Samankaltaisia teoksia: FREQUENCY STABILITY ENHANCEMENT WITH VSG CONTROL FOR A PV DOMINATED GRID /
- ANALYSIS OF ECONOMIC FEASIBILITY OF GRID CONNECTED PV-SYSTEM IN MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY /
- STABILITY ANALYSIS OF SMART GRID CONNECTED WITH RENEWABLE SOURCES /
- DESIGN OF A GRID CONNECTED ROOF TOP SOLAR PV SYSTEM AND ANALYSIS OF ITS EFFECT ON GRID IN CONTEXT OF BANGLADESH /
- PERFORMANCE ASSESSMENT OF METAHEURISTIC OPTIMIZED PID CONTROLLERS FOR MULTI-AREA LOAD FREQUENCY CONTROL /
- GRID-CONNECTED PV-FUEL CELL-BATTERY BASED HYBRID SYSTEMS IN UNIVERSITY BUILDINGS: A TECHNO-ECONOMIC ANALYSIS /
- A LIGHTWEIGHT FREQUENCY BASED LOAD SHEDDING CONTROL SYSTEM WITH CATEGORIZED LOAD DISTRIBUTION AND ENHANCED DOS DETECTION IN SMARTGRID SYSTEM /
Aihe: Electrical, Electronic and Communication Engineering.
- DESIGN AND ANALYSIS OF METAMATERIAL BASED BIOSENSOR TO DETERMINE BLOOD GLUCOSE CONCENTRATION /
- A COMPUTATIONALLY EFFICIENT DEEP LEARNING FRAMEWORK FOR SLEEP APNEA DETECTION /
- TEMPERATURE AND STATE OF CHARGE AWARE FAST CHARGING FOR LITHIUM-ION BATTERY SYSTEM /
- INTERPOLATION BASED ADAPTIVE REVERSIBLE DATA HIDING FOR DIGITAL IMAGE APPLICATIONS /
- ENERGY EFFICIENT OPERATIONS OF COMP BASED LTE-A CELLULAR NETWORKS WITH HYBERID POWER SUPPLIES /
- DESIGN AND CHARACTERIZATION OF BULK AND SOL FINFETS /
Aihe: This Thesis Paper of EECE in BSc Program.
- A COMPUTATIONALLY EFFICIENT DEEP LEARNING FRAMEWORK FOR SLEEP APNEA DETECTION /
- TEMPERATURE AND STATE OF CHARGE AWARE FAST CHARGING FOR LITHIUM-ION BATTERY SYSTEM /
- A COMPARATIVE STUDY OF INORGANIC LEAD HALIDE PEROVSKITES BY ANALYZING THEIR ELECTRICAL, OPTICAL AND MECHANICAL PROPERTIES /
- A COMPREHENSIVE INVESTIGATION OF GaSb/GaAs0.5Sb0.5/GaAs BASED DG-HJ-SP-HD VERTICAL TUNNEL FET FOR OPTIMIZED LOW POWER APPLICATIONS: EXPLORING THE IMPACT OF DEVICE PARAMETER VARIATIONS /
- PERFORMANCE ANALYSIS OF CONTEMPORARY POWER DISTRIBUTION NETWORKS USING IEEE 13 BUS SYSTEM /
- AN IOT BASED HEALTHCARE SOLUTION AND MEDICAL SPECIALTY CLASSIFICATION FROM BANGLA TEXT USING MACHINE AND DEEP LEARNING MODELS /