Compact Modules for Wireless Communication Systems in the E-Band (71–76 GHz)

The millimeter-wave spectrum above 70 GHz provides a cost-effective solution to increase the wireless communications data rates by increasing the carrier wave frequencies. We report on the development of two key components of a wireless transmission system, a high-speed photodiode (HS-PD) and a Schottky Barrier Diode (SBD). Both components operate uncooled, a key issue in the development of compact modules. On the transmitter side, an improved design of the HS-PD allows it to deliver an output RF power exceeding 0 dBm (1 mW). On the receiver side, we present the design process and achieved results on the development of a compact direct envelope detection receiver based on a quasi-optical SDB module. Different resonant (meander dipole) and broadband (Log-Spiral and Log-Periodic) planar antenna solutions are designed, matching the antenna and Schottky diode impedances at high frequency. Impedance matching at baseband is also provided by means of an impedance transition to a 50 Ohm output. From this comparison, we demonstrate the excellent performance of the broadband antennas over the entire E-band by setting up a short-range wireless link transmitting a 1 Gbps data signal.     

Chiral Excitonic Organic Photodiodes for Direct Detection of Circular Polarized Light

A facile route to soft matter self‐powered bulk heterojunction photodiode detectors sensitive to the circular polarization state of light is shown based on the intrinsic excitonic circular dichroism of the photoactive layer blend. As light detecting materials, enantiopure semiconducting small molecular squaraine derivates of opposite handedness are employed. Via Mueller matrix ellipsometry, the circular dichroism is proven to be of H‐type excitonic nature and not originating from mesoscopic structural ordering. Within the green spectral range, the photodiodes convert circular polarized light into a handedness‐dependent photocurrent with a maximum dissymmetry factor of ±0.1 corresponding to 5% overall efficiency for the polarization discrimination under short circuit conditions. On the basis of transfer matrix optical simulations, it is rationalized that the optical dissymmetry fully translates into a photocurrent dissymmetry for ease of device design. Thereby, the photodiode's ability to efficiently distinguish between left and right circularly polarized light without the use of external optical elements and voltage bias is demonstrated. This allows a straightforward and sustainable future design of flexible, lightweight, and compact integrated platforms for chiroptical imaging and sensing.     

Tuning the Properties of CZTS Films by Controlling the Process Parameters in Cost-Effective Non-vacuum Technique

Highly dispersive Cu₂ZnSnS₄ (CZTS) nanoparticles were successfully synthesized by a simple solvothermal route. A low cost, non-vacuum method was used to deposit CZTS nanoparticle ink on glass substrates by a doctor blade process followed by selenization in a tube furnace to form Cu₂ZnSn (S,Se)₄ (CZTSSe) layers. Different selenization conditions and particle concentrations were considered in order to improve the crystallinity and surface morphology; the annealing temperature was varied between 400°C and 550°C and the annealing time was varied between 5 min and 20 min in a selenium-nitrogen atmosphere. The influence of annealing conditions on structural, compositional, optical and electrical properties of CZTSSe thin films was studied. An improvement in the structural and surface morphology was observed with increasing of annealing temperature (up to 500°C). An enhancement in the crystallinity and surface morphology were observed for thin films annealed for 10–15 min. Absorption study revealed that the band gap energy of as-deposited CZTS thin film was approximately 1.43 eV, while for CZTSSe thin films it ranged from 1.15 eV to 1.34 eV at different annealing temperatures, and from 1.33 eV to 1.38 eV for different annealing times.     

Power consumption reduction through elastic data rate adaptation in survivable multi-layer optical networks

Network survivability requires the provisioning of backup resources in order to protect active traffic against any failure scenario. Backup resources, however, can remain unused most of the time while the network is not in failure condition, inducing high power consumption wastage, if fully powered on. In this paper, we highlight the power consumption wastage of the additional resources for survivability in IP/multi-protocol label switching (MPLS) over dense wavelength division multiplexing multi-layer optical networks. We assume MPLS protection switching as the failure recovery mechanism in the network, a solution interesting for current network operators to ensure fast recovery as well as fine-grained recovery treatment per label switched path. Next, we quantitatively show how elastic optical technologies can effectively reduce such a power consumption by dynamically adjusting the data rate of the transponders to the carried amount of traffic.     

