Numeric Keypad Research Articles

Wi-Crack: a smartphone keystroke recognition system based on multi-dimensional information

Abstract With the popularity of mobile payment, password protection has become more and more important. Channel state information (CSI) has recently been used to crack passwords in public WiFi environments. The feasibility of password cracking lies in the fact that different keystrokes lead to different finger movement directions and distances, resulting in unique interference to WiFi signal transmission. The unique interference can be recorded by CSI and used for keystroke inference. In this paper, we propose Wi-Crack, a keystroke recognition system for numerical keypad input on smartphones. Two computers equipped with commercial off-the-shelf WiFi NIC comprise the system, with one serving as the transmitter and the other as the receiver. Previous keystroke recognition systems only used the amplitude of CSI for keystroke recognition. Different from them, Wi-Crack combines the amplitude, phase, amplitude difference and phase difference of CSI for keystroke recognition. The use of multi-dimensional information has enabled Wi-Crack to improve keystroke recognition. Experimental results show that Wi-Crack improves the accuracy of keystroke segmentation to consistently above 90%. It also improves the keystroke recognition accuracy on DTW-KNN, SVM, 1D-CNN, and LSTM with over 90% in the best case.

LiKey: Location-independent keystroke recognition on numeric keypads using WiFi signal

As an application of wireless sensing, keystroke recognition can be used in a variety of scenarios, such as password protection and analog input for devices. Existing WiFi-based keystroke recognition systems have strong dependencies on environment and location. Once the environment or keyboard position changes, systems need to re-collect data and re-train the model, otherwise the performance will degrade significantly.In this paper, we propose LiKey, a location-independent keystroke recognition system for numeric keypads, consisting of one transmitter and two receivers. We specially place the transceiver devices, which makes it easy to analyze the variation of the reflection path length for keystrokes at different locations. The same keystroke at different positions has the same change rule of reflection path length, corresponding to the same change rule of rotational direction in the channel state information (CSI) complex plane. We design a method named SRDF to sum the rotational direction features in the CSI complex plane and obtain the location independent features. Since different keystrokes behave differently on the two receivers, a dual-receiver phase–amplitude joint optimization segmentation algorithm is designed to extract the complete keystroke segments. Finally, the dual-branch CNN model is used for training and classification based on the features of the two receivers. The experimental results show that LiKey has strong robustness to the keystroke position. It recognizes well even for keystrokes on positions not involved in the training.

Automatic Material Handling System Using PLC

This paper presents the design and development of an automatic material handling system utilizing a Programmable Logic Controller (PLC) capable of efficiently managing loads up to 40 kg. The system integrates various components including Schneider PLC with corresponding data cables, proximity and photoelectric sensors with a detection distance of 4mm, servo motors operating at a speed of 60-100 RPM, a 24V DC power supply unit (input: 230V, output: 24V DC), Siemens MCB for circuit protection, Elmex connectors for terminal connections, Phoenix Contact Glass Relays operating at 230V AC, and numeric keypad push buttons with an input supply of 4-5V. The integration of these components ensures reliable operation and efficient handling of materials within the specified load capacity. This research contributes to the advancement of automated material handling systems, offering potential applications across various industries. Key Words: PLC, Ladder, Automation, HMI, Interlocking security

Design and Development of an Automatic Material Handling System Using PLC for 40 kg Load Capacity

This paper presents the design and development of an automatic material handling system utilizing a Programmable Logic Controller (PLC) capable of efficiently managing loads up to 40 kg. The system integrates various components including Schneider PLC with corresponding data cables, proximity and photoelectric sensors with a detection distance of 4mm, servo motors operating at a speed of 60-100 RPM, a 24V DC power supply unit (input: 230V, output: 24V DC), Siemens MCB for circuit protection, Elmex connectors for terminal connections, Phoenix Contact Glass Relays operating at 230V AC, and numeric keypad push buttons with an input supply of 4-5V. The integration of these components ensures reliable operation and efficient handling of materials within the specified load capacity. This research contributes to the advancement of automated material handling systems, offering potential applications across various industries

