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In line-of-sight massive MIMO, the downlink channel vectors of few users may become highly correlated. This high correlation limits the sum-rates of systems employing linear precoders. To constrain the reduction of the sum-rate, few users can be dropped and served in the next coherence intervals. The optimal strategy for selecting the dropped users can be obtained by an exhaustive search at the cost of high computational complexity. To alleviate the computational complexity of the exhaustive search, a correlation-based dropping algorithm (CDA) is conventionally used, incurring a sum-rate loss with respect to the optimal scheme. In this paper, we propose a dropping algorithm based on neural networks (DropNet) to find the set of dropped users. We use appropriate input features required for the user dropping problem to limit the complexity of DropNet. DropNet is evaluated using two known linear precoders: conjugate beamforming (CB) and zero-forcing (ZF). Simulation results show that DropNet provides a trade-off between complexity and sum-rate performance. In particular, for a 64-antenna base station and 10 single-antenna users: (i) DropNet reduces the computational complexity of the exhaustive search by a factor of 46 and 3 for CB and ZF, respectively, (ii) DropNet improves the 5th percentile sum-rate of CDA by 0.86 and 2.33 bits/s/Hz for CB and ZF, respectively.

The field of generating movement profiles of individuals is valuable in many real-world applications (e.g., controlling disease spread or evaluating marketing engagement). Existing solutions often rely on global positioning systems (GPS) or similar systems, primarily targeted at outdoor use cases. However, the indoor tracking capabilities of current solutions either lack precision or are available in closed buildings only. The literature proposes sensor fusion approaches, but many of those are based on specific sensors. These approaches do not reveal implementation details or data to allow for their independent evaluation. Therefore, this article presents FusIon Data Tracking System (FITS) as an approach and proof-of-concept to facilitate the correlation of data from different indoor sensors to movement profiles of different individuals. Functionally, FITS does this by generating synthetic sensor measurement data based on real-world movement data and correlating objects tracked from distinct sensors by effectively solving clustering and position prediction tasks. This correlation is evaluated based on different metrics [multiple object tracker accuracy/precision (MOTA/MOTP)] in four different scenarios, for example, sparse data, high density of sensors, low density of sensors, and a base case. Finally, FITS’s performance was evaluated by increasing the load test (dataset up to 100000 measurements and 1000 visitors) to assess whether near real-time processing is feasible under a high workload. [ABSTRACT FROM AUTHOR]

We propose a storage memory device that enables bit densities of >1 Tbit/mm2 based on the electro- deposition and electrodissolution of multilayered metal stacks in deep nanometer-sized wells. This device addresses the challenge of bit density scaling slowdown expected for 3-D NAND flash beyond 2030. We describe in detail the operating principles and discuss the response time, bandwidth, retention, and cycling endurance requirements for the device to be viable. As a proof-of-principle, we provide a first demonstration of the write/read (W/R) mechanism on millimeter- and micrometer-sized electrodes and show the device’s potential for reaching very high bit densities. To evaluate how the response time scales for the envisioned nanometer-sized electrodes, we derive simple analytical expressions based on finite element simulations that relate the well depth, radius, and electrolyte composition to the deposition/dissolution rate. [ABSTRACT FROM AUTHOR]

Small fission chambers manufactured by Photonis, Reuter-Stokes, CEA, and Centronic are routinely calibrated in standard neutron fields of the BR1 reactor at SCK CEN in Mol, Belgium. Two irradiation fields are available: fast spectrum in the MARK3 convertor and thermal spectrum in the Empty Cavity. In this work, for the first time the calibration procedure of larger fission chambers (Photonis CFUL01 and CFUM21 type) with the deposit length exceeding the length of the convertor is presented. Spatial energy correction factors taking into account the neutron flux shape beyond the convertor and radial gradient of the neutron flux were calculated with MCNP and experimentally validated. The combination of calibration (i.e., effective mass measurement) in fast and thermal irradiation fields allows for determination of the purity of 238U deposit. This is demonstrated on three CFUL01-type fission chambers with purity between 99.8% and 99.998%. [ABSTRACT FROM AUTHOR]

Nano-scaling of metal-oxide-semiconductor (MOS) field-effect transistors (FETs) is exploited to benefit the interdisciplinary field of single-molecule biosensing. While single-molecule DNA sequencing is done successfully by ionic current sensing through nanopores, the unambiguous characterization of more complex single biomolecules, such as fast translocating proteins, remains challenging with existing techniques. However, the nanopore-FET (NP-FET), a device with a nanopore embedded within the channel of the FET, is a promising new device detecting the motion of a molecule through the nanopore based on the transistor’s electronic current. This nano-scale FET-based approach enables next-generation single-molecule sensing by offering larger signals and hence higher bandwidth, responding to a key challenge of detecting fast translocating molecules, and by offering denser electronic system packing and therefore more parallel sensing. However, the sensitivity of the nanopore-FET reported so far was limited to about 30%. In this paper, we show that the inherent potential of this hybrid nanofluidic-nanoelectronic device significantly exceeds the initial reportings by demonstrating sensitivity predictions up to 1000%. Our findings are supported with 3D nanofluidic-nanoelectronic open-source device simulations. Insight in the versatility of the device is provided through geometrical device optimization and demonstration that the device is sensitive to both positively and negatively charged molecules in both n- and p-channel FET configurations. These promising features, together with the immense expertise in MOS fabrication and scaling, offer a path to a highly parallelized and scalable sensor platform for genomics and proteomics. [ABSTRACT FROM AUTHOR]

Recently, a metric called energy dispersion index (EDI) was proposed to qualitatively indicate the nonlinear interference (NLI) induced by correlated symbols during optical transmission. In this paper, we propose a new shaper architecture to decrease the EDI of transmitted symbols and thus, increase the signal-to-noise ratio (SNR). We call this shaper the list-encoding constant-composition distribution matcher (L-CCDM). L-CCDM consists of an additional EDI selecting module, which is compatible with standard probabilistic amplitude shaping (PAS) architecture. Numerical results obtained from a multi-span multi-channel system show that when compared to standard CCDM with 256-ary quadrature amplitude modulation (256QAM), the proposed architecture offers an effective SNR gain of 0.35 dB, an achievable information rate gain of 0.22 b/4D-symbol, or equivalently an 8% reach extension. [ABSTRACT FROM AUTHOR]