Thursday, February 20, 2025

Exercises

 Predict the Number

Programming challenge description:

The example sequence 011212201220200112 ... is constructed as follows:

1. The first element in the sequence is 0.

2. For each iteration, repeat the following action: take a copy of the entire current sequence, replace 0 with 1, 1 with 2, and 2 with 0, and place it at the end of the current sequence. E.g.

0 -> 01 -> 0112 -> 01121220 -> ...

Create an algorithm which determines what number is at the Nth position in the sequence (using 0-based indexing).

Input:

Your program should read lines from standard input. Each line contains an integer N such that 0 <= N <= 3000000000.

Output:

Print out the number which is at the Nth position in the sequence.

Test 1

Test Input

Download Test 1 Input

5

Expected Output

Download Test 1 Output

2

Test 2

Test Input

Download Test 2 Input

25684

Expected Output

Download Test 2 Output

0

import java.io.BufferedReader;

import java.io.IOException;

import java.io.InputStreamReader;

import java.nio.charset.StandardCharsets;

public class Main {

  private static int getDigitAt(long position) {

    StringBuilder sb = new StringBuilder("0");

    int result = -1;

    long start = 1;

    for (long i = 0; i < Long.MAX_VALUE && start-1 <= position; i++) {

            String candidate = sb.toString();

            candidate = candidate.replace("0", "X").replace("1", "Y").replace("2","Z").replace("X","1").replace("Y", "2").replace("Z", "0");

            start += candidate.length();

            sb.append(candidate);

    }

    result = Integer.parseInt(String.valueOf(sb.charAt((int)position)));

    return result;

  }

  public static void main(String[] args) throws IOException {

    InputStreamReader reader = new InputStreamReader(System.in, StandardCharsets.UTF_8);

    BufferedReader in = new BufferedReader(reader);

    String line;

    while ((line = in.readLine()) != null) {

      long position = Long.MAX_VALUE;

      try {

        position = Long.parseLong(line);

      } catch (NumberFormatException e) {

      }

      if (position != Long.MAX_VALUE) {

        int digit = getDigitAt(position);

        System.out.println(digit);

      }

    }

  }

}

Running test cases... Done

– – – – – – – – – – – – – –

Test 1

Passed Collapse

Test Input:

5

Expected Output:

2

Your Output:

2

Test 2

Passed Collapse

Test Input:

25684

Expected Output:

0

Your Output:

0

Problem 2:

You are painting a fence of n posts with k different colors. You must paint the posts following these rules:

• Every post must be painted exactly one color.

• There cannot be three or more consecutive posts with the same color.

Given the two integers n and k, return the number of ways you can paint the fence.

Example 1:

Input: n = 3, k = 2

Output: 6

Explanation: All the possibilities are shown.

Note that painting all the posts red or all the posts green is invalid because there cannot be three posts in a row with the same color.

Example 2:

Input: n = 1, k = 1

Output: 1

Example 3:

Input: n = 7, k = 2

Output: 42

Constraints:

• 1 <= n <= 50

• 1 <= k <= 105

• The testcases are generated such that the answer is in the range [0, 231 - 1] for the given n and k.

Solution:

class Solution {

    public int numWays(int n, int k) {

     int[] dp = new int[n+2];

     dp[0] = 0;

     dp[1] = k;

     dp[2] = k + k*(k-1);

     for (int i = 3; i <=n; i++) {

         dp[i] = dp[i-1] * (k-1) + dp[i-2]*(k-1);

     }

     return dp[n];

    }

}

Accepted

Runtime: 0 ms

Case 1

Case 2

Case 3

Input

n =

3

k =

2

Output

6

Expected

6

Input

n =

1

k =

1

Output

1

Expected

1

Input

n =

7

k =

2

Output

42

Expected

42

Problem #3:

Make Array Zero by Subtracting Equal Amounts

You are given a non-negative integer array nums. In one operation, you must:

• Choose a positive integer x such that x is less than or equal to the smallest non-zero element in nums.

• Subtract x from every positive element in nums.

Return the minimum number of operations to make every element in nums equal to 0.

Example 1:

Input: nums = [1,5,0,3,5]

Output: 3

Explanation:

In the first operation, choose x = 1. Now, nums = [0,4,0,2,4].

