A 1-V 9.7-mW CMOS Frequency Synthesizer for IEEE 802.11a Transceivers

A 1-V CMOS frequency synthesizer is proposed for wireless local area network 802.11a transceivers using a novel transformer-feedback voltage-controlled oscillator (VCO) for low voltage and a stacked frequency divider for low power. Implemented in a 0.18-mum CMOS process and operated at 1-V supply, the VCO measures a phase noise of -140.5 dBc at an offset of 20 MHz with a center frequency of 4.26 GHz and a power consumption of 5.17 mW. Its tuning range is as wide as 920 MHz (23%). By integrating the VCO into a frequency synthesizer, a phase noise of -140.1 dBc/Hz at an offset of 20 MHz is measured at a center frequency of 4.26 GHz. Its output frequency can be changed from 4.112 to 4.352 GHz by switching the 3-bit modulus of the programmable divider. The synthesizer consumes only 9.7 mW and occupies a chip area of 1.28 mm².     

Process planning via computerassisted classification and coding

This paper describes the development of a classification system for process planning. The system generates both a process plan for production and a classification number based on classification theory. The system is implemented in the C programming language with interfaces to a relational database. This allows the generated process plan and the classification number to be loaded into the database for information retrieval.     

Secure communication in untrusted relay selection networks with wireless energy harvesting

In this paper, we consider the secrecy performance of an energy-harvesting relaying system with Kth best partial relay selection where the communication of a multi-antenna source-destination pair is assisted via single-antenna untrusted relays. To protect confidential source messages from untrusted relays, transmit beamforming and destination jamming signals are used. The relays are energy-constrained nodes that use the power-splitting policy to harvest energy through the wireless signals from both the source and destination. For performance evaluation, closed-form expressions of the secrecy outage probability and average secrecy capacity (ASC) are derived for Nakagami-m fading channels. The analytical results are confirmed via Monte Carlo simulations. Numerical results provide valuable insights into the effect of various system parameters, such as relay location, number of relays, and power splitting ratio, on the secrecy performance. Specifically, the maximum ASC is achieved when the relay is located between the source and destination.

     

Nanostructured β‐Bi2O3 Fractals on Carbon Fibers for Highly Selective CO2 Electroreduction to Formate

3D Bi₂O₃ fractal nanostructures (f‐Bi₂O₃) are directly self‐assembled on carbon fiber papers (CFP) using a scalable hot‐aerosol synthesis strategy. This approach provides high versatility in modulating the physiochemical properties of the Bi₂O₃ catalyst by a tailorable control of its crystalline size, loading, electron density as well as providing exposed stacking of the nanomaterials on the porous CFP substrate. As a result, when tested for electrochemical CO₂ reduction reactions (CO₂RR), these f‐Bi₂O₃ electrodes demonstrate superior conversion of CO₂ to formate (HCOO^?) with low onset overpotential and a high mass‐specific formate partial current density of ?52.2 mA mg^?1, which is ≈3 times higher than that of the drop‐casted control Bi₂O₃ catalyst (?15.5 mA mg^?1), and a high Faradaic efficiency (FE_HCOO^?) of 87% at an applied potential of ?1.2 V versus reversible hydrogen electrode. The findings reveal that the high exposure of roughened β‐phase Bi₂O₃/Bi edges and the improved electron density of these fractal structures are key contributors in attainment of high CO₂RR activity.     

Optimized cardinality-based generalized itemset mining using transaction ID and numeric encoding

In recent years, generalization-based data mining techniques have become an interesting topic for many data scientists. Generalized itemset mining is an exploration technique that focuses on extracting high-level abstractions and correlations in a database. However, the problem that domain experts must always deal with is how to manage and interpret a large number of extracted patterns from a massive database of transactions. In generalized pattern mining, taxonomies that contain abstraction information for each dataset are defined, so the number of frequent patterns can grow enormously. Therefore, exploiting knowledge turns into a difficult and costly process. In this article, we introduce an approach that uses cardinality-based constraints with transaction id and numeric encoding to mine generalized patterns. We applied transaction id to support the computation of each frequent itemset as well as to encode taxonomies into a numeric type using two simple rules. We also attempted to apply the combination of cardinality cons- traints and closed or maximal patterns. Experiments show that our optimizations significantly improve the performance of the original method, and the importance of comprehensive information within closed and maximal patterns is worth considering in generalized frequent pattern mining.     

