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ndian Institute of Technology Hyderabad’s Center for Healthcare Entrepreneurship (CfHE) will mark the graduation of its third batch on Thursday (8th August 2019) by hosting a ‘pitching session’ for its Fellows with Investors, Venture Capital and Private Equity firms. This pitching session will help the CfHE Fellows convert their ideas and innovations into health technology products that will cater to some of the unmet needs of Indian Healthcare Sector.

Indian Institute of Technology Hyderabad-incubated startup PURE EV has raised Venture Capital funding from Hyderabad-based pharmaceuticals visionary Shri V.C. Nannapaneni, in his personal capacity.

Indian Institute of Technology Hyderabad-incubated startup PURE EV has raised Venture Capital funding from Hyderabad-based pharmaceuticals visionary Shri V.C. Nannapaneni, in his personal capacity.

The third batch of fellows at Indian Institute of Technology Hyderabad Center for Healthcare Entrepreneurship (CfHE) will pitch their ideas to investors, venture capital firms and private equity players on August 8.

Traditional agriculture is transforming into smart agriculture due to the prominence of the Internet of Things (IoT). Low-cost and low-power are the key factors to make any IoT network useful and acceptable to the farmers. In this paper, we have proposed a low-power, low-cost IoT network for smart agriculture. For monitoring the soil moisture content, we have used an in-house developed sensor. In the proposed network, the IITH mote is used as a sink and sensor node which provides low-power communication. We have evaluated our network with state of the art networks, proposed for agriculture monitoring. Power and cost are the two metrics used for evaluation of these networks. Results show that the proposed network consumes less power and has prolonged lifetime in the agriculture field.

In conventional telesonography, the ultrasound video is encoded at a constant bit rate and transmitted to the expert side for diagnosis, this is highly bandwidth demanding and less adaptable to the varying network conditions. In contrary, Internet based videophone applications adapt to the network conditions with variable bit rate encoding and error concealment algorithms. Since portable ultrasound scanners are coming with Internet connectivity, this motivated us to evaluate the viability of using existing Internet based videophone services for real-time telesonography. Popular videophone services like Skype, Facebook and WebRTC is chosen for this purpose. The performance of these services is analyzed by varying network parameters like bandwidth, packet loss, delay and packet error in a controlled set up. Liver ultrasound video is considered for the study. A total of 273 different network settings is analyzed for evaluating the performance of each videophone service, constituting a total of 819 instances for evaluating three services. Three medical experts were participated in the subjective study. From an extensive analysis, an inference is made that the Internet based videophone services are reliable enough for telesonography provided a network with minimum bandwidth of 1600 kHz, delay up to 200 ms, packet loss up to 3% and packet error up to 3%.

IEEE 802.15.4 takes a center stage in IoT as Low-Rate Wireless Personal Area Networks(LR-WPANs). The standard specifies Offset Quadrature Phase Shift Keying Physical Layer (O-QPSK PHY) with half-sine pulse shaping which can be either categorized under the class of M-ary PSK signals (QPSK signal with offset) or as Minimum Shift Keying (MSK) signal. M-ary PSK demodulation requires perfect carrier synchronization and has minimal error. MSK signals which falls under Continuous Phase Frequency Shift Keying can be demodulated non-coherently but error performance is not as good. In our paper, this dual nature of IEEE 802.15.4 PHY is exploited to propose a dual mode receiver comprising of QPSK demodulator chain and MSK demodulator chain as a single system on chip. The mode can be configured manually depending on the type of application or based on feedback from a Signal to Noise (SNR) indicator employed in the proposed receiver. M-ary PSK chain is selected for lower SNRs and MSK for higher SNRs. Each of these properties are analyzed in detail for both demodulator chains and we go on to prove that MSK detection can be used for low power, low complex and low latency while QPSK detection is employed for minimal error.

