Energy Efficient Designs for Collaborative Signal and Information Processing inWireless Sensor Networks

Collaborative signal and information processing (CSIP) plays an important role in the deployment of wireless sensor networks. Since each sensor has limited computing capability, constrained power usage, and limited sensing range, collaboration among sensor nodes is important in order to compensate for each other’s limitation as well as to improve the degree of fault tolerance. In order to support the execution of CSIP algorithms, distributed computing paradigm and clustering protocols, are needed, which are the major concentrations of this dissertation. In order to facilitate collaboration among sensor nodes, we present a mobile-agent computing paradigm, where instead of each sensor node sending local information to a processing center, as is typical in the client/server-based computing, the processing code is moved to the sensor nodes through mobile agents. We further conduct extensive performance evaluation versus the traditional client/server-based computing. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this result, we propose a hybrid computing paradigm that adopts different computing models within different clusters of sensor nodes. Either the client/server or the mobile agent paradigm can be employed within clusters or between clusters according to the different cluster configurations. This new computing paradigm can take full advantages of both client/server and mobile agent computing paradigms. Simulations show that the hybrid computing paradigm performs better than either the client/server or the mobile agent computing. The mobile agent itinerary has a significant impact on the overall performance of the sensor network. We thus formulate both the static mobile agent planning and the dynamic mobile agent planning as optimization problems. Based on the models, we present three itinerary planning algorithms. We have showed, through simulation, that the predictive dynamic itinerary performs the best under a wide range of conditions, thus making it particularly suitable for CSIP in wireless sensor networks. In order to facilitate the deployment of hybrid computing paradigm, we proposed a decentralized reactive clustering (DRC) protocol to cluster the sensor network in an energy-efficient way. The clustering process is only invoked by events occur in the sensor network. Nodes that do not detect the events are put into the sleep state to save energy. In addition, power control technique is used to minimize the transmission power needed. The advantages of DRC protocol are demonstrated through simulations

Research Progress on Spatiotemporal Distribution Characteristics of Carbon Stable Isotopes in Wines from New and Old World Countries

With the continuous development of economic globalization, the production and consumption of wine in New and Old World countries have shown a steady growth trend. Wines of different grades and styles have been developed, which are related to geographical origin. Carbon stable isotopes are important indicators for geographical origin identification and have been of great concern to researchers due to their stability and objectivity. This paper summarizes the spatiotemporal distribution characteristics of carbon stable isotopes in ethanol, glycerol, total carbon and other components in wines from the Old (France, Italy, Spain, Germany, Switzerland, Croatia, and Romania) and New Worlds (Australia, South Africa, Chile, Argentina, Brazil, and China). In general, there are small differences in the carbon stable isotope ratio of various components between New and Old World wines, so it is impossible to achieve reliable results when only carbon stable isotope data is used for geographical origin identification. The combined use of carbon stable isotope data with other isotope data and mineral elements can improve the accuracy of geographical origin identification. Furthermore, the application of carbon stable isotope technology is also summarized over the past 15 years with the aim to provide a reference for the establishment of database

The Bantam microRNA Is Associated with Drosophila Fragile X Mental Retardation Protein and Regulates the Fate of Germline Stem Cells

Fragile X syndrome, a common form of inherited mental retardation, is caused by the loss of fragile X mental retardation protein (FMRP). We have previously demonstrated that dFmr1, the Drosophila ortholog of the fragile X mental retardation 1 gene, plays a role in the proper maintenance of germline stem cells in Drosophila ovary; however, the molecular mechanism behind this remains elusive. In this study, we used an immunoprecipitation assay to reveal that specific microRNAs (miRNAs), particularly the bantam miRNA (bantam), are physically associated with dFmrp in ovary. We show that, like dFmr1, bantam is not only required for repressing primordial germ cell differentiation, it also functions as an extrinsic factor for germline stem cell maintenance. Furthermore, we find that bantam genetically interacts with dFmr1 to regulate the fate of germline stem cells. Collectively, our results support the notion that the FMRP-mediated translation pathway functions through specific miRNAs to control stem cell regulation

The epidemiological patterns of non-Hodgkin lymphoma: global estimates of disease burden, risk factors, and temporal trends

