Optical orthogonal frequency division multiplexing (O-OFDM) schemes are variations of OFDM schemes which produce non-negative signals. Asymmetrically-clipped O-OFDM (ACO-OFDM) is a single-layer O-OFDM scheme, whose spectral efficiency can be enhanced by adopting multiple ACO-OFDM layers or a combination of ACO-OFDM and other O-OFDM schemes. However, since symbol detection in such enhanced ACO-OFDM (eACO-OFDM) is done iteratively, erroneous detection leads to residual clipping noise (RCN) which can degrade performance in practice. Thus, it is important to develop an accurate model for RCN which can be used to design RCN-aware eACO-OFDM schemes. To this end, this paper provides a mathematical analysis of RCN leading to an accurate model of RCN power. The obtained model is used to analyze the performance of various eACO-OFDM schemes. It is shown that the model provides an accurate evaluation of symbol error rate (SER), which would be underestimated if RCN is ignored. Moreover, the model is shown to be useful for designing an RCN-aware resource allocation that increases the robustness of the system in terms of meeting a target SER, compared to an RCN-unaware design.

Facial sketch recognition is one of the most commonly used method to identify a suspect when only witnesses are available, which, however, usually leads to four gaps, i.e. memory gap, communication gap, description-sketch gap, and sketch-image gap. These gaps limit its application in practice to some extent. To circumvent these gaps, this paper therefore focus on the problem: how to identify a suspect using partial photo information from different persons. Accordingly, we propose a new Logical Operation Oriented Face Retrieval (LOOFR) approach provided that partial information extracted from several different persons’ photos is available. The LOOFR defines the new AND and OR operators on these partial information. For example, ” eyes of person A AND mouth of person B” means retrieving the target person whose eyes and mouth are similar to that of person A and person B respectively, while “eyes of person A OR eyes of person B” means retrieving target person whose eyes are similar to both person A and B. Evidently, these logical operators cannot be directly implemented by INTERSECTION and UNION in set operations. Meanwhile, they are better for human understanding than set operators. Subsequently, we propose a two-stage LOOFR approach, in which the representations of partial information are learned in the first stage while the logical operations are processed in the second stage. As a result, the target photo of a suspect can be retrieved. Experiments show its promising results.

A document by Abhisha Mano . Click on the document to view its contents.

To data-driven estimate power system sensitivity matrix considering mitigating data saturation, a series of multivariable least squares (MLS) algorithms are proposed and compared, including the ordinary MLS (OMLS), the weighted MLS (WMLS), the memory-limited OMLS (ML-ORMLS), the memory-limited WRMLS (ML-WRMLS), and the memoryfading ML-WRMLS (MF-ML-WRMLS). Considering enhancing computational efficiency and accuracy by mitigating data saturation, the last three of them are specifically derived for sensitivity matrix estimation based on time-varying online-measured data. The effectiveness of the presented algorithms is verified and compared in the Nordic 32 system for voltage sensitivity matrix estimation. The results illustrate the prime algorithm in practice.

This paper aims to discuss the standard guidelines of the development process of secure software and will give justification on different types and ways of the software development processes. Additionally, a survey is conducted, the aim of which is to observe user behavior towards software system usage, user attitude in terms of privacy and policy awareness, security and privacy concerns. This is followed by discussion on how to secure software systems in development stage.

This work considers the exact bit error rate (BER) analysis of a two-user non-orthogonal multiple access (NOMA) system using quadrature amplitude modulation (QAM). Unlike existing work, no constraints on the modulation order of the QAM symbols for any user. Closed-form expressions are derived for the BER of joint multiuser detector (JMuD), which is demonstrated that it is equivalent to the successive interference cancellation (SIC) receiver. Moreover, a general expression is derived for the relation between the power allocation factors for the two users, which depends on the modulation order for each user.

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy-saving problem in mobile nodes. To mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms.

It is proposed that spacetime is not the most proper space to describe metamaterials with nonlocality. Instead, we show that the most general and suitable configuration space for doing electromagnetic theory in nonlocal domains is a proper function-space infinite-dimensional (Sobolev) vector bundle, a special case of the general topological structure known as fiber bundles. It appears that this generalized space explains why nonlocal metamaterials cannot have unique EM boundary conditions at interfaces involving spatially dispersive media.

Kalman filtering of measurement data from multiple sensors with time-varying delays and missing measurements is considered in this work. Two existing approaches to Kalman filtering with delays are extended by removing some assumptions in order to have equivalent filtering methods and making comparisons between them. The computational loads of the two methods are compared in terms of the average number of floating point operations required by each method for different system dimensionalities and delay upper bounds. The results show that the superiority of the methods over each other depends on the comparison conditions.

