�ݺ�ߣshows by User: gdr0059

�ݺ�ߣshows by User: gdr0059 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: gdr0059 / Thu, 06 Dec 2018 16:10:39 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: gdr0059 Resume of Gaurang Rathod, Embedded Software Developer /slideshow/gaurangresume-125176266/125176266 gaurangresume-181206161039
o 2.5+ years of experience in the embedded system domain o Expertise in C language, OS concepts and ARM cortex M3/M4 architecture o Strong Electronics engineering and research background]]>
o 2.5+ years of experience in the embedded system domain o Expertise in C language, OS concepts and ARM cortex M3/M4 architecture o Strong Electronics engineering and research background]]> Thu, 06 Dec 2018 16:10:39 GMT /slideshow/gaurangresume-125176266/125176266 gdr0059@slideshare.net(gdr0059) Resume of Gaurang Rathod, Embedded Software Developer gdr0059 o 2.5+ years of experience in the embedded system domain o Expertise in C language, OS concepts and ARM cortex M3/M4 architecture o Strong Electronics engineering and research background <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gaurangresume-181206161039-thumbnail.jpg?width=120&height=120&fit=bounds" /> o 2.5+ years of experience in the embedded system domain o Expertise in C language, OS concepts and ARM cortex M3/M4 architecture o Strong Electronics engineering and research background

Resume of Gaurang Rathod, Embedded Software Developer from Gaurang Rathod

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0 Trojan horse /slideshow/trojan-horse-47274836/47274836 trojanhorse-150422015552-conversion-gate02
Introduction to Trojan horse]]>
Introduction to Trojan horse]]> Wed, 22 Apr 2015 01:55:52 GMT /slideshow/trojan-horse-47274836/47274836 gdr0059@slideshare.net(gdr0059) Trojan horse gdr0059 Introduction to Trojan horse <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/trojanhorse-150422015552-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduction to Trojan horse

