570 research outputs found
Low Power Instrumentation Amplifier for a Fully Implantable Neural Recording System
No full textRecording neural signal from a living human body is a complex
task and it is an important research issues for neuroscientists and
researchers in biomedical engineering. The major issue to over-
come in the design of a system that is aimed at being implant
into the human body is having a low power consumption, low
noise circuit and small dimension to minimize tissue damage. In
this paper, specific issues of the most important part of such a
neural acquisition system are presented; in particular, the design
of a low-power amplifier, for a fully implantable neural recording
system, is described. The amplifier uses a differential pair as
input stage. Given that neural amplifiers must include differen-
tial input pair to achieve a high common-mode ratio rejection
(CMRR). The amplifier has been designed in the AMS 0.35 μm
standard CMOS process. The amplifier current consumption is
4.61 μA at ±1V supply, which gives a power consumption of
9.22 μW. The low cutoff frequency is adjustable from 21 Hz to
100 Hz, with four tunable gains of 43.6 dB, 48 dB, 50 dB and 52.8
dB. The upper cutoff frequency is about 7.6 kHz. The CMRR is
113 dB and the power supply ratio rejection is PSRR > 73dB.
The input referred noise is 14.8 μV rms over 100 10 kHz. The
amplifier gives an input DC offset of 196 μV
Biochemical characterization and structural insights into the high substrate affinity of a dimeric and Ca2+ independent Bacillus subtilis α-amylase
No full textAn extracellular amylase (AmyKS) produced by a newly isolated Bacillus subtilis strain US572 was purified and characterized. AmyKS showed maximal activity at pH 6 and 60 °C with a half-life of 10 min at 70 °C. It is a Ca2+ independent enzyme and able to hydrolyze soluble starch into oligosaccharides consisting mainly of maltose and maltotriose. When compared to the studied α-amylases, AmyKS presents a high affinity towards soluble starch with a Km value of 0.252 mg mL-1 . Coupled with the size-exclusion chromatography data, MALDI-TOF/MS analysis indicated that the purified amylase is a dimer with a molecular mass of 136,938.18 Da. It is an unusual feature of a non-maltogenic α-amylase. A 3D model and a dimeric model of AmyKS were generated showing the presence of an additional domain suspected to be involved in the dimerization process. This dimer arrangement could explain the high substrate affinity and catalytic efficiency of this enzyme
Extracting Electric Power From Human Body For Supplying Neural Recording System
No full textA powerful approach to the characterization of cellular electrical activity is electrical recording from cells or living tissues. The human central and / or peripheral nervous system has been a subject of study and fascination of the neuroscience and biomedical engineering communities for many decades. In this paper, we propose a new approach to feed implantable neural recording system, which based on extracting electrical power from human tissue warmth in order to supply a biomedical neural recording system. The major issue to overcome, in the design of a system that is aimed at being implant into the human body, is having a low power consumption, low noise circuit and small dimension to minimize tissue damage
Low Power Instrumentation Amplifier for a Fully Implantable Neural Recording System
No full textRecording neural signal from a living human body is a complex
task and it is an important research issues for neuroscientists and
researchers in biomedical engineering. The major issue to over-
come in the design of a system that is aimed at being implant
into the human body is having a low power consumption, low
noise circuit and small dimension to minimize tissue damage. In
this paper, specific issues of the most important part of such a
neural acquisition system are presented; in particular, the design
of a low-power amplifier, for a fully implantable neural recording
system, is described. The amplifier uses a differential pair as
input stage. Given that neural amplifiers must include differen-
tial input pair to achieve a high common-mode ratio rejection
(CMRR).
Extracting Electric Power From Human Body For Supplying Neural Recording System
No full textA powerful approach to the characterization of cellular electrical activity is electrical recording from cells or living tissues. The human central and / or peripheral nervous system has been a subject of study and fascination of
the neuroscience and biomedical engineering communities for many decades. In this paper, we propose a new approach to feed implantable neural recording system, which based on extracting electrical power from uman tissue warmth in order to supply a biomedical neural recording system. The major issue to overcome, in the design of a system that is aimed at being implant into the human body, is having a low power consumption, low
noise circuit and small dimension to minimize tissue damage
His-tag effect on biochemical properties of B. subtilis US572 a-amylase produced in E. coli: application of the recombinant enzyme in breadmaking
No full textThe gene encoding Bacillus subtilis α-amylase was cloned into pET-21a (+). The expression level of the recombinant enzyme is 10.7-fold higher than the expression level of the native one (0.13 mg mL-1). The recombinant enzyme (His6-rAmyKS) was purified in one step using Ni-NTA column affinity with a specific activity of 664.28 U.mg-1. The biochemical properties of the His6-rAmyKS were determined and compared to those of the non-tagged enzyme. Interestingly, differences were found between the two enzymes mainly for the optimal temperature and pH. Experiment tests and molecular modeling confirmed that the extra residues (C-terminal His-tag fusion peptide and cleavage thrombin site) could be responsible for the slight increase in total activity and the improvement of biochemical properties of the His-tagged enzyme compared to the native one. The His6-rAmyKS was used as an additive in breadmaking. It showed a significant effect in improving the dough texture and the bread quality
Variation along the year of trace metal levels in the compartments of the seagrass Posidonia oceanica in Port El Kantaoui, Tunisia
No full textThe accumulation of the five Trace Metals (TMs) cadmium, copper, lead, nickel and zinc was measured in Posidonia oceanica leaves. Shoots were seasonally sampled at 8 - 10 m depth from four stations located in Port El Kantaoui area, Tunisia during four campaigns performed in 2012. Levels of the five TMs were analysed using inductively coupled plasma atomic emission spectrometry (ICP-AES) in three compartments of P. oceanica shoots: blades and sheaths of adult leaves, and intermediates leaves.