“I Can See Your Password”: A Case Study About Cybersecurity Risks in Mid-Air Interactions of Mixed Reality-Based Smart Manufacturing Applications

Abstract This paper aims to present a potential cybersecurity risk existing in mixed reality (MR)-based smart manufacturing applications that decipher digital passwords through a single RGB camera to capture the user’s mid-air gestures. We first created a test bed, which is an MR-based smart factory management system consisting of mid-air gesture-based user interfaces (UIs) on a video see-through MR head-mounted display. To interact with UIs and input information, the user’s hand movements and gestures are tracked by the MR system. We setup the experiment to be the estimation of the password input by users through mid-air hand gestures on a virtual numeric keypad. To achieve this goal, we developed a lightweight machine learning-based hand position tracking and gesture recognition method. This method takes either video streaming or recorded video clips (taken by a single RGB camera in front of the user) as input, where the videos record the users’ hand movements and gestures but not the virtual UIs. With the assumption of the known size, position, and layout of the keypad, the machine learning method estimates the password through hand gesture recognition and finger position detection. The evaluation result indicates the effectiveness of the proposed method, with a high accuracy of 97.03%, 94.06%, and 83.83% for 2-digit, 4-digit, and 6-digit passwords, respectively, using real-time video streaming as input with known length condition. Under the unknown length condition, the proposed method reaches 85.50%, 76.15%, and 77.89% accuracy for 2-digit, 4-digit, and 6-digit passwords, respectively.

Harnessing highly efficient triboelectric sensors and machine learning for self-powered intelligent security applications

In the contemporary epoch, distinguished by a transition from the internet-of-things (IoT) to the artificial intelligence of things (AIoT), individual electronic appliances necessitate inherent power-generation, independence from internet connectivity, and an imbued degree of intellect. Devices governed by pressure or strain sensors particularly demand such attributes. Responding to this technological imperative, our study endeavored to conceive an intelligent door security apparatus grounded on the universally adopted numerical input system. Despite the commercialization of identification systems such as fingerprint, iris, or facial recognition, these mechanisms suffer from susceptibility to a variety of functional aberrations. Consequently, our investigation concentrated on a security system predicated on numerical input. This necessitated the formulation of a swift, self-powered pressure sensor characterized by sensitivity to minute pressure changes. As such, we engineered a triboelectric pressure sensor incorporating a composite of Ti3C2-based MXene and polydimethylsiloxane (PDMS) as the electronegative stratum, and Nylon functioning as the electropositive layer. Addressing the sensor's intrinsic deficiency in sensitivity to pressure, we augmented the MXene-PDMS composite's surface with an out-of-plane wavy structure, and utilized a Nylon stratum composed of nanofibers, thereby amplifying the contact area under pressurized conditions. This meticulously developed sensor displayed a sensitivity metric of 0.604 kPa−1 at 15 kPa, and notably, the swiftest response times recorded amongst triboelectric pressure sensors to date. Post attachment of the sensor to a numeric keypad (ranging from 0 to 9), we meticulously measured the signal alterations contingent on each key press, resulting in a comprehensive dataset. Employing a multitude of machine learning algorithms, we realized an exemplary degree of precision in both training and testing phases. The pragmatic implications of this work are noteworthy. Not only does our technology facilitate the unlocking of a door by entering the correct numerical code, but it is capable of recognizing distinct triboelectric signal patterns, corresponding to the specific manner of key entry by an authorized user, offering an additional dimension of security.

