Group Photo: Faculty of Engineering Sciences and Technology, Hamdard University, Karachi Campus

Tuesday 28th January 2025

Attending One-Day:Masterclass in Health Information Systems Global Perspective

Thursday 30th January 2025

Dr. Tariq Javid attended a one-day session entitled, Masterclass in Health Information Systems: Global Perspective." The session was organized by the Department of Biomedical Engineering, NED University of Engineering and Technology, LEJ Campus, Karachi; and held on Thursday 30th January 2025. The session started with a welcome speech by Prof. Dr. Eraj Humayun Mirza, Chair BME, NED UET, and a brief speech by Prof. Dr. Saad Ahmed Qazi, Meritorious Prof. & Dean Faculty of Electrical and Computer Engineering, NED UET. Prof. Dr. Maryati Mohd Yusof from Universiti Kebangsaan Malaysia was the resource person. The session was about the use of three life cycles: Plan-Do-Act-Check (PDAC) - a quality management methodology, Software Development Life Cycle (SDLC), and Business Process Management (BPM); as an integrated tool for identifying the errors and addressing the root cause. The first half was on the introduction of the health information system (HIS). After the prayer break and lunch, the second session - activity-based - was held. The session concluded by awarding certificates to participants and a group photograph. 

Demystifying the Shortest Path Algorithm with Example

Demystifying the Shortest Path Algorithm with Examples

Saturday 05 October 2024

Consider the complete network in Fig. 1, to find the shortest path from A to H using the Dijkstra algorithm [1]. A simple inspection reveals the optimal path ABEFGH with cost 10. However, implementing the algorithm through a computer program deserves some attention. 

Figure 1 

The procedure excerpt from [2]. 

Step1. A is marked. The nodes connected directly to A are labeled with (cost, from node). All other nodes in the network are labeled with (infinity, dash). B is marked for the next step due to lower cost than C, Fig. 2.

Figure 2

Step2. The nodes connected directly to B are relabeled with (cost, from node). E is marked for the next step due to lower cost than C and D, Fig. 3.

Figure 3

Step3. The nodes connected directly to E are relabeled with (cost, from node). F is marked for the next step due to lower cost than C and D. 

Step4. The modes connected directly to F are relabeled with (cost, from node). G is marked.

Step5. Figure 4 shows the optimal path from A to H: ABEFGH.

Figure 4

There are a lot of posts on the net. A good one is [3]. The Python code was modified for the above example and results are shown in Fig. 5. 

Figure 5

References: 

[1] Dijkstra (1959), [2] CN 6th Tanenbaum, [3] www.dougmahugh.com/dijkstra

Appendix: Output 

Reading the input file ...
('A', 'B', 2.0)
('A', 'C', 6.0)
('B', 'D', 7.0)
('B', 'E', 2.0)
('C', 'E', 1.0)
('C', 'G', 4.0)
('D', 'F', 3.0)
('D', 'H', 3.0)
('E', 'F', 2.0)
('F', 'G', 2.0)
('G', 'H', 2.0)
Building Adjacency List ...
{'C': {('E', 1.0), ('G', 4.0)}, 'F': {('G', 2.0)}, 'G': {('H', 2.0)}, 
'A': {('C', 6.0), ('B', 2.0)}, 'H': set(), 'E': {('F', 2.0)}, 
'B': {('D', 7.0), ('E', 2.0)}, 'D': {('F', 3.0), ('H', 3.0)}}
Unvisited Nodes ...
{'C', 'F', 'G', 'A', 'H', 'E', 'B', 'D'}
Distance from Start Node
{'C': inf, 'F': inf, 'G': inf, 'A': 0, 'H': inf, 'E': inf, 'B': inf, 'D': inf}
Current node =  A
	Unvisited nodes =  {'C', 'F', 'G', 'H', 'E', 'B', 'D'}
		neighbor =  C  distance =  6.0
			New path =  6.0
		neighbor =  B  distance =  2.0
			New path =  2.0
Current node =  B
	Unvisited nodes =  {'C', 'F', 'G', 'H', 'E', 'D'}
		neighbor =  D  distance =  7.0
			New path =  9.0
		neighbor =  E  distance =  2.0
			New path =  4.0
Current node =  E
	Unvisited nodes =  {'C', 'F', 'G', 'H', 'D'}
		neighbor =  F  distance =  2.0
			New path =  6.0
Current node =  C
	Unvisited nodes =  {'F', 'G', 'H', 'D'}
		neighbor =  E  distance =  1.0
		neighbor =  G  distance =  4.0
			New path =  10.0
Current node =  F
	Unvisited nodes =  {'G', 'H', 'D'}
		neighbor =  G  distance =  2.0
			New path =  8.0
Current node =  G
	Unvisited nodes =  {'H', 'D'}
		neighbor =  H  distance =  2.0
			New path =  10.0
Current node =  D
	Unvisited nodes =  {'H'}
		neighbor =  F  distance =  3.0
		neighbor =  H  distance =  3.0
Current node =  H
	Unvisited nodes =  set()
graph definition file: simple_graph3.txt
      start/end nodes: A -> H
        shortest path: ['A', 'B', 'E', 'F', 'G', 'H']
       total distance: 10