Results showed a preferential accumulation of Cd, Pb, Ni and Zn in adult leaf blades. Therefore, we focus on the study of this compartment. TM levels of blades of adult leaves decreased in the following order: Zn > Ni > Cu > Pb > Cd, irrespective of the season. Levels of the five TMs significantly differed between seasons (p<0.01). Levels of Cd and Cu showed a seasonal pattern: Cd decreased from spring to winter while Cu increased during that same period of time. A significant correlation (p<0.01) was found between Cd-Cu and Cd-Pb. A significant correlation (p<0.05) was also noted between Cd-Ni in the adult leaves blades. A relationship was recorded between the foliar surface of the adult leaves blades and Zn accumulation. This survey allowed to highlight the annual variation of TM accumulation in adult leaves blades of P. oceanica, in relation with ecophysiology of this seagrass. Therefore, this study reinforces the usefulness and the relevance of this compartment of P. oceanica, easy to sample without destruction of whole shoot, as a bioindicator of Zn, Ni, Cd and Pb contamination
Evaluation des performances des systèmes multi-agents
No full textCette thèse s’intéresse à la question de l’évaluation des Systèmes Multi-Agents (SMA). Les caractéristiques propres que possèdent ces derniers, notamment en termes d’autonomie, de distribution, de dynamique et de socialité, ont grandement contribué à l’élargissement de leurs champs d’application, mais en contrepartie, elles ont rendu leur analyse plus ardue. Ainsi, les méthodes d’évaluation dans les systèmes informatiques classiques s’avèrent insuffisantes à analyser les SMA, étant donné qu’elles ne tiennent pas compte de leurs spécificités. L’objectif de cette thèse consiste donc à proposer une approche générique pour l’évaluation des SMA en se basant sur la mesure de leurs caractéristiques fonctionnelles. A cet effet, le besoin de disposer d’informations sur l’exécution du système à évaluer est manifeste. C’est dans ce cadre qu’une nouvelle approche d’observation des SMA est proposée. Les résultats de ces observations sont exploités pour construire une abstraction du système sous forme d’un modèle, lequel est étudié pour définir les mesures de performances. L’analyse se focalise sur deux caractéristiques essentielles, à la base de la dynamique et de la socialité des SMA : la communication et l’organisation. Les expérimentations de la solution proposée portent sur deux applications multi-agents. La première est une application de diagnostic des pannes dans un environnement industriel et la seconde est une application de pilotage et de gestion de la production dans les chaînes logistiques.This thesis focuses on the issue of MultiAgent Systems (MAS) evaluation. The MAS own characteristics, namely autonomy, distribution, dynamicity and sociality, have greatly contributed to the expansion of their application scope; but in return they made their analysis more difficult. Thus, evaluation methods in classic computer systems are insufficient to analyse MAS, since they do not take into account their specificities. The objective of this thesis is to provide a generic approach for the evaluation of MAS by measuring their functional characteristics. To this end, the need for information about the execution of the system to be evaluated is evident. In this context, a new approach to observe MAS is proposed. The results of these observations are exploited to build an abstraction model of the system which is studied in order to define performance metrics. The analysis focuses on two key characteristics, at the basis on the dynamics and sociality in MAS: communication and organization. The experiments of the proposed solution are performed on two multiagent applications. The first is an application of fault diagnosis in an industrial environment and the second is an application of control and production planning in supply chains
Caractérisation du couvert végétal naturel à l’extérieur et à l’intérieur du parc national de Sidi Toui, zone aride de la Tunisie
No full textThis study was carried out in Sidi Toui national park belonging to El Ouara natural region (southern east of Tunisia). This region is the preferred destination of many herds (sheep, goats and camels) causing an increasing grazing pressure over its various ecosystems, leading to the loss of many plant species especially the highly palatable ones, such as Rhanterium suaveolens
Desf., Cenchrus ciliaris L., Macrochloa tenacissima