Keyless Security

This research paper introduces a novel approach to improve security and access control in smart door lock systems. The proposed system incorporates multi-factor authentication, combining a numerical keypad, mobile app, and RFID sensor. Each authentication method is associated with a specific lock, offering differentiated access control. Notably, the system requires the simultaneous use of the mobile app and RFID sensor to unlock a designated lock, adding an extra layer of security. To assess the system's effectiveness, a comprehensive study was conducted, considering security, usability, and user satisfaction. The study involved simulated attacks, user surveys, and usability tests, with data analysis providing insights into the system's resilience and user acceptance. This research contributes to the field of smart door lock systems by offering an innovative approach to enhance security through multi-factor authentication. The findings provide valuable insights for designing advanced access control mechanisms, empowering users with increased confidence in their security measures while maintaining a user-friendly experience.

A Fully Passive Ten-Digit Numeric Keypad Sensor Using Chipless RFID Technology

A ten-digit chipless radio frequency identification (CRFID)-based numeric keypad that works on the principle of touch event sensing is presented. The tag is designed to operate using the passive frequency-domain (FD) technique. The proposed tag has ten circular resonator rings for each of the keypad’s corresponding decimal digits. Each circular ring resonates at a particular frequency within the 400-MHz band from 2.7 to 7.6 GHz to uniquely identify a digit. Identification is performed by decoding radar cross Section (RCS) response of the whole keypad tag. A customized human finger using standard tissue characteristics is modeled to verify the keypad’s working mechanism. When the finger is placed on any of the nine resonators, its corresponding resonance disappears from the allocated band. The proposed CRFID keypad sensor tag is built upon a Rogers substrate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$85\times 95\times0.8$ </tex-math></inline-formula> mm volume. The backside of the proposed tag is fully copper filled for an easy interface with a variety of backgrounds, including metallic walls. The design exactly mimics the shape of ordinary keypad. The finger model used in the simulations realizes the practical scenario. This CRFID keypad is deemed an intriguing solution for future automated cities and will help in the conceptualization of Internet of Things (IoT) systems.

Customizable Graphite-On-Paper-Based Keypads: Toward Disposable and Recyclable Wireless Human–Machine Interfaces

There is a rapid increase in the use of affordable electronic devices and human-machine interfaces (HMIs) with short serviceable life in almost every aspect of our lives. It’s estimated that the Waste Electrical and Electronic Equipment (WEEE) and e-waste generated in year 2021 was 57.5 million metric tons (Mt) and it is expected that the production of e-waste will increase to 110 Mt by the end of 2050. To mitigate these wastes, green HMIs are required which can be customized for multiple applications and can be recycled or disposed of with minimal environmental impact. This work presents customizable graphite-on-paper (GOP) based keypad consists of interdigitated capacitive (IDC) touch sensors is demonstrated as HMIs to interact with different electronic and media applications wirelessly. The GOP keypads are fabricated through facile and green fabrication process by direct writing of graphite on flexible paper substrate. The GOP keypads can be fabricated in a home setting as the required materials are readily available, i.e., paper, pencils, Arduino. The GOP keypads can be easily disposed of or recycled at the end of its life or requirement due to the employment of biodegradable materials like paper and graphite. The IDC touch sensors are optimized by analyzing the number of electrode fingers, finger’s width, finger’s overlap length and spacing between the electrode fingers. The same GOP keypad is customized to interact with different electronic and media applications wirelessly i.e., laptop cursor navigation, calculator app on mobile, numeric keypad etc. The customizable GOP keypads have potential to be used as green wireless HMIs to enforce a circular economy by mitigating electronic and plastic waste, which leads to the vision of a sustainable and green world.

Analysis of four syringe dispensing machine point-of-access data 2017–2020 in Melbourne, Australia: machine utilisation and client demographics