In this paper, we propose an AC optimal power flow (ACOPF) model considering distributed flexible AC transmission system (D-FACTS) devices, in which the reactance of D-FACTS equipped lines are introduced as decision variables. This is motivated by increasing interests in using D-FACTS devices to address system operational and cyber-security concerns. First, D-FACTS devices can be incorporated in real-time operations for economic benefits such as managing power congestions and reducing system losses. Second, D-FACTS devices can be utilized by moving target defense (MTD), an emerging concept against cyber-attacks, to prevent attackers from knowing true system configurations. Therefore, system operators can use the proposed ACOPF model to achieve economic benefits and provide the setpoints of D-FACTS devices for MTD at the same time. In addition, we rigorously derive the gradient and Hessian matrices of the objective function and constraints, which are further used to build an interior-point solver of the proposed ACOPF. Numerical results on the IEEE 118-bus transmission system show the validity of the proposed ACOPF model as well as the efficacy of the interior-point solver in minimizing system losses and generation costs.

In this paper, a novel Robust-Synchronization-Loop (RSL) is proposed to track the phase and frequency of the grid voltage. The method can be widely applied to the grid-connected converters in integration of distributed generations (DGs). The detailed analytical model and the parameter tuning based on a small-signal model of the RSL are presented. It is shown that the eigenvalues associated with the RSL remain always on the left half-plane, thus, it provides better stability property with the grid-connected DG converters. In order to show the effectiveness of the proposed RSL, simulation results are provided for different scenarios of the grid condition such as voltage and frequency disturbance, unbalance grid condition and grid faults. The results show that the proposed method effectively tracks the phase and frequency of the grid voltage and maintains the good synchronism of the DG converters during these different small and large disturbances in the power system.

sampled data, grouped into equal length blocks, The blocks must be individually time-reversed.

The design of any artificial intelligence is an interdisciplinary pursuit that requires keeping within perspective, the various properties of matter and life in the known universe, while remaining cautious of biases and misconceptions that arise from the limitations of prior learning, available tools and sensory capabilities. This paper curates a vast collection of human knowledge gathered during the process of exploring and questioning the fundamentals of why the mechanisms that constitute life were built in specific ways, and re-questioning those facts using anomalies that sometimes contradict or expose errors in human assumptions of life and intelligence. A meta-analysis of such knowledge at a single glance also helps identify interesting patterns in various phenomena that can in-time, spur creative solutions, alter the direction of research, assist with intelligent inferences and hopefully result in the identification and creation of artificial intelligence.

Electrifying mobility-on-demand (MoD) fleets is an important step towards a more sustainable transportation system. With increasing fleet size, MoD operators will be able to participate in the energy exchange market and will have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of, e.g., a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a macroscopic model to study the tradeoffs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet size and different numbers of charging units, we compare the performance of several reactive and scheduling policies in a simulation framework based on a macroscopic model. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.

We report a photonic-based radio frequency (RF) arbitrary waveform generator (AWG) using a soliton crystal micro-comb source with a free spectral range (FSR) of 48.9 GHz. We successfully achieve arbitrary shapes including square waveforms with a tunable duty ratio ranging from 10% to 90%, sawtooth waveforms with a tunable slope ratio of 0.2 to 1, and a symmetric concave quadratic chirp waveform with an instantaneous frequency of sub GHz. We achieve good agreement between theory and experiment, validating the effectiveness of this approach towards realizing high-performance, broad bandwidth, nearly user-defined RF waveform generation.

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overftting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog datasets. Experiments demonstrate that these approaches are promising in generating meaningful medical dialogues about COVID-19. But more advanced approaches are needed to build a fully useful dialogue system that can offer accurate COVID-related consultations. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue

A unified method is presented which allows to estimate all harmonic components, DC-offset and fundamen- tal frequency in arbitrarily distorted single-phase grids us- ing a frequency-adaptive observer (FAO) consisting of modi- fied Second-Order Generalized Integrators (mSOGIs), a DC- Integrator (DCI) and a modified Frequency Locked Loop (mFLL). DCI and mSOGIs are tuned by pole placement which allows for an arbitrarily fast detection of DC-offset and harmonic components if the fundamental frequency is known. If the fundamental frequency must be estimated as well, a mFLL with Gain Normalization (GN), Rate Limitation (RL), Anti-Windup (AW) strategy and low-pass filters (LPF) must be employed. The effectiveness of the proposed FAO is validated by experimental results and its enhanced performance is shown by comparisons to existing estimation methods.

In this world of communication, the security of the messages to be transferred plays a very important role. Strategies like Encryption/Decryption, Digital Signatures, Steganography etc., have been developed to ensure the security, privacy and integrity of these messages. Playfair cipher is one of the well known polyalphabetic ciphers used for the encryption and decryption. However in account of few drawbacks inherent in existing 5*5 playfair cipher, recently improved version of Playfair cipher i.e., Extended 8*8 Playfair cipher has been developed. In this work, extended playfair cipher with Least Significant bit steganography are applied to hide the presence of any such messages. The main objective of this work is to build a secure mechanism of sending and receiving messages. The results show that combination of both extended 8 * 8 playfair cipher and stegnography increases the security of messages in contrast to the existing traditional Playfair ciphers.