(L.) Kunth., Gymnocarpos decandrus Forssk. et Echiochilon fruticosum Desf. This region is also subjected to extreme weather conditions (high temperature, low rainfall). The combined effect of these constraints accelerates the degra- dation processes of natural resources and imposes the implementation of adequate measures for the sustainable management of this floristic heritage. The creation of Sidi Toui national park in 1991 was one of the proposed solutions. This study aims to assess the vegetation status outside and inside the park after 20 years of protection using the quadrat point methods and some ecological indicators (vegetation cover, species richness, alpha and beta diversities). The main results show that all indicators variations are closely related to the rainfall season. Vegetation cover, species richness and diversity are always higher inside than outside the park. It has also been noted that longterm protection greatly reduces vegetation dynamics.Cette étude a été menée au niveau du parc national de Sidi Toui, appartenant à la région naturelle d’El Ouara (Sud tunisien). Cette dernière représente la destination préférée de plusieurs troupeaux (ovins, caprins et camelins) dont la pression croissante est à l’origine de la disparition/ raréfaction des plantes ayant une très bonne valeur pastorale comme Rhanterium suaveolens Desf., Cenchrus ciliaris L., Macrochloa tenacissima (L.) Kunth., Gymnocarpos decandrus Forssk. et Echiochilon fruticosum
Desf. Cette région est également soumise à des conditions climatiques extrêmes (température élevée, faible pluviométrie). L’effet combiné de ces deux contraintes accélère la dégradation des ressources naturelles de la région, d’où la nécessité de prendre des mesures adéquates pour la gestion durable de ce patrimoine floristique. Parmi les solutions proposées était la création du parc national de Sidi Toui en 1991. La présente étude vise au suivi de l’état du couvert végétal à l’extérieur et à l’intérieur du parc après 20 ans de protection, au moyen de la méthode des points quadrats et un ensemble d’indicateurs écologiques (recouvrement de la végétation, richesse spécifique, diversité alpha et diversité béta). Les principaux résultats obtenus montrent que les indicateurs suivis dépendent étroitement des précipitations saisonnières. Le recouvrement, la richesse et la diversité floristique sont toujours plus élevés à l’intérieur du parc qu’à l’extérieur. On a pu également remarquer que la longue protection réduit fortement la dynamique de la végétation.Tarhouni Mohamed, Ben Hmida Walid, Neffati Mohamed. Caractérisation du couvert végétal naturel à l’extérieur et à l’intérieur du parc national de Sidi Toui, zone aride de la Tunisie. In: Ecologia mediterranea, tome 40 n°2, 2014. pp. 41-52
Ein neuer Ansatz zur Optimierung der Verwendung von Algorithmen für Situationserkennung im IoT-Bereich
No full textOver the past few years technological advancements have supported the growth of the Internet of Things (IoT). The Internet of Things consists of (smart) objects embedded with sensors, actuators and controllers. These objects are connected to the Internet and are able to communicate with each other. The interconnection and communication of objects enable the creation of different application domains within the Internet of Things. Smart living is one of the major application areas for the Internet of Things. Sensors, actuators and controllers in a smart living environment (e.g. smart homes) are deployed anywhere; on objects or even on persons. As sensors have the capability to sense the environment, they can be used to collect useful information on location, motion, temperature, humidity, light, etc. Actuators can perform different actions based on data gathered from sensors, and controllers can process that data. Real-time situation awareness is one of the key tasks in a smart living environment. Real-time recognition of situations is especially important in ambient assisted living environments, where elderly or disabled people need support in their everyday lives. Recognition of situations in real-time enables immediate identification of critical situations and provides just-in-time assistance. To detect situations, data needs to be monitored, collected, analyzed and processed. Due to the increasing number of IoT connected devices, the amount of generated data is increasing too. Processing huge amounts of data is complex due to the inefficiency of continuously-running pattern/situation recognition algorithms, high requirement for processing capability and high frequency of the recognition process. Situation recognition algorithms must be executed constantly to handle the continuously generated data. For real-time recognition of situations in particular, these algorithms need to be executed permanently for all received data. The continuously-running recognition algorithms require high processing capabilities. The resource consumption of these algorithms is especially high when they are running on large sets of data. To overcome these problems there is a need for more intelligent approaches that are able to decide - based on target situation