BackgroundAustralian needle and syringe distribution occurs via a mix of modalities, including syringe dispensing machines (SDMs). SDMs are electronic vending machines providing (often) 24-h access to needles/syringes and may attract greater numbers of people who are younger, female, and/or have limited connection to health care services compared to individuals accessing fixed-site needle and syringe programs (NSPs). However, validating the demographic characteristics of SDM clients has proven difficult in previous research.MethodsIn this paper, we analyse SDM order and client demographic data from four SDMs located in South-East Melbourne, Australia, and compare this against the managing fixed-site NSP between May 2017 and December 2020. SDM data were collected via a novel 0–9 numeric keypad input tool. Via the tool, SDM clients were requested to input their categorised age, gender and postcode. Given the novelty of the tool, we evaluate the feasibility of the data collection method. We analysed data according to: (1) total SDM orders made, (2) estimated ‘unique SDM presentations’ and (3) describing the demographics of unique SDM clients. Importantly, we noted substantial invalid demographic data, and consequently, severely restricted data for analysis.ResultsThere were 180,989 SDM orders made across the four SDMs to an estimated 90,488 unique SDM presentations. There was little variation in unique presentations across days of the week, but 69% occurred out of NSP operating hours. Across the study period, the SDMs distributed 66% of the number of syringes distributed by the fixed-site NSP. Due to invalid demographic data, our restriction method provided only 10,914 (6% of all data) unique presentations for analysis. There were some demographic differences between SDM and NSP client, but these should be treated with caution.ConclusionsThe data collection tool provides a novel means of comparing SDM and fixed-site presentations, demonstrating the substantial expansion of service via the SDMs. However, the validity of the demographic data was highly questionable and requires significant data coding, meaning it is not feasible for community NSPs. While we recommend the inclusion of automatically collected SDM order data, the use of a 0–9 numeric keypad to collect demographic data—while an innovation—requires alteration to support NSP data.

Handedness did not affect motor skill acquisition by the dominant hand or interlimb transfer to the non-dominant hand regardless of task complexity level

Patients undergoing unilateral orthopedic or neurological rehabilitation have different levels of impairments in the right- or left-dominant hand. However, how handedness and the complexity of the motor task affect motor skill acquisition and its interlimb transfer remains unknown. In the present study, participants performed finger key presses on a numeric keypad at 4 levels of sequence complexities with each hand in a randomized order. Furthermore, they also performed motor sequence practice with the dominant hand to determine its effect on accuracy, reaction time, and movement time. The NASA-TLX at the end of each block of both testing and practice was used to confirm participants’ mental workload related to sequence complexity. Both right- and left-handed participants performed the motor sequence task with faster RT when using their right hand. Although participants had increasing RT with increasing sequence complexity, this association was unrelated to handedness. Motor sequence practice produced motor skill acquisition and interlimb transfer indicated by a decreased RT, however, these changes were independent of handedness. Higher sequence complexity was still associated with longer RT after the practice, moreover, both right- and left-handed participants’ RT increased with the same magnitude with the increase in sequence complexity. Similar behavioral pattern was observed in MT as in RT. Overall, our RT results may indicate left-hemisphere specialization for motor sequencing tasks, however, neuroimaging studies are needed to support these findings. On the other hand, handedness did not affect motor skill acquisition by the dominant hand or interlimb transfer to the non-dominant hand regardless of task complexity level.

Flexible, durable, and washable triboelectric yarn and embroidery for self-powered sensing and human-machine interaction

The novel combination of textiles and triboelectric nanogenerators (TENGs) successfully achieves self-powered wearable electronics and sensors. However, the fabrication of Textile-based TENGs remains a great challenge due to complex fabrication processes, low production speed, high cost, poor electromechanical properties, and limited design capacities. Here, we reported a new route to develop Textile-based TENGs with a facile, low-cost, and scalable embroidery technique. 5-ply ultrathin enameled copper wires, low-cost commercial materials, were utilized as embroidery materials with dual functions of triboelectric layers and electrodes in the Textile-based TENGs. A single enameled copper wire with a diameter of 0.1 mm and a length of 30 cm can produce over 60 V of open-circuit voltage and 0.45 µA of short circuit current when in contact with polytetrafluoroethylene (PTFE) fabric at the frequency of 1.2 Hz and the peak value of contact force of 70 N. Moreover, the triboelectric performance of enameled copper wire after plasma treatment can be better than that without plasma treatment, such as the maximum instantaneous power density can reach 245 μW/m which is ∼ 1.5 times as much as the untreated wire. These novel embroidery TENGs possess outstanding triboelectric performance and super design capacities. A 5 × 5 cm2 embroidery sample can generate an open-circuit voltage of 300 V and a short circuit current of 8 μA under similar contact conditions. The wearable triboelectric embroidery can be employed in different parts of the wear. A self-powered, fully fabric-based numeric keypad was designed based on triboelectric embroidery to serve as a human-machine interface, showing good energy harvesting and signal collection capabilities. Therefore, this study opens a new generic design paradigm for textile-based TENGs that are applicable for next-generation smart wearable devices.

