home page images

Author: aybanerj

_data/people.yml

@@ -1,3 +1,6 @@
+- name: Dinesh Bharadia
+  role: Principal Investigator
+  picture: /assets/images/teampic/dinesh.jpg
 - name: Roshan Ayyalasomayajula
   role: PhD
   picture: /assets/images/teampic/roshan.jpg

car1.png

@@ -3,10 +3,12 @@ layout: article
 title: Wireless Communications Sensing and Networking
 carousels:
   - images:
-    - image: /atkinson.jpeg
-    - image: /rice_chip.jpeg
-    - image: /pcb.jpeg
-    - image: /bearl.jpeg
+    - image: /assets/images/picpic/graduation_2023.jpg
+    - image: /assets/images/picpic/mobicom_2021.jpg
+    - image: /assets/images/picpic/roshan_graduation.jpg
+    - image: /assets/images/picpic/birch_2022.jpg
+    - image: /assets/images/picpic/christmas_2021.jpg
+    - image: /assets/images/picpic/diwali.jpg
 article_header:
   type: overlay
   theme: dark
@@ -15,17 +17,21 @@ article_header:
     src: /atkinson.jpeg
     gradient: 'linear-gradient(135deg, rgba(34, 139, 87 , .4), rgba(139, 34, 139, .4))'
 ---
-Our group develops theories and algorithms with a special focus on the implementation and prototyping of solutions in the following impact areas:
-{% include carousel.html height="50" unit="%" duration="7" number="1" %}
-
-<h2>Wireless Sensing</h2>
+<h2><a href="/wireless.html">Wireless Sensing</a></h2>
 <div><img src="/wiros.mov" height="400" width="100%"/></div>
-<h2>Communications</h2>
+<h2><a href="/6g.html">Towards 6G</a></h2>
 <div><img src="/rice_chip.jpeg" height="400" width="100%"/></div>
-<h2>Vehicular</h2>
+<h2><a href="/vehicle.html">Autonomous Vehicles</a></h2>
 <div><img src="/vehicular.mov" height="400" width="100%"/></div>
+<h2><a href="/iot.html">Ubiquitous IoT</a></h2>
+<div><img src="/pcb.jpeg" height="400" width="100%"/></div>
+<h2><a href="/spectrum.html">Spectrum Sensing</a></h2>
+<div><img src="/sweepsense.png" height="400" width="100%"/></div>
+<h2><a href="/security.html">Security and Privacy</a></h2>
+<div><img src="/bearl.jpeg" height="400" width="100%"/></div>
 <h2> Join Our Team! </h2>
 We are always looking for new group members with passion, talent, and grit!
+{% include carousel.html height="50" unit="%" duration="7" number="1" %}
 <h5> PhD and Postdoc </h5>
 Send an email with the subject "Application PhD" or "Application Postdoc" directly to <i>dineshb [at] ucsd.edu</i> with your CV and a brief statement of why you are interested.
 <h5> UCSD students </h5>

car2.png

@@ -4,10 +4,13 @@ heading: Autonomous Vehicles
 pubtag: Vehicle
 carousels:
   - images:
-    - image: /atkinson.jpeg
-    - image: /rice_chip.jpeg
-    - image: /pcb.jpeg
-    - image: /bearl.jpeg
+    - image: /car1.png
+    - image: /car2.png
+    - image: /car3.png
+    - image: /car4.png
+people: 
+  - Kshitiz Bansal
+  - Dinesh Bharadia
 ---
 
-We at the Wireless Sensing group of WCSNG, focus on designing, researching, developing, and deploying wireless localization and sensing systems for a wide range of applications, including Extended Reality, Indoor Robotics, Navigation, Warehouse management and Industrial IoT 4.0, with the goal of providing accurate and reliable location and sensing estimates for humans, devices, and robots.
+Autonomous perception requires high-quality environment sensing in the form of 3D bounding boxes of dynamic objects. The primary sensors used in automotive systems are light-based cameras and LiDARs. However, they are known to fail in adverse weather conditions. Radars can potentially solve this problem as they are barely affected by adverse weather conditions. However, specular reflections of wireless signals cause poor performance of radar point clouds.We introduce Pointillism, a system that combines data from multiple spatially separated radars with an optimal separation to mitigate these problems. We introduce a novel concept of Cross Potential Point Clouds, which uses the spatial diversity induced by multiple radars and solves the problem of noise and sparsity in radar point clouds. Furthermore, we present the design of RP-net, a novel deep learning architecture, designed explicitly for radar’s sparse data distribution, to enable accurate 3D bounding box estimation. The spatial techniques designed and proposed in this paper are fundamental to radars point cloud distribution and would benefit other radar sensing applications.