In the second operation, choose x = 2. Now, nums = [0,2,0,0,2].

In the third operation, choose x = 2. Now, nums = [0,0,0,0,0].

Example 2:

Input: nums = [0]

Output: 0

Explanation: Each element in nums is already 0 so no operations are needed.

Constraints:

• 1 <= nums.length <= 100

• 0 <= nums[i] <= 100

import java.util.*;

import java.util.stream.*;

class Solution {

    public int minimumOperations(int[] nums) {

        List<Integer> list = Arrays.stream(nums).boxed().collect(Collectors.toList());

        var nonZero = list.stream().filter(x -> x > 0).collect(Collectors.toList());

        int count = 0;

        while(nonZero.size() > 0) {

            var min = nonZero.stream().mapToInt(x -> x).min().getAsInt();

            nonZero = nonZero.stream().map(x -> x - min).filter(x -> x > 0).collect(Collectors.toList());

            count++;

        }

        return count;

    }

}

Input

nums =

[1,5,0,3,5]

Output

3

Expected

3

Input

nums =

[0]

Output

0

Expected

0

SQL Schema

Table: Books

+----------------+---------+

| Column Name | Type |

+----------------+---------+

| book_id | int |

| name | varchar |

| available_from | date |

+----------------+---------+

book_id is the primary key of this table.

Table: Orders

+----------------+---------+

| Column Name | Type |

+----------------+---------+

| order_id | int |

| book_id | int |

| quantity | int |

| dispatch_date | date |

+----------------+---------+

order_id is the primary key of this table.

book_id is a foreign key to the Books table.

Write an SQL query that reports the books that have sold less than 10 copies in the last year, excluding books that have been available for less than one month from today. Assume today is 2019-06-23.

Return the result table in any order.

The query result format is in the following example.

Example 1:

Input:

Books table:

+---------+--------------------+----------------+

| book_id | name | available_from |

+---------+--------------------+----------------+

| 1 | "Kalila And Demna" | 2010-01-01 |

| 2 | "28 Letters" | 2012-05-12 |

| 3 | "The Hobbit" | 2019-06-10 |

| 4 | "13 Reasons Why" | 2019-06-01 |

| 5 | "The Hunger Games" | 2008-09-21 |

+---------+--------------------+----------------+

Orders table:

+----------+---------+----------+---------------+

| order_id | book_id | quantity | dispatch_date |

+----------+---------+----------+---------------+

| 1 | 1 | 2 | 2018-07-26 |

| 2 | 1 | 1 | 2018-11-05 |

| 3 | 3 | 8 | 2019-06-11 |

| 4 | 4 | 6 | 2019-06-05 |

| 5 | 4 | 5 | 2019-06-20 |

| 6 | 5 | 9 | 2009-02-02 |

| 7 | 5 | 8 | 2010-04-13 |

+----------+---------+----------+---------------+

Output:

+-----------+--------------------+

| book_id | name |

+-----------+--------------------+

| 1 | "Kalila And Demna" |

| 2 | "28 Letters" |

| 5 | "The Hunger Games" |

+-----------+--------------------+

SELECT DISTINCT b.book_id, b.name

FROM books b

LEFT JOIN Orders o on b.book_id = o.book_id

GROUP BY b.book_id, b.name,

DATEDIFF(day, DATEADD(year, -1, '2019-06-23'), o.dispatch_date),

DATEDIFF(day, b.available_from, DATEADD(month, -1, '2019-06-23'))

HAVING SUM(o.quantity) IS NULL OR

DATEDIFF(day, DATEADD(year, -1, '2019-06-23'), o.dispatch_date) < 0 OR

(DATEDIFF(day, DATEADD(year, -1, '2019-06-23'), o.dispatch_date) > 0 AND DATEDIFF(day, b.available_from, DATEADD(month, -1, '2019-06-23')) > 0 AND SUM(o.quantity) < 10);

