Նմանատիպ նյութեր: TEMPERATURE AND STATE OF CHARGE AWARE FAST CHARGING FOR LITHIUM-ION BATTERY SYSTEM /
- HYBRID QUANTUM-CLASSICAL LEARNING APPROACH FOR PREDICTING THE STATE OF HEALTH OF LITHIUM-ION BATTERIES /
- DESIGN AND DEVELOPMENT OF AN EFFICIENT CHARGING SYSTEM FOR THE BATTERY OPERATED VEHICLES IN BANGLADESH / Md Rezaul Awal .
- PIONEER: A PHYSICS-INFORMED CONTRASTIVE AND INVARIANT FRAMEWORK FOR EV CHARGING DEMAND PREDICTION /
- DESIGN, DEVELOPMENT AND PERFORMANCE ANALYSIS OF SOLAR CHARGE CONTROLLER FOR VARIOUS EQUIPMENT OF BANGLADESH ARMY /
- DESIGN AND IMPLEMENTATION OF A MICRO CONTROLLER BASED MAXIMUM POWER POINT TRACKING SOLAR CHARGE CONTROLLER /
- Design and Development of an Efficient Charging System for the Battery Operated Vehicles in Bangladesh
Խորագիր: 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 /
Խորագիր: 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 /