Trojan horse from Gaurang Rathod

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0 Precision based data aggregation to extend life of wsn /gdr0059/precision-based-data-aggregation-to-extend-life-of-wsn precisionbaseddataaggregationtoextendlifeofwsn-150422015548-conversion-gate02
The fast advancement of hardware technology has enabled the development of tiny and powerful sensor nodes, which are capable of sensing, computation and wireless communication. This revolutionizes the deployment of wireless sensor network for monitoring some area and collecting regarding information. However, limited energy constraint presents a major challenge such vision to become reality. Data communication between nodes consumes a large portion of the total energy consumption of the WSNs. Consequently, Wireless sensor nodes are very small in size and have limited processing capability with very low battery power. This restriction of low battery power makes the sensor network prone to failure. Data aggregation may be effective technique because it reduces the number of packets to be sent to sink by aggregating the similar packets. Data aggregation has been put forward as an essential technique to achieve power efficiency in sensor networks. The main goal of data aggregation is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. The data aggregation technique of precision allocation helps to balance the energy consumption of network. By optimum precision allocation given to node, helps to control the frequency of communication between node and base station. This way, effectively it reduces less communication between sources and sink, which helps to reduce the energy consumption. In experiment work, assigning same precision, random precision and precision based on distance and residual energy of node to all nodes in network and summarize energy consumption of node. By periodically adjusting the precision of node extend the life time of network compared to without aggregation and random precision allocation method. This technique suits to problem of continues data measuring, like temperature, humidity, water level, etc.]]>
The fast advancement of hardware technology has enabled the development of tiny and powerful sensor nodes, which are capable of sensing, computation and wireless communication. This revolutionizes the deployment of wireless sensor network for monitoring some area and collecting regarding information. However, limited energy constraint presents a major challenge such vision to become reality. Data communication between nodes consumes a large portion of the total energy consumption of the WSNs. Consequently, Wireless sensor nodes are very small in size and have limited processing capability with very low battery power. This restriction of low battery power makes the sensor network prone to failure. Data aggregation may be effective technique because it reduces the number of packets to be sent to sink by aggregating the similar packets. Data aggregation has been put forward as an essential technique to achieve power efficiency in sensor networks. The main goal of data aggregation is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. The data aggregation technique of precision allocation helps to balance the energy consumption of network. By optimum precision allocation given to node, helps to control the frequency of communication between node and base station. This way, effectively it reduces less communication between sources and sink, which helps to reduce the energy consumption. In experiment work, assigning same precision, random precision and precision based on distance and residual energy of node to all nodes in network and summarize energy consumption of node. By periodically adjusting the precision of node extend the life time of network compared to without aggregation and random precision allocation method. This technique suits to problem of continues data measuring, like temperature, humidity, water level, etc.]]> Wed, 22 Apr 2015 01:55:48 GMT /gdr0059/precision-based-data-aggregation-to-extend-life-of-wsn gdr0059@slideshare.net(gdr0059) Precision based data aggregation to extend life of wsn gdr0059 The fast advancement of hardware technology has enabled the development of tiny and powerful sensor nodes, which are capable of sensing, computation and wireless communication. This revolutionizes the deployment of wireless sensor network for monitoring some area and collecting regarding information. However, limited energy constraint presents a major challenge such vision to become reality. Data communication between nodes consumes a large portion of the total energy consumption of the WSNs. Consequently, Wireless sensor nodes are very small in size and have limited processing capability with very low battery power. This restriction of low battery power makes the sensor network prone to failure. Data aggregation may be effective technique because it reduces the number of packets to be sent to sink by aggregating the similar packets. Data aggregation has been put forward as an essential technique to achieve power efficiency in sensor networks. The main goal of data aggregation is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. The data aggregation technique of precision allocation helps to balance the energy consumption of network. By optimum precision allocation given to node, helps to control the frequency of communication between node and base station. This way, effectively it reduces less communication between sources and sink, which helps to reduce the energy consumption. In experiment work, assigning same precision, random precision and precision based on distance and residual energy of node to all nodes in network and summarize energy consumption of node. By periodically adjusting the precision of node extend the life time of network compared to without aggregation and random precision allocation method. This technique suits to problem of continues data measuring, like temperature, humidity, water level, etc. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/precisionbaseddataaggregationtoextendlifeofwsn-150422015548-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> The fast advancement of hardware technology has enabled the development of tiny and powerful sensor nodes, which are capable of sensing, computation and wireless communication. This revolutionizes the deployment of wireless sensor network for monitoring some area and collecting regarding information. However, limited energy constraint presents a major challenge such vision to become reality. Data communication between nodes consumes a large portion of the total energy consumption of the WSNs. Consequently, Wireless sensor nodes are very small in size and have limited processing capability with very low battery power. This restriction of low battery power makes the sensor network prone to failure. Data aggregation may be effective technique because it reduces the number of packets to be sent to sink by aggregating the similar packets. Data aggregation has been put forward as an essential technique to achieve power efficiency in sensor networks. The main goal of data aggregation is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. The data aggregation technique of precision allocation helps to balance the energy consumption of network. By optimum precision allocation given to node, helps to control the frequency of communication between node and base station. This way, effectively it reduces less communication between sources and sink, which helps to reduce the energy consumption. In experiment work, assigning same precision, random precision and precision based on distance and residual energy of node to all nodes in network and summarize energy consumption of node. By periodically adjusting the precision of node extend the life time of network compared to without aggregation and random precision allocation method. This technique suits to problem of continues data measuring, like temperature, humidity, water level, etc.