Effects of smartphone numeric keypad designs on performance and satisfaction of elderly users

Background and objectiveAs cognitive, motor, and sensory skills decline with age, the interface needs of elderly users differ from that of young adults, especially when entering important information in healthcare applications (e.g. blood glucose values) via a numeric keypad on a smartphone touchscreen. The aim of this study is to propose an optimal numeric keypad design for elderly users. A total of 51 participants greater than or equal to 65 years old completed a 6-digit numeric entry task on a smartphone touchscreen. The Java-based experimental program allows participants to operate 45 different keypad designs consisting of three button shapes (circle, vertical rectangle, and square), five button sizes with 2.5-mm increments (7.5 mm, 10 mm, 12.5 mm, 15 mm, and 17.5 mm), and three button spacings (0 mm, 1 mm, and 3 mm). The usability of the keypad design was compared based on task completion time, error rate, and subjective satisfaction, while the data was analyzed using repeated measures ANOVA. The results show that circle and square buttons outperform the vertical rectangle button in completion time, accuracy, and satisfaction. Square and circle buttons in 15 mm size led to improved efficiency and decreased error rate. Moreover, task completion time for 3 mm is significantly shorter than 0 mm spacing. Given sufficient space on the touchscreen, the 3 mm spacing can be considered optimal when designing a numeric keypad interface. This study presents a comparison of different numeric keypad designs and the impact on the performance and satisfaction of elderly users. The result can be applied to smartphone numeric keypad design to increase input accuracy and speed. In addition, both circle and square buttons can be adopted in user interfaces for elderly users without the decline of usability. Relevance to industryThe proposed configuration of button shape, size, and spacing can be applied to the numeric keypad user interface (UI) of smartphones targeting elderly users, especially in healthcare applications.

Ohm's Law + Kirchhoff's Current Law = Better AI: Neural-Network Processing Done in Memory with Analog Circuits will Save Energy

Machine learning and artificial intelligence (AI) have already penetrated so deeply into our life and work that you might have forgotten what interactions with machines used to be like. We used to ask only for precise quantitative answers to questions conveyed with numeric keypads, spreadsheets, or programming languages: “What is the square root of 10?” “At this rate of interest, what will be my gain over the next five years?”

Chipless RFID Label with Identification and Touch-Sensing Capabilities.

This article presents a 14-bit chipless RFID label which, in addition to classical identification feature, can be used as decimal numeric keypad, allowing the deployment of secure access control applications. A low-cost single layer label comprising 10 RF loop scatterers is used to code information in the frequency domain. In addition, each resonator is associated to a digit in the decimal number system, and the difference in the spectrum caused by the touch event is exploited for the detection of each key pressing. The shape of the resonators has been carefully selected to be both highly resonant and to show high sensitivity to the presence or absence of the human finger. The concept is validated by measurements in an office environment using an FCC compliant low-cost chipless reader and microstrip vivaldi antennas. Simple detection algorithms are proposed for both identification and touch sensing in real environment.

