iosr-JVSP   Volume-16 ~ Issue-4 ~ Jul. - Aug. 2026

ABSTRACT: One of the main disadvantages of voltage-mode flash ADCs is their limited speed in real-time applications. Due to the charging and discharging of parasitic capacitances with the full voltage swing, the speed of the ADC is adversely affected. In current-mode flash ADCs, there is no need to charge and discharge parasitic capacitances because currents flow in closed loops. Therefore, current-mode flash ADCs are more suitable for high-speed real-time operation. Current-mode TIQ (Threshold Inverter Quantization) based comparators are gaining attention in flash analogue-to-digital converter (ADC) designs due to their high-speed operation and low power consumption. A current-mode TIQ comparator generates an internal quantization reference value, thereby eliminating......

Keywords - Current-mode, Current-mode TIQ comparator with Wilson current mirror, Flash ADC, Low power design

[1]. Abumurad And K. Choi, “Design Procedure And Selection Of Tiq Comparators For Flash Adcs: Tiq Comparator Generation Procedure And Tiq Comparator Selection Algorithm,” Circuits, Systems, And Signal Processing, Vol. 37, No. 2, Pp. 500–531, 2018. [Online]. Available: Https://Link.Springer.Com/Article/10.1007/S00034-017-0574-X
[2]. Kawatra And V. Bhatia, “A Reference Generating Voltage Wide Range Low Power Current Comparator,” In 2018 International Conference On Advances In Computing, Communication Control And Networking (ICACCCN). IEEE, 2018, Pp. 788–791. [Online]. Available: Https://Ieeexplore.Ieee.Org/Abstract/Document/8748345
[3]. L. F. Rahman, M. B. I. Reaz, M. Marufuzzaman, M. B. Mashur, And M. T. I. Badal, “Evaluation Of Low Power And High-Speed CMOS Current Comparators,” Transactions On Electrical And Electronic Materials, Vol. 17, No. 6, Pp. 317–328, 2016. [Online]. Available: Https://Koreascience.Kr/Article/JAKO201607365702140.Page
[4]. M. Maghraby, I. Bozyel, I. T. Abougindia, And S. U. Ay, “Design Of A High-Precision Inverter-Based CMOS Comparator For High Accuracy Applications,” In 2024 IEEE 67th International Midwest Symposium On Circuits And Systems (MWSCAS). IEEE, 2024, Pp. 455–459. [Online]. Available: Https://Ieeexplore.Ieee.Org/Abstract/Document/10658797
[5]. M. K. Adimulam And M. Srinivas, “A 12-Bit, 1.1-Gs/S, Low-Power Flash ADC,” IEEE Transactions On Very Large Scale Integration (VLSI) Systems, Vol. `Https://Ieeexplore.Ieee.Org/Abstract/Document/9674740

ABSTRACT: Pipelining overlaps the execution of successive instructions to approach a throughput of one instruction per cycle, but the data hazards it creates force a designer to choose between stalling, which is simple but wastes cycles, and forwarding, which recovers those cycles at the cost of extra hardware. This trade-off is taught in.....

Keywords - RISC-V, RV32I, instruction pipelining, data hazards, forwarding, bypassing, load-use hazard, cycles per instruction, FPGA synthesis, performance–area trade-off

[1]. D. A. Patterson And J. L. Hennessy, Computer Organization And Design Risc-V Edition: The Hardware/Software Interface, 2nd Ed. Cambridge, Ma: Morgan Kaufmann, 2020, Ch. 4, "The Processor."
[2]. A. Waterman And K. Asanović, Eds., The Risc-V Instruction Set Manual, Volume I: Unprivileged Isa, Risc-V International, Document Version 20191213.
[3]. C. Wolf, "Picorv32 – A Size-Optimized Risc-V Cpu," Yosyshq / Clifford Wolf, Open-Source Rtl Repository, Accessed 2026.
[4]. J. L. Hennessy And D. A. Patterson, Computer Architecture: A Quantitative Approach, 6th Ed. Cambridge, Ma: Morgan Kaufmann, 2019, Ch. 3, "Instruction-Level Parallelism And Its Exploitation

ABSTRACT: Good management of medical equipment is very important to improve healthcare services and reduce operational expenses. Current hospital asset management systems are reliant on manual tracking, which can cause asset misplacement, the increase in search time and wasteful energy usage. The suggested system comprises a microprocessor and current and voltage sensors for monitoring energy consumption of medical equipment and Bluetooth Low Energy (BLE) beacons for indoor location tracking. The data is then transferred over Wi-Fi to a cloud platform for real-time monitoring and analysis. Status of the equipment is shown on an OLED display and a buzzer generates alarms in case of abnormal conditions. The solution allows medical staff to rapidly find equipment, monitor energy....