BackgroundThe incidence of non-Hodgkin’s lymphoma (NHL) has increased steadily over the past few decades. Elucidating its global burden will facilitate more effective disease management and improve patient outcomes. We explored the disease burden, risk factors, and trends in incidence and mortality in NHL globally.MethodsThe up-to-date data on age-standardized incidence and mortality rates of NHL were retrieved from the GLOBOCAN 2020, CI5 volumes I-XI, WHO mortality database, and Global Burden of Disease (GBD) 2019, focusing on geographic disparities worldwide. We reported incidence and mortality by sex and age, along with corresponding age-standardized rates (ASRs), the average annual percentage change (AAPC), and future burden estimates to 2040.ResultsIn 2020, there were an estimated 545,000 new cases and 260,000 deaths of NHL globally. In addition, NHL resulted in 8,650,352 age-standardized DALYs in 2019 worldwide. The age-specific incidence rates varied drastically across world areas, at least 10-fold in both sexes, with the most pronounced increase trend found in Australia and New Zealand. By contrast, North African countries faced a more significant mortality burden (ASR, 3.7 per 100,000) than highly developed countries. In the past decades, the pace of increase in incidence and mortality accelerated, with the highest AAPC of 4.9 (95%CI: 3.6-6.2) and 6.8 (95%CI: 4.3-9.2) in the elderly population, respectively. Considering risk factors, obesity was positively correlated with age-standardized incidence rates (P< 0.001). And North America was the high-risk region for DALYs due to the high body mass index in 2019. Regarding demographic change, NHL incident cases are projected to rise to approximately 778,000 by 2040.ConclusionIn this pooled analysis, we provided evidence for the growing incidence trends in NHL, particularly among women, older adults, obese populations, and HIV-infected people. And the marked increase in the older population is still a public health issue that requires more attention. Future efforts should be directed at cultivating health awareness and formulating effective and locally tailored cancer prevention strategies, especially in most developing countries

Computational modeling for visual attention analysis

Abstract Visual scenes typically contain massive amounts of content that cannot be processed in a short time due to the limited processing capacity of the human visual system. The term, visual attention, is a biologically inspired and psychologically driven mechanism that works by selecting visually relevant information and filtering out the redundant contents. This thesis is a thorough summary of the main subjects around computational modeling for visual attention analysis, consisting of several published papers corresponding to my research progress. First, the data preparation for computational modeling will be introduced, including eye movement data, eye tracking data collection and eye tracking datasets facilitating the evaluation of computational modeling of visual attention. Second, computational models for visual attention analysis, or saliency models, are presented from traditional unsupervised methods to deep saliency models. Third, the subject about saliency integration will be illustrated that unifies multiple saliency maps from the multiple candidate saliency models for better accuracy. The contributions of this study are three folds. Firstly, we collect a task-driven eye tracking dataset for visual attention analysis. Secondly, we propose three saliency models for in-depth investigation in modeling visual attention, including an unsupervised model using the bi-directional propagation method, a Convolutional Neural Networks based model by connecting the Dense Conditional Random Fields for multi-scale saliency refinement, and a Convolutional Neural Networks based model with cascade Conditional Random Fields for joint model training. Thirdly, we propose a saliency integration method and conduct comprehensive experiments and analysis on the topic. Finally, we summarize the contributions of the work and propose the potential applications of saliency models and the extended saliency related topics to boost applications of saliency approaches on other computer vision topics.Tiivistelmä Kuvat sisältävät tyypillisesti valtavan määrän informaatiota, jota ei pystytä prosessoimaan lyhyessä ajassa ihmisen näköjärjestelmän rajoitetun prosessointi kapasiteetin takia. Termi, visuaalinen huomio, on biologian ja psykologian motivoima mekanismi, joka toimii valiten oleellisen informaation ja suodattaen ylimääräisen informaation. Mallintaaksemme huomio mekanismia konenäön käyttöön on olennaista, että laskennallinen malli visuaaliselle huomiolle ehdottaa tärkeät alueet kuvista, jotka ihmisen näköjärjestelmä on nähnyt. Tämä väitöskirja on perusteellinen yhteenveto tärkeimmistä osa-alueista liittyen visuaalisen huomion analyysin laskennalliseen mallintamiseen, koostuen useasta julkaisusta vastaten minun tutkimukseni etenemiseen. Ensimmäiseksi, esittelemme datan esikäsittelyn laskennallista mallia varten, mukaan ottaen silmänliike datan, silmänjäljitys datan kerääminen ja silmänjäljitys tietokantojen hyödyntäminen visuaalisen huomion laskennallisen mallien evaluoinnissa. Toiseksi, laskennalliset mallit visuaalisen huomioon, tai tärkeys mallit, esitellään perinteisistä ohjaamattomista menetelmistä syviin tärkeys malleihin. Kolmanneksi, havainnollistamme tärkeys integraation, joka yhdistää useita tärkeys ehdotuksia useista eri tärkeys malli ehdokkaista, jolla saavutamme paremman tarkkuuden. Kontribuutiomme ovat seuraavat kolme asiaa. Ensimmäiseksi, keräämme silmänjäljitys tietokannan visuaalisen huomion analyysiin. Toiseksi, ehdotamme kolmea tärkeys mallia visuaalisen huomion perusteelliseen tarkasteluun, sisältäen ohjaamattoman mallin, joka käyttää kaksisuuntaista etenemismallia, konvoluutioneuroverkko pohjainen malli, joka yhdistää syvän ehdollisen satunnaiskentän monitaso tärkeys tarkistukseen, ja konvoluutioneuroverkko pohjainen malli ehdollisella sarja satunnaiskentällä usean mallin yhteisopetukseen. Kolmanneksi, ehdotamme tärkeys integraatio menetelmää ja suoritamme kattavia testejä ja analyysejä aiheesta. Lopuksi, tiivistämme kontribuution työstämme ja ehdotamme mahdollisia sovelluksia tärkeys malleista ja laajennamme tärkeys- aiheeseen liittyviä sovelluksia tehostamaan tärkeys menetelmiä eri konenäön aiheisiin

