Short-window spectral analysis using AMVAR and multitaper methods: a comparison

We compare two popular methods for estimating the power spectrum from short data windows, namely the adaptive multivariate autoregressive (AMVAR) method and the multitaper method. By analyzing a simulated signal (embedded in a background Ornstein-Uhlenbeck noise process) we demonstrate that the AMVAR method performs better at detecting short bursts of oscillations compared to the multitaper method. However, both methods are immune to jitter in the temporal location of the signal. We also show that coherence can still be detected in noisy bivariate time series data by the AMVAR method even if the individual power spectra fail to show any peaks. Finally, using data from two monkeys performing a visuomotor pattern discrimination task, we demonstrate that the AMVAR method is better able to determine the termination of the beta oscillations when compared to the multitaper method.

Evidence for a thermally reversing window in bulk Ge-Te-Si glasses revealed by alternating differential scanning calorimetry

Experiments on Ge15Tc85-xSix glasses (2 <= x <= 12) using alternating differential scanning calorimetry (ADSC) indicate that these glasses exhibit one glass transition and two crystallization reactions upon heating. The glass transition temperature has been found to increase almost linearly with silicon content, in the entire composition tie-line. The first crystallization temperature (T-cl) exhibits an increase with silicon content for x<5; T-cl remains almost a constant in the composition range 5 < x <= 10 and it increases comparatively more sharply with silicon content thereafter. The specific heat change (Delta C-p) is found to decrease with an increase in silicon content, exhibiting a minimum at x=5 (average coordination number, (r) = 2.4); a continuous increase is seen in Delta C-p with silicon concentration above x = 5. The effects seen in the variation with composition of T-cl and Delta C-p at x=5, are the specific signatures of the mean-field stiffness threshold at (r) = 2.4. Furthermore, a broad trough is seen in the enthalpy change (Delta H-NR), which is indicative of a thermally reversing window in Ge15Te85-xSix glasses in the composition range 2 <= x <= 6 (2.34 <= (r) <= 2.42).

Novel window functions for digital filters

Window technique is one of the simplest methods to design Finite Impulse Response (FIR) filters. It uses special functions to truncate an infinite sequence to a finite one. In this paper, we propose window techniques based on integer sequences. The striking feature of the proposed work is that it overcomes all the problems posed by floating point numbers and inaccuracy, as the sequences are made of only integers. Some of these integer window sequences, yield sharp transition, while some of them result in zero ripple in passband and stopband.

Observation of a thermally reversing window in bulk Ge15Te85-xInx glasses

Alternating differential scanning calorimetry measurements have been undertaken on the Ge15Te85-xInx (1 <= x <= 11) series of glasses. It is found that there is a marginal decrease in the glass transition temperature (T-g) in the composition range 1 <= x <= 3. Above x = 3, a monotonic increase is seen in T-g which indicates a continuous build-up in network connectivity and absence of any nanophase separation. The non-reversing heat flow (Delta H-NR) has been found to exhibit a broad trough between the compositions x = 3 and 7, which clearly indicates the presence of a thermally reversing window in Ge15Te85-xInx glasses in the composition range 3 <= x <= 7.

Ternary Iron(III) complex showing photocleavage of DNA in the photodynamic therapy window

A new ternary iron(III) complex [FeL(dpq)] containing dipyridoquinoxaline (dpq) and 2,2-bis(3,5-di-tert-butyl-2-hydroxybenzyl)aminoacetic acid (H3L) is prepared and structurally characterized by X-ray crystallography. The high-spin complex with a FeN3O3 core shows a quasi-reversible iron(III)/iron(II) redox couple at -0.62 V (vs SCE) in DMF/0.1 M TBAP and a broad visible band at 470 nm in DMF/Tris buffer. Laser photoexcitation of this phenolate (L)-to-iron(III) charge-transfer band at visible wavelengths including red light of >= 630 nm leads to cleavage of supercoiled pUC19 DNA to its nicked circular form via a photoredox pathway forming hydroxyl radicals.

Double time window targeting technique: Real-time DMRG dynamics in the Pariser-Parr-Pople model

We present a generalized adaptive time-dependent density matrix renormalization-group (DMRG) scheme, called the double time window targeting (DTWT) technique, which gives accurate results with nominal computational resources, within reasonable computational time. This procedure originates from the amalgamation of the features of pace keeping DMRG algorithm, first proposed by Luo et al. [Phys. Rev. Lett. 91, 049701 (2003)] and the time-step targeting algorithm by Feiguin and White [Phys. Rev. B 72, 020404 (2005)]. Using the DTWT technique, we study the phenomena of spin-charge separation in conjugated polymers (materials for molecular electronics an spintronics), which have long-range electron-electron interactions and belong to the class of strongly correlated low-dimensional many-body systems. The issue of real-time dynamics within the Pariser-Parr-Pople (PPP) model which includes long-range electron correlations has not been addressed in the literature so far. The present study on PPP chains has revealed that, (i) long-range electron correlations enable both the charge and spin degree of freedom of the electron, to propagate faster in the PPP model compared to Hubbard model, (ii) for standard parameters of the PPP model as applied to conjugated polymers, the charge velocity is almost twice that of the spin velocity, and (iii) the simplistic interpretation of long-range correlations by merely renormalizing the U value of the Hubbard model fails to explain the dynamics of doped holes/electrons in the PPP model.

Smart applications for energy harvested WSNs

A Wireless Sensor Network (WSN) powered using harvested energies is limited in its operation by instantaneous power. Since energy availability can be different across nodes in the network, network setup and collaboration is a non trivial task. At the same time, in the event of excess energy, exciting node collaboration possibilities exist; often not feasible with battery driven sensor networks. Operations such as sensing, computation, storage and communication are required to achieve the common goal for any sensor network. In this paper, we design and implement a smart application that uses a Decision Engine, and morphs itself into an energy matched application. The results are based on measurements using IRIS motes running on solar energy. We have done away with batteries; instead used low leakage super capacitors to store harvested energy. The Decision Engine utilizes two pieces of data to provide its recommendations. Firstly, a history based energy prediction model assists the engine with information about in-coming energy. The second input is the energy cost database for operations. The energy driven Decision Engine calculates the energy budgets and recommends the best possible set of operations. Under excess energy condition, the Decision Engine, promiscuously sniffs the neighborhood looking for all possible data from neighbors. This data includes neighbor's energy level and sensor data. Equipped with this data, nodes establish detailed data correlation and thus enhance collaboration such as filling up data gaps on behalf of nodes hibernating under low energy conditions. The results are encouraging. Node and network life time of the sensor nodes running the smart application is found to be significantly higher compared to the base application.

Integer Sequence window based Reconfigurable FIR filters

Conventional hardware implementation techniques for FIR filters require the computation of filter coefficients in software and have them stored in memory. This approach is static in the sense that any further fine tuning of the filter requires computation of new coefficients in software. In this paper, we propose an alternate technique for implementing FIR filters in hardware. We store a considerably large number of impulse response coefficients of the ideal filter (having box type frequency response) in memory. We then do the windowing process, on these coefficients, in hardware using integer sequences as window functions. The integer sequences are also generated in hardware. This approach offers the flexibility in fine tuning the filter, like varying the transition bandwidth around a particular cutoff frequency.