Cognitive radio (CR) systems are potentially capable of mitigating the spectrum shortage of contemporary wireless systems. In this paper, we provide a brief overview of CR systems and the important research milestones of their evolution, along with their standardization activities, as a result of their research. This is followed by the detailed analysis of the interweave policy-based CR network (CRN) and by a detailed comparison with the family of underlay-based CRNs. In the interweave-based CRN, sensing of the primary user's (PU) spectrum by the secondary user's (SU) has remained a challenge, because the sensing errors prevent us from fulfilling the significant throughput gains that the concept of CR promises. Since missed detection and false alarm errors in real-time spectrum sensing cannot be avoided, based on a new approach, we quantify the achievable rates of the interweave CR by explicitly incorporating the effect of sensing errors. The link between the PU transmitter and the SU transmitter is assumed to be fast fading. Explicitly, the achievable rate degradation imposed by the sensing errors is analyzed for two spectrum sensing techniques, namely, for energy detection and for magnitude squared coherence-based detection. It is demonstrated that when the channel is sparsely occupied by the PU, the reusing techniques that are capable of simultaneously providing low missed detection and false alarm probabilities cause only a minor degradation to the achievable rates. Furthermore, based on the achievable rates derived for underlay CRNs, we compare the interweave CR and the underlay CR paradigms from the perspective of their resilience against spectrum sensing errors. Interestingly, in many practical regimes, the interweave CR paradigm outperforms the underlay CR paradigm in the presence of sensing errors, especially when the SNR at the SU is below 10 dB and when the SNR at the PU is in the range of 10-40 dB. Furthermore, we also provide rules of thumb that identif

The evolution of the concept of cloud communications has posed a growing emphasis on virtual and abstract environments for the flow of information, structuring it in similitude to a natural cloud. The Green Symbiotic Cloud Communications (GSCC) paradigm created on this concept facilitates the use of multiple communication mediums concomitantly creating a first of its kind communication cloud. This paper specifically corroborates a virtualized transport layer and network ports and an abstracted Internet protocol scheme in defining the GSCC architecture. We further address the issue of formulating a cognitive decision function based on utility theory, which allows users with GSCC enabled devices to intelligently distribute its bandwidth requirement amongst the available communication mediums. Considering the multiple criteria associated with different networks we formulate an optimization problem to find the solution for this resource allocation problem for single user. We further address the multi-user scenario and formulate and solve the multi-objective optimization problem using goal attainment technique. Results in single and multiple user scenarios, demonstrate that by utilizing multiple mediums as per GSCC paradigm coupled with our proposed decision function improves the functionality of the communication cloud. The proposed architecture is dynamic and evolving, embedding greenness by efficiently utilizing the available resources as and when required. The multiple virtual links equate a linearly increasing relationship with the throughput achieved. Experimental results for both real time and static data through the proposed schematic are documented. The augmented paradigm enhances the quality of service, linearly increases throughput and increases the overall security in communications.

The Green Symbiotic Cloud Communications (GSCC) paradigm created on the concept of cloud communications facilitates the use of multiple communication mediums concomitantly. In this paper, we address the issue of formulating a cognitive decision function based on utility theory, which allows users with GSCC enabled devices to intelligently distribute its bandwidth requirement amongst the available communication mediums. The green and symbiotic approach entails the simultaneous use of multiple communication interfaces, enabling efficient resource utilization. The judiciously designed architecture allows multiple users to access multiple mediums concomitantly, for both uplink and downlink, via virtualized communication ports and Internet Protocol schematic. Design postulates for technological systems of future are proposed and GSCC is evolved using these concepts.

Unification of varied signal transmission through a cloud structure of communications is one of the major postulates of the GSCC paradigm. Unification yields new possibilities in architecture evolution wherein a single access point can manage communication demands of multiple standards. In this chapter, the authors propose a novel access point (modem design), which allows for one modem to control all types of Communication Mediums (LTE, 3G, Wi-Fi, etc.) effectively delineating a universal communication modem (UCM). Three innovative modules are proposed, which when amalgamated together, result in UCM. Firstly, when different types of signals are sent and received on different mediums, the receiver needs intelligent channel estimation for coupling and decoupling these signals. An intelligent blind channel estimation scheme is proposed for achieving this. Secondly, the received signals of different or the same mediums need to be classified and routed to the appropriate devices. To accomplish, this we propose a novel Optimized Coulomb Energy Neural Network (OCENN) for classification of incoming signals. Finally, an innovative Internet Protocol (IP) based addressing schematic is proposed, which allows bidirectional communications.