Case 1

Input

Books =

| book_id | name | available_from |

| ------- | ---------------- | -------------- |

| 1 | Kalila And Demna | 2010-01-01 |

| 2 | 28 Letters | 2012-05-12 |

| 3 | The Hobbit | 2019-06-10 |

| 4 | 13 Reasons Why | 2019-06-01 |

| 5 | The Hunger Games | 2008-09-21 |

Orders =

| order_id | book_id | quantity | dispatch_date |

| -------- | ------- | -------- | ------------- |

| 1 | 1 | 2 | 2018-07-26 |

| 2 | 1 | 1 | 2018-11-05 |

| 3 | 3 | 8 | 2019-06-11 |

| 4 | 4 | 6 | 2019-06-05 |

| 5 | 4 | 5 | 2019-06-20 |

| 6 | 5 | 9 | 2009-02-02 |

| 7 | 5 | 8 | 2010-04-13 |

Output

| book_id | name |

| ------- | ---------------- |

| 2 | 28 Letters |

| 1 | Kalila And Demna |

| 5 | The Hunger Games |

Expected

| book_id | name |

| ------- | ---------------- |

| 1 | Kalila And Demna |

| 2 | 28 Letters |

| 5 | The Hunger Games |


Wednesday, February 19, 2025

 

This is a review of the book titled “Founder Brand” written by Dave Gerhardt and published by Lioncrest Publishing in 2022. As a marketing expert, he explains why and how a company’s founder must turn out to be an effective personal brand. His framework for creating “a founder brand” is a worthwhile investment for all entrepreneurs as his consultancy has demonstrated.  A founder brand provides both an ongoing connection with a new and existing customer base and continuous public feedback for strategic growth and without spending on polling and advertising.

A founder brand is crucial for building awareness, trust, and credibility in marketing. Focusing on the founder provides a personal connection, establishing trust with customers and connecting them to a human being. Dave Gerhardt, a conversation-focused marketing company, created a brand for Drift founder David Cancel, which led to a successful podcast and increased revenue. To create a founder brand, tell the founder's story, create content, publish, and master the feedback loop. Transparent, open, and emotionally vulnerable stories make it easier for the niche to connect. Gerhardt recommends targeting a specific group of potential customers and marketing the founder brand directly to them.

Drift transformed its lead form issue into a problem-solving case history by offering articles, videos, and podcasts to solve the issue. This led to a book and a better marketing channel. To determine your niche, define the ideal customer for your product, identify a common problem it solves, offer your solution, and end with the product name. To become a social media presence, post short messages on Twitter and LinkedIn, create a podcast, and engage in public speaking. Share your reality with online followers to build credibility and trust.

Starting a niche podcast requires determining expertise, researching similar podcasts, and showcasing your knowledge and personality. Plan the format, length, and frequency, and prepare a backlog of prerecorded episodes. Utilize a hosting site like Transistor.fm to publish your podcast and promote it on social media. Maximize podcast content to generate traffic to your blog or corporate website and offer perks for subscribers. Stay with your podcast for at least 12 months, build a community, and accept speaking opportunities. A continuous loop of online followers and integrating content from outside activities into your podcast can transform you into a mini media company.

Dave Gerhardt's book offers both actionable insights and basic information about social media and podcasts, making it useful for beginners and those fluent in the web and social media. Gerhardt presents a clever and useful strategy, providing general tactics and direction.

#Codingexercise: Codingexercise-02-19-2025.docx

Tuesday, February 18, 2025

 Infrastructure engineering for AI projects often deal with text based inputs for analysis and predictions whether they are sourced from chatbots facing the customers, service ticket notes, or a variety of data stores and warehouses but the ability to convert text into audio format is also helpful for many scenarios, including DEI requirements and is quite easy to setup and offers the convenience to listen when screens are small, inadequate for usability and are difficult to read. Well-known audio formats can be played on any device and not just phones.

Although there are many dedicated text-to-speech software and online services available, some for free, and with programmability via web requests, there are built-in features of the public cloud service portfolio that make such capabilities more mainstream and at par with the rest of the AI/ML pipelines. This article includes one such implementation towards the end but first an introduction to this feature and capabilities as commercially available.

Most audio is characterized by amplitude as measured in decibels preferably at least 24db, the stream or bit rate which should preferably be at least 192kbps, and a limiter. Different voices can be generated with variations in amplitude, pitch, and tempo much like how singing is measured. Free text-to-speech software, whether standalone, or online gives conveniences in the input and output formats. For example, Natural Reader allows you to load documents and convert them to audio files. Balabolka can save narrations as a variety of audio file formats with customizations for pronunciations and voice settings. Panopreter Basic, also free software, add both input formats and mp3 output format. TTSMaker supports 100+ languages and 600+ AI voices for commercial purposes. Murf AI although not entirely free has a converter that supports 200+ realistic AI voices and 20+ languages. Licensing and distribution uses varies with each software maker.

