Enhancement of the piezoelectric coefficient in PVDF-TrFe/CoFe2O4 nanocomposites through DC magnetic poling

In the last years flexible, low-cost, wearable, and innovative piezoelectric nanomaterials have attracted considerable interest regarding the development of energy harvesters and sensors. Among the piezoelectric materials, special attention has been paid to electroactive polymers such as poly(vinylidene fluoride) (PVDF) and its copolymer poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFe), which is one of the most extensively investigated piezoelectric polymers, due to the high β phase content resulting from specific curing or processing conditions. However, to obtain a high piezoelectric coefficient (d33) alignment of the β phase domains is needed, which is usually reached through applying a high electric field at moderate temperatures. This process, usually referred to as electrical poling, requires the deposition of contact electrodes on the sample surface and the use of high-voltage apparatus. In the present work, in order to overcome these constraints, we have produced, characterized, and studied a polymer nanocomposite consisting of CoFe2O4 nanoparticles dispersed in PVDF-TrFe with enhancement of the β phase alignment through an applied DC magnetic field. The magnetic poling was demonstrated to be particularly effective, leading to a piezoelectric coefficient d33 with values up to 39 pm/V. This type of poling does not need the use of a top electrode or of high magnetic fields (the maximum value of d33 was obtained at 50 mT, using a current of 0.4 A) making the PVDF-TrFE/CoFe2O4 nanocomposite suitable for the fabrication of highly efficient devices for energy harvesting and wearable sensors.

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics

Non-sinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase-grid space, makes it possible to compare cycles of different durations and shapes. 'Normalised shapes' can then be constructed with high temporal detail whilst accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average, yet exhibiting high variability on a cycle-by-cycle basis. We show how Principal Components Analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of enquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.

Enhancement of Piezoelectric Coefficient (d33) in PVDF-TrFe/CoFe2O4 nanocomposites through DC magnetic poling

In the last years flexible, low-cost, wearable and innovative piezoelectric nanomaterials, have attracted a considerable interest to develop energy harvesters and sensors. Among the piezoelectric materials, a special focus was paid on electroactive polymers such as Poly(vinylidene fluoride) [PVDF] and on its copolymer Poly(vinylidene fluoride-co-trifluoroethylene) [PVDF-TrFe], which is one of the most investigated piezoelectric polymers, due to the high β-phase content resulting under specific curing or processing conditions. However, to get high piezoelectric coefficient (d33), alignment of the β-phase domains is needed, which is usually obtained by applying a high electric fields at moderate temperatures. This process, usually referred as electrical poling, requires the deposition of contact electrodes over the sample surface, and the use of high voltage apparatus. In the present work, in order to overcome these constraints we have produced, characterized and studied a polymer nanocomposite, consisting of CoFe2O4 nanoparticles dispersed in PVDF-TrFe with enhancement of the β-phase alignment through and applied a DC magnetic fields. The magnetic poling was demonstrated to be particular effective, leading to a piezoelectric coefficient, d33, with values up to 39 pm/V. The magnetic poling does not need the use a top electrode and of high magnetic fields (the maximum value of d33 was obtained at 50 mT, using a current of 0.4 A) making the PVDF-TrFE/CoFe2O4 nanocomposite suitable for the fabrication of highly efficient devices for energy harvesting and wearable sensors.

The statistical characteristics of knock signal waveforms

Individual instances of the knock resonant response are easy to acquire but these are subject to noise and vary considerably from cycle to cycle due to random variations in the knock process. This work provides a new way to quantify and model the stochastic properties of knock signals, capturing both the time domain resonant characteristics within a cycle as well as the random variations from cycle to cycle. A new phase alignment method enables the ensemble mean knock waveform to be identified from the data which also removes noise components without the need for narrowband filtering. This ensemble waveform shows the empirical characteristics of knock onset, decay, and frequency slurring within the cycle as the gas expands and cools. The phase-aligned cyclic variations of the knock waveform are also shown to approximate a (time-varying) dual-Gaussian distribution, and fitting such a model to the data enables the statistical properties of the dataset as a whole to be decomposed into separate knocking and non-knocking populations providing further insight into the knock process. The technique is applied both to filtered cylinder pressure signals and to accelerometer-based knock signals, and the results are compared and contrasted.

BlueFMCW: Random Frequency Hopping Radar for Mitigation of Interference and Spoofing

Radars form a central piece in a variety of emerging applications requiring higher degrees of localization. However, two problems are anticipated as more radars are deployed: viz., (i) inter-radar interference and (ii) security attacks. While many prior proposals have addressed the problems, no work in the radar literature addressed them simultaneously. In this context, we introduce a novel frequency-modulated continuous-wave (FMCW) radar scheme (namely, BlueFMCW) that aims to alleviate the damage from interference and active attacks (e.g., spoofing). The technique designs that the waveform randomly hops across multiple frequencies to dilute the damage at a certain frequency. Moreover, we propose a phase alignment algorithm to remove the phase discontinuity while combining the beat signals from the randomly-hopped chirps. The simulation results show that the proposed technique can efficiently mitigate the interference and spoofing signals in various scenarios without costing its resolution.

Spatial structure, phase, and the contrast of natural images

The sensitivity of the human visual system is thought to be shaped by environmental statistics. A major endeavour in visual neuroscience, therefore, is to uncover the image statistics that predict perceptual and cognitive function. When searching for targets in natural images, for example, it has recently been proposed that target detection is inversely related to the spatial similarity of the target to its local background. We tested this hypothesis by measuring observers' sensitivity to targets that were blended with natural image backgrounds. Importantly, targets were designed to have a spatial structure that was either similar or dissimilar to the background. Contrary to masking from similarity, however, we found that observers were most sensitive to targets that were most similar to their backgrounds. We hypothesised that a coincidence of phase-alignment between target and background results in a local contrast signal that facilitates detection when target-background similarity is high. We confirmed this prediction in a second experiment. Indeed, we show that, by solely manipulating the phase of a target relative to its background, the target can be rendered easily visible or completely undetectable. Our study thus reveals a set of image statistics that predict how well people can perform the ubiquitous task of detecting an object in clutter.

Theta phase mediates deliberate action switching in human Supplementary Motor Areas

The ability to deliberately overwrite ongoing automatic actions is a necessary feature of adaptive behavior. It has been proposed that the supplementary motor areas (SMAs) operate as a controller that orchestrates the switching between automatic and deliberate processes by inhibiting ongoing behaviors and so facilitating the execution of alternative ones. In addition, previous studies support the involvement of SMAs theta waves (4-9 Hz) in cognitive control. However, the exact role of such oscillatory dynamics and their contribution to the control of action are not fully understood. To investigate the mechanisms by which the SMAs support direct control of deliberate behavior, we recorded intracranial electroencephalography (iEEG) activity in humans performing a motor sequence task. Subjects had to perform a "change of plans" motor task requiring habitual movements to be overwritten at unpredictable moments. We found that SMAs were exclusively active during trials that demand action reprogramming in response to the unexpected cue but were silent during automatic action execution. Importantly, SMAs activity was characterized by a distinct temporal pattern, expressed in a stereotypical phase alignment of theta oscillations. More specifically, single trial motor performance was correlated with the trial contribution to the global inter-trial phase coherence, with higher coherence associated with faster trials. In addition, theta phase modulated the amplitude of gamma oscillations, with higher cross-frequency coupling in faster trials. Our results suggest that within frontal cortical networks, theta oscillations could encode a control signal that promotes the execution of deliberate actions.