This paper establishes a solid mathematical model for Over the Air (OTA) test and characterization of 5G millimeter wave User Equipment (UE) in Compact Antenna Test Ranges (CATR). Special attention is paid to receiver characteristics and polarization mismatch problem which is encountered in spherical coverage tests. The received power of a UE with dual-polarized antenna system is derived and output SNR is calculated using Maximal Ratio Combining (MRC) principle. And finally, some of the misconceptions in 3rd Generation Partnership Project (3GPP) standards publications is addressed

Reconfigurable intelligent surface (RIS)-empowered communication is being considered as an enabling technology for sixth generation (6G) wireless networks. The key idea of RIS-assisted communication is to enhance the capacity, coverage, energy efficiency, physical layer security, and many other aspects of modern wireless networks. At the same time, mobile edge computing (MEC) has already shown its huge potential by extending the computation, communication, and caching capabilities of a standalone cloud server to the network edge. In this article, we first provide an overview of how MEC and RIS can benefit each other. We envision that the integration of MEC and RIS will bring an unprecedented transformation to the future evolution of wireless networks. We provide a system-level perspective on the MEC-aided RIS (and RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline some of the fundamental challenges that pertain to the implementation of MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends in the RIS-assisted MEC are discussed.

This paper investigates the seamless mapping of wireless mmWave phase-levels to the optical phase-levels in a seamless digital mmWave-to-photonic converter. We report a non-linear mapping of phase-levels from the wireless to the optical domain which results in digital symbol degeneracy. Our work provides various optimum design guidelines to make such seamless converters practically feasible in digital communication links. The latency and the design complexity of such a seamless converter are expected to be appreciably lower than those of a conventional converter, thereby making it more suitable for 6G communication systems. Although the wireless operating frequency considered in this paper belongs to the mmWave band, all the contributions presented are equally applicable and relevant in the THz wireless band as well.

We develop and evaluate an automated data-driven framework for providing reviewer recommendations for submitted manuscripts. Given inputs comprising a set of manuscripts for review and a listing of a pool of prospective reviewers, our system uses a publisher database to extract papers authored by the reviewers from which a Paragraph Vector (doc2vec ) neural network model is learned and used to obtain vector space embeddings of documents. Similarities between embeddings of an individual reviewer’s papers and a manuscript are then used to compute manuscript-reviewer match scores and to generate a ranked list of recommended reviewers for each manuscript. Our mainline proposed system uses full text versions of the reviewers’ papers, which we demonstrate performs significantly better than models developed based on abstracts alone, which has been the predominant paradigm in prior work. Direct retrieval of reviewer’s manuscripts from a publisher database reduces reviewer burden, ensures up-to-date data, and eliminates the potential for misuse through data manipulation. We also propose a useful evaluation methodology that addresses hyperparameter selection and enables indirect comparisons with alternative approaches and on prior datasets. Finally, the work also contributes a large scale retrospective reviewer matching dataset and evaluation that we hope will be useful for further research in this field. Our system is quite effective; for the mainline approach, expert judges rated 38% of the recommendations as Very Relevant; 33% as Relevant; 24% as Slightly Relevant; and only 5% as Irrelevant.

This paper reports the design, fabrication, and measurement of a millimeter-wave solid-state ?pi-match waveguide switch using bulk silicon micromachining. A photogenerated plasma within a silicon post is utilized as the switching element within the waveguide channel. Not only does this isolate the switch bias network from the RF signal path, but allows for tuning of the OFF-state isolation with increasing optical power for application as a variable attenuator. A measured OFF-state isolation greater than 25 dB up to 40 GHz is reported, with a measured extracted ON-state insertion loss of 0.52 dB at 35 GHz, and less than 0.88 dB across the entire band from 30-40 GHz. The proposed switch illustrates the significant potential for photogenerated silicon plasma switching of high-performance bulk micromachined millimeter-wave waveguides.

In this paper, we present a novel design framework of synthetic radial aperture focusing for 3-dimensional (3-D) transrectal ultrasound imaging (TRUS-rSAF), in which multiple transmittance/reception events at different scanning angles are synthesized to reconstruct a radial plane in the target volume, securing high spatial resolution and texture uniformity. A theory-based design approach has not been available to push the envelope of the 3-D rSAF technique. Herein, a closed-form analytical description of the TRUS-rSAF method is presented for the first time, effectively delineating spatial resolution and grating lobe positions in the radial dimension of a TRUS transducer. We demonstrate a solid optimization workflow based on the theoretical foundation to improve its spatiotemporal resolution, grating lobe artifacts, and signal-to-noise ratio (SNR). A specific design criterion was considered to outperform a clinical 3-D TRUS imaging (TRUS-REF), where each radial plane is reconstructed with a single transmittance/reception event using a motorized actuator. The optimized TRUS-rSAF method significantly enhanced spatial resolution up to 50 % over the TRUS-REF method while providing clinically-effective temporal resolution (2-8 volumes per second) with negligible grating lobe artifacts. The results indicate that the proposed design approach would enable a novel TRUS imaging solution in clinics.

