Samankaltaisia teoksia: DESIGN AND ANALYSIS OF ORIENTATION-INDEPENDENT CHIPLESS RFID SENSORS /
- ENHANCED DETECTION AND ANOMALY IDENTIFICATION IN CHIPLESS RFID SYSTEMS USING SCALABLE MACHINE LEARNING MODELS /
- ESTABLISHING IOT BASED SMART TOLL MANAGEMENT SYSTEM USING RFID /
- DESIGN AND PERFORMANCE ANALYSIS OF A LOW POWER SMALL AREA RFID TRANSPONDER FOR WIRELESS COMMUNICATION APPLICATIONS /
- DESIGN AND NUMERICAL ANALYSIS OF A HIGH-SENSITIVITY SPR-BASED PHOTONIC CRYSTAL FIBER SENSOR FOR SUCROSE DETECTION /
- DESIGN AND NUMERICAL ANALYSIS OF A HIGH-SENSITIVITY GOLD-COATED D-SHAPED PCF-BASED SPR SENSOR FOR BIOMEDICAL SENSING APPLICATIONS /
- BI-LEVEL GATED UNET FOR POLYP SEGMENTATION WITH ADVERSARIAL RESILIENCE AND SEMI-SUPERVISED LEARNING /
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 /