Application of Signal Processing and Soft Computing To Genomics

A major challenge for genomic research is to establish a relationship among sequences,structures and function of genes. In addition processing and analyzing this information are of prime importance. Basically genes are repositories for protein coding information and proteins in turn are responsible for most of the important biological functions in all cells. These in turn gives rise to analysis of DNA sequences in proteins, designing of various drugs for genetic diseases. This thesis deals with the applications of signal processing and soft computing algorithms to the field of genomics and proteinomics. Diseases like SARS and Migraine have been modeled using these tools and potential druggable compounds have been proposed which are better than the previous available drugs. Protein structural classes have been identified more accurately based on Genetic Algorithm and Particle Swarm Optimization.Better and efficient methods like Sliding-DFT and Adaptive AR Modeling were proposed to identify Protein coding regions in genes. The proposed methods showed better results as compared to existing methods

Recent advances in industrial wireless sensor networks towards efficient management in IoT

With the accelerated development of Internet-of- Things (IoT), wireless sensor networks (WSN) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for wireless sensor networks design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross layer design of lightweight and cloud-based RESTful web service

Topology design and cross-layer optimization for wireless body sensor networks

Wireless Body Sensor Networks play a crucial role in digital health care nowadays. Due to the size limitation on the sensor nodes and the life critical characteristics of the signals, there are stringent requirements on network’s reliability and energy efficiency. In this article, we propose a mathematical optimization problem that jointly considers network topology design and cross-layer optimization in WBSNs. We introduce multilevel primal and dual decomposition methods and manage to solve the proposed non-convex mixed-integer optimization problem. A solution with fast convergence rate based on binary search is provided. Simulation results have been supplemented to show that our proposed method yields much better performance than existing solutions

Optimal power control in green wireless sensor networks with wireless energy harvesting, wake-up radio and transmission control

Wireless sensor networks (WSNs) are autonomous networks of spatially distributed sensor nodes which are capable of wirelessly communicating with each other in a multi-hop fashion. Among different metrics, network lifetime and utility and energy consumption in terms of carbon footprint are key parameters that determine the performance of such a network and entail a sophisticated design at different abstraction levels. In this paper, wireless energy harvesting (WEH), wake-up radio (WUR) scheme and error control coding (ECC) are investigated as enabling solutions to enhance the performance of WSNs while reducing its carbon footprint. Specifically, a utility-lifetime maximization problem incorporating WEH, WUR and ECC, is formulated and solved using distributed dual subgradient algorithm based on Lagrange multiplier method. It is discussed and verified through simulation results to show how the proposed solutions improve network utility, prolong the lifetime and pave the way for a greener WSN by reducing its carbon footprint

Carbon nanotube hybrid materials: efficient and pertinent platforms for antifungal drug delivery

Carbon nanotubes (CNTs) have emerged as the brightest nascent artifact to deliver antifungal drugs in drug delivery applications, health care, and pharmaceutical industries. Excellent physio-chemical features such as huge surface area, tunable side wall, and microneedle-like morphology make CNTs suitable for drug carriers. Chemical attachments (covalent and non-covalent functionalization) result in the formation of functionalized CNTs (F-CNTs) and CNT-based hybrid materials (CNT-HMs). These F-CNTs and CNT-HMs have substantial antifungal activity and also have the potential to immobilize antifungal drugs such as amphotericin B, nystatin, curcumin, etc. on the exterior or interior surface, securely transport to the target sites, permeate through bio-barriers, and release these drugs in a controlled manner. As antifungal drug carriers, F-CNT and CNT-HMs exhibit more excellent antifungal activity than other conventional drug delivery systems and have the potency to invade biofilm to circumvent the multidrug resistance of fungal species. This review focuses on CNTs and CNT-HMs for antifungal drug delivery, including their functionalization methods, drug loading approaches, drug release mechanism, cellular internalization, delivery efficiency, and cellular toxicities with their workaround.C.M. acknowledges the National Institute of Technology Raipur for SeedGrant, project no: NITRR/Seed Grant/2021-22/30. C.M. gratefully accepted the Science & Engineering Research Board (SERB) research support vide grant number SRG/2022/000348 from the Department of Science and Technology, India