This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties. A novel feature of the method is the use of a deep meta-learning framework to learn a prior energy model, which can efficiently adapt to the local terrain conditions based on small quantities of exteroceptive and proprioceptive data. A meta-adaptive heuristic function is also proposed for the integration of the energy model into an A* path planner. The performance of the proposed approach is assessed in a 3D-body dynamic simulator over several typologies of deformable terrains, and compared with alternative machine learning solutions. We provide evidence of the advantages of the proposed method to adapt to unforeseen terrain conditions, thereby yielding more informed estimations and energy-efficient paths, when navigating on unknown terrains. Submitted for revision to IEEE Transaction on Cybernetics.

The hybrid software-defined networks (SDN) architectures are beneficial for a smooth transition and less costly SDN deployment. However, legacy switches and SDN switches coexistence brings new challenges of deployment inconsistency management and security. Security is not well studied for hybrid SDN architecture. In this paper, we study the topology poisoning attacks in hybrid SDN for the first time. We propose new attack vectors for link fabrication in hybrid SDN. The new attack is named “multi-hop link fabrication”, in which an adversary successfully injects a fake multi-hop link (MHL) by exploiting the link discovery protocols. We presented the Hybrid-Shield, a link verification framework for hybrid SDN link discovery. Hybrid-Shield introduces a novel verification technique that includes: i) monitoring legacy switch and host generated traffic at MHL and ii) validating the existence of legacy switches contained in an MHL. This paper presents the prototype implementation of Hybrid-Shield over a real SDN controller. The experimental evaluation is performed with the mininet virtual network emulation. Our evaluation shows that Hybrid-Shield is capable of detecting MHL fabrication attacks in real-time with high accuracy. Hybrid-Shield’s performance evaluation shows that it is lightweight at the controller as it causes less overhead and requires no additional functionalities at the SDN controller for deployment.

In toy environments like video games, a reinforcement learning agent is deployed and operates within the same state space in which it was trained. However, in robotics applications such as industrial systems or autonomous vehicles, this cannot be guaranteed. A robot can be pushed out of its training space by some unforeseen perturbation, which may cause it to go into an unknown state from which it has not been trained to move towards its goal. While most prior work in the area of RL safety focuses on ensuring safety in the training phase, this paper focuses on ensuring the safe deployment of a robot that has already been trained to operate within a safe space. This work defines a condition on the state and action spaces, that if satisfied, guarantees the robot's recovery to safety independently. We also propose a strategy and design that facilitate this recovery within a finite number of steps after perturbation. This is implemented and tested against a standard RL model, and the results indicate a much-improved performance.

Isolated multilevel inverters are widely used in renewable energy systems and industrial applications. Isolated IGBT topologies exploit the usage of low-frequency transformers that improve robustness and reliability. However, critical failure mechanisms should be considered at the design stage to ensure proper performance. This paper describes these critical failure mechanisms, such as short circuit, cross-conduction, IGBT high inductive load avalanche, IGBT second turn-on, VS-undershoot, transformer inrush current, IGBT thermal runaway, and cable switching interference. Furthermore, this paper comprises design techniques to prevent these failures. The previous failure mechanisms come from the inverter’s power stage, except switching interference from control signal cables and directly affecting the control device functionality. This work also proposes a circuit topology based on FPGA resources to reduce switching interference from control signal cables. It behaves like a fault tolerant digital input that effectively filters bouncing events shorter than 2μs. Measurements report satisfactory experimental results upon constructing a 45kVA ac-side-isolated 13-level inverter.

This letter introduces the concept of antisparse Blind Source Separation (BSS), proposing a suitable criterion based on the $\ell_\infty$ norm to explore the antisparsity feature. The effectiveness of the criterion is theoretically demonstrated and it is also evaluated by computational simulations, which consider up to ten distinct sources with different correlation levels. Moreover, we simulated a scenario in wireless communication with binary sources, comparing our approach to the Constant Modulus algorithm. Both the theoretical and the simulation results highlight the potentiality of using antisparsity as a prior in BSS.