Cloud computing, a TCP/IP based development, is essentially an integration of computer technologies such as HPC, massive memory resource handling, high-speed networks and reliable system architecture. A unified definition of cloud computing doesn’t exist with researchers and industrialists globally having listed up to 22 definitions to provide a comprehensive analysis of all the characteristics of Cloud Computing. However Cloud Computing mainly entails as a service that is outsourced, and does not symbolically represent a cloud as we observe in Nature. Classified exhaustively, clouds fit into the following categories—public, private, community and hybrid—however, without much exclusivity. This chapter views the emblem of cloud computing from a different perspective by emulating the geographical cloud as it appears in nature with properties of abstraction and virtualization. The chapter further introduces a first of its kind concept of Cloud Communications. To the best of our knowledge this is an archetype approach of incorporating the communications infrastructure into the cloud. The chapter proposes a Green Symbiotic Cloud (GSC) paradigm, which is an amalgamation of all sorts of clouds, with the elimination/minimization of reliance on data-centers, agent-based cooperative approaches and self-managed platforms inherent to systems of the future. Backed by concepts of abstraction and virtualized infrastructure and shared resource pools in one’s own local area network, the proposed paradigm offers impetus to revolutionize cloud computing. Taking virtualization to an entirely new level by offering a more local, energy-efficient, synergistic system comprised of individual agents sharing not just resources but knowledge/intelligence in the cloud, it basically emulates the cloud as it appears in nature.

The determination of Optimal Packet Size (OPS) for Cognitive Radio assisted Sensor Networks (CRSNs) architecture is non-trivial. State of the art in this area describes various complex techniques to determine OPS for CRSNs. However, it is observed that under high interference from the surrounding users, it is not possible to determine a feasible optimal packet size of data transmission under the simple point-to-point CRSN network topology. This is contributed primarily due to the peak transmit power constraint of the cognitive nodes. To address this specific challenge, this paper proposes a Multiple Input Multiple Output based Cognitive Radio Sensor Networks (MIMO-CRSNs) architecture for futuristic technologies like Internet of Things (IoT) and machine-to-machine (M2M) communications. A joint optimization problem is formulated taking into account network constraints like the overall end to end latency, interference duration caused to the non-cognitive users, average BER and transmit power.We propose our Algorithm-1 based on generic exhaustive search technique blue to solve the optimization problem. Furthermore, a low complexity suboptimal Algorithm-2 based on solving classical Karush-Kuhn-Tucker (KKT) conditions is proposed. These algorithms for MIMO-CRSNs are implemented in conjunction with two different channel access schemes. These channel access schemes are Time Slotted Distributed Cognitive Medium Access Control denoted as MIMO-DTS-CMAC and CSMA/CA assisted Centralized Common Control Channel based Cognitive Medium Access Control denoted as MIMO-CC-CMAC. Simulations reveal that the proposed MIMO based CRSN network outperforms the conventional point-to-point CRSN network in terms of overall energy consumption. Moreover, the proposed Algorithm-1 and Algorithm2 shows perfect match and the implementation complexity of Algorithm-2 is much lesser than Algorithm-1. Algorithm-1 takes almost 680 ms to execute and provides OPS value for a given number of users while Algor

elesonography suffers from inherent limitations due to the need of all time availability of experts in cloud and data connectivity to the device. Computer-aided diagnosis (CAD) used for automatic detection of abnormalities without manual intervention can overcome these limitations. Commercially available ultrasound scanners restrict the installation of new softwares and hence CAD algorithms cannot be integrated into the existing ultrasound scanners. There is a need for an external computing device, which can acquire image data from ultrasound scanners, perform CAD and generate result. Smart-phones are now widely used in personalized healthcare due to its ubiquitous computing capability. Smartphones with embedded CAD can be used as a computing device for automated diagnosis. In this paper, we have developed an Application (APP) for a smartphone to automatically diagnose the kidney in the ultrasound image. With the developed APP, the smartphone can acquire images from any ultrasound scanner, process it and give the diagnostic result. Automatic abnormality detection of kidney is based on Viola Jones algorithm, texture feature extraction followed by SVM classifier. Stones and cysts are the abnormalities detected using the algorithm. The developed APP resulted with an accuracy of 90.91% in detecting the abnormalities.

