Computer vision based traffic monitoring system for multi-track freeways

Nowadays, development is synonymous with construction of infrastructure. Such road infrastructure needs constant attention in terms of traffic monitoring as even a single disaster on a major artery will disrupt the way of life. Humans cannot be expected to monitor these massive infrastructures over 24/7 and computer vision is increasingly being used to develop automated strategies to notify the human observers of any impending slowdowns and traffic bottlenecks. However, due to extreme costs associated with the current state of the art computer vision based networked monitoring systems, innovative computer vision based systems can be developed which are standalone and efficient in analyzing the traffic flow and tracking vehicles for speed detection. In this article, a traffic monitoring system is suggested that counts vehicles and tracks their speeds in realtime for multi-track freeways in Australia. Proposed algorithm uses Gaussian mixture model for detection of foreground and is capable of tracking the vehicle trajectory and extracts the useful traffic information for vehicle counting. This stationary surveillance system uses a fixed position overhead camera to monitor traffic

Engineering the Cost Function of a Variational Quantum Algorithm for Implementation on Near-Term Devices

Variational hybrid quantum-classical algorithms are some of the most promising workloads for near-term quantum computers without error correction. The aim of these variational algorithms is to guide the quantum system to a target state that minimizes a cost function, by varying certain parameters in a quantum circuit. This paper proposes a new approach for engineering cost functions to improve the performance of a certain class of these variational algorithms on today's small qubit systems. We apply this approach to a variational algorithm that generates thermofield double states of the transverse field Ising model, which are relevant when studying phase transitions in condensed matter systems. We discuss the benefits and drawbacks of various cost functions, apply our new engineering approach, and show that it yields good agreement across the full temperature range.Comment: 8 pages, 4 figure

Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning

In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the spin of single electrons. In this hardware architecture, the control landscape is vast and complex, so we use the deep reinforcement learning method to design optimal control pulses to achieve high fidelity multi-qubit gates. In our learning model, a simulator models the physical system of quantum dots and performs the time evolution of the system, and a deep neural network serves as the function approximator to learn the control policy. We evolve the Hamiltonian in the full state-space of the system, and enforce realistic constraints to ensure experimental feasibility

Quantum electrodynamical theory of high-efficiency excitation energy transfer in laser-driven nanostructure systems

A fundamental theory is developed for describing laser-driven resonance energy transfer (RET) in dimensionally constrained nanostructureswithin the framework of quantum electrodynamics. The process of RET communicates electronic excitation between suitably disposed emitter and detector particles in close proximity, activated by the initial excitation of the emitter. Here, we demonstrate that the transfer rate can be significantly increased by propagation of an auxiliary laser beam through a pair of nanostructure particles. This is due to the higher order perturbative contribution to the F¨orster-type RET, in which laser field is applied to stimulate the energy transfer process. We construct a detailed picture of how excitation energy transfer is affected by an off-resonant radiation field, which includes the derivation of second and fourth order quantum amplitudes. The analysis delivers detailed results for the dependence of the transfer rates on orientational, distance, and laser intensity factor, providing a comprehensive fundamental understanding of laser-driven RET in nanostructures. The results of the derivations demonstrate that the geometry of the system exercises considerable control over the laser-assisted RET mechanism. Thus, under favorable conformational conditions and relative spacing of donor-acceptor nanostructures, the effect of the auxiliary laser beam is shown to produce up to 70% enhancement in the energy migration rate. This degree of control allows optical switching applications to be identified

Gender Inequality in Digital Transformation: Evidence from Business Process Management Industry in Sri Lanka

This research examines whether gender inequality exists in Leadership Style, Organizational Culture, and Digital Competence of digital transformation of the Business Process Management (BPM) industry. Data were collected from 507 employees of 40 Sri Lankan BPM companies through a web-based survey. Mann-Whitney U test with descriptive statistics provided evidence to strengthen the findings. The findings confirmed that gender inequality exists in Leadership Style, Organizational Culture, and Digital Competence of digital transformation in the BPM industry in Sri Lanka. This research contributes to "Acker's Theory of Gendered Organizations" by identifying areas that reproduce gender inequality in the new digital economy workplace. This study recommends controlling if not eradicating the gender inequality through proper Human Resource (HR) policies and procedures since it may hinder organizational performance. Digital workplace will improve employee retention, satisfaction, and productivity. Keywords: Business Process Management, Gender Inequality, Leadership Style, Organizational Culture, Digital Competenc