Precision based data aggregation to extend life of wsn from Gaurang Rathod

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0 Load balancing to extend life of wireless sensor network /slideshow/load-balancing-to-extend-life-of-wireless-sensor-network/47274794 loadbalancingtoextendlifeofwirelesssensornetwork-150422015448-conversion-gate02
Wireless sensor network is energy constrain network. Lifetime of network is defined by life of first certain percentage of dying nodes. Load balancing is a method to make energy consumption of all nodes equal and this way all nodes will die together. By load balancing, lifetime of network does not depend only on life of weak node but depends on life all nodes in network which helpful to increase life of network. In this paper two methods are proposed for load balancing which are also called data aggregation methods. In first method, nodes which are far from the sink consume more energy and load balancing is achieved by increasing the interval of communication based on residual energy of these nodes. In second method, load balancing is achieved by tolerating the quality of data. Nodes which have less energy send data only when data is sufficient deviate from past data. Quality of data is based on deviation control function and this deviation control function is based on residual energy of nodes. Simulation results show proposed methods significantly increases the lifetime of wireless sensor network.]]>
Wireless sensor network is energy constrain network. Lifetime of network is defined by life of first certain percentage of dying nodes. Load balancing is a method to make energy consumption of all nodes equal and this way all nodes will die together. By load balancing, lifetime of network does not depend only on life of weak node but depends on life all nodes in network which helpful to increase life of network. In this paper two methods are proposed for load balancing which are also called data aggregation methods. In first method, nodes which are far from the sink consume more energy and load balancing is achieved by increasing the interval of communication based on residual energy of these nodes. In second method, load balancing is achieved by tolerating the quality of data. Nodes which have less energy send data only when data is sufficient deviate from past data. Quality of data is based on deviation control function and this deviation control function is based on residual energy of nodes. Simulation results show proposed methods significantly increases the lifetime of wireless sensor network.]]> Wed, 22 Apr 2015 01:54:48 GMT /slideshow/load-balancing-to-extend-life-of-wireless-sensor-network/47274794 gdr0059@slideshare.net(gdr0059) Load balancing to extend life of wireless sensor network gdr0059 Wireless sensor network is energy constrain network. Lifetime of network is defined by life of first certain percentage of dying nodes. Load balancing is a method to make energy consumption of all nodes equal and this way all nodes will die together. By load balancing, lifetime of network does not depend only on life of weak node but depends on life all nodes in network which helpful to increase life of network. In this paper two methods are proposed for load balancing which are also called data aggregation methods. In first method, nodes which are far from the sink consume more energy and load balancing is achieved by increasing the interval of communication based on residual energy of these nodes. In second method, load balancing is achieved by tolerating the quality of data. Nodes which have less energy send data only when data is sufficient deviate from past data. Quality of data is based on deviation control function and this deviation control function is based on residual energy of nodes. Simulation results show proposed methods significantly increases the lifetime of wireless sensor network. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/loadbalancingtoextendlifeofwirelesssensornetwork-150422015448-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Wireless sensor network is energy constrain network. Lifetime of network is defined by life of first certain percentage of dying nodes. Load balancing is a method to make energy consumption of all nodes equal and this way all nodes will die together. By load balancing, lifetime of network does not depend only on life of weak node but depends on life all nodes in network which helpful to increase life of network. In this paper two methods are proposed for load balancing which are also called data aggregation methods. In first method, nodes which are far from the sink consume more energy and load balancing is achieved by increasing the interval of communication based on residual energy of these nodes. In second method, load balancing is achieved by tolerating the quality of data. Nodes which have less energy send data only when data is sufficient deviate from past data. Quality of data is based on deviation control function and this deviation control function is based on residual energy of nodes. Simulation results show proposed methods significantly increases the lifetime of wireless sensor network.

Load balancing to extend life of wireless sensor network from Gaurang Rathod

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0 Extend life of wireless sensor network /slideshow/extend-life-of-wireless-sensor-network/47274777 extendlifeofwirelesssensornetwork-150422015400-conversion-gate01
Lfe time of wireless sensor network]]>
Lfe time of wireless sensor network]]> Wed, 22 Apr 2015 01:54:00 GMT /slideshow/extend-life-of-wireless-sensor-network/47274777 gdr0059@slideshare.net(gdr0059) Extend life of wireless sensor network gdr0059 Lfe time of wireless sensor network <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/extendlifeofwirelesssensornetwork-150422015400-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Lfe time of wireless sensor network