Wireless energy harvesting for Internet of Things

Internet of Things (IoT) is an emerging computing concept that describes a structure in which everyday physical objects, each provided with unique identifiers, are connected to the Internet without requiring human interaction. Long-term and self-sustainable operation are key components for realization of such a complex network, and entail energy-aware devices that are potentially capable of harvesting their required energy from ambient sources. Among different energy harvesting methods such as vibration, light and thermal energy extraction, wireless energy harvesting (WEH) has proven to be one of the most promising solutions by virtue of its simplicity, ease of implementation and availability. In this article, we present an overview of enabling technologies for efficient WEH, analyze the life-time of WEH-enabled IoT devices, and briefly study the future trends in the design of efficient WEH systems and research challenges that lie ahead

High speed and energy efficient hardware architectures for LTE-advanced systems

The explosive growth of internet traffic, fueled by an ever increasing availability of mobile wireless devices and demands of end users to be always connected, provides a challenge for cellular and broadband wireless access technologies. In this thesis, we present novel approaches of physical layer architectures Orthogonal Wavelet Division Multiple Access (OWDMA) & Fast Inverse Square Root based Matrix Inverse (FISRMI) that is shown to substantially improve bit error rate (BER), increase data rate, accommodating more number of users, low power consumption and cover dead zones effectively. The work presented in this thesis consists of basically two parts which provides solutions to different problems in the Long Term Evolution (LTE) networks. In LTE-Advanced (LTE-A), heterogeneous networks (HetNet) concept using centralized coordinated multipoint (CoMP) transmitting Radio resources over optical fibers LTE-A Radio-Over-Fiber (ROF) has provided a feasible way of satisfying user demands. A OWDMA processor architecture is proposed and evaluated. To validate the architecture, circuit is designed and synthesized on a Xilinx vertex-6 Field Programmable Gate Array (FPGA). We compare our architecture with similar available architectures for resource utilization & timing and provide performance comparison with OFDMA for different quality metrics of communication systems. The OWDMA architecture is found to perform better than OFDMA for BER performance versus signal to noise ratio (SNR) in ROF media. It also gives higher throughput and mitigates the bad effect of Peak to Average Power ratio (PAPR) and Inter carrier interference (ICI). Secondly, a low complexity and high speed matrix inversion algorithm FISRMI using fast inverse square root based on QR-decomposition and systolic array was designed. Matrix operations are costliest computational module within multiple input multiple output (MIMO)-LTE receivers. The capital expenditure (CAPEX) is reduced by implementing a 4x4 matrix inverse in Xilinx Virtex-6 FPGA by optimizing the module for speed and power by pipelining. The results are compared with state of art techniques of Coordinate Rotation Digital Computer (CORDIC) based algorithms and the various Minimum Mean Squared Error channel matrices of size 4x4 and 8x8 are inverted at different bit precision on a BER plot.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Energy-efficient device architecture and technologies for the internet of everything

Around the globe, integrating information and communication technologies with physical infrastructure is a top priority in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Internet-of-Everything (IoE) is a network of uniquely identifiable, accessible, and manageable smart things that are connected through a network of heterogeneous devices and people, usually consisting of battery-operated nodes, and mostly working at remote places, without human intervention. This leads us to issues concerning IoE Systems such as network lifetime, battery efficiency, carbon emissions, low-power security and efficient data transmission, which have been analysed in this thesis and solutions have been proposed for them. First, we investigate wireless energy harvesting (WEH), wake-up radio (WUR) scheme, and error control coding (ECC) as enabling solutions to enhance the performance of sensor networks-based IoE systems while reducing their carbon footprints. Specifically, a utility-lifetime maximization problem incorporating WEH, WUR, and ECC, is formulated and solved using a distributed dual sub gradient algorithm based on the Lagrange multiplier method. Discussion and verification through simulation results show how the proposed solutions improve network utility, prolong the lifetime, and pave the way for a greener IoE by reducing their carbon footprints. Next, we introduce active radio frequency identification tags based cluster head selection, data-awareness and energy harvesting in IoE systems. The results show that such IoE systems are better equipped to deal with energy efficiency and data delivery problems. Simulation results support our data aware energy saving approach and show significant improvement over state-of-the art techniques. To design an energy-efficient and low-resource consuming security solution for IoE systems, we propose a Physically Unclonable Function based security scheme that exploits variations of physical sensor characteristics through a prototype printed circuit board design and challengeresponse pair generation using the quadratic residue property. Through simulations and measurements, we show that our design scheme is better in terms of energy and computation requirements and provides a two-fold secure data transfer. Finally, we apply our solutions to a home energy management system and find an optimal model to save energy in a broad IoE system application.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Detection And Management of Lower Body Deformity And Ulceration Extremity In People With A Lived Experience of Diabetes