Controlling resonance energy transfer in nanostructure emitters by positioning near a mirror

The ability to control light-matter interactions in quantum objects opens up many avenues for new applications. We look at this issue within a fully quantized framework using a fundamental theory to describe mirror-assisted resonance energy transfer (RET) in nanostructures. The process of RET communicates electronic excitation between suitably disposed donor and acceptor particles in close proximity, activated by the initial excitation of the donor. Here, we demonstrate that the energy transfer rate can be significantly controlled by careful positioning of the RET emitters near a mirror. The results deliver equations that elicit new insights into the associated modification of virtual photon behavior, based on the quantum nature of light. In particular, our results indicate that energy transfer efficiency in nanostructures can be explicitly expedited or suppressed by a suitably positioned neighboring mirror, depending on the relative spacing and the dimensionality of the nanostructure. Interestingly, the resonance energy transfer between emitters is observed to “switch off” abruptly under suitable conditions of the RET system. This allows one to quantitatively control RET systems in a new way

Feeding Patterns and Milk Production of Small-Scale Dairy Farmers under Semi-Intensive and Extensive Cattle Management Systems in Sri Lanka

The main objective of the present study was to determine the feeding pattern and milk production of small-scale dairy farmers under semi-intensive and extensive management systems in the intermediate zone of Sri Lanka. This region is sandwiched between the Wet and Dry Zones, receives a mean annual rainfall of 1750-2500 mm, and covers an area of about 1.2 million ha. A survey was conducted with 60 farmers and data on their herd size, herd composition and breeds, management system, breeding method, milk production, feeding costs and returns of raising animals were collected. The results indicated that the majority of farmers conducted dairying as a part-time business in both semi-intensive (80%) and extensive (66%) management systems in the study area. The highest (P \u3c 0.05) average herd size was observed under semi-intensive systems (3.7 animal units (AU)), compared to extensive systems (2.7 AU). The farmers under the semi-intensive system maintained better feeding levels compared with the extensive system. The majority of farmers in the area depended on tethering and stall feeding as their main source of animal feed. Grasses grown on roadsides, paddy fields, neighbours’ land, government estates and tree leaves were the main feed resources available for both management systems. Rice (Oriza sativa) bran and coconut (Cocos nucifera) poonac were the main concentrate feed ingredients in the study area. Jersey crosses were the most popular dairy animals among semi-intensively managed farms, whereas Sahiwal crosses were most popular in extensive management systems. The average milk production under extensive systems was significantly lower (P \u3c 0.01) at 3.9 l/AU/day, compared to 5.4 l/AU/day under semi-intensive systems. Semi-intensive management systems also had the highest average monthly return per AU

Maximization of Gain in Slow-Light Silicon Raman Amplifiers

We theoretically study the problem of Raman gain maximization in uniform silicon photonic-crystal waveguides supporting slow optical modes. For the first time, an exact solution to this problem is obtained within the framework of the undepleted-pump approximation. Specifically, we derive analytical expressions for the maximum signal gain, optimal input pump power, and optimal length of a silicon Raman amplifier and demonstrate that the ultimate gain is achieved when the pump beam propagates at its maximum speed. If the signal’s group velocity can be reduced by a factor of 10 compared to its value in a bulk silicon, it may result in ultrahigh gains exceeding 100 dB. We also optimize the device parameters of a silicon Raman amplifier in the regime of strong pump depletion and come up with general design guidelines that can be used in practice

Customer Behavior Analysis for Social Media

It is essential for a business organization to get the customer feedback in order to grow as a company. Business organizations are collecting customer feedback using various methods. But the question is ‘are they efficient and effective?' In the current context, there is more of a customer oriented market and all the business organizations are competing to achieve customer delight through their products and services. Social Media plays a huge role in one's life. Customers tend to reveal their true opinion about certain brands on social media rather than giving routine feedback to the producers or sellers. Because of this reason, it is identified that social media can be used as a tool to analyze customer behavior. If relevant data can be gathered from the customers' social media feeds and if these data are analyzed properly, a clear idea to the companies what customers really think about their brand can be provided