Extend life of wireless sensor network from Gaurang Rathod

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0 Energy management issues in lte.pptx /slideshow/energy-management-issues-in-ltepptx/47274775 energymanagementissuesinlte-150422015350-conversion-gate02
Mobile Internet applications running on devices such as smart phones and tablets have dramatically changed the landscape of application-generated network traffic. Third Generation Partnership Project (3GPP) Releases 8, 9, and 10 (LTE and LTE-Advanced) were proposed before many such applications. Now such applications are widely used in modern smart phones and other mobile devices. In LTE’s power management model, where the user equipment (UE) is stays in radio resource control (RRC)-Connected state during active sessions and moves to RRC-Idle during Inactive sessions. It was well suited to the previous generation of popular applications and was effective at minimizing UE power consumption and other air interface resources. However, newer applications generate a constant stream of autonomous and user generated traffic at all times. This has, thus erasing the previously clear demarcation between active and Inactive states. This means, a given mobile device often ends up moving between connected and idle states very frequently to send mostly short bursts of data. This is draining device battery and causing excessive signaling overhead in LTE networks. This problem has grown and attracted the research community’s attention to address the negative effects of frequent back and forth transitions between LTE radio states. This seminar presents various methods for monitoring and controlling techniques for energy usages. Also explore the solution adopted by 3GPP is included in the latest development of release 11, to handle energy management issues. At the end, the future scope related to energy utilization is discussed]]>
Mobile Internet applications running on devices such as smart phones and tablets have dramatically changed the landscape of application-generated network traffic. Third Generation Partnership Project (3GPP) Releases 8, 9, and 10 (LTE and LTE-Advanced) were proposed before many such applications. Now such applications are widely used in modern smart phones and other mobile devices. In LTE’s power management model, where the user equipment (UE) is stays in radio resource control (RRC)-Connected state during active sessions and moves to RRC-Idle during Inactive sessions. It was well suited to the previous generation of popular applications and was effective at minimizing UE power consumption and other air interface resources. However, newer applications generate a constant stream of autonomous and user generated traffic at all times. This has, thus erasing the previously clear demarcation between active and Inactive states. This means, a given mobile device often ends up moving between connected and idle states very frequently to send mostly short bursts of data. This is draining device battery and causing excessive signaling overhead in LTE networks. This problem has grown and attracted the research community’s attention to address the negative effects of frequent back and forth transitions between LTE radio states. This seminar presents various methods for monitoring and controlling techniques for energy usages. Also explore the solution adopted by 3GPP is included in the latest development of release 11, to handle energy management issues. At the end, the future scope related to energy utilization is discussed]]> Wed, 22 Apr 2015 01:53:50 GMT /slideshow/energy-management-issues-in-ltepptx/47274775 gdr0059@slideshare.net(gdr0059) Energy management issues in lte.pptx gdr0059 Mobile Internet applications running on devices such as smart phones and tablets have dramatically changed the landscape of application-generated network traffic. Third Generation Partnership Project (3GPP) Releases 8, 9, and 10 (LTE and LTE-Advanced) were proposed before many such applications. Now such applications are widely used in modern smart phones and other mobile devices. In LTE’s power management model, where the user equipment (UE) is stays in radio resource control (RRC)-Connected state during active sessions and moves to RRC-Idle during Inactive sessions. It was well suited to the previous generation of popular applications and was effective at minimizing UE power consumption and other air interface resources. However, newer applications generate a constant stream of autonomous and user generated traffic at all times. This has, thus erasing the previously clear demarcation between active and Inactive states. This means, a given mobile device often ends up moving between connected and idle states very frequently to send mostly short bursts of data. This is draining device battery and causing excessive signaling overhead in LTE networks. This problem has grown and attracted the research community’s attention to address the negative effects of frequent back and forth transitions between LTE radio states. This seminar presents various methods for monitoring and controlling techniques for energy usages. Also explore the solution adopted by 3GPP is included in the latest development of release 11, to handle energy management issues. At the end, the future scope related to energy utilization is discussed <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/energymanagementissuesinlte-150422015350-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Mobile Internet applications running on devices such as smart phones and tablets have dramatically changed the landscape of application-generated network traffic. Third Generation Partnership Project (3GPP) Releases 8, 9, and 10 (LTE and LTE-Advanced) were proposed before many such applications. Now such applications are widely used in modern smart phones and other mobile devices. In LTE’s power management model, where the user equipment (UE) is stays in radio resource control (RRC)-Connected state during active sessions and moves to RRC-Idle during Inactive sessions. It was well suited to the previous generation of popular applications and was effective at minimizing UE power consumption and other air interface resources. However, newer applications generate a constant stream of autonomous and user generated traffic at all times. This has, thus erasing the previously clear demarcation between active and Inactive states. This means, a given mobile device often ends up moving between connected and idle states very frequently to send mostly short bursts of data. This is draining device battery and causing excessive signaling overhead in LTE networks. This problem has grown and attracted the research community’s attention to address the negative effects of frequent back and forth transitions between LTE radio states. This seminar presents various methods for monitoring and controlling techniques for energy usages. Also explore the solution adopted by 3GPP is included in the latest development of release 11, to handle energy management issues. At the end, the future scope related to energy utilization is discussed