Cognitive Radio Sensor Networks (CRSN) is state of the art communication paradigm for power constrained short range data communication. It is one of the potential technology adopted for Internet of Things (IoT) and other futuristic Machine to Machine (M2M) based applications. Many of these applications are power constrained and delay sensitive. Therefore, CRSN architecture must be coupled with different adaptive and robust communication schemes to take care of the delay and energy-efficiency at the same time. Considering the tradeoff that exists in terms of energy efficiency and overhead delay for a given data packet length, it is proposed to transmit the physical layer payload with an optimal packet size (OPS) depending on the network condition. Furthermore, due to the cognitive feature of CRSN architecture overhead energy consumption due to channel sensing and channel handoff plays a critical role. Based on the above premises, in this paper we propose a heuristic exhaustive search based Algorithm-1 and a computationally efficient suboptimal low complexity Karuh-Kuhn- Tucker (KKT) condition based Algorithm-2 to determine the optimal packet size in CRSN architecture using variable rate m-QAM modulation. The proposed algorithms are implemented along with two main cognitive radio assisted channel access strategies based on Distributed Time Slotted-Cognitive Medium Access Control (DTS-CMAC) and Centralized Common Control Channel based Cognitive Medium Access Control (CC-CMAC) and their performances are compared. The simulation results reveals that proposed Algorithm-2 outperforms Algorithm-1 by a significant margin in terms of its implementation time. For the exhaustive search based Algorithm-1 the average time consumed to determine OPS for a given number of cognitive users is 1.2 seconds while for KKT based Algorithm-2 it is of the order of 5 to 10 ms. CC-CMAC with OPS is most efficient in terms of overall energy consumption but incurs more delay as compared to DTS-CM

Embodiments herein disclose a method and a base station for accessing a channel of an unlicensed band in a wireless communication network. The method includes maintaining a plurality of virtual stations by the base station in the wireless communication network based on a value. Further, the method includes contending to access the channel using the plurality of virtual stations. Each virtual station in the plurality of virtual stations includes a contention window and a counter value.

A distributed sensor network deployed to detect a binary event using a hard decision fusion scheme is considered. Earlier works show that the K-out-of-N counting rule is optimum in minimizing the total error rate when the sensor nodes participating in the event detection have identical performance indexes (detection and false-alarm probability pairs). In most scenarios, this optimum rule turns out to be the half-voting or majority rule. However, when the sensor nodes have non-identical performance indexes, the optimum value of K that gives the maximum correct decision probability may be anything from 1 to N and no general expression for it is available. Trying all possibilities before choosing the best value of K is computationally very demanding and the complexity grows rapidly as the number of sensor nodes increases, making it an impractical approach for a large sensor network. We propose a multistage decision fusion scheme for such networks using unanimity fusion rules and show that it gives an improved performance over unanimity, half-voting, majority, and some soft fusion rules and a comparable performance to the best K-out-of-N fusion rule in many cases with considerably reduced computational complexity in determining the global detection and false-alarm probabilities.

The mathematical characterization of a concept embeds a logical element in it, giving the invention a concrete structure. The conducting and radiating properties of tuPOY are given a mathematical emblem in this chapter. The behavioral pattern of tuPOY to a stimuli and its subsequent molecular state change variation due to its conduction, and radiation properties are mathematically modeled, providing an insight into its metallic behavior. A new mathematical concept of interchange phenomenon at molecular level is proposed to support the claims. The numerical manifestations spout a gamut of operations of tuPOY in technologies of future.

A new information-theoretic model is proposed for underlay-based cognitive radio (CR), which imposes rate limitation on the secondary user (SU), whereas the traditional systems impose either interference or transmit power limitations. The channel is modeled as a twin-user interference channel constituted by the primary user (PU) and the SU. The achievable rate of the SU is derived based on the inner bound formulated by Han and Kobayashi, where the PU achieves the maximum attainable rate of the single-user point-to-point link. We show that it is necessary for the SU to allocate its full power for the "public" message that can be decoded both by the SU and by the PU. We also demonstrate that it is optimal for the PU to allocate its full power for the "private" message that can only be decoded by the PU if the level of interference imposed by the PU on the SU is "ergodically strong." Similarly, it is optimal for the PU to allocate its full power for the public message that can be decoded both by the SU and PU if this interference is "ergodically weak." These findings suggest that this power allocation is independent of the level of interference imposed by the SU on the PU. Furthermore, the achievable rate is analyzed as a function of the average level of interference. An interesting observation is that if the level of interference imposed by the SU on the PU is "ergodically weak," the achievable rate becomes a monotonically increasing function of this interference, and it is independent of the level of interference imposed by the PU on the SU. Furthermore, we analyze the realistic imperfect channel estimation scenario and demonstrate that the channel estimation errors will not affect the optimal nature of the SU's power allocation.