FYDP First Evaluation Presentation by Students of BE BME 2021

Tuesday 03 September 2024

The first evaluation presentations were delivered by the students for five final year design projects at FEST conference room after Zuhr prayer, 2pm.

1. Noninvasive Cardiac Activity Analyzer by Team Aqsa, Supervisor: Dr. Tariq

2. Noninvasive Brain Activity (Depression) Analyzer by Team Atisam, Supervisor: Dr. Tariq

3. Design the Circuit for Single Lead ECG and PPG to Calculate Pulse Transient Time by Team Anees, Supervisor: Dr. Faris

4. Detection of Retina Deformation in Diabetes Mellitus through Machine Learning by Using Jetson Nano by Team Fakiha, Supervisor: Dr. Faris

5. Multi-Class Skin Disease Detecton by using U Net and CNN in Deep Learning by Team Ramsha, Supervisor: Dr. Faris

Publication of Book: The Fundamentals of Biomedical Image Processing and its Impact on Cancer Research

Wednesday 07 August 2024 

The book includes topics on image processing basics for the biomedical image and segmentation techniques applied on MR images with a detailed overview of brain anatomy from a functional point of view. The establishment of tumour boards across the region was discussed from the multidisciplinary team platform. The book was published on Wednesday 7th August 2024 as a joint effort of co-authors Dr. Jawwad Sami Ur Rahman from Riphah International University, Prof. Dr. Tariq Javid from Hamdard University, and Prof. Dr. Ahmed Nadeem Abbasi from Aga Khan University Hospital. 

Object-Oriented Programming (OOP) Rubrics

Saturday 24 August 2024 

This generic rubric (Fig. 1) is developed to assess the work of student groups solving problems using the object-oriented programming (OOP) paradigm. The generic rubric is used to construct the rubric for developing the clean program code using OOP concepts (Fig. 2) and the rubric for problem analysis and solution design with OOP (Fig. 3). The other rubrics include rubric final performance (Fig. 4) and rubric for theory teacher viva (Fig. 5). 

Figure 1 Generic

Figure 2 Clean code

Figure 3 Problem analysis and solution design

Figure 4 Final performance

Figure 5 Theory teacher viva

PPG Processed Data

Sunday 11 August 2024

The data array is processed photoplethysmography (PPG) data.

11   6  48  62  21  13   5   7   5  44
14   8   5   2   2  51  13  11   6   0
 5  62  23  13   5   2  10  49  12   6
 5   0  40  46  14   9   0   3  52  31
13   5   4   0  67  28  13   9   5   8
83  40  29  11  10  16  96  57  38  17
10   9 100  65  43  21  11  11  75  84
35  26  14   5  11  90  41  32  13  10
 5  84  35  15   9   4   6  59  14  14
 2   4  81  45  22   8   2  44  76  27

Above 100 samples were acquired at 1 ms. No time correction.

By: 

Engr. Prof. Dr. Tariq Javid, Chair BME, FEST, HU, PK

As of 11 August 2024