Design of 2.4 GHz Improved Current Reuse Gilbert Mixer with Source Degeneration Technique

An improved Gilbert mixer with the current reuse and source degeneration techniques is investigated. This design and simulation carried out by considering UMC 180 nm CMOS process in the Cadence Tool 6.1.6 with 2.4 GHz. The investigation is presented with the current reuse topology by stacking both pMOS and nMOS transistors. The reduction of the power consumption is obtained due to the self-biasing of the shunt feedback resistor. An improvement in the conversion gain and linearity is shown through the source degeneration in this mixer design. The proposed design achieves the maximum conversion gain (CG) of 13.2 dB with the noise figure (NF) of 8.6 dB. The design circuit consumes 0.7 mW power from 1.2 V with the 1 dB compression point of ?2.63 dBm and third-order input intercept point (IIP3) of 8.2 dBm. The chip area occupied is 0.128?×?0.180 mm² shown in the layout design. This compact layout of the mixer helps to create an opportunity as a suitable building block for RF integrated circuit (RFIC) applications with moderately high performance in the receiver front end.

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Terahertz Spectra of Ninhydrin and Indane-1,2,3-Trione

Distinctive terahertz (THz) absorption spectra of ninhydrin and indane-1,2,3-trione ranging from 0.5 to 4.5 THz were observed firstly in our experiment by terahertz time-domain spectroscopy (THz-TDS). The dehydration process of ninhydrin was also monitored online. The experimental results indicate that THz spectroscopy is highly sensitive to the crystal structure, weak intermolecular interactions, and the environmental change. Multitechniques including differential scanning calorimetry (DSC) and powder X-ray diffraction (PXRD) were also carried out to further investigate ninhydrin and indane-1,2,3-trione. And the results support the reliability of THz spectroscopy. Density functional theory (DFT) calculations based on the samples’ crystalline structures were performed for better understanding the THz characteristic spectra. The calculations agree with the experimental observation, and the corresponding vibrational modes of ninhydrin and indane-1,2,3-trione are assigned.

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Graphical Abstract Cover art for Terahertz spectra of ninhydrin and indane-1,2,3-trione.

     

Local structure of germanium-sulfur,germanium-selenium,and germanium-tellurium vitreous alloys

¹¹⁹Sn and ¹²⁹Te (¹²⁹I) Mössbauer spectroscopy showed that chalcogen-enriched Ge_{100 ? y} X _y (X = S, Se, Te) glasses are constructed of structural units including two-coordinated chalcogen atoms in chains such as

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and

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. Germanium in these glasses is only tetravalent and four-coordinated, and only chalcogen atoms are in the local environment of germanium atoms. Chalcogen-depleted glasses are constructed of structural units including two-coordinated (in

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chains) and three-coordinated chalcogen atoms (in

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chains). Germanium in these glasses stabilizes in both the tetravalent four-coordinated and divalent three-coordinated states, and only chalcogen atoms are in the local environment of germanium atoms.

     

Optimising Residual Energy Transmission Head with SNR Value in Multiple Clusters

WSN consists of independent device spatially distributed in the wireless network with sensor that observes the environment conditions. In this paper we propose a new algorithm for transmission head and its function. The cluster head (CH) activities, the role of transmission head (TH) and the selection of TH in various scenarios are discussed. Moreover the residual energy of the head nodes, signal–noise ratio for CH and TH are analysed with simulation results. Section 3 defines the cluster region and the active nodes of the cluster. It defines the identity of each cluster node. Section 3 deals with the selection of CH and the role of TH. Also, the algorithmic approach of TH is explained in this section. Section 4 explains the process of TH to TH communication. The communication of the various clusters is discussed in this section. The final section presents the analysis of TH and CH work with a comparison based on simulation.