Public-cloud based capabilities for text-to-speech can be instantiated with a resource initialization from the corresponding service in their service portfolio. The following explains just how to do that.

Sample implementation:

1. Get text input over a web api:

from flask import Flask, request, jsonify, send_file

import os

import azure.cognitiveservices.speech as speechsdk

app = Flask(__name__)

# Azure Speech Service configuration

SPEECH_KEY = "<your-speech-api-key>"

SERVICE_REGION = "<your-region>"

speech_config = speechsdk.SpeechConfig(subscription=SPEECH_KEY, region=SERVICE_REGION)

speech_config.set_speech_synthesis_output_format(speechsdk.SpeechSynthesisOutputFormat.Audio16Khz32KBitRateMonoMp3)

speech_config.speech_synthesis_voice_name = "en-US-GuyNeural" # Set desired voice

@app.route('/text-to-speech', methods=['POST'])

def text_to_speech():

    try:

        # Check if text is provided directly or via file

        if 'text' in request.form:

            text = request.form['text']

        elif 'file' in request.files:

            file = request.files['file']

            text = file.read().decode('utf-8')

        else:

            return jsonify({"error": "No text or file provided"}), 400

        # Generate speech from text

        audio_filename = "output.mp3"

        file_config = speechsdk.audio.AudioOutputConfig(filename=file_name)

        synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=file_config)

        result = synthesizer.speak_text_async(text).get()

        if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:

            # Save audio to file

            with open(audio_filename, "wb") as audio_file:

                audio_file.write(result.audio_data)

            return send_file(audio_filename, as_attachment=True)

        else:

            return jsonify({"error": f"Speech synthesis failed: {result.reason}"}), 500

    except Exception as e:

        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":

    app.run(host="0.0.0.0", port=5000)

2. Prerequisites to run the script:

a. pip install flask azure-cognitiveservices-speech

b. Create an Azure Speech resource in the Azure portal and retrieve the SPEECH_KEY and SERVICE_REGION from the resources’ keys and endpoint section and use them in place of `<your-speech-api-key>` and `<your-region>` above

c. Save the script and run it in any host as `python app.py`

3. Sample trial

a. With curl request as `curl -X POST -F "text=Hello, this is a test." http://127.0.0.1:5000/text-to-speech --output output.mp3`

b. Or as file attachment with `curl -X POST -F "file=@example.txt" http://127.0.0.1:5000/text-to-speech --output output.mp3`

c. The mp3 audio file generated can be played.

Sample output: https://b67.s3.us-east-1.amazonaws.com/output.mp3

Pricing: Perhaps the single most sought-after feature on text-to-speech is the use of natural sounding voice and service providers often markup the price or even eliminate programmability options for the range of the natural-voices offered. This severely limits the automation of audio books. A comparison of costs might also illustrate the differences between the service providers. Public Cloud text-to-speech services typically charge $4 and $16 per million characters for standard and neural voices respectively which is about 4-5 audio books. Custom voices require about $30 per million characters while dedicated providers such as Natural Voice with more readily available portfolio of voices charge about $60/month as a subscription fee and limits on words. This is still costly but automation of audio production for books is here to stay simply because of the time and effort saved.



Sunday, February 16, 2025

 Public-cloud based capabilities for text-to-speech can be instantiated with a resource initialization from the corresponding service in their service portfolio. The following explains just how to do that.

Sample implementation:

1. Get text input over a web api:

from flask import Flask, request, jsonify, send_file

import os

import azure.cognitiveservices.speech as speechsdk

app = Flask(__name__)

# Azure Speech Service configuration

SPEECH_KEY = "<your-speech-api-key>"

SERVICE_REGION = "<your-region>"

speech_config = speechsdk.SpeechConfig(subscription=SPEECH_KEY, region=SERVICE_REGION)

speech_config.set_speech_synthesis_output_format(speechsdk.SpeechSynthesisOutputFormat.Audio16Khz32KBitRateMonoMp3)

speech_config.speech_synthesis_voice_name = "en-US-GuyNeural" # Set desired voice