Toward a direct measure of video quality perception using EEG

An approach to the direct measurement of perception of video quality change using electroencephalography (EEG) is presented. Subjects viewed 8-s video clips while their brain activity was registered using EEG. The video signal was either uncompressed at full length or changed from uncompressed to a lower quality level at a random time point. The distortions were introduced by a hybrid video codec. Subjects had to indicate whether they had perceived a quality change. In response to a quality change, a positive voltage change in EEG (the so-called P3 component) was observed at latency of about 400-600 ms for all subjects. The voltage change positively correlated with the magnitude of the video quality change, substantiating the P3 component as a graded neural index of the perception of video quality change within the presented paradigm. By applying machine learning techniques, we could classify on a single-trial basis whether a subject perceived a quality change. Interestingly, some video clips wherein changes were missed (i.e., not reported) by the subject were classified as quality changes, suggesting that the brain detected a change, although the subject did not press a button. In conclusion, abrupt changes of video quality give rise to specific components in the EEG that can be detected on a single-trial basis. Potentially, a neurotechnological approach to video assessment could lead to a more objective quantification of quality change detection, overcoming the limitations of subjective approaches (such as subjective bias and the requirement of an overt response). Furthermore, it allows for real-time applications wherein the brain response to a video clip is monitored while it is being viewed.     

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time‐of‐Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.     

Adaptive phase extraction: incorporating the Gabor transform in the matching pursuit algorithm

Short-time Fourier transform (STFT), Gabor transform (GT), wavelet transform (WT), and the Wigner-Ville distribution (WVD) are just some examples of time-frequency analysis methods which are frequently applied in biomedical signal analysis. However, all of these methods have their individual drawbacks. The STFT, GT, and WT have a time-frequency resolution that is determined by algorithm parameters and the WVD is contaminated by cross terms. In 1993, Mallat and Zhang introduced the matching pursuit (MP) algorithm that decomposes a signal into a sum of atoms and uses a cross-term free pseudo-WVD to generate a data-adaptive power distribution in the time-frequency space. Thus, it solved some of the problems of the GT and WT but lacks phase information that is crucial e.g., for synchronization analysis. We introduce a new time-frequency analysis method that combines the MP with a pseudo-GT. Therefore, the signal is decomposed into a set of Gabor atoms. Afterward, each atom is analyzed with a Gabor analysis, where the time-domain gaussian window of the analysis matches that of the specific atom envelope. A superposition of the single time-frequency planes gives the final result. This is the first time that a complete analysis of the complex time-frequency plane can be performed in a fully data-adaptive and frequency-selective manner. We demonstrate the capabilities of our approach on a simulation and on real-life magnetoencephalogram data.     

A New Design Concept for High-Performance Fading Channel Simulators Using Set Partitioning

In this paper, we introduce a new technique for the design of high-performance Rayleigh fading channel simulators. The proposed design method uses set partitioning – a technique, which plays a key role in the design of trellis-coded modulation schemes. We show how set partitioning can be used to design multiple uncorrelated fading waveforms enabling the simulation of Rayleigh fading channels. For the important case of isotropic scattering, we show that the sample average of the generated waveforms results in a deterministic process, the autocorrelation function (ACF) of which tends to the zeroth-order Bessel function of the first kind as the number of sample functions increases. The proposed procedure is completely deterministic. The comparison with a stochastic procedure using Monte Carlo techniques will be made. A study of the performance shows clearly that the new technique using set partitioning outperforms by far existing Monte Carlo methods.     

On the Static Resolution of Digitally Corrected Analog-to-Digital and Digital-to-Analog Converters With Low-Precision Components

This semi-tutorial paper considers the effect of component mismatch on the static accuracy of analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) with digital correction. First, it is noted that the effective static resolution of flash ADCs is not much reduced by component mismatch: with proper digital correction, the loss due to mismatch is only about 1.3 bit, virtually independently of the mismatch level unless the mismatch is very small. Second, it is noted that current steering DACs may actually benefit from component mismatch. Moreover, with proper digital correction, current steering DACs can achieve an effective static resolution of m bits with as few as m+2 near-unit low-precision current sources