In this paper, we successfully construct the universal quantum gate for Shor’s algorithm and derive the cost of this quantum circuit to estimate the complexity. The circuit we design to implement Shor’s algorithm’s first complete quantum circuit rather than a simplified circuit. This is important that the quantum circuit design proposed in this paper can decompose larger composite numbers directly if the hardware can be improved in the future. Although the time and space complexity are still greater than traditional computers, it proves that Shor’s algorithm can be implemented in quantum computers.

This paper focuses on the control of microgrids where both gas and electricity are provided to the final customer, i.e., multi-carrier microgrids. Hence, these microgrids include thermal and electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power units. The parameters characterizing the multi-carrier microgrid are subject to several disturbances, such as fluctuations in the provision of renewable energy, variability in the electrical and thermal demand, and uncertainties in the electricity and gas pricing. With the aim of accounting for the data uncertainties in the microgrid, we propose a Robust Model Predictive Control (RMPC) approach whose goal is to minimize the total economical cost, while satisfying comfort and energy requests of the final users. In the related literature various RMPC approaches have been proposed, focusing either on electrical or on thermal microgrids. Only a few contributions have addressed the robust control of multi-carrier microgrids. Consequently, we propose an innovative RMPC algorithm that employs on an uncertainty set-based method and that can provide better performance compared with deterministic model predictive controllers applied to multi-carrier microgrids. With the aim of mitigating the conservativeness of the approach, we define suitable robustness factors and we investigate the effects of such factors on the robustness of the solution against variations of the uncertain parameters. We show the effectiveness of the proposed RMPC approach by applying it to a realistic residential multi-carrier microgrid and comparing the obtained results with the ones of a baseline robust method. Postprint accepted for publication in IEEE Transactions on Automation Science and Engineering (T-ASE). How to cite: R. Carli, G. Cavone, T. Pippia, B. De Schutter, and M. Dotoli, ”Robust Optimal Control for Demand Side Management of Multi-Carrier Microgrids,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1338-1351, July 2022. DOI:  https://doi.org/10.1109/TASE.2022.3148856

We present a reflection mode saturable absorber based on nonlinear multimode interference in a graded index multimode fiber. The saturable absorber provides a modulation depth of 12% at a low saturation intensity of 18.5 MW/cm2. The all-fiber structure and reflection mode design of this saturable absorber makes it an ideal device for the fabrication of fiber lasers with linear resonant cavity. When inserted in a linear laser cavity comprising a thulium doped fiber as the gain medium, the saturable absorber triggers modelocked noise like pulses with a center wavelength of 1905 nm and spectral width of 11.2 nm.

Today, Mental health problems are getting grave and need technological solutions. Irrational anticipated fear is Anxiety Disorder. Specific Phobia disorders are a type of Anxiety disorder; these phobias are rarely detected in clinical settings and are presence indicators of other serious mental problems. VR is considered a potent tool for treatment and diagnosis. In this study, we investigated the parameters for predicting participants’ severity level of Cynophobia and Astraphobia by using the following measures: “APA Specific Phobia Severity Measure - Adult”, “Distance and Time”, “Heart Rate and Oxygen levels for each level” in VR-specific-phobia diagnostic environment, “symptoms” observed during experimentation, and “causes” described by DSM-5. The “APA Specific Phobia Severity Measure - Adult” is attributed as the standard used by psychiatrists for clinical evaluation. We used the score of this measure to classify instances for each participant. The other parameters serve as attributes for predicting class, implementing the process of Data Mining. The literature supports the prior mentioned parameters for assessing severity levels for specific phobia. The participant walks or runs along a road in a Virtual Reality Environment to achieve the objective. The first scenario is a neutral environment with no phobic stimulus; the afterward situations pose for a dog cue, thunder lightning stimulus, and a combination of both stimulation consecutively. The ‘Distance’ traveled and ‘Time’ taken in units for each VR scenario generated using a Bluetooth controller is saved in a file with time stamps. The participant subsequently fills Google Form to record the parameters. The dataset is converted to ARFF format, and the process of Knowledge Discovery is applied using the WEKA tool. The results suggest that the presence of Cynophobia and Astraphobia are highly interrelated. The study advised that Dog-Phobia severity level confidently predicts with the parameters “Age”, “Time” in Neutral scenario, “Distance” covered in Cynophobic scenario“, ”Difference in Oxygen levels” of Cynophobic VRE and scenario with both (Dog and Thunder Lightning) stimuli and “DSMAstraphobia”. The research analysis concludes that thunder-lightning phobia severity level effectively forecasts with these attributes: “Velocity”, “Distance” and “Time” in Neutral VRE scenario“; ”Velocity“, ”Time” VRE scenario for both pre-mentioned phobic stimuli; “Time” in Cynophobic scenario, “Velocity” calculated in Astraphobic VRE, “Age” of the participant and DSMCynophobia. This study will help in suggesting standards for diagnosing mental health problems with the advantages of VR.

