure. The sul3 gene was detected associated with non-classic class 1 integrons in 4 out of 46 sul3-positive isolates. The sul2 gene was detected in 66 isolates, 51 of them were linked to strA/B genes in seven different genetic structures. Seventy-three-per-cent of integron-positive isolates presented resistance to at least five different antimicrobial families versus 38.7% of integron-negative isolates. Our study highlights the role of commensal E. coli isolates from poultry meat as an important reservoir for sulphonamide resistance genes and integrons carrying antimicrobial resistance genes.     

Chitosan nanoparticles with controlled size and zeta potential

Optimization of chitosan nanoparticles (ChNs) production process employing a 2^(5–2) fractional factorial design was performed to analyze the influence of viscosity average molecular weight (40–120 kDa), the initial concentration of chitosan (2–5 g/L), the initial tripolyphosphate (TPP) concentration (0.8–1.2 g/L), the ratio chitosan/TPP (4/1–10/1) (V/V), and the stirring speed (300–700 rpm), on final nanoparticles size and zeta potential. The measured responses of average particle size and surface charge were determined on Zetasizer Nano ZS. ChNs were prepared using ionotropic cross-linking of chitosan and TPP and were characterized by Fourier-transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), and differential scanning calorimetry (DSC). The experiments showed that the size of synthesized nanoparticles depended on initial concentration and molecular weight of chitosan, TPP concentration and stirring speed within the chosen levels. However, the zeta potential was significantly influenced by chitosan molecular weight, chitosan concentration and stirring speed. The FTIR analysis confirmed the interaction between negative charge of TPP with positive charge of chitosan through the appearance of new peaks at 1222 and 895 cm⁻¹ in produced ChNs. XRD and DSC analysis were used to evaluate the effect of crosslinking of chitosan on crystal structure of ChNs.     

HMDCS-UV: A concept study of Hybrid Monitoring,Detection and Cleaning System for Unmanned Vehicles

This paper proposes a modular system of precision agriculture to automate sprayers, optimizing the application of pesticides through a robotic system based on computer vision and individual nozzle on/off control. The system uses low-cost equipment such as Arduino boards, solenoid valves, pressure and flow sensors, smartphone, webcam, and Raspberry Pi. The motivation is to reduce the amount of pesticides applied in crops, not just for potential savings for the farmers, but also for environment protection issues, as well as for food safety. The system can be used in any crop planted in rows such as onion, soybean, corn, beans, and rice. The results show that our system can detect lines in plantations and can be used to retrofit conventional boom sprayers, so it is an important step to develop a kit capable of upgrade a conventional sprayer to a fully autonomous robotic sprayer even at affordable cost in the context of small and medium size farms.