There are millions of scanned documents worldwide in around 4 thousand languages. Searching for information in a scanned document requires a text layer to be available and indexed. Preparation of a text layer requires recognition of character and sub-region patterns and associating with a human interpretation. Developing an optical character recognition (OCR) system for each and every language is a very difficult task if not impossible. There is a strong need for systems that add on top of the existing OCR technologies by learning from them and unifying disparate multitude of many a system. In this regard, we propose an algorithm that leverages the fact that we are dealing with scanned documents of handwritten text regions from across diverse domains and language settings. We observe that the text regions have consistent bounding box sizes and any large font or tiny font scenarios can be handled in preprocessing or postprocessing phases. The image subregions are smaller in size in scanned text documents compared to subregions formed by common objects in general purpose images. We propose and validate the hypothesis that a much simpler convolution neural network (CNN) having very few layers and less number of filters can be used for detecting individual subregion classes. For detection of several hundreds of classes, multiple such simpler models can be pooled to operate simultaneously on a document. The advantage of going by pools of subregion specific models is the ability to deal with incremental addition of hundreds of newer classes over time, without disturbing the previous models in the continual learning scenario. Such an approach has distinctive advantage over using a single monolithic model where subregions classes share and interfere via a bulky common neural network. We report here an efficient algorithm for building a subregion specific lightweight CNN models. The training data for the CNN proposed, requires engineering synthetic data points that consider both pattern of interest and non-patterns as well. We propose and validate the hypothesis that an image canvas in which optimal amount of pattern and non-pattern can be formulated using a means squared error loss function to influence filter for training from the data. The CNN hence trained has the capability to identify the character-object in presence of several other objects on a generalized test image of a scanned document. In this setting some of the key observations are in a CNN, learning a filter depends not only on the abundance of patterns of interest but also on the presence of a non-pattern context. Our experiments have led to some of the key observations - (i) a pattern cannot be over-expressed in isolation, (ii) a pattern cannot be under-xpressed as well, (iii) a non-pattern can be of salt and pepper type noise and finally (iv) it is sufficient to provide a non-pattern context to a modest representation of a pattern to result in strong individual sub-region class models. We have carried out studies and reported \textit{mean average precision} scores on various data sets including (1) MNIST digits(95.77), (2) E-MNIST capital alphabet(81.26), (3) EMNIST small alphabet(73.32) (4) Kannada digits(95.77), (5) Kannada letters(90.34), (6) Devanagari letters(100) (7) Telugu words(93.20) (8) Devanagari words(93.20) and also on medical prescriptions and observed high-performance metrics of mean average precision over 90%. The algorithm serves as a kernel in the automatic annotation of digital documents in diverse scenarios such as annotation of ancient manuscripts and hand-written health records.

A new way of analyzing the effect of a control action for sub-synchronous and super-synchronous operation of a doubly-fed induction generator is discussed, accompanied by an illustrative example.

Hydrogen can be key in the energy system transition. We investigate the role of offshore hydrogen generation in a future integrated energy system, and its interaction with other system elements. By performing energy system optimisation in a model application of the Northern-central European energy system and the North Sea offshore grid towards 2050, we find that offshore hydrogen generation may likely only play a limited role, and that offshore wind energy has higher value when sent to shore in the form of electricity. Forcing all hydrogen generation offshore would lead to increased energy system costs (9-28 b\EUR2016/year by 2045). Under the assumed scenario conditions, hydrogen generation - both onshore and offshore - follows solar PV generation patterns. Combined with hydrogen storage, this is the most cost-effective solution to satisfy future hydrogen demand. Overall, we find that the role of future offshore hydrogen generation should not simply be derived from minimizing costs for the offshore sub-system, but by also considering the value that such generation would create for the whole integrated energy system. Based on our results, a stronger political effort to promote the integration of offshore wind in onshore energy markets via electrical connection is called for.

On the seasonal time scale, for accessible locations and when manpower is available, direct observations and field survey are the most useful and standard approaches. However very limited studies have been conducted on direct observation at the decennial to century time-scale due to observational constrains. Here, we present an open and reproducible pipeline based on historical aerial images (up to 70 yrs time span) that includes sensor calibration, dense matching and elevation reconstruction over two areas of interest that represent pristine examples for tropical and alpine environments. The Remparts Canyon and Langevin River in Reunion Island, and the Bossons glacier in the French Alps share a limited accessibility (in time and space) that can be overcome only from remote-sensing. We reach a metric to sub-metric resolution close to the nominal images spatial sampling. This provides elevation time series with a better resolution to most recent satellite images such as Pleiades over decennial time period.

In this paper, it has been explained how the equipotential surface is influenced by the shape of the symmetric polyhedral conductor and how it finally becomes spherical as it would be if the polyhedral conductor is replaced by a point charge placed at the centre of that conductor. As we move away from the polyhedral conductor the consecutive equipotential surfaces curve more at the sharp bends. A patter n is observed when the diagonal distance from the vertex of the conductor to the first occurrence of its spherical equipotential surface for all symmetric polyhedral conductors is mathematically calculated which is half of the length of its side.

The primary purpose of this paper was to propose a way to alert sleepy drivers in the act of driving. Most of the traditional methods to detect drowsiness are based on behavioral aspects while some are intrusive and may distract drivers, while some require expensive sensors/hardware. Therefore, in this paper, driver’s drowsiness detection system is developed and implemented to aid drowsy drivers from falling asleep and to prevent accidents. The system takes images from the device as input. Using these image templates, the trained model starts execution and predicts/classifies whether the face of the person in the image is drowsy or alert. The proposed model is able to achieve accuracy of 99.93% using CNN on trained image dataset.

The paper presents an effective particle filtering using the Ensemble Kalman Filter based proposal density to improve the computational efficiency