@app.route('/text-to-speech', methods=['POST'])

def text_to_speech():

    try:

        # Check if text is provided directly or via file

        if 'text' in request.form:

            text = request.form['text']

        elif 'file' in request.files:

            file = request.files['file']

            text = file.read().decode('utf-8')

        else:

            return jsonify({"error": "No text or file provided"}), 400

        # Generate speech from text

        audio_filename = "output.mp3"

        file_config = speechsdk.audio.AudioOutputConfig(filename=file_name)

        synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=file_config)

        result = synthesizer.speak_text_async(text).get()

        if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:

            # Save audio to file

            with open(audio_filename, "wb") as audio_file:

                audio_file.write(result.audio_data)

            return send_file(audio_filename, as_attachment=True)

        else:

            return jsonify({"error": f"Speech synthesis failed: {result.reason}"}), 500

    except Exception as e:

        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":

    app.run(host="0.0.0.0", port=5000)

2. Prerequisites to run the script:

a. pip install flask azure-cognitiveservices-speech

b. Create an Azure Speech resource in the Azure portal and retrieve the SPEECH_KEY and SERVICE_REGION from the resources’ keys and endpoint section and use them in place of `<your-speech-api-key>` and `<your-region>` above

c. Save the script and run it in any host as `python app.py`

3. Sample trial

a. With curl request as `curl -X POST -F "text=Hello, this is a test." http://127.0.0.1:5000/text-to-speech --output output.mp3`

b. Or as file attachment with `curl -X POST -F "file=@example.txt" http://127.0.0.1:5000/text-to-speech --output output.mp3`

c. The mp3 audio file generated can be played.

Sample output: https://b67.s3.us-east-1.amazonaws.com/output.mp3

Pricing: Perhaps the single most sought-after feature on text-to-speech is the use of natural sounding voice and service providers often markup the price or even eliminate programmability options for the range of the natural-voices offered. This severely limits the automation of audio books. A comparison of costs might also illustrate the differences between the service providers. Public Cloud text-to-speech services typically charge $4 and $16 per million characters for standard and neural voices respectively which is about 4-5 audio books. Custom voices require about $30 per million characters while dedicated providers such as Natural Voice with more readily available portfolio of voices charge about $60/month as a subscription fee and limits on words. This is still costly but automation of audio production for books is here to stay simply because of the time and effort saved.

#codingexercise: CodingExercise-02-16-2025.docx

Saturday, February 15, 2025

 This is the summary of a book titled “Generative AI in practice” written by Bernard Marr and published by Wiley in 2024 so it’s almost hot off the press. It surveys 100+ amazing ways Generative Artificial Intelligence is changing business and society.  GenAI gives companies a competitive advantage but also poses unintended risks at societal and organizational levels. Urging for its use towards good, the author explains how it is poised to disrupt industries across sectors and calls for usage in a way that inspires innovation, openness and collaboration. It provides a leap in our understanding and efficiency and will likely result in productivity savings across  Carefully identifying and mitigating the risks when harnessing this power, is going to be a ubiquitous requirement. Higher engagement with the potential to draw customers towards retail, gaming, software and product development and a shift that goes beyond organizational hierarchies is going to be the norm. 

Gen AI, a groundbreaking subset of AI, is poised to disrupt work and life in radical ways. The concept of machines displaying human-like intelligence dates back to 1950 when Alan Turing introduced the Turing Test. Advances in predictive analytics, data mining, big data, and deep learning have set the stage for GenAI models. GenAI demonstrates significant creative power through the development of neural networks, particularly "generative models," which push the boundaries of what humans once believed machines could do. The use of automation and human-machine collaboration could save 70% of the time some processes now require. GenAI will affect nearly every industry, making many workers redundant while creating productivity savings. McKinsey anticipates that GenAI will boost the global economy by US$4.4 trillion each year. Jobs that may experience redundancies include content creators, graphic designers, stock traders, translators, paralegals, factory workers, clerical workers, and journalists. While AI will eliminate some jobs, it will also create new ones, such as data curators and cleaners, and AI prompt engineers, trainers, and ethics officers. 