Energy management issues in lte.pptx from Gaurang Rathod

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0 Energy efficient node deployment for target coverage in wireless sensor network /slideshow/energy-efficient-node-deployment-for-target-coverage-in-wireless-sensor-network/47274760 energyefficientnodedeploymentfortargetcoverageinwirelesssensornetwork-150422015337-conversion-gate01
Network lifetime plays an integral role in setting up an efficient wireless sensor network. Coverage in a network needs to guarantee that the region is monitored with the required degree of reliability. Locations of sensor nodes constitute the basic input for the algorithms that examine coverage of the network. Coverage problems can be broadly classified as area coverage problem and target coverage problem. Area coverage focuses on monitoring the entire region of interest, whereas target coverage concerns monitoring only certain specific points in a given region. Target coverage can be categorized as simple coverage, k-coverage and Q-coverage. Lower coverage level (simple coverage) is enough for environmental or habitat monitoring or applications like home security. Higher degree of coverage (k-coverage) will be required for some applications like target tracking to track the targets accurately, or if sensors work in a hostile environment such as battle fields or chemically polluted areas. More reliable results are produced for higher degree of coverage which requires multiple sensor nodes to monitor the region/targets. An example of Q-coverage is video surveillance systems deployed for monitoring hostile territorial area where some sensitive targets like a nuclear plant may need more sensors cooperate to ensure source redundancy for precise data. Sensor nodes deterministically deployed by using artificial bee colony algorithm, so as to achieve the required target coverage level and maximize the network lifetime.]]>
Network lifetime plays an integral role in setting up an efficient wireless sensor network. Coverage in a network needs to guarantee that the region is monitored with the required degree of reliability. Locations of sensor nodes constitute the basic input for the algorithms that examine coverage of the network. Coverage problems can be broadly classified as area coverage problem and target coverage problem. Area coverage focuses on monitoring the entire region of interest, whereas target coverage concerns monitoring only certain specific points in a given region. Target coverage can be categorized as simple coverage, k-coverage and Q-coverage. Lower coverage level (simple coverage) is enough for environmental or habitat monitoring or applications like home security. Higher degree of coverage (k-coverage) will be required for some applications like target tracking to track the targets accurately, or if sensors work in a hostile environment such as battle fields or chemically polluted areas. More reliable results are produced for higher degree of coverage which requires multiple sensor nodes to monitor the region/targets. An example of Q-coverage is video surveillance systems deployed for monitoring hostile territorial area where some sensitive targets like a nuclear plant may need more sensors cooperate to ensure source redundancy for precise data. Sensor nodes deterministically deployed by using artificial bee colony algorithm, so as to achieve the required target coverage level and maximize the network lifetime.]]> Wed, 22 Apr 2015 01:53:37 GMT /slideshow/energy-efficient-node-deployment-for-target-coverage-in-wireless-sensor-network/47274760 gdr0059@slideshare.net(gdr0059) Energy efficient node deployment for target coverage in wireless sensor network gdr0059 Network lifetime plays an integral role in setting up an efficient wireless sensor network. Coverage in a network needs to guarantee that the region is monitored with the required degree of reliability. Locations of sensor nodes constitute the basic input for the algorithms that examine coverage of the network. Coverage problems can be broadly classified as area coverage problem and target coverage problem. Area coverage focuses on monitoring the entire region of interest, whereas target coverage concerns monitoring only certain specific points in a given region. Target coverage can be categorized as simple coverage, k-coverage and Q-coverage. Lower coverage level (simple coverage) is enough for environmental or habitat monitoring or applications like home security. Higher degree of coverage (k-coverage) will be required for some applications like target tracking to track the targets accurately, or if sensors work in a hostile environment such as battle fields or chemically polluted areas. More reliable results are produced for higher degree of coverage which requires multiple sensor nodes to monitor the region/targets. An example of Q-coverage is video surveillance systems deployed for monitoring hostile territorial area where some sensitive targets like a nuclear plant may need more sensors cooperate to ensure source redundancy for precise data. Sensor nodes deterministically deployed by using artificial bee colony algorithm, so as to achieve the required target coverage level and maximize the network lifetime. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/energyefficientnodedeploymentfortargetcoverageinwirelesssensornetwork-150422015337-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Network lifetime plays an integral role in setting up an efficient wireless sensor network. Coverage in a network needs to guarantee that the region is monitored with the required degree of reliability. Locations of sensor nodes constitute the basic input for the algorithms that examine coverage of the network. Coverage problems can be broadly classified as area coverage problem and target coverage problem. Area coverage focuses on monitoring the entire region of interest, whereas target coverage concerns monitoring only certain specific points in a given region. Target coverage can be categorized as simple coverage, k-coverage and Q-coverage. Lower coverage level (simple coverage) is enough for environmental or habitat monitoring or applications like home security. Higher degree of coverage (k-coverage) will be required for some applications like target tracking to track the targets accurately, or if sensors work in a hostile environment such as battle fields or chemically polluted areas. More reliable results are produced for higher degree of coverage which requires multiple sensor nodes to monitor the region/targets. An example of Q-coverage is video surveillance systems deployed for monitoring hostile territorial area where some sensitive targets like a nuclear plant may need more sensors cooperate to ensure source redundancy for precise data. Sensor nodes deterministically deployed by using artificial bee colony algorithm, so as to achieve the required target coverage level and maximize the network lifetime.