Background: Diabetes is a silent killer, wherein prolonged poor glucose control could lead to acute diabetic ulcers that are responsible for foot ulcers in the lower body extremities. A diabetic foot is a skin sore formed as a result of skin tissue breaking down and exposing the tissue layers underneath. Chronic conditions of the disease lead to amputation of the limb which is a lifelong disability as well as morbidity. Objective: We have compiled an interesting and informative review on diabetic foot ulceration. Topics and subtopics discussed in the article have scientific relevance for the readers of health management journals. Main Outcome and Results: The cascade of events that lead to ulceration is responsible for degrading vascular changes in nerve fibers, resulting in poor motor neuropathy in the lower extremities. Therefore, detection of diabetic foot and ulceration in the early stage is crucial for proper disease management. Various tools in this regard have been used to detect and monitor diabetic foot occurrence apart from a conventional assessment such as the severity of the infection, infection of the skin, extent or size of the ulcer, depth of tissue infection, and loss of sensation from various parts of the lower body. Furthermore, recent advancement in medical technology has also given some critical diagnostic tools EMG (Electromyography), NCV (Nerve Conduction Velocity), PPG (Photoplethysmography), and SSEP (Somatosensory Evoked Potential). Conclusion: The review discusses various complications related to people with a lived experience of diabetic foot ulcers and some advanced tools to diagnose them. Furthermore, a conclusive discussion on a holistic view of diabetic foot diagnosis methods and available treatments has been summarized which could be more explored for better detection/management of the disease

Modeling and predicting mobility in wireless ad hoc networks

Wireless Ad Hoc Networks are a particular paradigm where wireless devices communicate in a decentralized fashion, without any centralized infrastructure or decision. In order to avoid a situation where nodes chaotically try to communicate, distributed and localized structures (graphs, trees, etc.) need to be built. Mobility brings challenging issues to the maintenance and to the optimality of such structures. In conventional approaches, structures are adapted to the current topology by each node periodically sending beacon messages, which is a significant waste of network resources. If each node can obtain some a priori knowledge of future topology configurations, it could decide to send maintenance messages only when a change in the topology effectively requires updating the structure. In this Doctoral Thesis, we investigate this approach and define the Kinetic Graphs, a novel paradigm regrouping mobility predictions for a kinetic mobility management, and localized and distributed graph protocols to insure a high scalability. The Kinetic Graph framework is able to naturally capture the dynamics of mobile structures, and is composed of four steps: (i) a representation of the trajectories, (ii) a common message format for the posting of those trajectories, (iii) a time varying weight for building the kinetic structures, (iv) an aperiodic neighborhood maintenance. By following this framework, we show that any structure-based ad-hoc protocol may benefit from the kinetic approach. A significant challenge of Kinetic Graphs comes from prediction errors. In order to analyze them, we illustrate the relationship between the prediction model and the mobility model. We decompose the prediction errors into three metrics: the adequacy between the prediction and the mobility models, the predicability of the mobility model, and the mobility model's realism. Following the framework, we define a kinetic model for the modeling of the trajectories and then analyze the extents of the effects of each error metric and develop solutions in order to reduce them. We finally adapt the Multipoint Relaying (MPR) protocol, used by the Optimized Link State Routing protocol (OLSR), and show the significant improvements that may be obtained by using the Kinetic Graph Framework, even on the very challenging vehicular networks