GenAI has the potential to revolutionize various industries, including education, healthcare, and legal services. However, it is crucial to identify and mitigate risks, such as the spread of misinformation, dependence on AI, and potential harms in critical sectors like defense. Organizations must ensure the ethical use of GenAI by assessing and mitigating risks, implementing robust data governance, preventing data bias, and embracing transparency and accountability. 

In education, GenAI can help teachers personalize student learning experiences, providing feedback on struggling students. In healthcare, GenAI chatbots can provide individualized health advice and assist doctors in providing hyperpersonalized patient care. GenAI can also accelerate drug discovery and development by generating new molecular structures. 

 

In law, GenAI can create efficiencies for law firms by predicting case outcomes, using chatbots to expand services, and improving legal services. Additionally, GenAI and intelligent systems will revolutionize customer service and engagement, with companies like Octopus Energy seeing significant gains. 

In conclusion, while GenAI has the potential to transform various industries, organizations must ensure the ethical use of AI and seek support from AI and data experts when faced with complex issues. 

GenAI is set to revolutionize the video game industry by assisting developers in every stage of game development, including character creation, 2D model creation, and non-linear storylines. It can also automate musical scores, produce immersive soundscapes, and generate quests and missions. GenAI can also create simulated players to test gameplay experiences and use data analysis to predict potential issues. Unity's AI marketplace offers developers solutions like voice generators and asset creation mechanisms, while the Auctoria platform allows users to create game assets more easily. GenAI is expected to accelerate software and product development, speeding up delivery and boosting creative ideation. It can also help designers complete their code and detect bugs, and open up the possibility of creating digital twins. However, embracing GenAI requires a shift in organizational culture, shifting towards curiosity, humility, adaptability, and collaboration. Companies must rethink traditional approaches to work, giving employees autonomy to collaborate on cross-functional teams within a learning culture that prioritizes openness.  

Friday, February 14, 2025

 

A previous article described the formation of a UAV swarm when aligning to a point, line and plane. While the shape formations in these cases are known, the size depends on the number of units in the formation, the minimum distance between units, the presence of external infringements and constraints and the margin required to maintain from such constraints. The article also described the ability to distribute drones to spread as close to the constraints using self-organizing maps which is essentially drawing each unit to the nearest real-world element that imposes a constraint. This establishes the maximum boundaries for the space that the UAV swarm occupies with the core being provided by the point, line, or plane that the units must align to. Given the minimum-maximum combination and the various thresholds for the factors cited, the size of the shape for the UAV swarm at a point of time can be determined.

This article argues that the vectorization, clustering and model does not just apply to the UAV swarm formation in space but also applies to maintaining a balance between constraints and sizing and determining the quality of the formation, using Vector-Search-as-a-judge.  The idea is borrowed from LLM-as-a-judge which helps to constantly evaluate and monitor many AI applications of various LLMs used for specific domains including Retrieval Augmented Generation aka RAG based chatbots. By virtue of automated evaluation with over 80% agreement on human judgements and a simple 1 to 5 grading scale, the balance between constraints and sizing can be consistently evaluated and even enforced. It may not be at par with human grading and might require several auto-evaluation samples but these can be conducted virtually without any actual flights of UAV swarms. A good choice of hyperparameters is sufficient to ensure reproducibility, single-answer grading, and reasoning about the grading process. Emitting the metrics for correctness, comprehensiveness and readability is sufficient in this regard. The overall workflow for this judge is also similar to the self-organizing map in terms of data preparation, indexing relevant data, and information retrieval.

As with all AI models, it is important to ensure AI safety and security to include a diverse set of data and to leverage the proper separation of the read-write and read-only accesses needed between the model and the judge. Use of a feedback loop to emit the gradings as telemetry and its inclusion into the feedback loop for the model when deciding on the formation shape and size, albeit optional, can ensure the parameters of remaining under the constraints imposed is always met.

The shape and size of the UAV formation is deterministic at a point of time but how it changes over time depends on the selection of waypoints between source and destination as well as the duration permitted for the swarm to move collectively or stream through and regroup at the waypoint. A smooth trajectory was formed between the waypoints and each unit could adhere to the trajectory by tolerating formation variations.

Perhaps, the biggest contribution of the vectorization of all constraints in a landscape is that a selection of waypoints offering the least resistance for the UAV swarm to keep its shape and size to pass through can be determined by an inverse metric to the one that was used for self-organizing maps.