Energy efficient node deployment for target coverage in wireless sensor network from Gaurang Rathod

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0 Empolyee welfare /slideshow/empolyee-welfare/47274754 empolyeewelfare-150422015319-conversion-gate01
Employee Welfare]]>
Employee Welfare]]> Wed, 22 Apr 2015 01:53:19 GMT /slideshow/empolyee-welfare/47274754 gdr0059@slideshare.net(gdr0059) Empolyee welfare gdr0059 Employee Welfare <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/empolyeewelfare-150422015319-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Employee Welfare

Empolyee welfare from Gaurang Rathod

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0 Bcd counter with mode control & parallel load capability /slideshow/bcd-counter-with-mode-control-parallel-load-capability/47274747 bcdcounterwithmodecontrolparallelloadcapability-150422015258-conversion-gate02
Basic BCD counter impletation]]>
Basic BCD counter impletation]]> Wed, 22 Apr 2015 01:52:58 GMT /slideshow/bcd-counter-with-mode-control-parallel-load-capability/47274747 gdr0059@slideshare.net(gdr0059) Bcd counter with mode control & parallel load capability gdr0059 Basic BCD counter impletation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bcdcounterwithmodecontrolparallelloadcapability-150422015258-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Basic BCD counter impletation

Bcd counter with mode control & parallel load capability from Gaurang Rathod

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0 https://cdn.slidesharecdn.com/profile-photo-gdr0059-48x48.jpg?cb=1652087985 1. Programming Skills - C, C++ 2. Micro-controllers - AT89S51(8051), PIC16, LPC2148 and STM32F4xx(Cortex-M4). 3.Communication protocol - I2C,SPI,USB,RS232, Ethernet 4. Peripheral programming - Timers, Interrupts, RTC, ADC, PWM, EEPROM, UART. 5. Hardware / IC Interfacing - LED, LCD, Matrix Keypad, DC motor, Line Driver, 870 Decoder, BCD to 7 segment converter IC’S. 6. Devices and Sensors Interfacing - Devices like GSM, and RFID and sensors like pressure sensor, gas sensor, IR, LDR, TSOP etc. 7. Operating System - Windows XP, GNU/Linux 8. Software tools - RCS, make, gdb. 9. Measuring & Testing instruments - Multi-meter and Logic Analyzer. 10. Able to write tight and bug-free code. 11. Good... http://stackoverflow.com/story/gauran https://cdn.slidesharecdn.com/ss_thumbnails/gaurangresume-181206161039-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/gaurangresume-125176266/125176266 Resume of Gaurang Rath... https://cdn.slidesharecdn.com/ss_thumbnails/trojanhorse-150422015552-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/trojan-horse-47274836/47274836 Trojan horse https://cdn.slidesharecdn.com/ss_thumbnails/precisionbaseddataaggregationtoextendlifeofwsn-150422015548-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds gdr0059/precision-based-data-aggregation-to-extend-life-of-wsn Precision based data a...