A valences-totaling model for English sentiment classification

Sentiment classification plays an important role in everyday life, in political activities, activities of commodity production and commercial activities. Finding a time-effective and highly accurate solution to the classification of emotions is challenging. Today, there are many models (or methods) to classify the sentiment of documents. Sentiment classification has been studied for many years and is used widely in many different fields. We propose a new model, which is called the valences-totaling model (VTM), by using cosine measure (CM) to classify the sentiment of English documents. VTM is a new model for English sentiment classification. In this study, CM is a measure of similarity between two words and is used to calculate the valence (and polarity) of English semantic lexicons. We prove that CM is able to identify the sentiment valence and the sentiment polarity of the English sentiment lexicons online in combination with the Google search engine with AND operator and OR operator. VTM uses many English semantic lexicons. These English sentiment lexicons are calculated online and are based on the Internet. We present a full range of English sentences; thus, the emotion expressed in the English text is classified with more precision. Our new model is not dependent on a special domain and training data set—it is a domain-independent classifier. We test our new model on the Internet data in English. The calculated valence (and polarity) of English semantic words in this model is based on many documents on millions of English Web sites and English social networks.     

Hydrodynamically Guided Hierarchical Self‐Assembly of Peptide–Protein Bioinks

Effective integration of molecular self‐assembly and additive manufacturing would provide a technological leap in bioprinting. This article reports on a biofabrication system based on the hydrodynamically guided co‐assembly of peptide amphiphiles (PAs) with naturally occurring biomolecules and proteins to generate hierarchical constructs with tuneable molecular composition and structural control. The system takes advantage of droplet‐on‐demand inkjet printing to exploit interfacial fluid forces and guide molecular self‐assembly into aligned or disordered nanofibers, hydrogel structures of different geometries and sizes, surface topographies, and higher‐ordered constructs bound by molecular diffusion. PAs are designed to co‐assemble during printing in cell diluent conditions with a range of extracellular matrix (ECM) proteins and biomolecules including fibronectin, collagen, keratin, elastin‐like proteins, and hyaluronic acid. Using combinations of these molecules, NIH‐3T3 and adipose derived stem cells are bioprinted within complex structures while exhibiting high cell viability (>88%). By integrating self‐assembly with 3D‐bioprinting, the study introduces a novel biofabrication platform capable of encapsulating and spatially distributing multiple cell types within tuneable pericellular environments. In this way, the work demonstrates the potential of the approach to generate complex bioactive scaffolds for applications such as tissue engineering, in vitro models, and drug screening.     

Fuzzy critical chain method for project scheduling under resource constraints and uncertainty

This paper develops a fuzzy critical chain method for project scheduling under resource constraints and uncertainty. The method consists of developing a desirable deterministic schedule under resource constraints, and adding a project buffer (PB) to the end of the schedule to deal with uncertainty. The size of the project buffer is determined by computations with fuzzy numbers. During project execution, the proposed method focuses on the penetration level in the project buffer, and dynamically updates the schedule to provide a more accurate schedule for actual progress. The use of a project buffer makes the method akin to critical chain project management (CCPM), although no feeding buffers are used. The proposed method is useful for both project planning and execution.     

Reusable platinum nanoparticle modified boron doped diamond microelectrodes for oxidative determination of arsenite

Boron doped diamond (BDD) macro- and microelectrodes were modified by electrodeposition of platinum nanoparticles using a multipotential step electrodeposition technique and used for the oxidative determination of arsenite, As(III). The formation of Pt nanoparticles was evident from cyclic voltammetry measurement, whereas AFM and SEM revealed the size and size distribution of deposited Pt nanoparticles. Raman spectroscopy illustrated a correlation between the typical BDD signature and the number of platinum deposition cycles. Linear sweep voltammetry performed with the modified BDD microelectrode outperformed its macrocounterpart and resulted in very low detecting currents with enhanced signal-to-noise ratios. With linearity up to 100 ppb and a detection limit of 0.5 ppb, the electrochemical system was applicable for processing tap and river water samples. Over 150 repetitive runs could be performed, and electrochemical etching of platinum allowed the reuse of the BDD microelectrode. The presence of copper and chloride ions, the two most severe interferents at levels commonly found in groundwater, did not interfere with the assay.