     

Distributed Self-Stabilizing Capacitated Maximal Independent Set Construction in Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of a large number of wireless self-organized sensor nodes connected through a wireless decentralized distributed network without the aid of a predefined infrastructure. Fault-tolerance and power management are fundamental challenges in WSNs. A WSN is self-stabilizing if it can initially start at any state and obtain a legitimate state in a finite time without any external intervention. Self-stabilization is an important method for providing fault-tolerance in WSNs. Maximal independent set (MIS) is an extensively used structure for many important applications such as clustering (Randhawa and Jain in Wirel Personal Commun 97(3):3355, 2017. https://doi.org/10.1007/s11277-017-4674-5) and routing (Attea et al. in Wirel Personal Commun 81(2):819, 2015. https://doi.org/10.1007/s11277-014-2159-3; Lipiński in Wirel Personal Commun 101(1):251, 2018. https://doi.org/10.1007/s11277-018-5686-5) in WSNs. The capacitated MIS (CapMIS) problem is an extension of MIS in that each node has a capacity that determines the number of nodes it may dominate. In this paper, we propose a distributed self-stabilizing capacitated maximal independent set algorithm (CapMIS) in order to reduce energy consumption and support load balancing in WSNs. To the best of our knowledge, this is the first algorithm in this manner. The algorithm is validated through theoretical analysis as well as testbed implementations and simulations.

     

Estimation of neural dynamics from MEG/EEG cortical current density maps: application to the reconstruction of large-scale cortical synchrony

There is a growing interest in elucidating the role of specific patterns of neural dynamics--such as transient synchronization between distant cell assemblies--in brain functions. Magnetoencephalography (MEG)/electroencephalography (EEG) recordings consist in the spatial integration of the activity from large and multiple remotely located populations of neurons. Massive diffusive effects and poor signal-to-noise ratio (SNR) preclude the proper estimation of indices related to cortical dynamics from nonaveraged MEG/EEG surface recordings. Source localization from MEG/EEG surface recordings with its excellent time resolution could contribute to a better understanding of the working brain. We propose a robust and original approach to the MEG/EEG distributed inverse problem to better estimate neural dynamics of cortical sources. For this, the surrogate data method is introduced in the MEG/EEG inverse problem framework. We apply this approach on nonaveraged data with poor SNR using the minimum norm estimator and find source localization results weakly sensitive to noise. Surrogates allow the reduction of the source space in order to reconstruct MEG/EEG data with reduced biases in both source localization and time-series dynamics. Monte Carlo simulations and results obtained from real MEG data indicate it is possible to estimate non invasively an important part of cortical source locations and dynamic and, therefore, to reveal brain functional networks.     

Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks

This paper proposes enhancements to the channel(-state) estimation phase of a cognitive radio system. Cognitive radio devices have the ability to dynamically select their operating configurations, based on environment aspects, goals, profiles, preferences etc. The proposed method aims at evaluating the various candidate configurations that a cognitive transmitter may operate in, by associating a capability e.g., achievable bit-rate, with each of these configurations. It takes into account calculations of channel capacity provided by channel-state estimation information (CSI) and the sensed environment, and at the same time increases the certainty about the configuration evaluations by considering past experience and knowledge through the use of Bayesian networks. Results from comprehensive scenarios show the impact of our method on the behaviour of cognitive radio systems, whereas potential application and future work are identified.

Evaluating the Performance of Kalman-Filter-Based EEG Source Localization

Electroencephalographic (EEG) source localization is an important tool for noninvasive study of brain dynamics, due to its ability to probe neural activity more directly, with better temporal resolution than other imaging modalities. One promising technique for solving the EEG inverse problem is Kalman filtering, because it provides a natural framework for incorporating dynamic EEG generation models in source localization. Here, a recently developed inverse solution is introduced, which uses spatiotemporal Kalman filtering tuned through likelihood maximization. Standard diagnostic tests for objectively evaluating Kalman filter performance are then described and applied to inverse solutions for simulated and clinical EEG data. These tests, employed for the first time in Kalman-filter-based source localization, check the statistical properties of the innovation and validate the use of likelihood maximization for filter tuning. However, this analysis also reveals that the filter's existing space- and time-invariant process model, which contains a single fixed-frequency resonance, is unable to completely model the complex spatiotemporal dynamics of EEG data. This finding indicates that the algorithm could be improved by allowing the process model parameters to vary in space.      

