Green Biocomposites for Thermoelectric Wearable Applications

The materials commonly used to fabricate thermoelectric devices are tellurium, lead, and germanium. These materials ensure the best thermoelectric performance, but exhibit drawbacks in terms of availability, sustainability, cost, and manufacturing complexity. Moreover, they do not guarantee a safe and cheap implementation in wearable thermoelectric applications. Here, p‐Type and n‐type flexible thermoelectric textiles are produced with sustainable and low‐cost materials through green and scalable processes. Cotton is functionalized with inks made with biopolyester and carbon nanomaterials. Depending on the nanofiller, i.e., graphene nanoplatelets, carbon nanotubes, or carbon nanofibers, positive or negative Seebeck coefficient values are obtained, resulting in a remarkable electrical conductivity value of 55 S cm^?1 using carbon nanotubes. The best bending and washing stability are registered for the carbon nanofiber‐based biocomposites, which increase their electrical resistance by 5 times after repeated bending cycles and only by 30% after washing. Finally, in‐plane flexible thermoelectric generators coupling the best p‐ and n‐type materials are fabricated and analysed, resulting in an output voltage of ≈1.65 mV and a maximum output power of ≈1.0 nW by connecting only 2 p/n thermocouples at a temperature difference of 70 °C.     

Connectivity of Ad Hoc Networks with Link Asymmetries Induced by Shadowing

The aim of this letter is to determine the minimum node density to achieve a connected large-scale ad hoc network, where every node has the same transmitting and receiving capabilities. Due to the log-normal shadowing, links are unidirectional in general. Contrary to the prevailing opinion, we argue that such asymmetries result into a "reduced" connectivity graph, which, from the point of view of MAC and routing protocols, is to be considered the true or effective connectivity graph. Accordingly, we derive a new formula for the connection probability between two nodes in order to compute global connectivity. Finally, theoretical findings, borrowed from random graphs theory, are compared to numerical simulation results in synthetic wireless network scenarios.     

A 1.44-mW, 1-MHz bandwidth continuous-time Σ∆ modulator with 10-bit ENOB in 130-nm CMOS

A low-power, low-pass continuous-time sigma-delta A/D modulator with 1-MHz bandwidth is implemented in a 130-nm CMOS process. The circuit is targeted for an IEEE 802.15.4 direct conversion receiver operating in the 2.4-GHz band. It has a third-order feedforward single-loop filter and a 1-bit quantizer to minimize power consumption. Non-return-to-zero DAC pulse shaping is used to reduce sensitivity to clock jitter. Clocked at 64 MHz, the prototype chip achieves 62-dB peak SNR, −70-dB peak THD, 63-dB dynamic range with an oversampling ratio of 32. The built-in anti-alias filter provides alias attenuation greater than 57 dB, which improves coexistence of the receiver with other devices that operate in the 2.4-GHz band. The circuit dissipates 1.44 mW from the 1.2-V supply and the active die area is 0.1 mm².     

Adaptive Call Admission Control for QoS/Revenue Optimization in CDMA Cellular Networks

In this paper, we show how online management of both quality of service (QoS) and provider revenue can be performed in CDMA cellular networks by adaptive control of system parameters to changing traffic conditions. The key contribution is the introduction of a novel call admission control and bandwidth degradation scheme for real-time traffic as well as the development of a Markov model for the admission controller. This Markov model incorporates important features of 3G cellular networks, such as CDMA intra- and inter-cell interference, different call priorities and soft handover. From the results of the Markov model the threshold for maximal call degradation is periodically adjusted according to the currently measured traffic in the radio access network. As a consequence, QoS and revenue measures can be optimized with respect to a predefined goal. To illustrate the effectiveness of the proposed QoS/revenue management approach, we present quantitative results for the Markov model and a comprehensive simulation study considering a half-day window of a daily usage pattern.     

On the variability of manual spike sorting

The analysis of action potentials, or "spikes," is central to systems neuroscience research. Spikes are typically identified from raw waveforms manually for off-line analysis or automatically by human-configured algorithms for on-line applications. The variability of manual spike "sorting" is studied and its implications for neural prostheses discussed. Waveforms were recorded using a micro-electrode array and were used to construct a statistically similar synthetic dataset. Results showed wide variability in the number of neurons and spikes detected in real data. Additionally, average error rates of 23% false positive and 30% false negative were found for synthetic data.     

A meshless method for solving the EEG forward problem

We present a numerical method to solve the quasistatic Maxwell equations and compute the electroencephalography (EEG) forward problem solution. More generally, we develop a computationally efficient method to obtain the electric potential distribution generated by a source of electric activity inside a three-dimensional body of arbitrary shape and layers of different electric conductivities. The method needs only a set of nodes on the surface and inside the head, but not a mesh connecting the nodes. This represents an advantage over traditional methods like boundary elements or finite elements since the generation of the mesh is typically computationally intensive. The performance of the proposed method is compared with the boundary element method (BEM) by numerically solving some EEG forward problems examples. For a large number of nodes and the same precision, our method has lower computational load than BEM due to a faster convergence rate and to the sparsity of the linear system to be solved.     

MEG forward problem formulation using equivalent surface current densities

We present a formulation for the magnetoencephalography (MEG) forward problem with a layered head model. Traditionally the magnetic field is computed based on the electric potential on the interfaces between the layers. We propose to express the effect of the volumetric currents in terms of an equivalent surface current density on each interface, and obtain the magnetic field based on them. The boundary elements method is used to compute the equivalent current density and the magnetic field for a realistic head geometry. We present numerical results showing that the MEG forward problem is solved correctly with this formulation, and compare it with the performance of the traditional formulation. We conclude that the traditional formulation generally performs better, but still the new formulation is useful in certain situations.