On Randomized Algorithms for Matching in the Online Preemptive Model

We investigate the power of randomized algorithms for the maximum cardinality matching (MCM) and the maximum weight matching (MWM) problems in the online preemptive model. In this model, the edges of a graph are revealed one by one and the algorithm is required to always maintain a valid matching. On seeing an edge, the algorithm has to either accept or reject the edge. If accepted, then the adjacent edges are discarded. The complexity of the problem is settled for deterministic algorithms. Almost nothing is known for randomized algorithms. A lower bound of $1.693$ is known for MCM with a trivial upper bound of $2$. An upper bound of $5.356$ is known for MWM. We initiate a systematic study of the same in this paper with an aim to isolate and understand the difficulty. We begin with a primal-dual analysis of the deterministic algorithm due to McGregor. All deterministic lower bounds are on instances which are trees at every step. For this class of (unweighted) graphs we present a randomized algorithm which is $\frac{28}{15}$-competitive. The analysis is a considerable extension of the (simple) primal-dual analysis for the deterministic case. The key new technique is that the distribution of primal charge to dual variables depends on the "neighborhood" and needs to be done after having seen the entire input. The assignment is asymmetric: in that edges may assign different charges to the two end-points. Also the proof depends on a non-trivial structural statement on the performance of the algorithm on the input tree. The other main result of this paper is an extension of the deterministic lower bound of Varadaraja to a natural class of randomized algorithms which decide whether to accept a new edge or not using independent random choices

Chapter Spectral Efficiency Analysis of Filter Bank Multi‐Carrier (FBMC)‐ Based 5G Networks with Estimated Channel State Information (CSI)

The heterogeneous cellular network (HCN) is most significant as a key technology for future fifth-generation (5G) wireless networks. The heterogeneous network consists of randomly macrocell base stations (MBSs) overlaid with femtocell base stations (FBSs). Stochastic geometry has been shown to be a very powerful tool to model, analyze, and design networks with random topologies such as wireless ad hoc, sensor networks, and multi-tier cellular networks. HCNs can be energy-efficiently designed by deploying various BSs belonging to different networks, which has drawn significant attention to one of the technologies for future 5G wireless networks. In this chapter, we propose switching off/on systems enabling the BSs in the cellular networks to efficiently consume the power by introducing active/sleep modes, which is able to reduce the interference and power consumption in the MBSs and FBSs on an individual basis as well as improve the energy efficiency of the cellular networks. We formulate the minimization of the power consumption for the MBSs and FBSs as well as an optimization problem to maximize the energy efficiency subject to throughput outage constraints, which can be solved by the Karush-Kuhn-Tucker (KKT) conditions according to the femto tier BS density. We also formulate and compare the coverage probability and the energy efficiency in HCN scenarios with and without coordinated multi-point (CoMP) to avoid coverage holes

Spectral Efficiency Analysis of Filter Bank Multi‐Carrier (FBMC)‐ Based 5G Networks with Estimated Channel State Information (CSI)

Filter bank multi‐carrier (FBMC) modulation, as a potential candidate for physical data communication in the fifth generation (5G) wireless networks, has been widely investigated. This chapter focuses on the spectral efficiency analysis of FBMC‐based cognitive radio (CR) systems, and spectral efficiency comparison is conducted with another three types of multi‐carrier modulations: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), and universal‐filtered multi‐carrier (UFMC). In order to well evaluate and compare the spectral efficiency, we propose two resource allocation (RA) algorithms for single‐cell and two‐cell CR systems, respectively. In the single‐cell system, the RA algorithm is divided into two sequential steps, which incorporate subcarrier assignment and power allocation. In the two‐cell system, a noncooperative game is formulated and the multiple access channel (MAC) technique assists to solve the RA problem. The channel state information (CSI) between CR users and licensed users cannot be precisely known in practice, and thus, an estimated CSI is considered by defining a prescribed outage probability of licensed systems. Numerical results show that FBMC can achieve the highest channel capacity compared with another three waveforms

Evidence of Strong Guest–Host Interactions in Simvastatin Loaded in Mesoporous Silica MCM-41