Lp norm iterative sparse solution for EEG source Localization

How to localize the neural electric activities effectively and precisely from the scalp EEG recordings is a critical issue for clinical neurology and cognitive neuroscience. In this paper, based on the spatial sparse assumption of brain activities, proposed is a novel iterative EEG source imaging algorithm, Lp norm iterative sparse solution (LPISS). In LPISS, the lp (p < or =1) norm constraint for sparse solution is integrated into the iterative weighted minimum norm solution of the underdetermined EEG inverse problem, and it is the constraint and the iteratively renewed weight that forces the inverse problem to converge to a sparse solution effectively. The conducted simulation studies with comparison to LORETA and FOCUSS for various dipoles configurations confirmed the validation of LPISS for sparse EEG source localization. Finally, LPISS was applied to a real evoked potential collected in a study of inhibition of return (IOR), and the result was consistent with the previously suggested activated areas involved in an IOR process.     

A Bit-Error Rate Measurement and Error Analysis of Wireline Data Transmission using Current Source Model for Single Event Effect under Irradiation Environment

A high-speed wireline interfaces, e.g. LVDS (Low Voltage Differential Signaling), are widely used in the aerospace field for powerful computing in artificial satellites and aircraft [19]. This paper describes Bit Error Rate (BER) prediction methodology for wireline data transmission under irradiation environment at the design stage of data transmitter, which is useful in proactively determining if the design circuit meets the BER criteria of the target system. Using a custom-designed LVDS transmitter (TX) to enhance latch-up immunity [42], the relationship between transistor size and BER has been analyzed with focusing on Single Event Effect (SEE) as a cause of the bit error. The measurement was executed under ⁸⁴Kr¹⁷⁺ exposure of 322.0 MeV at various flux condition from 1?×?10³ to 5?×?10⁵ count/cm²/sec using cyclotron facility. For the analysis of the bit error, circuit simulation by SPICE was utilized with expressing the irradiation environment by a current source model. The current source model represents a single event strike into the circuit at drain and substrate junctions in bulk MOSFETs. For the construction of the current source model, a charge collection was simulated at the single particle strike with the creation of 3D Technology CAD (TCAD) models for the MOS devices of bulk transistor process technology. The simulation result of the charge correction was converted to a simple time-domain equation, and the single-event current source model was produced using the equation. The single-event current source was applied to SPICE simulation at bias current related circuits in the LVDS transmitter, then simulation results are carefully verified whether the output data is disturbed enough to cause bit errors on wireline data transmission. By the simulation, sensitive MOSFETs have been specified and a sum of the gate area for these MOSFETs has 29% better correlation than the normal evaluation index (sum of the drain area) by comparison to the actual BER measurement. Through the precise revelation of the sensitive area by SPICE simulation using the current model, it became possible to estimate BER under irradiation environment at the pre-fabrication design stage.

     

Mode‐SVD‐Based Maximum Likelihood Source Localization Using Subspace Approach

A mode‐singular‐value‐decomposition (SVD) maximum likelihood (ML) estimation procedure is proposed for the source localization problem under an additive measurement error model. In a practical situation, the noise variance is usually unknown. In this paper, we propose an algorithm that does not require the noise covariance matrix as a priori knowledge. In the proposed method, the weight is derived by the inverse of the noise magnitude square in the ML criterion. The performance of the proposed method outperforms that of the existing methods and approximates the Taylor‐series ML and Cramér‐Rao lower bound.     

Conventional and reciprocal approaches to the inverse dipole localization problem of electroencephalography

Forward transfer matrices relating dipole source to surface potentials can be determined via conventional or reciprocal approaches. In numerical simulations with a triangulated boundary-element three-concentric-spheres head model, we compare four inverse electroencephalogram (EEG) solutions: those obtained utilizing conventional or reciprocal forward transfer matrices, relating in each case source dipole components to potentials at either triangle centroids or triangle vertices. Single-dipole inverse solutions were obtained using simplex optimization with an additional position constraint limiting solution dipoles to within the brain region. Dipole localization errors are presented in all four cases, for varying dipole eccentricity and two different values of skull conductivity. Both conventional and reciprocal forward transfer matrices yielded inverse dipole solutions of comparable accuracy. Localization errors were low even for highly eccentric source dipoles on account of the nonlinear nature of the single-dipole solution and the position constraint. In the presence of Gaussian noise, both conventional and reciprocal approaches were also found to be equally robust to skull conductivity errors.     