Scrambling Keypad for Secure Pin Entry to Defeat Shoulder Surfing and Inference Attacks

Personal identification number (PIN) is a common user authentication method widely used especially for automated teller machines and point-of-sales devices. The user's PIN entry is susceptible to shoulder-surfing and inference attacks, where the attacker can obtain the PIN by looking over the user's shoulder. The conventional keypad with a fixed layout makes it easy for the attacker to infer the PIN entered by casual observation. This paper proposes a method of authentication to address these challenges. The paper develops a prototype numeric keypad with a layout akin to the conventional keypad, with the keys randomized for each PIN entry. The shuffle algorithm, Durstenfeld shuffle algorithm, is implemented in an application developed using JavaScript, which is a prototype-based object-oriented programming application that conforms to the ECMAScript specification. The prototype is implemented on three computing platforms for evaluation. The test proves the effectiveness of the system to mitigate shoulder-surfing and inference attacks.

Secure Numeric Keypad against Attacks Guessing Passwords from Key Touching

Secure Numeric Keypad against Attacks Guessing Passwords from Key Touching

Bootstrapping the visuospatial bootstrapping effect and testing its spatialisation

ABSTRACT When sequences of digits are visually presented within a numerical keypad on a screen, memory span increases, this effect was named visuospatial bootstrapping. The aim of the first experiment was to know if this effect could emerge without presenting a keypad on the screen. For this purpose, a three-phase experiment was designed. During phase 1, the immediate serial recall of two groups of participants was compared (pre-training): the first group saw sequences of one-digit numbers displayed on a screen within a keypad (the keypad group) whereas the second group heard the (same) sequences (the auditive group). During phase 2, all participants underwent a training session to help them visualise in their mind a keypad. Finally, in the third phase, participants were tested again with an immediate serial recall task (post-training). Results showed that both groups had comparable performance in post-training indicating that the visuospatial bootstrapping could be obtained without displaying a numerical keypad. The second experiment also involved a keypad group and an auditive group and was designed to investigate their spatial representation. Results showed that both groups spatialised the digits following the keypad spatial configuration: digits 1-4-7 were associated to left, 2-5-8 to middle and 3-6-9 to right.

Visuospatial bootstrapping: spatialized displays enhance digit and nonword sequence learning.

Visuospatial bootstrapping describes the observation that performance on a verbal memory task is enhanced by presenting the to-be-remembered material in a format with additional embedded spatial information. Thus far, it has only been reported in short-term memory tasks. Here, we report two experiments assessing the impact of spatial information on the learning of sequences in long-term memory. Experiment 1 used digits presented within a familiar numeric keypad as stimuli compared against single digits presented in one location. Experiment 2 used novel nonwords, which were either presented in an unchanging arrangement permitting the building-up of location knowledge or in a constantly changing arrangement. Both experiments demonstrated strong evidence that reliable spatial information facilitated sequence learning, particularly in later sequence positions. It is concluded that the incidental availability of spatialized information during study can facilitate learning of sequences of digits and nonwords. Furthermore, the spatial information can be learned during the task itself and does not need to be preexistent in long-term knowledge.

Transfer of incompatible spatial mapping to the vertical Simon task generalizes across effectors but not stimulus features.

For the vertical Simon task, in which stimuli and responses are arrayed along the vertical dimension and stimulus location is irrelevant, a Simon effect (benefit for stimulus-response correspondence) is typically obtained. Results have been mixed about whether performing fewer than 100 trials of a spatially incompatible mapping prior to a Simon task reduces or eliminates this vertical Simon effect in a transfer session. Several reasons have been suggested to explain why previous studies show disparate results. Previously, we ruled out orientation of the response panel in the transverse or horizontal plane as a critical factor. The present experiments evaluated two other possible factors: finger/hand placement and relevant stimulus dimension. In Experiment 1, we found reduction of the vertical Simon effect for a circle-square discrimination after incompatible practice using a separate numeric keypad as the response device, regardless of whether the keypad was placed on a table and operated by index fingers or held in the hands and operated by thumbs. In Experiment 2, we replicated the reduction for the circle-square discrimination but found no evidence of reduction for a red-green color discrimination. Overall, our results suggest that the relevant discrimination of red-green color versus circle-square shape is responsible for the discrepancy in results across prior studies.