Ultrasound imaging has been widely used for preliminary diagnosis as it is non-invasive and has good scope for the doctors to analyse many diseases. Due to lack of trained radiologists in remote areas, tele-radiology is used to diagnose the scanned ultrasound data. Availability of online Radiographers and having communication facility for the portable ultrasound is an issue in tele-radiology for using ultrasound scanning in remote health-care. In this scenario, Computer Aided Diagnosis (CAD) will be beneficial in diagnosing the patients with minimal manual intervention. Hence, in this paper we propose FPGA based CAD algorithm for abnormality detection of kidney in ultrasound images. As a pre-processing, ultrasound image is denoised and region of interest of kidney in ultrasound image is segmented. Intensity histogram features and Haralick features are extracted from kidney region. The algorithm is implemented in two stages. In first stage, a Look Up Table (LUT) based approach is used to differentiate between normal and abnormal kidney images. If abnormality is detected then SVM classifier is used to further classify the presence of stone or cyst in kidney ultrasound image. SVM with Multi Layer Perceptron (MLP) kernel approach is used to detect the exact abnormality. The propose algorithm resulted with an accuracy of 98.14% in detecting the cyst or stone in kidney ultrasound images. The proposed algorithm is implemented on FPGA based Xilinx Kintex-7 board

The mathematical characterization of a concept embeds a logical element in it, giving the invention a concrete structure. The conducting and radiating properties of tuPOY are given a mathematical emblem in this chapter. The behavioral pattern of tuPOY to a stimuli and its subsequent molecular state change variation due to its conduction, and radiation properties are mathematically modeled, providing an insight into its metallic behavior. A new mathematical concept of interchange phenomenon at molecular level is proposed to support the claims. The numerical manifestations spout a gamut of operations of tuPOY in technologies of future.

The rise of opportunistic mobile phone sensing paradigm is due to ubiquitous mobile phones usage. Optimisation of battery energy consumption and achieving required spatial coverage of sensing area are important issues under opportunistic mobile phone sensing networks. Human mobility enable collection of mobile phones sensor data and sensing area coverage by forming communication network with surrounding neighbors devices (mobile phones). This paper proposes a human mobility based selective sensing framework called as HWSS (Human Walk based Selective Sensing) for sensor data collection in opportunistic mobile phone sensing networks. In proposed HWSS method, mobile phones are selected for sensing task based on users current and predicted future locations and sensing overlap area. The proposed HWSS method is compared with non-selective sensing method (NSS), where all mobile nodes are allowed for sensing during sensor data collection process, in an given application area. The simulation results show that compared to NSS method proposed HWSS method achieves reduction in energy consumption and required spatial coverage.

Significant energy consumption by buildings from utility grid has made researchers revisit existing Building Energy Management Systems (BEMS). Most of the developing countries have taken a green initiative of Net Zero Energy Buildings (NZEB) to reduce carbon foot print and fast depletion of conventional energy sources. Though the integration of solar and wind based systems to grid is successful in recent years, residential building energy management systems with renewable energy sources is still an evolving research area. Monitoring, control and actuation systems should be tightly coupled with the help of any to any device communication namely Internet of Things (IoT) to realize an efficient NZEB. In this paper a real time NZEB is proposed and developed with bi-directional wireless sensor and actuation system. Proposed NZEB central server collects and maintains a database of on site solar generation, battery state of charge and load power consumption data of a building with help of IEEE 802.15.4 and IEEE 802.11 wireless networks. Proposed system was deployed as a test bed with sensing, control, actuation and server modules and connecting them with a bi-directional wireless network architecture similar to IoT. Data observed at experimental test bed confirm that developed system can estimate on site solar power generation, state of charge on battery bank and load power consumption with negligible error. A simulation study with experimental data collected at NZEB test bed shows that NZEB can optimally schedule loads between local generation and utility grid thereby minimizing peak demand on the grid.

In this study, we propose a novel physical layer architecture which is a hybrid of IEEE 802.15.4 and IEEE 802.11b for providing ubiquitous connectivity in IoT remote sensing platforms. The proposed architecture multiplexes the commonly available functional units of IEEE 802.15.4 and IEEE 802.11b, thereby achieving significant area and power savings. In order to achieve this, we modified the existing PHY layer to incorporate QPSK and RC pulse shaping for both the radios during the process of modulation. Performance analysis shows that the proposed architecture resulted in an area savings of 17.2 % and power savings of 17.8 % compared to traditional architecture without any performance degradation. In addition the CMOS transistors count is reduced by 6592, which is a significant reduction in complexity when compared to traditional independent radio's architectures.