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Made with Unity Developed the game logic with dedicated server and Network Management Developed Complex skill types for each character and correlated design patterns. Developed Matchmaking and Messaging with Google Firebase. HLAPI, DOTS, Timeline, Cine-machine, Lighting, Shaders, Coding Extendable components for MOBA style specific hero abilities Graphical assets: animation, VFX, etc.

A new solution for neuro navigation surgeries makes the surgeon able to see through a head-mounted device (HoloLens 2) and watch different modalities inside the patient’s head. Registration system capable of automatically detecting the patient’s face and registering the CT-scanned 3D-Reconstructed mesh on it. Assessment scenario for measuring registration errors. Tracking system using retro reflective probes and QR code localization. Assessment scenario for tracking errors. MVP app and integrated the plugins for HoloLens 2 (XR Interaction) showcased at GITEX Dubai 2023 Extended packages for custom usage (painting in VR, Cylindrical Clipping, …) Backend. (Sockets, TCP, Docker compose, FASTAPI, Python). Volumetric Rendering, Clipping Shaders, etc.

IELTS VR for smart NPCs and Lip-syncing over audio stream through microphone React JS  and WebRTC with websockets communications for signalling Express js and MongoDB with JWT authorization and webrtc communications

WebAR unity game mechanic with more than 20,000 verified users   so far: OTP REST API scalable online video streaming Fluid simulation webgl optimizations Zapworks Image tracking CDN setup used for: Unity WebGL app object storage Dockerized ubuntu server: nodejs postgres-db nginx-server and static landings

Tracking Enscopy with Opti Track Accurate Tracking Calibration Optimized Computations PMS through Node.j and MongoDB Face Registration Remote Rendering volumetric layered data

  Farhad Piri¶ Adversarial Networks¶ Generate Synthetic Images with DCGANs in Keras Import Libraries¶ In [3]: import sys sys.path.insert(1, ‘/kaggle/input/module-of-plot-utils’) In [4]: %matplotlib inline import tensorflow as tf from tensorflow import keras import numpy as np import plot_utils import matplotlib.pyplot as plt from tqdm import tqdm print(‘Tensorflow version:’, tf.__version__) /opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3 warnings.warn(f”A NumPy version >={np_minversion} and <{np_maxversion}” Tensorflow version: 2.13.0 Load and Preprocess the Data¶ In [5]: (x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data() x_train = x_train.astype(np.float32) / 255.0 x_test = x_test.astype(np.float32) / 255.0 Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz