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Ultrathin Junctionless Nanowire FET Model, Including 2-D Quantum Confinements
No full textIn this paper, we develop an explicit model to predict the dc electrical behavior in ultrathin surrounding gate junctionless (JL) nanowire field-effect transistors (FETs). The proposed model considers 2-D electrical and geometrical confinements of carrier charge density within few discrete subbands. Combining a parabolic approximation of the Poisson equation, the first-order perturbation theory for the Schrodinger subband energy eigenvaluesand the Fermi-Dirac statistics for the confined carrier density lead to an explicit solution of the dc characteristic in ultrathin JL devices. Validity of the model has been verified with technology computer-aided design simulations. The results confirm its validity for all regions of operation, i.e., from deep depletion to accumulation and from linear to saturation. This represents an essential step toward analysis of circuits based on JL nanowire devices.ICLA
Coupling models for lightning-induced overvoltage calculations: a comparison and consolidation
No full textLRESCI-STI-F
Strong LP Formulations for Scheduling Splittable Jobs on Unrelated Machines
No full textWe study a natural generalization of the problem of minimizing makespan on unrelated machines in which jobs may be split into parts. The different parts of a job can be (simultaneously) processed on different machines, but each part requires a setup time before it can be processed. First we show that a natural adaptation of the seminal approximation algorithm for unrelated machine scheduling [11] yields a 3-approximation algorithm, equal to the integrality gap of the corresponding LP relaxation. Through a stronger LP relaxation, obtained by applying a lift-and-project procedure, we are able to improve both the integrality gap and the implied approximation factor to 1 + φ, where φ ≈ 1.618 is the golden ratio. This ratio decreases to 2 in the restricted assignment setting, matching the result for the classic version. Interestingly, we show that our problem cannot be approximated within a factor better than e/e-1 ≈ 1.582 (unless P = NP). This provides some evidence that it is harder than the classic version, which is only known to be inapproximable within a factor 1.5 - ε. Since our 1 + φ bound remains tight when considering the seemingly stronger machine configuration LP, we propose a new job based configuration LP that has an infinite number of variables, one for each possible way a job may be split and processed on the machines. Using convex duality we show that this infinite LP has a finite representation and can be solved in polynomial time to any accuracy, rendering it a promising relaxation for obtaining better algorithms. © 2014 Springer International Publishing Switzerland.THL
Inelastic wide-column models for U-shaped reinforced concrete walls
No full textAlthough core structures are often used in reinforced concrete buildings as members providing lateral strength and stiffness, experimental and numerical studies on their inelastic behavior are scarce. In an experimental program recently completed at the ETH Zurich, two U-shaped walls were subjected to a bi-directional quasi-static cyclic loading regime. In this article, inelastic wide-column models for these two test units are developed. The wide-column analogy was chosen because it combines the merits of representing the U-shaped wall as a three-dimensional structure with inelastic properties while still being relatively simple and easy to set up when compared to shell or solid element models. It is therefore a tool which is not only available to researchers but also to design engineers. The article commences with the analysis of wide-column models that have been built according to recommendations found in the literature. Since these recommendations had been derived from analyzes of elastic systems, they are then revisited in a sensitivity study in which the effects of different modeling assumptions on the inelastic behavior of wide-column models are investigated. Finally, comparing the numerical results with the experimental evidence from the tests, the article concludes with practical recommendations for setting up wide-column models of U-shaped walls subjected to large inelastic deformations.EES
Galois cohomology of linear algebraic groups over fields of cohomological dimension ≦ 2
No full textCSA
Plasma-activated water retains antimicrobial properties against <i>Escherichia coli</i> after 72 h of storage
No full textPlasma-activated water (PAW) is increasingly recognized for its bactericidal properties. To advance this technology toward practical applications and deepen the understanding of its mechanisms, it is crucial to study the storage stability of PAW, focusing on both its chemical composition and antimicrobial properties over time. In this study, PAW was produced using a surface dielectric barrier discharge applied to ultra-pure water. Six PAW samples were analyzed, with plasma exposure times of 10, 20, or 30 min, and with or without water recirculation. Chemical properties such as pH, electrical conductivity, and oxidation-reduction potential were monitored over 72 h of storage at 25 degrees C. The kinetics of long-lived reactive oxygen and nitrogen species were also studied, with H2O2 measured by visible spectrophotometry and NO2- and NO3- analyzed via ion chromatography. While H2O2 and NO2- concentrations decreased during the storage, NO3- levels increased in all samples, reaching similar final concentrations independently on the water recirculation during plasma exposure. After 72 h, the bactericidal effect of PAW on Escherichia coli was evaluated for 10 and 30 min treatment times and compared to fresh samples. Although water recirculation initially provided stronger antimicrobial effects, after storage, both recirculated and non-recirculated PAW samples exhibited similar antimicrobial activity. This study demonstrates that PAW samples, regardless of the initial chemical composition, associated to water recirculation, achieve comparable antimicrobial properties after 72 h of storage, providing valuable insights for practical PAW applications.SPC-LTP2ASPC-P
Parallel convolutional processing using an integrated photonic tensor core
No full textWith the proliferation of ultrahigh-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence (AI)(1), the world is generating exponentially increasing amounts of data that need to be processed in a fast and efficient way. Highly parallelized, fast and scalable hardware is therefore becoming progressively more important(2). Here we demonstrate a computationally specific integrated photonic hardware accelerator (tensor core) that is capable of operating at speeds of trillions of multiply-accumulate operations per second (10(12) MAC operations per second or tera-MACs per second). The tensor core can be considered as the optical analogue of an application-specific integrated circuit (ASIC). It achieves parallelized photonic in-memory computing using phase-change-material memory arrays and photonic chip-based optical frequency combs (soliton microcombs(3)). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 gigahertz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates(3-5), ultralow-loss silicon nitride waveguides(6,7), and high-speed on-chip detectors and modulators, our approach provides a path towards full complementary metal-oxide-semiconductor (CMOS) wafer-scale integration of the photonic tensor core. Although we focus on convolutional processing, more generally our results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services.LPQ
Thematic Indexing of Spoken Documents by Using Self-Organizing Maps
No full textA method is presented to provide a useful searchable index for spoken audio documents. The task differs from the traditional (text) document indexing, because large audio databases are decoded by automatic speech recognition and decoding errors occur frequently. The idea in this paper is to take advantage of the large size of the database and select the best index terms for each document with the help of the other documents close to it using a semantic vector space. First, the audio stream is converted into a text stream by a speech recognizer. Then the text of each story is represented by a document vector which is the normalized sum of the word vectors in the story. A large collection of document vectors is used to train a self-organizing map to find the clusters and latent semantic structures in the collection. Because the news stories are quite short and include speech recognition errors, the idea of smoothing the document vectors using the thematic clusters determined by the self-organizing map is introduced to get a better index. The application in this paper is the indexing and retrieval of broadcast news on radio and TV. Test results are given using the evaluation data from the TREC spoken document retrieval task.LIDIA
Nonlinear Data-Enabled Prediction and Control
No full textBehavioral theory, which characterizes linear dynamics with measured trajectories, has found successful applications in controller design and signal processing. However, the extension of behavioral theory to general nonlinear system remains an open question. In this work, we propose to apply behavioral theory to a reproducing kernel Hilbert space in order to extend its application to a class of nonlinear systems and we show its application in prediction and in predictive control.LA