This letter presents an easy-to-use accurate high-frequency (HF) behavioral model of star-connected induction motors with a simple and straightforward parameterization process for conducted electromagnetic interference (EMI) analysis. The HF model contains nine circuit elements per phase with a simple structure. These circuit elements are determined using a multilayer perceptron (MLP) neural network, where only the individual phase-to-frame impedance is needed for training purposes. Compared with parameterization methods in conventional motor modeling, the MLP significantly simplifies the parameterization process. Using two different induction motors (1.5 hp and 7.5 hp) as case studies, experiments verify the capability and accuracy of the HF model to predict the motor common-mode (CM) and differential-mode (DM) impedances in the HF range of 150 kHz to 30 MHz.

A PEEC formulation is applied to compute Joule losses and currents in the wires composing the screens of the phase conductors in a submarine tripolar cable. In order to model the infinite extent of the cable and to reduce the computational burden, simmetries of the geometry are exploited. A good agreement is obtained with brute force FEM simulations, with a dramatic reduction of computational times and memory requirement.

A document by Ritwik Ghosh . Click on the document to view its contents.

StyleGAN is now capable of achieving excellent results, especially high-quality face synthesis. Recently, some studies have tried to invert real face images into style latent space via StyleGAN. However, morphing real faces via latent representation is still in its infancy. Training costs are high and/or require huge samples with labels. By adding regularization to style optimization, we propose a novel method to morph real faces based on StyleGAN. To do the supervised task, we label latent vectors via synthesized faces and release the label set; then we utilise logistic regression to fast discover interpretable directions in latent space. Appropriate regularization helps us to optimize both latent vectors (faces and directions). Moreover, we use learned directions under different layer representations to handle real face morphing. Compared to the existing methods, our method faster yields a larger diverse and realistic output. Code and cases are available at \url{https://github.com/disanda/RFM}.

We propose a deep neural network framework for analysing time-to-event under different treatment regimes.

The orientation-dependence of the diffraction patterns of the 1D-grating experiments have been studied experimentally and theoretically. However, the experiments were limited to certain orientations, namely rotate the grating around two axes. We introduce the 3-axis-rotation apparatus, which can rotate the 1D-grating/2D-cross-grating, CW and CCW respectively, by 00-3600, around three axes independently and sequentially. By this apparatus, the orientation-dependence of the patterns of the 1D-grating is systematically studied. The experiments show the novel phenomena: when rotating the grating around three axes, not only the patterns are curved, expanded and inclined, but also the spacings between the adjacent orders of the patterns are non-uniform.

IoT-technology is gaining a wide popularity over a large range of applications including not only monitoring of structures but also management and control of smart-systems. An IoT-system, in general, is composed of a number of IoT-devices which form a wireless decentralized setting as they get installed over a specific area to serve a particular purpose. The structure of the underlying wireless network depends on the structure of the target where the system gets deployed and hence, widely varies based on the exact application. Such structural variations often have an impact on the performance of the underlying IoT-protocols. Unfortunately most of the network protocols do not take care of such issues explicitly. For instance, although there have been quite significant development in the data-sharing protocols, especially with the advent of Synchronous-Transmission (ST), most of them are designed without considering the variation in the structural formation of the base networks. These protocols are tested over either in small scale simulated networks or in testbed settings bearing fixed/homogeneous structures. In this work, we demonstrate that the property of self-adaptability in an IoT-system can enable it not only to run faster but also save substantial energy which is an extremely important issue in the context of low-power system, in general. In particular, we design and implement a flexible and structure-adaptive many-to-many data-sharing protocol FlexiCast. Through extensive experiments under emulation-settings and IoT-testbeds we demonstrate that FlexiCast performs upto 49% faster and consumes upto 53% lesser energy compared to the case when it does not adapt to the network structure.

A novel base-prime set was presented generalizing bases of floating point numbers. An asymptotic analysis is presented to reasonably suggest that it might have cardinality strictly between integers and real numbers. This concrete counter-example reasonably suggests that the Continuum Hypothesis (CH) might be false.