Partially Constrained Blind Source Separation for Localization of Unknown Sources Exploiting Non-homogeneity of the Head Tissues

A new brain source localization technique using electroencephalograms (EEGs) is investigated in this paper. The information which describes the location of certain known sources is used as the constraint within the proposed blind source separation (BSS) algorithm and leads to a solution to the ill-posed inverse problem of source localization. Non-homogeneity of the head tissues, on the other hand, is exploited by introducing a realistic model of the mixing system. This model is used to better identify the location of the unknown sources within the brain from projection of the separated independent components on to the scalp. A separate procedure is employed to highlight the rhythmic EEG sources such as Alpha rhythm as the known sources. The performance of the scheme is shown on real EEG measurements and compared with that of “conventional dipole fitting algorithm”.

Electromagnetic brain mapping

There has been tremendous advances in our ability to produce images of human brain function. Applications of functional brain imaging extend from improving our understanding of the basic mechanisms of cognitive processes to better characterization of pathologies that impair normal function. Magnetoencephalography (MEG) and electroencephalography (EEG) (MEG/EEG) localize neural electrical activity using noninvasive measurements of external electromagnetic signals. Among the available functional imaging techniques, MEG and EEG uniquely have temporal resolutions below 100 ms. This temporal precision allows us to explore the timing of basic neural processes at the level of cell assemblies. MEG/EEG source localization draws on a wide range of signal processing techniques including digital filtering, three-dimensional image analysis, array signal processing, image modeling and reconstruction, and, blind source separation and phase synchrony estimation. We describe the underlying models currently used in MEG/EEG source estimation and describe the various signal processing steps required to compute these sources. In particular we describe methods for computing the forward fields for known source distributions and parametric and imaging-based approaches to the inverse problem     

Estimating stationary dipoles from MEG/EEG data contaminated withspatially and temporally correlated background noise

The stationary dipole model for the inverse problem of magnetoencephalographic (MEG) and electroencephalographic (EEG) data is extended by including spatio-temporal correlations of the background noise. For that purpose, the spatio-temporal covariances are described as a Kronkecker product of a spatial and a temporal covariance matrix. The maximum likelihood method is used to estimate this Kronecker product from a series of trials of MEG/EEG data. A simulation study shows that the inclusion of the background noise generally improves the dipole estimate substantially. When the frequency of the source time functions, however, coincides with the frequency contents of the covariance function, the dipole estimate worsens when the temporal correlations are included. The inclusion of spatial correlations always improves the estimates     

Effect of Head Shape Variations Among Individuals on the EEG/MEG Forward and Inverse Problems

We study the effect of the head shape variations on the EEG/magnetoencephalography (MEG) forward and inverse problems. We build a random head model such that each sample represents the head shape of a different individual and solve the forward problem assuming this random head model, using a polynomial chaos expansion. The random solution of the forward problem is then used to quantify the effect of the geometry when the inverse problem is solved with a standard head model. The results derived with this approach are valid for a continuous family of head models, rather than just for a set of cases. The random model consists of three random surfaces that define layers of different electric conductivity, and we built an example based on a set of 30 deterministic models from adults. Our results show that for a dipolar source model, the effect of the head shape variations on the EEG/MEG inverse problem due to the random head model is slightly larger than the effect of the electronic noise present in the sensors. The variations in the EEG inverse problem solutions are due to the variations in the shape of the volume conductor, while the variations in the MEG inverse problem solutions, larger than the EEG ones, are caused mainly by the variations of the absolute position of the sources in a coordinate system based on anatomical landmarks, in which the magnetometers have a fixed position.     

Some New Expressions for Nakagami Fading Based on Order Statistics

Alternative representations based on order statistics are derived for the probability of error for orthogonal, biorthogonal, and transorthogonal signaling. Short programs in are developed for the computation of these representations and to furnish evidence to show that their performance is superior to the traditional Monte Carlo approach.