Funding Information: This research was funded by the Associate Laboratory for Green Chemistry LAQV, which is financed by national funds from FCT/MEC (UID/QUI/50006/2019) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER—007265). This research was funded by the Interreg 2 Seas program 2014–2020, and co-funded by the European Regional Development Fund (FEDER) under subsidy contract 2S01-059_IMODE and 2S07-033_ Site Drug. This research was funded by the Program PHC PESSOA 2018 project nbr 4340/40868R. This research was funded by National Funds through FCT—Portuguese Foundation for Science and Technology, reference UIDB/00100/2020, UIDP/00100/2020, LA/P/0056/2020, UIDB/50025/2020-2023, and PTNMR (ROTEIRO/0031/2013; PINFRA/22161/2016), co-financed by ERDF through COMPETE 2020, Portugal, POCI and PORL and FCT through PIDDAC (POCI-01-0145-FEDER-007688). M.C.C. acknowledges PTNMR&i3N for the researcher contract. T. Cordeiro acknowledges Fundação para a Ciência e a Tecnologia (FCT) for the scholarship SFRH/BD/114653/2016. I. Matos acknowledges FCT for the Investigator FCT contract IF/01242/2014/CP1224/CT0008. Publisher Copyright: © 2023 by the authors.A rational design of drug delivery systems requires in-depth knowledge not only of the drug itself, in terms of physical state and molecular mobility, but also of how it is distributed among a carrier and its interactions with the host matrix. In this context, this work reports the behavior of simvastatin (SIM) loaded in mesoporous silica MCM-41 matrix (average pore diameter ~3.5 nm) accessed by a set of experimental techniques, evidencing that it exists in an amorphous state (X-ray diffraction, ssNMR, ATR-FTIR, and DSC). The most significant fraction of SIM molecules corresponds to a high thermal resistant population, as shown by thermogravimetry, and which interacts strongly with the MCM silanol groups, as revealed by ATR-FTIR analysis. These findings are supported by Molecular Dynamics (MD) simulations predicting that SIM molecules anchor to the inner pore wall through multiple hydrogen bonds. This anchored molecular fraction lacks a calorimetric and dielectric signature corresponding to a dynamically rigid population. Furthermore, differential scanning calorimetry showed a weak glass transition that is shifted to lower temperatures compared to bulk amorphous SIM. This accelerated molecular population is coherent with an in-pore fraction of molecules distinct from bulklike SIM, as highlighted by MD simulations. MCM-41 loading proved to be a suitable strategy for a long-term stabilization (at least three years) of simvastatin in the amorphous form, whose unanchored population releases at a much higher rate compared to the crystalline drug dissolution. Oppositely, the surface-attached molecules are kept entrapped inside pores even after long-term release assays.publishersversionpublishe

Development and Packaging of Microsystems Using Foundry Services

Micro-electro-mechanical systems (MEMS) are a new and rapidly growing field of research. Several advances to the MEMS state of the art were achieved through design and characterization of novel devices. Empirical and theoretical model of polysilicon thermal actuators were developed to understand their behavior. The most extensive investigation of the Multi-User MEMS Processes (MUMPs) polysilicon resistivity was also performed. The first published value for the thermal coefficient of resistivity (TCR) of the MUMPs Poly 1 layer was determined as 1.25 x 10(exp -3)/K. The sheet resistance of the MUMPs polysilicon layers was found to be dependent on linewidth due to presence or absence of lateral phosphorus diffusion. The functional integration of MEMS with CMOS was demonstrated through the design of automated positioning and assembly systems, and a new power averaging scheme was devised. Packaging of MEMS using foundry multichip modules (MCMs) was shown to be a feasible approach to physical integration of MEMS with microelectronics. MEMS test die were packaged using Micro Module Systems MCM-D and General Electric High Density Intercounect and Chip-on-Flex MCM foundries. Xenon difluoride (XeF2) was found to be an excellent post-packaging etchant for bulk micromachined MEMS. For surface micromachining, hydrofluoric acid (HF) can be used

Automated CNN pipeline generation for heterogeneous architectures

Heterogeneity is a vital feature in emerging processor chip designing. Asymmetric multicore-clusters such as high-performance cluster and power efficient cluster are common in modern edge devices. One example is Intel\u27s Alder Lake featuring Golden Cove high-performance cores and Gracemont power-efficient cores. Chiplet-based technology allows organization of multi cores in form of multi-chip-modules, thus housing large number of cores in a processor. Interposer based packaging has enabled embedding High Bandwidth Memory (HBM) on chip and reduced transmission latency and energy consumption of chiplet-chiplet interconnect.\ua0For Instance Intel\u27s XeHPC Ponte Vecchio package integrates multi-chip GPU organization along with HBM modules.Since new devices feature heterogeneity at the level of cores, memory and on-chip interconnect, it has become important to steer optimization at application level in order to leverage the new heterogeneous, high-performing and power-efficient features of underlying computing platforms. An important high-performance application paradigm is Convolution Neural Networks (CNN). CNNs are widely used in many practical applications. The pipelined parallel implementation of CNN is favored for inference on edge devices. In this Licentiate thesis we present a novel scheme for automatic scheduling of CNN pipelines on heterogeneous devices. A pipeline schedule is a configuration that provides information on depth of pipeline, grouping of CNN layers into pipeline stages and mapping of pipeline stages onto computing units. We utilize simple compile-time hints which consists of workload information of individual CNN layers and performance hints of computing units.The proposed approach provides near optimal solution for a throughput maximizing pipeline. We model the problem as a design space exploration technique. We developed a time-efficient design space navigation through heuristics extracted from the knowledge of CNN structure and underlying computing platform. The proposed search scheme converges faster and utilizes real-time performance measurements as fitness values. The results demonstrate that the proposed scheme converges faster and can scale when used with larger networks and computing platforms. Since the scheme utilizes online performance measurements, one of the challenges is to avoid expensive configurations during online tuning. The results demonstrate that on average, ~80\% of the tested configurations are sub-optimal solutions.Another challenge is to reduce convergence time. The experiments show that proposed approach is 35x faster than stochastic optimization algorithms. Since the design space is large and complex, We show that the proposed scheme explores only ~0.1% of the total design space in case of large CNNs (having 50+ layers) and results in near-optimal solution