This paper builds upon the theoretical foundations of the Accountable explainable Artificial Intelligence (AXAI) capability framework presented in part one of this paper. This part demonstrates the incorporation of the AXAI capability in the real time Affective State Assessment Module (ASAM) of a robotic system. The paper argues that adhering to the extreme Programming (XP) practices would help in understanding user behavior while systematically incorporating the AXAI capability in AI systems. Issues pertaining to identification of ethical, technical, functional, and domain-specific system requirements were resolved using an appropriate software design process, thus establishing confidence in the system. The presented ASAM synthesizes discrete and continuous models of affective state expressions and classifies them in real-time. Input, processed and output data are continuously shared with users via a graphical user interface (GUI) for providing reasons behind decisions. The GUI also disseminates information about local reasoning, data handling and decision-making. We hope this work will initiate further investigations on selection of suitable software design practices for incorporating the AXAI capability in AI systems, enhancing AI system acceptability and utility and, establishing a chain of responsibility.

A variant of a robust description source coding framework motivated by goal-oriented semantic information transmission is studied here. Considering two individual distortion constraints and input and output data that takes values in finite sets, we prove a general result that provides in parametric form the various cases of optimal solutions of this problem. Then, we derive structural properties of the solution when this achieves the best rates. Capitalizing on these results, we examine the structure of the solution for one case study of general binary alphabets under Hamming distortions and solve in closed form a special case. We also solve another general binary alphabet case where a Hamming and an erasure distortion are used, as a means to highlight the importance of selecting the type of the distortion constraint in the problem.

Unmanned Aerial Vehicles (UAVs) have emerged as suitable platforms for transporting and positioning communications nodes on demand, including Wi-Fi Access Points and cellular Base Stations. This paved the way for the deployment of flying networks capable of temporarily providing wireless connectivity and reinforcing coverage and capacity of existing networks. Several solutions have been proposed for the positioning of UAVs acting as Flying Access Points (FAPs). Yet, the positioning of Flying Communications Relays (FCRs) in charge of forwarding the traffic to/from the Internet has not received equal attention. In addition, state of the art works are focused on optimizing both the flying network performance and the energy-efficiency from the communications point of view, leaving aside a relevant component: the energy spent for the UAV propulsion. We propose the Energy and Performance Aware relay Positioning (EPAP) algorithm. EPAP defines target performance-aware Signal-to-Noise Ratio (SNR) values for the wireless links established between the FCR UAV and the FAPs and, based on that, computes the trajectory to be completed by the FCR UAV so that the energy spent for the UAV propulsion is minimized. EPAP was evaluated in terms of both the flying network performance and the FCR UAV endurance, considering multiple networking scenarios. Simulation results show gains up to 25% in the FCR UAV endurance, while not compromising the Quality of Service offered by the flying network.

Twitter allows its users to share their concerns, ideas and daily activities online. This shared content by individuals provides a rich source of natural occurring data. The rapidity and flexibility of tweets have motivated its use as a tool for mobilizing masses for organizing strikes, protests, and other demonstrations. In this paper, we hypothesized that unrestricted data available on Twitter has potential power to predict day and location of major protests when deep learning and data mining is applied to its unstructured data. The prediction model used in the study is Bidirectional Long Short Term Memory (BiLSTM) Model. The results of our experiment shows that our model performs better than existing baselines models achieving an accuracy of 82.02% on testing data and 95.28% on training data. Overall, the aim of the paper is to study the efficacy of data mining techniques on publicly available data on Twitter in predicting mass protests and demonstrations.

This is the preprint version of ZTA operationalization approaches. We discuss three distinct categories: i) centralized operationalization of ZTA (cZTA), ii) federated operationalization of ZTA (fZTA), and iii) decentralized operationalization of ZTA (dZTA).

The small arms generally capture a significant range of infantry weapons that an individual soldier can carry and operate. Excessive use with inadequate maintenance of these weapons gradually forms abnormalities like corrosion, crack, bulge, and particle deposition on their inner surface. Such abnormalities inspection has been conventionally manual (with open-eyes), tedious, inefficient requiring much effort and time, and susceptible to incorrect detection. Therefore, this paper presents a new software-based inspection system with automatic detection and estimation of the surface abnormality as a timely requirement. The state of abnormalities is determined and characterized for the small arms made of steel that eventually result in different faults and reduce the weapons’ efficiency. A software-based system is designed with a high definition, small-aperture, and low-powered camera to acquire, process, and analyze the inner-surface images in real-time. The surface abnormalities are then determined and estimated according to the detected features of the abnormalities. The proposed system would offer an effortless early-detection and more accurate estimation of the inner-surface abnormalities to ensure appropriate diagnosis and longer lifecycle of the small arms.