Keywords - Medical Equipment Management, Energy Monitoring, Bluetooth Low Energy (BLE), Hospital Asset Management, Smart Healthcare

[1]. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, And M. Ayyash, "Internet Of Things: A Survey On Enabling Technologies, Protocols, And Applications," Ieee Communications Surveys & Tutorials, Vol. 17, No. 4, Pp. 2347–2376, 2015.
[2]. M. M. Islam, M. Rahaman, And M. R. Islam, "Development Of Smart Healthcare Monitoring System In Iot Environment," Sn Computer Science, Vol. 1, No. 185, Pp. 1–11, 2020.
[3]. N. F. Tzeng, S. Y. Chen, And Y. C. Chen, "Iot-Based Smart Healthcare Monitoring System: A Review," Ieee Access, Vol. 9, Pp. 18173–18195, 2021.
[4]. A. Yassin, Y. Nasser, M. Awad, A. Al-Dubai, R. Raulefs, E. Aboutanios, M. Abaza, And H. Yanikomeroglu, "Recent Advances In Indoor Localization: A Survey On Theoretical Approaches And Applications," Ieee Communications Surveys & Tutorials, Vol. 19, No. 2, Pp. 1327–1346, 2017.

ABSTRACT: This GSM based Children’s Rescue System checks health, location recording and contacting earthquake rescue authorities. Earthquake damage hampers rescuers’ search for casualties. Rescuers need a dependable automatic health and finding system for vulnerable children. Rescue operations are speeding up because of inexpensive, tiny and efficient sensors, processing and wireless communication. Steps involve data collection, processing and alerting. The......

Keywords - Earthquake, Rescue System, Raspberry Pi Pico, GSM, GPS, Child Safety, Vital Sign Monitoring, Emergency Alert System, Disaster Management.

[1]. D. Sunehra And A. Sreshta, "Raspberry Pi Based Smart Wearable Device For Women And Child Safety," In Proc. International Conference On Innovative Computing And Communications (Icicc), New Delhi, India, 2020, Pp. 1–6.
[2]. V. Lavanya, M. Priyanka, And K. Swathi, "Child Safety Wearable Device Using Iot," Ssrg International Journal Of Electronics And Communication Engineering, Vol. 6, No. 5, Pp. 15–20, May 2019.
[3]. A. Srinivasan, P. Kumar, And R. Sharma, "Intelligent Child Safety System Using Machine Learning And Iot," In Proc. Ieee International Conference On Intelligent Computing And Control Systems (Iciccs), Madurai, India, 2020, Pp. 847–852.
[4]. H. Somashekar, R. Kumar, And S. Prakash, "Enhancing Women's Safety Using Raspberry Pi Pico And Iot Technologies," Journal Of Nonlinear Analysis And Optimization, Vol. 14, No. 2, Pp. 87–96, 2023.
[5]. A. Srinivas, "Iot-Based Child Safety Monitoring System Using Gsm And Gps Technologies," Journal Of Nonlinear Analysis And Optimization, Vol. 15, No. 1, Pp. 112–121, 2024

ABSTRACT: This paper presents a concise review of research on sleep apnea event detection using biomedical signals and machine learning techniques. The study explains the clinical importance of sleep apnea and the limitations of conventional polysomnography, which motivate the development of automated diagnostic solutions. The research paper reviews commonly used physiological signals, feature extraction methods, and machine learning and deep......

Keywords - Sleep apnea; Electrocardiogram (ECG); Electroencephalogram (EEG); Multimodal signal fusion; Machine learning; Deep learning; One-dimensional Convolutional Neural Network (1D-CNN)

[1]. C. Hannachi, S. Kouah, M. Ammi, And O. Cheikhrouhou, “Artificial Intelligence In Sleep Apnea Detection: A Review,” In Distributed Computing For Emerging Smart Networks, I. Jemili And M. Mosbah, Eds., Commun. Comput. Inf. Sci., Vol. 2633. Cham, Switzerland: Springer, 2026, Pp. 1–14, Doi: 10.1007/978-3-032-06777-7_9.
[2]. S. Fathima And M. Ahmed, “Sleep Apnea Detection Using Eeg: A Systematic Review Of Datasets, Methods, Challenges, And Future Directions,” Annals Of Biomedical Engineering, Vol. 53, 2025, Doi: 10.1007/S10439-025-03691-5.
[3]. P. Hemrajani, V. S. Dhaka, G. Rani, S. Verma, Kavita, M. Woźniak, J. Shafi & M. F. Ijaz, “Integrating Physiological Signals For Enhanced Sleep Apnea Diagnosis With Sleepnet,” Scientific Reports, Vol. 15, No. 1, Art. No. 31715, 2025, Doi: 10.1038/S41598-025-16154-7.
[4]. Y. Zhang, L. Zhou, S. Zhu, Y. Zhou, Z. Wang, L. Ma, Y. Yuan, Y. Xie, X. Niu, Y. Su, H. Liu, X. Hei, Z. Shi, X. Ren, And Y. Shi, “Deep Learning For Obstructive Sleep Apnea Detection And Severity Assessment: A Multimodal Signals Fusion Multiscale Transformer Model,” Nature And Science Of Sleep, Vol. 17, Pp. 1–15, 2025, Doi: 10.2147/Nss.S492806.
[5]. A. Bhongade, T. K. Gandhi, P. Ap, “Automatic Identification Of Obstructive Sleep Apnea Using Multimodal Features,” Biomedical Signal Processing And Control, Vol. 105, Art. No. 107609, 2025, Doi: 10.1016/J.Bspc