recognition purposes - which data is important and should be processed and which algorithm should be used to process this data. This study proposes an approach for optimizing the usage of situation recognition algorithms in Internet of Things domains. The key idea of our approach is to select important data, based on situation recognition purposes, and to execute the situation recognition algorithms after all relevant data have been collected. The main advantage of our approach is that situation recognition algorithms will not be executed each time new data is received. This allows reduction of the frequency of execution of the situation recognition algorithms, thus saving computational resources, such as CPU, memory, storage, bandwidth and power. Another advantage of our approach is that it can be applied to recognize situations in real-time, which is useful for ambient assisted living environments. We apply the proposed approach to implement a use case scenario on top of the universAAL IoT platform, which is an open-source platform for the development of IoT solutions.In den letzten Jahren gab es viele Fortschritte in Technologien, die zur Entwicklung des Internet der Dinge (Internet of Things) beigetragen haben. Das Internet der Dinge besteht aus (intelligenten) Objekten, in denen Sensoren, Aktuatoren und Controller eingebettet sind. Diese Objekte sind mit einem Netzwerk, wie dem Internet, verbunden, wodurch sie die Möglichkeit besitzen, miteinander zu kommunizieren. Die Vernetzung und Kommunikation von verschiedenen Objekten ermöglicht die Entwicklung unterschiedlicher Anwendungsdomänen für das Internet der Dinge. Smart Living Umgebungen sind eine der wichtigsten Anwendungsdomänen im Internet der Dinge. In einer Smart Living Umgebung werden Sensoren, Aktuatoren und Controller überall auf Objekten oder sogar auf Personen platziert. Sensoren können die Umgebung wahrnehmen und nützliche Informationen über den Standort, die Bewegung, die Temperatur, die Feuchtigkeit, das Licht und vieles mehr sammeln. Aktuatoren können verschiedene Aktionen basierend auf Daten, die von Sensoren gesammelt werden, durchführen und Controller können diese Daten verarbeiten. Echtzeit-Situationserkennung ist eine der wichtigsten Aufgaben in einer Smart Living Umgebung. Die Echtzeit-Erkennung von Situationen ist besonders wichtig in Ambient Assisted Living Umgebungen, in denen ältere oder Menschen mit Behinderung in ihrem Alltag unterstützt werden. Die Echtzeit-Situationserkennung in diesen Umgebungen ermöglicht es, kritische Situationen sofort zu erkennen und ggf. Hilfe zu leisten. Um Situationen zu erkennen, müssen Daten gesammelt, gespeichert, analysiert und verarbeitet werden. Aufgrund der zunehmenden Anzahl von Geräten im Internet der Dinge nimmt die Menge der erzeugten Daten zu. Die Verarbeitung größerer Datenmengen ist nicht trivial aufgrund der Ineffizienz von ständig laufenden Situationserkennungsalgorithmen, der hohen erforderlichen Verarbeitungsfähigkeit und der erforderlichen Erkennungshäufigkeit. Die Situationserkennungsalgorithmen müssen kontinuierlich laufen, um die kontinuierlich erzeugten Datenmengen zu verarbeiten. Darüber hinaus erfordern kontinuierlich laufende Erkennungsalgorithmen eine hohe Verarbeitungsfähigkeit. Der Ressourcenverbrauch dieser Algorithmen ist besonders hoch, wenn sie auf grosse Datensätze angewandt werden. Um diese Probleme zu überwinden, ist es notwendig, intelligentere Ansätze zu entwickeln, die auf der Grundlage der Zielsituationserkennung entscheiden können, welche Daten wichtig sind und welche verarbeitet werden sollten und welche Algorithmen zur Verarbeitung dieser Daten verwendet werden sollten. In dieser Arbeit wird ein Ansatz vorgeschlagen, der die Optimierung der Nutzung von Situationserkennungsalgorithmen im Bereich des Internet der Dinge optimiert. Die Grundidee unseres Ansatzes besteht darin, relevante Daten basierend auf den Situationserkennungszwecken auszuwählen und die Situationserkennungsalgorithmen erst dann auszuführen, nachdem alle relevanten Daten gesammelt wurden. Der Hauptvorteil dieses Ansatzes ist, dass die Situationserkennungsalgorithmen nicht jedes Mal ausgeführt werden müssen, wenn neue Daten empfangen werden. Dies ermöglicht die Häufigkeit der Ausführung von Situationserkennungsalgorithmen zu verringern und die Rechenressourcen wie CPU, Speicher, Bandbreite und Strom zu sparen. Ein weiterer Vorteil ist, dass der vorgeschlagene Ansatz Situationen in Echtzeit erkennen kann, was in einer Ambient Assisted Living Umgebung sehr nützlich ist. Wir werden den vorgeschlagenen Ansatz verwenden, um ein Use Case-Szenario basierend auf der Plattform universAAL IoT zu implementieren, die eine Open-Source-Plattform für die Entwicklung von Lösungen für das Internet der Dinge ist
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