On Mobile Agent Itinerary for Collaborative Processing in Sensor Networks

Abstract — One of the most important problems studied in wireless sensor network applications is the collaborative processing among sensor nodes in order to compensate for each other’s sensing and computing capabilities and to tolerate faults. The client/server based paradigm and the mobile agent based paradigm are two popular computing models used in collaborative processing. One of the most challenging problems in mobile agent based computing is the design of mobile agent itinerary. An improper design of itinerary (or route) of mobile agent migration can largely deteriorate the performance of collaborative processing. In this paper, we study the key problem of mobile agent planning for collaborative processing in sensor networks. This paper first models the dynamic mobile agent planning problem. It then presents three itinerary planning algorithms, the static, the dynamic, and the predictive dynamic approaches. We design three metrics (energy consumption, network lifetime, and the number of hops) and use simulation tools to quantitatively measure the performance of different itinerary planning algorithms. Simulation results show considerable improvement over the static itinerary and the dynamic itinerary approaches using the predictive dynamic itinerary algorithm. I

Performance evaluation of distributed computing paradigms in mobile ad hoc sensor networks

The emergence of mobile ad hoc sensor networks has brought new challenges to traditional network design. This paper focuses on the study at the application layer. In specific, it compares the performance of two distributed computing paradigms, the client/server-based paradigm and the mobile-agent-based paradigm, through mathe-matical modeling and simulation. The differences between the two computing paradigms can be characterized by what is transferred over the network and correspondingly, where data processing is carried out. In the client/server-based computing, clients send data to a server and data processing is done at the server; while in the mobile-agent-based computing, the server dispatches mobile agents which carry executable codes to clients and data processing is done locally. Previous works have shown that the mobile-agent-based paradigm is more appropriate to handle computations in ad hoc sensor networks. However, no simulation work has done to quantitatively measur

Mobile Agent Migration Models and Algorithms for Collaborative Processing

Abstract — The mobile agent based paradigm is a promising computing paradigm in wireless sensor networks, which has many unique advantages over the traditional client/server paradigm. However, the question on how to model the mobile agent itinerary in sensor networks, is left unsolved. The design of the mobile agent migration itinerary has a large impact on the performance of collaborative processing, which is the focus of this paper. We first model both the static and dynamic mobile agent planning problems and design two planning algorithms. Then a performance evaluation between these two algorithms is conducted. Three metrics (energy consumption, network lifetime, and the number of hops) are used in the simulation to quantitatively measure the performance of different itinerary planning methods. Simulation results show that the dynamic itinerary planning has considerable advantages over the static itinerary. I